1. Computational and Systems Biology
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Biological constraints on GWAS SNPs at suggestive significance thresholds reveal additional BMI loci

  1. Reza K Hammond
  2. Matthew C Pahl
  3. Chun Su
  4. Diana L Cousminer
  5. Michelle E Leonard
  6. Sumei Lu
  7. Claudia A Doege
  8. Yadav Wagley
  9. Kenyaita M Hodge
  10. Chiara Lasconi
  11. Matthew E Johnson
  12. James A Pippin
  13. Kurt D Hankenson
  14. Rudolph L Leibel
  15. Alessandra Chesi
  16. Andrew D Wells
  17. Struan FA Grant  Is a corresponding author
  1. Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, United States
  2. Division of Human Genetics, The Children’s Hospital of Philadelphia, United States
  3. Naomi Berrie Diabetes Center, Vagelos College of Physicians and Surgeons, Columbia University, United States
  4. Department of Pathology and Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University, United States
  5. Columbia Stem Cell Initiative, Vagelos College of Physicians and Surgeons, Columbia University, United States
  6. Department of Orthopaedic Surgery, University of Michigan Medical School, United States
  7. Division of Molecular Genetics (Pediatrics) and the Naomi Berrie Diabetes Center, Columbia University Vagelos College of Physicians and Surgeons, United States
  8. Department of Pathology, The Children’s Hospital of Philadelphia, United States
  9. Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, United States
  10. University of Pennsylvania, United States
  11. The Children’s Hospital of Philadelphia, United States
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Cite this article as: eLife 2021;10:e62206 doi: 10.7554/eLife.62206

Abstract

To uncover novel significant association signals (p<5×10−8), genome-wide association studies (GWAS) requires increasingly larger sample sizes to overcome statistical correction for multiple testing. As an alternative, we aimed to identify associations among suggestive signals (5 × 10−8≤p<5×10−4) in increasingly powered GWAS efforts using chromatin accessibility and direct contact with gene promoters as biological constraints. We conducted retrospective analyses of three GIANT BMI GWAS efforts using ATAC-seq and promoter-focused Capture C data from human adipocytes and embryonic stem cell (ESC)-derived hypothalamic-like neurons. This approach, with its extremely low false-positive rate, identified 15 loci at p<5×10−5 in the 2010 GWAS, of which 13 achieved genome-wide significance by 2018, including at NAV1, MTIF3, and ADCY3. Eighty percent of constrained 2015 loci achieved genome-wide significance in 2018. We observed similar results in waist-to-hip ratio analyses. In conclusion, biological constraints on sub-significant GWAS signals can reveal potentially true-positive loci for further investigation in existing data sets without increasing sample size.

Introduction

Genome-wide association studies (GWAS) have been widely employed to identify genetic variants that are associated with risk for disease and physiologically relevant traits (Tam et al., 2019; Visscher et al., 2017). GWAS was first described in 2005 (Klein et al., 2005) and has since been utilized to study a large range of common and complex traits in humans. As of July 2020, the GWAS Catalog consisted of 4628 publications reporting a total of 189,811 associations that achieved genome-wide significance (p<5×10−8) between genetic variants and human common complex traits. For instance, in 2009, 54 near-independent genome-wide significant (GWS) signals associated with variation in height had been identified in a population of tens of thousands of individuals (Visscher, 2008), while by 2014, that number had jumped to 697 and was estimated to explain more than 20% of the trait’s heritability (Wood et al., 2014). The number of significant GWS signals increased to 3,290 in 2018, accounting for nearly a quarter of the heritability (Yengo et al., 2018). And yet, despite these successes, GWAS has a clear shortcoming in that a large proportion of predicted heritability remains unexplained despite a constantly growing number of implicated loci (Tam et al., 2019). These novel loci are only identifiable using larger sample sizes, which come at a significant cost of both time and money, and each successive independent signal explains less and less of the overall estimated heritability (Zuk et al., 2012).

The additional signals achieving genome-wide significance in successive rounds of GWAS are often those that failed to achieve the commonly utilized p=5×10−8 threshold in initial rounds, a strict bar that can only be met when larger cohort sizes are recruited. Various approaches have been previously utilized to identify sub-threshold signals that go on to achieve genome-wide significance. Sub-threshold signals have been selected for replication in a stage 2 GWAS due to their proximity to putative candidate genes to identify novel loci, as with the identification of the obesity locus NPC1 and replicated subsequently in a large meta-analysis (Meyre et al., 2009; Turcot et al., 2018). Epigenomic maps have also been utilized to implicate biologically relevant sub-threshold variants that were subsequently experimentally validated (Wang et al., 2016). We explored if it would be possible to apply a systematic genome-wide molecular-genetics-based approach to sub-threshold signals to predict single nucleotide polymorphisms (SNPs) that would go on to achieve genome-wide significance in a future round of GWAS. Over the past decade, sequencing technologies such aa RNA-seq (Wang et al., 2009), ATAC-seq (Buenrostro et al., 2013), and high-resolution promoter-focused Capture C Chesi et al., 2019; Hughes et al., 2014; Su et al., 2020 have been developed to facilitate the annotation of genes and their regulatory elements. Such data have been utilized to identify physical variant-to-gene interactions via three-dimensional genomics to implicate effector genes at GWAS loci where both sequence variant and gene reside within regions of open chromatin (Arnold et al., 2015; Çalışkan et al., 2019; Chesi et al., 2019; Cousminer et al., 2020; Javierre et al., 2016; Smemo et al., 2014; Su et al., 2019; Su et al., 2020). By leveraging high-resolution promoter-focused Capture C with ATAC-seq, it is possible to physically connect putatively functional non-coding elements, such as enhancers, harboring disease-relevant SNPs to promoters of specific genes thereby potentially mechanistically implicated in the SNP-associated phenotype. We hypothesized that by applying this variant-to-gene mapping method to sub-threshold SNP signals, the molecular constraints on these signals would result in a set of SNPs that are plausibly related to the biology of the trait in question, thereby bypassing the requirement of a larger GWAS to implicate these signals.

To test this hypothesis, we leveraged multiple GWAS data sets, increasing in sample size over time, to determine whether our sub-threshold implicated leads became genome-wide significant in a subsequent larger study. We chose to test this method utilizing GWAS data sets for body mass index (BMI) and waist-to-hip ratio adjusted for BMI (WHRadjBMI) as initial traits because they have both been the subject of particularly large GWAS efforts by the GIANT consortium. Additionally, both traits have implicated a large number of loci and therefore provide optimal statistical power for our purposes. With respect to variant-to-gene mapping, we utilized our existing 3D genomic data sets for adipocytes as well as hypothalamic neurons as previous studies have implicated these cell types in BMI-associated variants (Locke et al., 2015).

Results

Suggestive proxy SNP identification

We first applied our chromatin-based variant-to-gene mapping approach to predict BMI loci that would be subsequently reported as genome-wide significant in a 2015 GWAS (n = 339,224) but were only suggestive in 2010 (n = 249,796). Because the sentinel SNPs reported from a GWAS are not necessarily causal with regard to phenotype, but very likely in linkage disequilibrium (LD) with the responsible allele, we first identified proxy SNPs (r2 > 0.8) for each of the 2010 suggestive SNPs, 5×10−8≤p<5×10−4. Here, we detected 26,343 proxy SNPs; however, some of these proxy signals were in LD with an already established genome-wide significant signal. Given the purpose of this analysis was to identify new signals that were not yet genome-wide significant, we eliminated all proxies that were in even very modest LD, with an already genome-wide significant signal using an r2 > 0.1. This filtering left a residual of 23,197 signals.

ATAC-seq and promoter-focused capture C

We generated ATAC-seq and promoter-focused Capture C libraries from mesenchymal stem cell (MSC)-derived adipocytes and leveraged our existing comparable data from human ESC-derived hypothalamic neurons to query open chromatin maps for open sub-threshold SNPs that contact open gene promoters (Pahl et al., 2020). The adipocytes were derived from mesenchymal stem cells. Three ATAC-seq libraries were sequenced and analyzed with the ENCODE pipeline (https://github.com/kundajelab/atac_dnase_pipelines). Peaks from all replicates were merged by bedtools (v2.25.0) provided peaks were present in at least two biological replicates. These resulted in 2,225,635 adipocyte peaks and 179,212 hypothalamic neuron peaks.

The adipose capture C libraries had an average of 1.4 billion reads per adipose library, with an average of 41% valid read pairs and 89% capture efficiency. We then called significant interactions using the CHiCAGO pipeline and performed analyses at a 1-fragment resolution to identify short-distance interactions (258,882 interactions) and at 4-fragment resolution to identify long-distance interactions (278,040) (Cairns et al., 2016).

Variant-to-gene mapping reveals suggestive 2010 BMI loci that subsequently achieved genome-wide significance in 2015

We next identified physical contacts between any of the remaining proxy SNPs and gene promoters, with the additional constraint that both points of contact were within regions of open chromatin and that the SNP itself did not map to a gene promoter. This approach favors SNPs most likely to have a functional role in the regulation of genes relevant to a given trait and is therefore dependent on the cell type utilized to make such inferences. For BMI variant-to-gene mapping, we used data derived from MSC-derived adipocytes and human ESC-derived hypothalamic neurons because of the known roles of these cell types in BMI. To identify the point at which this approach is no longer viable due to noise, that is, at point of negligible return and excessive false positives, we stratified the suggestive regions into several bins: 5×10−8≤p<5×10−7, 5×10−8≤p<5×10−6, 5×10−8≤p<5×10−5, and 5×10−8≤p<5×10−4. Note that each successive bin is inclusive of the SNPs from the previous bin.

Upon identifying loci that passed these filters, we quantified those that had reached genome-wide significance by 2015. To avoid redundant inclusion of SNPs in LD with one another, we collapsed all biologically constrained SNPs into independent SNP clusters, designated here as separate independent ‘loci’. We defined such loci as the set of SNPs surviving the variant-to-gene mapping filter that were in LD with one another at a relatively relaxed r2 threshold of >0.4.

One hundred and sixty-one of the 23,197 suggestive proxy SNPs survived these biological filters at our most relaxed p-value threshold. These SNPs corresponded to 78 loci, of which 11 achieved genome-wide significance by 2015 (Table 1); these are annotated on the 2015 BMI Manhattan plot (Figure 1). Four of these loci were highlighted in the 2010 study, but the associations were only considered ‘suggestive’ within the stage one discovery set at that time, that is at p>5×10−8. Across all suggestive bins and cell types, the positive predictive value was low through 2015, and the proportion of constrained signals actually achieving genome-wide significance (GWS) did not differ significantly from the proportion of the unconstrained signals achieving GWS within the corresponding p-value bin (Figure 2). This is likely a function of the relatively modest increase in sample size (+89,428) between 2010 and 2015. At the relatively relaxed p-value threshold of 5×10−8≤p<5×10−5, we observed that 6/15 2010 biologically constrained loci were GWS in 2015, whereas 43/163 unconstrained loci were GWS by 2015. A flowchart describing this pipeline using the 2010–2015 BMI data is available in Figure 3.

2015 BMI Manhattan plot depicting loci identified with 2010 salvaged SNPs 2015 BMI loci identifiable with 2010 salvaged SNPs.

Cell type where locus was identified indicated below locus name. Color indicates the p-value threshold where the locus became implicated (Locke et al., 2015). Color key: Green – 5×10−8≤p<5×10−7, blue – 5×10−7≤p<5×10−6, orange – 5×10−6≤p<5×10−5, red – 5×10−5≤p<5×10−4.

Independent 2010 BMI SNPs identified via variant-to-gene mapping that go on to reach genome-wide significance by 2015, as well as the set of unconstrained 2010 suggestive SNPs that achieve genome-wide significance by 2015.

Positive predictive value is depicted as a percentage for each bar. Above these percentages, the p-value, as identified through Fisher’s exact test, is posted. These p-values depict the probability that the proportions of salvaged SNPs using variant-to-gene mapping differ from simply salvaging all suggestive SNPs within the same suggestive bin.

Figure 2—source data 1

Number of 2010 loci identified by constrained method and the number that achieved GWS by 2015 in each cell type.

https://cdn.elifesciences.org/articles/62206/elife-62206-fig2-data1-v2.csv
Figure 2—source data 2

Number of 2010 loci identified with no constraint and the number that achieved GWS by 2015.

https://cdn.elifesciences.org/articles/62206/elife-62206-fig2-data2-v2.csv
Flowchart of the pipeline describing each computational step.

BMI 2010–2015 data is utilized here as an example to report the number of SNPs and loci that occur at each step of the analysis.

Table 1
2015 BMI loci that were implicated with our method in the 2010 data set.

The 2015 genome-wide significant BMI loci whose sentinel SNP was in LD with SNPs implicated from suggestive association in the 2010 BMI GWAS. Key: Notable genes from biological relevance to obesity (B); copy number variation (C); DEPICT analyses (D); GRAIL results (G); BMI-associated variant is in strong LD (r2 ≥ 0.8) with a missense variant in the indicated gene (M); gene nearest to index SNP (N); association and eQTL data converge to affect gene expression (Q) (Locke et al., 2015).

Novel as of 2015 (Locke et al.)
2015 sentinel SNP2010 implicated SNPs2015 assigned locus nameInteracting gene
rs4740619rs10810462C9orf93(C,M,N)TCONS_00015651
rs17094222rs117597828HIF1AN(N)PAX2
rs2820292rs12086240, rs2820315NAV1(N)TIMM17A
rs758747rs2238435NLRC3(N)TCONS_00024950TCONS_00024564TCONS_00024949TCONS_00024562RP11-462G12.1TCONS_00024568TCONS_00024570RP11-95P2.1TCONS_00024567TCONS_00024569TCONS_00024320TCONS_00024952
rs3736485rs7183479SCG3(B,D); DMXL2(M,N)LYSMD2SCG3, CTD-2308G16.1TMOD2
Identified between 2010 (Speliotes et al.) and 2015 (Locke et al.)
2015 Sentinel SNP2010 Implicated SNPs2015 Assigned locus nameInteracting gene
rs17024393rs72705210GNAT2(N); AMPD2(D)GSTM3, AHCYL1
rs4256980rs10840079, rs10840087, rs11041999, rs11042023, rs12803166, rs4256980,TRIM66(D,M,N); TUB(B)PBLD, RPL27A, TRIM66
Identified in 2010 (Speliotes et al.), but was not genome-wide significant
2015 Sentinel SNP2010 Implicated SNPs2015 Assigned Locus NameInteracting gene
rs10182181rs12713419, rs13012304, rs6718510, rs7597332, rs7608976ADCY3(B,M,N,Q); POMC(B,G); NCOA1(B); SH2B1(B,M,Q); APOBR(M,Q);ADCY3, TCONS_00003602
rs12016871rs7988412,MTIF3(N); GTF3A(Q) *~1 Mb from sentinelMTIF3
rs1808579rs1788783NPC1(B,G,M,Q); C18orf8(N,Q)NPC1
rs2287019rs11672660, rs34783010QPCTL(N); GIPR(B,M)GIPR

BMI GWAS variant-to-gene mapping constraints between 2010 and 2018

Using the same set of 2010 surviving SNPs from the previous section, we next identified how many of these SNPs reached GWS by the 2018 BMI GWAS (N = 681,275), representing a nearly tripling in cohort size compared to 2010. Here, we observed that 45 of the 78 2010 biologically constrained loci achieved genome-wide significance by 2018 at the most relaxed p-value threshold (Table 2).

Table 2
2018 BMI loci that were identified using 2010 salvaged.

SNPs 2018 BMI loci identified as genes nearest to genome-wide significant SNPs that could be identified using SNPs salvaged from suggestive regions of the 2010 BMI GWAS.

Nearest gene to sentinelSurviving proxy SNPsLowest threshold found
ABHD17Ars893543, rs893542, rs116713475 × 10−4
AC007879.5rs11677847, rs72951700, rs11689163, rs72966483, rs11694560, rs11692026, rs964621, rs9646225 × 10−4
ADCY3rs6718510, rs7597332, rs7608976, rs13012304, rs127134195 × 10−4
ADCY9rs710893, rs2531993, rs22384355 × 10−5
AK5rs127299145 × 10−5
AP000439.5rs116057295 × 10−4
BCL7Ars72998425 × 10−4
C10orf32rs70851045 × 10−4
C18orf8rs17888265 × 10−4
C1orf61rs112644835 × 10−4
CCDC171rs108104625 × 10−4
CNNM2rs19260325 × 10−4
COQ4rs14686485 × 10−4
CRTC1rs4808845, rs48088445 × 10−4
DPYDrs120774425 × 10−4
EIF2B5rs3914188, rs356374225 × 10−4
EXOSC10rs1884429, rs120417405 × 10−4
FAIM2rs4220225 × 10−4
GAB2rs8692025 × 10−4
GIPRrs34783010, rs116726605 × 10−7
GPR61rs727052105 × 10−4
HIF1ANrs1175978285 × 10−4
HOXB1rs23260135 × 10−4
IFNGR1rs172587505 × 10−4
IPO9rs28203155 × 10−5
KCNJ12rs99060725 × 10−4
LMOD1rs20472645 × 10−4
MAP2K3rs2001651, rs37855425 × 10−4
MAP3K7CLrs9282775 × 10−4
MEF2Drs2274319, rs1925950, rs12038396, rs3818463, rs2274320, rs22743175 × 10−4
MLNrs11752353, rs6921487, rs72880511, rs1887340, rs737465095 × 10−4
MLXIPrs28530689, rs10773037, rs28737311, rs36158849, rs22805735 × 10−4
MST1Rrs3774758, rs2252833, rs64461875 × 10^−4
MTIF3rs79884125 × 10−7
MTORrs11581010, rs108644905 × 10−4
NAV1rs120862405 × 10−5
NPC1rs17887835 × 10−4
RASA2rs20428645 × 10−4
RCAN2rs39343935 × 10−5
RNU6-543Prs107616895 × 10−4
RP11-493K19.3rs131009035 × 10−4
RP11-562L8.1rs128876365 × 10−5
RP11-68I18.10rs107888005 × 10−5
RP11-707P17.1rs71834795 × 10−4
SAE1rs4664775 × 10−4
SKAP1rs16951519, rs22401215 × 10−5
STK33rs10840087, rs11041999, rs340099215 × 10−4
TNRC6Brs6001834, rs48204095 × 10−4
TRIM66rs10840079, rs4256980, rs11042023, rs128031665 × 10−6
TTC34rs64240625 × 10−5
URM1rs7859557, rs22409485 × 10−4
XXYLT1rs584349655 × 10−4

The proportion of 2010 suggestive SNPs meeting these criteria that achieved GWS by 2018 within suggestive bin 5×10−8≤p<5×10−5 was particularly striking (Figure 4). At this threshold, 86.7% (13/15) of biologically constrained loci identified achieved genome-wide significance by 2018, whereas only 40% (6/15) had achieved genome-wide significance by 2015 (Figure 2); clearly, this improvement is a function of relative cohort size. While an increase was also observed with loci identified with no constraint (43/163 suggestive 2010 loci achieving GWS by 2015 and 105/163 achieving GWS by 2018), the precision was significantly higher with the biological constraint than without.

Figure 4 with 1 supplement see all
Independent 2010.

BMI SNPs salvaged via variant-to-gene mapping that go on to reach genome-wide significance by 2018, as well as the set of unconstrained 2010 suggestive SNPs that achieve genome-wide significance by 2018. Positive predictive value is depicted for each bar. Above these percentages, the p-value, as identified through Fisher’s exact test, is posted. These p-values depict the probability that the proportions of salvaged SNPs using variant-to-gene mapping differ from simply salvaging all suggestive SNPs within the same suggestive bin.

Figure 4—source data 1

Number of 2010 loci identified by constrained method and the number that achieved GWS by 2018 in each cell type.

https://cdn.elifesciences.org/articles/62206/elife-62206-fig4-data1-v2.csv
Figure 4—source data 2

Number of 2010 loci identified with no constraint and the number that achieved GWS by 2018.

https://cdn.elifesciences.org/articles/62206/elife-62206-fig4-data2-v2.csv

We assessed the positive predictive capability of this method across the stratified p-value bins to determine at which level of significance this biologically constrained analytic no longer outperformed randomly selected signals from the same bin. As shown in Figure 4—figure supplement 1, there is no point across either cell type or p-value bin where randomly sampled SNPs outperform the chromatin-constrained approach. Forty-four of 78 suggestive loci were identified in the 5×10−8≤p<5×10−4 bin, with a positive predictive value approximately 1.2-fold higher than random selection. As anticipated, fewer novel chromatin-implicated loci were detected in the 5×10−8≤p<5×10−5 bin, though the positive predictive value was approximately 1.5-fold higher than random. Taken together, these results suggest that this chromatin-constrained approach consistently performs better than random within these serial data sets, though there are diminishing returns as the p-value threshold is made increasingly less stringent.

BMI GWAS variant-to-gene mapping constraints between 2015 and 2018

We also applied the chromatin-based variant-to-gene mapping approach to the 2015 BMI GWAS data to assess the efficiency of this analysis in identifying loci that would achieve genome-wide significance by 2018. The identified proxy SNPs, their nearest 2018 GWS proxy, and the nearest gene to those proxies are described in Supplementary file 1 (see Supplementary file 2 for the implicated genes). We observed that across all cell types with 5×10−8≤p<5×10−4, 117 of the implicated 184 loci reached GWS in 2018. At the 5×10−8≤p<5×10−5 threshold, 80% (57/71) of the constrained 2015 implicated loci reached GWS in 2018, while 59% (148/248) of the unconstrained 2015 loci reached GWS in 2018 (Figure 5).

Independent 2015.

BMI SNPs salvaged via variant-to-gene mapping that go on to reach genome-wide significance by 2018, as well as the set of unconstrained 2015 suggestive SNPs that achieve genome-wide significance by 2018. Positive predictive value is depicted for each bar. The posterior probability that loci identified by our chromatin-based constraint more often achieve GWS than loci with no constraint is posted above these percentages.

Figure 5—source data 1

Number of 2015 loci identified by constrained method and the number that achieved GWS by 2018 in each cell type.

https://cdn.elifesciences.org/articles/62206/elife-62206-fig5-data1-v2.csv
Figure 5—source data 2

Number of 2015 loci identified with no constraint and the number that achieved GWS by 2018.

https://cdn.elifesciences.org/articles/62206/elife-62206-fig5-data2-v2.csv

The results from the analyses at these years present the greatest levels of significance observed thus far. Given the presence of many more loci in each bin, we observed significance at the 5×10−8≤p<5×10−6 threshold in both cell types, where 93% (26/28) of the surviving signals reached GWS by 2018 in both cell types. This appears to be principally due to the larger sample sizes at this bin size relative to 2010, which provided additional power to observe such differences. Considering the results of all BMI retrospective analyses through 2018, we found that this method positively identified sub-threshold BMI signals that went on to achieve GWS at a later date significantly more often than when we apply no biological constraint.

Constraining WHRadjBMI GWAS reports: 2010–2018

We also assessed the performance of this method in the 2010 WHRadjBMI GWAS (n = 77,167) relative to 2018 (n = 694,649) (The results of the WHRadjBMI 2010 WHRadjBMI relative to 2015 [n = 224,459,459], and 2015 WHRadjBMI relative to 2018, are available in our Figure 6—figure supplement 2, Figure 6—figure supplement 2, and Supplementary file 2). Here, we found 15,457 proxies within the most relaxed p-value threshold of 5×10−8≤p<5×10−4. One hundred and fifty-seven of these proxies survived our biological constraints, corresponding to 71 independent loci. Thirty-six of these ultimately achieved GWS by the 2018 GWAS (Figure 6).

Figure 6 with 3 supplements see all
Independent 2010.

WHRadjBMI SNPs salvaged via variant-to-gene mapping that go on to reach genome-wide significance by 2018, as well as the set of unconstrained 2010 suggestive SNPs that achieve genome-wide significance by 2018. Positive predictive value is depicted for each bar. Above these percentages, the p-value, as identified through Fisher’s exact test, is posted. These p-values depict the probability that the proportions of salvaged SNPs using variant-to-gene mapping differ from simply salvaging all suggestive SNPs within the same suggestive bin.

Figure 6—source data 1

Number of 2010 loci identified by constrained method and the number that achieved GWS by 2018 in each cell type.

https://cdn.elifesciences.org/articles/62206/elife-62206-fig6-data1-v2.csv
Figure 6—source data 2

Number of 2010 loci identified with no constraint and the number that achieved GWS by 2018.

https://cdn.elifesciences.org/articles/62206/elife-62206-fig6-data2-v2.csv

Precision of the biologically constrained 2010 loci reaching genome-wide significance in 2018 for both traits through 5×10−8≤p<5×10−6 was 100% for each cell type, except for the 2010 WHRadjBMI SNPs constrained by adipose chromatin data which equals 83% (Figure 6—figure supplement 1). This represents a 1.3- to 1.6-fold increase over the mean PPV of a randomly sampled unconstrained SNP set at the same p-value threshold. The number of loci surviving this constraint present in this p-value threshold is quite modest (BMI: 4/4 vs WHRadjBMI: 5/6). The threshold may be further relaxed (5×10−8<p<5×10−5) to identify additional loci: BMI: 13/15 vs WHRadjBMI: 13/20. Relaxing even further to p<5×10−4 yielded far more loci, but brought in more potential false positives (BMI: 44/78 vs WHRadjBMI: 36/71), although they could still become GWS at a future time point. Despite the larger rate of false positives at a threshold of 5×10−8<p<5×10−5 in WHRadjBMI relative to BMI, we still observe that the chromatin-constraint surviving sub-threshold WHRadjBMI signals went on to achieve GWS at a later date significantly more often than when we apply no constraint.

Predictive power of negative control does not differ from the unconstrained set

We assessed whether there was any period (2010–2018) in which PPV for the set of SNPs that do not physically contact gene promoters or are not located within regions of open chromatin differed significantly from the unconstrained signals, that is, if there was any period in which such a negative control outperformed, the unconstrained signals. We found that there was no p-value bin, cell type or trait for which there was a difference between this negative set and the unconstrained set (Figure 7 and Figure 8). The absence of observable differences between the negative control and unconstrained sets supports the inference that the differences observed between our biological constrained data and the unconstrained data are in fact attributable to the chromatin-constrained analytic strategy.

Independent 2010.

BMI SNPs failing the variant-to-gene mapping filter that go on to reach genome-wide significance by 2018. Positive predictive value is depicted for each bar. The posterior probability that loci identified by our chromatin-based constraint more often achieve GWS than loci with no constraint is posted above these percentages. There is no threshold where this data differs significantly from the unconstrained set.

Figure 7—source data 1

Number of 2010 loci identified by constrained method and the number that achieved GWS by 2018 in each cell type.

https://cdn.elifesciences.org/articles/62206/elife-62206-fig7-data1-v2.csv
Independent 2010.

WHRadjBMI SNPs failing the variant-to-gene mapping filter that go on to reach genome-wide significance by 2018. Positive predictive value is depicted for each bar. The posterior probability that loci identified by our chromatin-based constraint more often achieve GWS than loci with no constraint is posted above these percentages. There is no threshold where this data differs significantly from the unconstrained set.

Figure 8—source data 1

Number of 2010 loci identified by constrained method and the number that achieved GWS by 2018 in each cell type.

https://cdn.elifesciences.org/articles/62206/elife-62206-fig8-data1-v2.csv

Finally, we assessed the classification rates of this approach for both traits in all testable years. At each threshold for prior GWAS significance, we used our biological constraints to predict whether an implicated locus would achieve GWS in a subsequent GWAS. We found that, across all thresholds and chromatin-constrained loci in each cell type, sensitivity was generally less than 25%, indicating a high rate of false negatives. This is expected because the chromatin-constrained data represent effects that are only captured by the cell-types (and their respective developmental maturities) that were profiled. By combining the loci identified in either adipose or hypothalamic neurons, sensitivity was increased, though the increment was modest. Specificity, however, was consistently >80%, and often >90%, indicating few false positives. The precision was particularly high through 5×10−8<p<5×10−5; however, expanding through to 5×10−8<p<5×10−4 resulted in relatively high proportions of false positives in most analyses, although always at a lower rate than without a chromatin-constraint. As a control, we analyzed classifications of a negative control set in which loci failed to survive the variant-to-gene mapping filter for each cell type and suggestive threshold. There was no instance across trait, cell type, or p-value bin in which the specificity of the negative control outperformed the variant-to-gene mapping filter (Source data 1). All together, the consistently high specificity and precision observed conveys the capacity of this chromatin-constraint to preferentially retain signals that are likely to achieve GWS in larger data sets while minimizing the presence of false positives through 5×10−8<p<5×10−5.

Discussion

In this study, we used our recently described variant-to-gene structural mapping approach (Chesi et al., 2019; Su et al., 2020) to conduct retrospective biologically constrained analyses of previous sequential GWAS reports to determine whether we could implicate statistically suggestive SNPs that would subsequently achieve GWS in incrementally larger data sets. Using a combination of high-resolution promoter-focused Capture C and ATAC-seq, this method enables the prioritization of statistically suggestive loci.

We assessed the extent to which this method can be applied to ‘salvage’ sub-threshold loci for possible consideration. We used BMI and WHR adjusted for BMI because of the relatively large number of loci identified in GWAS efforts conducted in large cohorts that continued to increase over time. The number of SNPs surviving these filters for each cell type and trait at each successively relaxed statistical cutoff are described in Source data 1.

Given the fairly modest increase in sample size between 2010 (N = 249,796) and 2015 (N = 339,224) for BMI, it was not particularly surprising that majority of the surviving 2010 BMI signals did not achieve GWS until 2018 (N = 681,275). Only six of the 15 surviving signals achieved GWS by 2015, but 13 eventually achieved GWS by 2018. Reaching deeper into the 2010 suggestive signals through p<5×10−4 showed a much clearer trend for both traits: roughly 20% of the surviving 2010 signals achieved GWS by 2015 and nearly 50% achieved GWS by 2018. Despite positive predictive values consistently greater than 80% for loci with 5×10−8≤p<5×10−6, it remains to be seen how many of the remaining surviving SNPs with less significant p-values will achieve GWS by the publication of the next GWAS in the future for each corresponding trait.

In this study, we never identified a particular sub-threshold bin where no constraint was as precise as this chromatin-based constraint. We did, however, find that the precision observed for loci in data sets between 2010 and 2018 varied by trait. While the positive predictive value was nearly 90% for 2010 BMI constrained loci reaching GWS by 2018 at 5×10−8≤p<5×10−5, only 65% of the surviving 2010 WHRadjBMI achieved GWS by the subsequent 2018 study. At the next, more restrictive, threshold, 5×10−8≤p<5×10−6, we found that nearly all of the surviving loci for both traits of the same years reached GWS by their respective 2018 study, although the number of loci within this threshold is small (BMI: 4/4, WHRadjBMI: 5/6). Thus, our findings suggest that this strategy is capable of identifying loci that will achieve GWS at 5×10−8≤p<5×10−6. Additional loci can be identified at 5×10−8≤p<5×10−5, at the expense of a degree of false positives. Although as noted above, such false-positive signals could possibly go on to be GWS in a future study.

We also observed that across all suggestive thresholds, specificity was consistently >80%, and often >90%. The consistently high specificity and precision, at least through 5×10−8≤p<5×10−6, suggest that true negatives are largely properly identified without the generation of large amounts of false positives. The low false-positive rate did come at a cost, however. Sensitivity was extremely low at all thresholds, often below 20% (Source data 1). The lack of sensitivity conveys high levels of false negatives, meaning many signals that would eventually achieve GWS would not be properly classified with this method.

The strength of this method is in its ability to attach biological relevance to sub-threshold SNPs in individual cell types, but this can also serve as its weakness. However, given the cost in time and resources to test candidate loci, we believe that high specificity and precision are more important characteristics for such a classifier. While we believe this trade-off is acceptable for the identification of novel biologically relevant loci prior to their confirmation via GWAS, we recognize that there are many loci that are falsely identified as not relevant. In utilizing the chromatin state of individual cell types in such a manner, we may reject loci that are biologically relevant in a different cell type or those that simply lack such an epigenetic mechanism. Provided a more comprehensive library of chromatin state in a much more diverse set of cell types, or the inclusion of additional biological filters that could identify loci that lack this epigenetic mechanism, we could potentially increase sensitivity in a substantial manner while retaining high rates of precision and specificity.

Despite any such limitations, we implicated loci most likely to become significant in the context of larger data sets with just our chromatin-based constraint approach. Using this variant-to-gene mapping approach, one can prioritize loci/genes of borderline statistical significance that may have important candidacy based upon functional considerations. Confirmation could come via larger data sets and/or by direct molecular physiological analyses of the candidates.

Materials and methods

ATAC-seq library generation and peak calls

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Tn5 transposase transposition (Illumina Cat #FC-121–1030, Nextera) and purification of the Tn5 transposase derived DNA from 100,000 cells of each investigated cell type. The samples were then shipped to the Center of Spatial and Functional Genomics at CHOP where the ATAC-seq process was completed. Live cells were harvested via trypsinization, followed by a series of wash steps. One hundred thousand cells from each sample were pelleted at 550 × g for 5 min at 4°C. The cell pellet was then resuspended in 50 μl cold lysis buffer (10 mM Tris–HCl, pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% IGEPAL CA-630) and centrifuged immediately at 550 × g for 10 min at 4°C. The nuclei were resuspended in the transposition reaction mix (2× TD Buffer [Illumina Cat #FC-121–1030, Nextera], 2.5 µl Tn5 transposase [Illumina Cat #FC-121–1030, Nextera], and nuclease-free H2O) on ice and then incubated for 45 min at 37°C. The transposed DNA was then purified using the MinElute Kit (Qiagen), eluted with 10.5 μl elution buffer (EB), frozen, and sent to the Center for Spatial and Functional Genomics at CHOP. The transposed DNA was PCR amplified using Nextera primers for 12 cycles to generate each library. The PCR was subsequently cleaned up using AMPureXP beads (Agencourt), and libraries were paired-end sequenced on an Illumina HiSeq 4000 (100 bp read length) and the Illumina NovaSeq platform. Open chromatin regions were called using the ENCODE ATAC-seq pipeline (https://www.encodeproject.org/atac-seq/), selecting the resulting IDR conservative peaks (with all coordinates referring to hg19). We define a genomic region open if it has 1 bp overlap with an ATAC-seq peak.

Cell fixation for chromatin capture

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The protocol used for cell fixation was similar to previous methods (Hughes et al., 2014). Cells were collected and single-cell suspension were made with aliquots of 107 cells in 10 ml media (e.g. RPMI + 10% FCS). Five hundred and forty microliters of 37% formaldehyde was added and incubated for 10 min at RT on a platform rocker. The reaction was quenched by adding 1.5 ml 1 M cold glycine (4°C) for a total volume of 12 ml. Fixed cells were centrifuged at 1000 rpm for 5 min at 4°C and supernatant removed. The cell pellets were washed in 10 ml cold PBS (4°C) followed by centrifugation as above. Supernatant was removed and cell pellets were resuspended in 5 ml of cold lysis buffer (10 mM Tris pH8, 10 mM NaCl, 0.2% NP-40 [Igepal] supplemented with protease inhibitor cocktails). Resuspended cells were incubated for 20 min on ice, centrifuged as above, and the lysis buffer removed. Finally, cell pellets were resuspended in 1 ml fresh lysis buffer, transferred to 1.5 ml Eppendorf tubes, and snap frozen (ethanol/dry ice or liquid nitrogen). Cells were stored at −80°C until they were thawed for 3C library generation.

Capture C

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Custom capture baits were designed using an Agilent SureSelect library design targeting both ends of DpnII restriction fragments encompassing promoters (including alternative promoters) of all human coding genes, noncoding RNA, antisense RNA, snRNA, miRNA, snoRNA, and lincRNA transcripts, totaling 36,691 RNA-baited fragments. The library was designed using scripts generously provided by Dr. Hughes (Oxford, UK), utilizing the RefSeq, lincRNA transcripts, and sno/miRNA tracks in the hg19 assembly downloaded from the UCSC Table Browser on 16 September 2015. The capture library design covered 95% of all coding RNA promoters and 88% of RNA types described above. The missing 5% of coding genes that could not be designed were either duplicated genes or contained highly repetitive DNA in their promoter regions.

The isolated DNA from the 3C libraries was quantified using a Qubit fluorometer (Life Technologies), and 10 μg of each library was sheared in dH2O using a QSonica Q800R to an average DNA fragment size of 350 bp. QSonica settings used were 60% amplitude, 30 s on, 30 s off, 2 min intervals, for a total of five intervals at 4°C. After shearing, DNA was purified using AMPureXP beads (Agencourt). Sample concentration was checked via Qubit fluorometer and DNA size was assessed on a Bioanalyzer 2100 using a 1000 DNA Chip. Agilent SureSelect XT Library Prep Kit (Agilent) was used to repair DNA ends and for adaptor ligation following the standard protocol. Excess adaptors were removed using AMPureXP beads. Size and concentration were checked again before hybridization. One microgram of adaptor ligated library was used as input for the SureSelect XT capture kit using their standard protocol and our custom-designed Capture C library. The quantity and quality of the captured library were assessed by Qubit fluorometer and Bioanalyser using a high-sensitivity DNA Chip. Each SureSelect XT library was initially sequenced on one lane HiSeq 4000 paired-end sequencing (100 bp read length) for QC. All six Capture C promoter interactome libraries were then sequenced three at a time on an S2 flow cells on an Illumina NovaSeq, generating ~1.6 billion paired-end reads per sample (50 bp read length).

Analysis of promoter-focused capture C data

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Quality control of the raw fastq files was performed with FastQC. Paired-end reads were pre-processed with the HiCUP pipeline (Wingett et al., 2015), with bowtie2 as aligner and hg19 as reference genome. Significant promoter interactions at 1-DpnII fragment resolution were called using CHiCAGO (Cairns et al., 2016), with default parameters except for binsize which was set to 2500. Significant interactions at 4-DpnII fragment resolution were also called with CHiCAGO using artificial .baitmap and .rmap files where DpnII fragments were grouped into four consecutively and using default parameters except for removeAdjacent that was set to false. We define PIR a promoter-interacting region, irrespective of whether it is a baited region or not. The CHiCAGO function peakEnrichment4Features() was used to assess enrichment of genomic features in promoter-interacting regions at both 1-fragment and 4-fragment resolution.

Variant-to-gene mapping pipeline

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BMI and WHRadjBMI GWAS summary statistics derived from European ancestry, each from 2010, 2015, and 2018, were downloaded from the Genetic Investigation of Anthropometric Traits (GIANT) consortium https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files (Heid et al., 2010a; Locke et al., 2015; Pulit et al., 2019; Shungin et al., 2015; Speliotes et al., 2010b; Yengo et al., 2018). We identified all genome-wide significant variants from each data set as any SNP with p<5×10−8. We then identified the sets of variants in the varying suggestive bins of 5×10−8≤p<5×10−7, 5 × 10−8 ≤ p<5×10−6, 5 × 10−8 ≤ p<5×10−5, and 5 × 10−8 ≤ p<5×10−4. Upon the identification of these variants within each bin, we utilized SNiPA v3.3 (Arnold et al., 2015) to find their proxy SNPs, which we define as any SNP with r2 > 0.8 in Europeans for each region. Only signals with LD information within SNiPA were considered.

We next conducted a variant-to-gene mapping for each cell type within each of the suggestive bins to identify the gene promoters that are in contact with these SNPs. Promoter Capture C libraries are utilized to identify the genes with which these variants have interactions. Significant interactions were called using the CHiCAGO pipeline (Cairns et al., 2016) utilizing both 1-fragment and 4-fragment resolutions. We chose to focus on non-gene-to-gene interactions; thus, we ignored interactions that were identified as bait-to-bait as these represent the interactions between gene promoters. ATAC-seq libraries were also utilized to identify only those gene promoters that interact with SNPs in regions of open chromatin. Genes within the annotated MHC region were removed due to its highly polymorphic nature.

We next removed SNPs within the suggestive bins that were in linkage disequilibrium with any SNP that was significant genome wide. This was done to determine an entirely novel set of variant interacting genes that are entirely independent of those that are already known. To accomplish this, we removed all variants that were found to be in LD with a variant that is significant at a genome level. Thus, we identified all proxies of genome-wide significant variants at r2 > 0.1 and remove any variant within the suggestive region that is found to be a proxy of these genome-wide significant variants. This provides a filtered set of SNPs that are independent of any SNP that is significant at a genome-wide scale.

Retrospective analysis

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Interacting SNP-gene promoter pairs were identified by the variant-to-gene mapping pipeline described above for each GWAS across all suggestive zones for each cell type. Of these SNP-gene promoter pairs, we identify those that are in tight LD (r2 >0.8) with an SNP that is genome-wide significant in a future study. The implicated genes derived from our variant-to-gene mapping approach were frequently different from the locus ‘names’ traditionally used in publications reporting GWAS findings, typically based on the nearest gene. In contrast, our variant-to-gene mapping used the integration of ATAC-seq and promoter-focused Capture C data to identify the gene promotor(s) physically in contact with a genomic region (almost always non-coding) harboring an associated variant. However, in order to assess the 2015 loci that were identifiable in 2010 with this method, for consistency’s sake, we annotated the implicated 2010 loci with the published locus ‘name’ annotation from the respective 2015 GWAS report. However, unlike the 2015 efforts, the 2018 studies did not annotate the reported loci with an arbitrary gene notation; thus, we simply labeled the nearest gene to the 2018 sentinel SNP in which the surviving signals were in tight LD. The gene promoters in contact with each surviving signal are also available in Source data 1. The surviving SNPs that were in tight LD with an SNP that achieved GWS in 2015 were annotated with the published 2015 locus name of a sentinel SNP residing up to 1 MB away. We indicate the locus name that was provided in each 2015 study, as well as provide the set of gene promoters that were identified by the variant-to-gene mapping pipeline. Manhattan plots were generated in R (R Development Core Team, 2019), utilizing p-values of SNPs from the corresponding summary statistics. SNP positions were identified with dbSNP Build 151 to plot the relative location of the SNPs on the Manhattan plot. The highlighted loci point to the SNP with the lowest p-value identified at each locus.

In the case of all remaining comparisons, retrospective annotation was performed differently. Rather than trying to provide a locus name to the set of SNPs that were identified to be in tight LD with a genome-wide significant SNP in a later year, we elected to identify the surviving SNP, the gene promoters it physically contacts, and its best proxy that reached GWS (as defined by both r2 and p-value in the summary statistics).

To assess the predictive power of this method, we identified the positive predictive value of each set of salvaged SNPs across each retrospective analysis. To accurately quantify the number of loci that are identifiable using salvaged suggestive SNPs, we group the surviving variants based on their LD (r2 > 0.4) to one another as a locus. We extend this one additional time to include the proxies (r2 > 0.4) of each previously identified proxy SNP as a component of each locus. This allows SNPs that failed to be identified as a proxy of the lead SNP to be included within the locus via the transitive property, but only via one iteration of this process. The resulting loci identified are independent from one another, which prevents double counting of loci. We count these loci for each cell type across each suggestive bin, and we also identify the number of distinct loci regardless of cell type. We then identify the set of true positives as a locus that is in tight LD with a genome-wide significant SNP in a future GWAS. Those failing to meet these criteria are deemed false positives. We also identified loci of all genome-wide significant independent suggestive SNPs (r2 < 0.4) for each retrospective analysis and identified true-positive, false-positive, false-negative, and true-negative counts for this set using the same metrics as the constrained data sets. We used a beta-binomial model and used Monte Carlo approximation over 100,000 iterations to identify the posterior probability that loci identified by our chromatin-based constraint more often achieve GWS than loci with no constraint. We also assessed significance of the positive predictive values by calculating an empirical distribution from 10,000 iterations of a randomly sampled population of the unconstrained suggestive signals within each p-value threshold bin. At each iteration, we randomly selected N loci, where N is the corresponding number of loci surviving our chromatin-constraints from either cell type, and quantified the positive predictive value from this sampling. We then plotted the location where the positive predictive values fell for each cell type to determine whether these different significantly from the random population.

We calculated sensitivity, specificity, false-negative rate, and false positive rate of this approach across both traits for all testable years. To do this, we identified binary classifications of sub-threshold loci that were predicted to either reach or not reach GWS by the future year in each cell type separately. We then created confusion matrices for each cell type and p-value bin to calculate each binary classification performance metric. Additionally, we identified these values for the previously described negative control data (the effective inverse of the biological constrained data) as well as the randomly sampled data. For both of these data sets, we identified these metrics across 10,000 randomly sampled populations where we randomly selected N loci, where N is the corresponding number of loci surviving our chromatin-constraints from either cell type at the corresponding p-value threshold.

All source code available on Github: https://github.com/rkweku/SubThresholdProjectScripts.

Data availability

Adipose ATAC-seq and promoter-focused capture C data will be made available on GEO prior to publication. Hypothalamic Neuron ATAC-eq and promoter-focused capture C data is the subject of another atlas-based manuscript currently under peer review and through that process that dataset will be made available once the paper is published- the corresponding hypothalamus preprint can be found at: https://doi.org/10.1101/2020.07.06.146951v1.full.

The following data sets were generated
The following previously published data sets were used

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    132. Hengstenberg C
    133. Holmen O
    134. Hottenga JJ
    135. James AL
    136. Jeff JM
    137. Johansson Å
    138. Jolley J
    139. Juliusdottir T
    140. Kinnunen L
    141. Koenig W
    142. Koskenvuo M
    143. Kratzer W
    144. Laitinen J
    145. Lamina C
    146. Leander K
    147. Lee NR
    148. Lichtner P
    149. Lind L
    150. Lindström J
    151. Lo KS
    152. Lobbens S
    153. Lorbeer R
    154. Lu Y
    155. Mach F
    156. Magnusson PKE
    157. Mahajan A
    158. McArdle WL
    159. McLachlan S
    160. Menni C
    161. Merger S
    162. Mihailov E
    163. Milani L
    164. Moayyeri A
    165. Monda KL
    166. Morken MA
    167. Mulas A
    168. Müller G
    169. Müller-Nurasyid M
    170. Musk AW
    171. Nagaraja R
    172. Nöthen MM
    173. Nolte IM
    174. Pilz S
    175. Rayner NW
    176. Renstrom F
    177. Rettig R
    178. Ried JS
    179. Ripke S
    180. Robertson NR
    181. Rose LM
    182. Sanna S
    183. Scharnagl H
    184. Scholtens S
    185. Schumacher FR
    186. Scott WR
    187. Seufferlein T
    188. Shi J
    189. Smith AV
    190. Smolonska J
    191. Stanton AV
    192. Steinthorsdottir V
    193. Stirrups K
    194. Stringham HM
    195. Sundström J
    196. Swertz MA
    197. Swift AJ
    198. Syvänen AC
    199. Tan ST
    200. Tayo BO
    201. Thorand B
    202. Thorleifsson G
    203. Tyrer JP
    204. Uh HW
    205. Vandenput L
    206. Verhulst FC
    207. Vermeulen SH
    208. Verweij N
    209. Vonk JM
    210. Waite LL
    211. Warren HR
    212. Waterworth D
    213. Weedon MN
    214. Wilkens LR
    215. Willenborg C
    216. Wilsgaard T
    217. Wojczynski MK
    218. Wong A
    219. Wright AF
    220. Zhang Q
    221. Brennan EP
    222. Choi M
    223. Dastani Z
    224. Drong AW
    225. Eriksson P
    226. Franco-Cereceda A
    227. Gådin JR
    228. Gharavi AG
    229. Goddard ME
    230. Handsaker RE
    231. Huang J
    232. Karpe F
    233. Kathiresan S
    234. Keildson S
    235. Kiryluk K
    236. Kubo M
    237. Lee JY
    238. Liang L
    239. Lifton RP
    240. Ma B
    241. McCarroll SA
    242. McKnight AJ
    243. Min JL
    244. Moffatt MF
    245. Montgomery GW
    246. Murabito JM
    247. Nicholson G
    248. Nyholt DR
    249. Okada Y
    250. Perry JRB
    251. Dorajoo R
    252. Reinmaa E
    253. Salem RM
    254. Sandholm N
    255. Scott RA
    256. Stolk L
    257. Takahashi A
    258. Tanaka T
    259. van 't Hooft FM
    260. Vinkhuyzen AAE
    261. Westra HJ
    262. Zheng W
    263. Zondervan KT
    264. Heath AC
    265. Arveiler D
    266. Bakker SJL
    267. Beilby J
    268. Bergman RN
    269. Blangero J
    270. Bovet P
    271. Campbell H
    272. Caulfield MJ
    273. Cesana G
    274. Chakravarti A
    275. Chasman DI
    276. Chines PS
    277. Collins FS
    278. Crawford DC
    279. Cupples LA
    280. Cusi D
    281. Danesh J
    282. de Faire U
    283. den Ruijter HM
    284. Dominiczak AF
    285. Erbel R
    286. Erdmann J
    287. Eriksson JG
    288. Farrall M
    289. Felix SB
    290. Ferrannini E
    291. Ferrières J
    292. Ford I
    293. Forouhi NG
    294. Forrester T
    295. Franco OH
    296. Gansevoort RT
    297. Gejman PV
    298. Gieger C
    299. Gottesman O
    300. Gudnason V
    301. Gyllensten U
    302. Hall AS
    303. Harris TB
    304. Hattersley AT
    305. Hicks AA
    306. Hindorff LA
    307. Hingorani AD
    308. Hofman A
    309. Homuth G
    310. Hovingh GK
    311. Humphries SE
    312. Hunt SC
    313. Hyppönen E
    314. Illig T
    315. Jacobs KB
    316. Jarvelin MR
    317. Jöckel KH
    318. Johansen B
    319. Jousilahti P
    320. Jukema JW
    321. Jula AM
    322. Kaprio J
    323. Kastelein JJP
    324. Keinanen-Kiukaanniemi SM
    325. Kiemeney LA
    326. Knekt P
    327. Kooner JS
    328. Kooperberg C
    329. Kovacs P
    330. Kraja AT
    331. Kumari M
    332. Kuusisto J
    333. Lakka TA
    334. Langenberg C
    335. Marchand LL
    336. Lehtimäki T
    337. Lyssenko V
    338. Männistö S
    339. Marette A
    340. Matise TC
    341. McKenzie CA
    342. McKnight B
    343. Moll FL
    344. Morris AD
    345. Morris AP
    346. Murray JC
    347. Nelis M
    348. Ohlsson C
    349. Oldehinkel AJ
    350. Ong KK
    351. Madden PAF
    352. Pasterkamp G
    353. Peden JF
    354. Peters A
    355. Postma DS
    356. Pramstaller PP
    357. Price JF
    358. Qi L
    359. Raitakari OT
    360. Rankinen T
    361. Rao DC
    362. Rice TK
    363. Ridker PM
    364. Rioux JD
    365. Ritchie MD
    366. Rudan I
    367. Salomaa V
    368. Samani NJ
    369. Saramies J
    370. Sarzynski MA
    371. Schunkert H
    372. Schwarz PEH
    373. Sever P
    374. Shuldiner AR
    375. Sinisalo J
    376. Stolk RP
    377. Strauch K
    378. Tönjes A
    379. Trégouët DA
    380. Tremblay A
    381. Tremoli E
    382. Virtamo J
    383. Vohl MC
    384. Völker U
    385. Waeber G
    386. Willemsen G
    387. Witteman JC
    388. Zillikens MC
    389. Adair LS
    390. Amouyel P
    391. Asselbergs FW
    392. Assimes TL
    393. Bochud M
    394. Boehm BO
    395. Boerwinkle E
    396. Bornstein SR
    397. Bottinger EP
    398. Bouchard C
    399. Cauchi S
    400. Chambers JC
    401. Chanock SJ
    402. Cooper RS
    403. de Bakker PIW
    404. Dedoussis G
    405. Ferrucci L
    406. Franks PW
    407. Froguel P
    408. Groop LC
    409. Haiman CA
    410. Hamsten A
    411. Hui J
    412. Hunter DJ
    413. Hveem K
    414. Kaplan RC
    415. Kivimaki M
    416. Kuh D
    417. Laakso M
    418. Liu Y
    419. Martin NG
    420. März W
    421. Melbye M
    422. Metspalu A
    423. Moebus S
    424. Munroe PB
    425. Njølstad I
    426. Oostra BA
    427. Palmer CNA
    428. Pedersen NL
    429. Perola M
    430. Pérusse L
    431. Peters U
    432. Power C
    433. Quertermous T
    434. Rauramaa R
    435. Rivadeneira F
    436. Saaristo TE
    437. Saleheen D
    438. Sattar N
    439. Schadt EE
    440. Schlessinger D
    441. Slagboom PE
    442. Snieder H
    443. Spector TD
    444. Thorsteinsdottir U
    445. Stumvoll M
    446. Tuomilehto J
    447. Uitterlinden AG
    448. Uusitupa M
    449. van der Harst P
    450. Walker M
    451. Wallaschofski H
    452. Wareham NJ
    453. Watkins H
    454. Weir DR
    455. Wichmann HE
    456. Wilson JF
    457. Zanen P
    458. Borecki IB
    459. Deloukas P
    460. Fox CS
    461. Heid IM
    462. O'Connell JR
    463. Strachan DP
    464. Stefansson K
    465. van Duijn CM
    466. Abecasis GR
    467. Franke L
    468. Frayling TM
    469. McCarthy MI
    470. Visscher PM
    471. Scherag A
    472. Willer CJ
    473. Boehnke M
    474. Mohlke KL
    475. Lindgren CM
    476. Beckmann JS
    477. Barroso I
    478. North KE
    479. Ingelsson E
    480. Hirschhorn JN
    481. Loos RJF
    482. Speliotes EK
    483. LifeLines Cohort Study
    484. ADIPOGen Consortium
    485. AGEN-BMI Working Group
    486. CARDIOGRAMplusC4D Consortium
    487. CKDGen Consortium
    488. GLGC
    489. ICBP
    490. MAGIC Investigators
    491. MuTHER Consortium
    492. MIGen Consortium
    493. PAGE Consortium
    494. ReproGen Consortium
    495. GENIE Consortium
    496. International Endogene Consortium
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    (2019) R: A Language and Environment for Statistical Computing
    R Foundation for Statistical Computing, Vienna, Austria.
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    2. Winkler TW
    3. Croteau-Chonka DC
    4. Ferreira T
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    26. Vedantam S
    27. Wood AR
    28. Chen J
    29. Fehrmann R
    30. Karjalainen J
    31. Kahali B
    32. Liu CT
    33. Schmidt EM
    34. Absher D
    35. Amin N
    36. Anderson D
    37. Beekman M
    38. Bragg-Gresham JL
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    40. Demirkan A
    41. Ehret GB
    42. Feitosa MF
    43. Goel A
    44. Jackson AU
    45. Johnson T
    46. Kleber ME
    47. Kristiansson K
    48. Mangino M
    49. Leach IM
    50. Medina-Gomez C
    51. Palmer CD
    52. Pasko D
    53. Pechlivanis S
    54. Peters MJ
    55. Prokopenko I
    56. Stančáková A
    57. Sung YJ
    58. Tanaka T
    59. Teumer A
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    61. Yengo L
    62. Zhang W
    63. Albrecht E
    64. Ärnlöv J
    65. Arscott GM
    66. Bandinelli S
    67. Barrett A
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    72. Böhringer S
    73. Bonnet F
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    82. Delgado G
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    84. Eklund N
    85. Erdos MR
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    87. Eury E
    88. Friedrich N
    89. Garcia ME
    90. Giedraitis V
    91. Gigante B
    92. Go AS
    93. Golay A
    94. Grallert H
    95. Grammer TB
    96. Gräßler J
    97. Grewal J
    98. Groves CJ
    99. Haller T
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    101. Hartman CA
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    106. Helmer Q
    107. Hillege HL
    108. Holmen O
    109. Hunt SC
    110. Isaacs A
    111. Ittermann T
    112. James AL
    113. Johansson I
    114. Juliusdottir T
    115. Kalafati IP
    116. Kinnunen L
    117. Koenig W
    118. Kooner IK
    119. Kratzer W
    120. Lamina C
    121. Leander K
    122. Lee NR
    123. Lichtner P
    124. Lind L
    125. Lindström J
    126. Lobbens S
    127. Lorentzon M
    128. Mach F
    129. Magnusson PK
    130. Mahajan A
    131. McArdle WL
    132. Menni C
    133. Merger S
    134. Mihailov E
    135. Milani L
    136. Mills R
    137. Moayyeri A
    138. Monda KL
    139. Mooijaart SP
    140. Mühleisen TW
    141. Mulas A
    142. Müller G
    143. Müller-Nurasyid M
    144. Nagaraja R
    145. Nalls MA
    146. Narisu N
    147. Glorioso N
    148. Nolte IM
    149. Olden M
    150. Rayner NW
    151. Renstrom F
    152. Ried JS
    153. Robertson NR
    154. Rose LM
    155. Sanna S
    156. Scharnagl H
    157. Scholtens S
    158. Sennblad B
    159. Seufferlein T
    160. Sitlani CM
    161. Smith AV
    162. Stirrups K
    163. Stringham HM
    164. Sundström J
    165. Swertz MA
    166. Swift AJ
    167. Syvänen AC
    168. Tayo BO
    169. Thorand B
    170. Thorleifsson G
    171. Tomaschitz A
    172. Troffa C
    173. van Oort FV
    174. Verweij N
    175. Vonk JM
    176. Waite LL
    177. Wennauer R
    178. Wilsgaard T
    179. Wojczynski MK
    180. Wong A
    181. Zhang Q
    182. Zhao JH
    183. Brennan EP
    184. Choi M
    185. Eriksson P
    186. Folkersen L
    187. Franco-Cereceda A
    188. Gharavi AG
    189. Hedman ÅK
    190. Hivert MF
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    192. Kanoni S
    193. Karpe F
    194. Keildson S
    195. Kiryluk K
    196. Liang L
    197. Lifton RP
    198. Ma B
    199. McKnight AJ
    200. McPherson R
    201. Metspalu A
    202. Min JL
    203. Moffatt MF
    204. Montgomery GW
    205. Murabito JM
    206. Nicholson G
    207. Nyholt DR
    208. Olsson C
    209. Perry JR
    210. Reinmaa E
    211. Salem RM
    212. Sandholm N
    213. Schadt EE
    214. Scott RA
    215. Stolk L
    216. Vallejo EE
    217. Westra HJ
    218. Zondervan KT
    219. Amouyel P
    220. Arveiler D
    221. Bakker SJ
    222. Beilby J
    223. Bergman RN
    224. Blangero J
    225. Brown MJ
    226. Burnier M
    227. Campbell H
    228. Chakravarti A
    229. Chines PS
    230. Claudi-Boehm S
    231. Collins FS
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    234. de Faire U
    235. de Geus EJ
    236. Dörr M
    237. Erbel R
    238. Eriksson JG
    239. Farrall M
    240. Ferrannini E
    241. Ferrières J
    242. Forouhi NG
    243. Forrester T
    244. Franco OH
    245. Gansevoort RT
    246. Gieger C
    247. Gudnason V
    248. Haiman CA
    249. Harris TB
    250. Hattersley AT
    251. Heliövaara M
    252. Hicks AA
    253. Hingorani AD
    254. Hoffmann W
    255. Hofman A
    256. Homuth G
    257. Humphries SE
    258. Hyppönen E
    259. Illig T
    260. Jarvelin MR
    261. Johansen B
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    300. Ritchie MD
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    302. Salomaa V
    303. Samani NJ
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    309. Steinthorsdottir V
    310. Stolk RP
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    312. Tönjes A
    313. Tremblay A
    314. Tremoli E
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    344. Njølstad I
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    349. Pérusse L
    350. Peters U
    351. Power C
    352. Quertermous T
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    354. Rivadeneira F
    355. Saaristo TE
    356. Saleheen D
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    358. Slagboom PE
    359. Snieder H
    360. Spector TD
    361. Stefansson K
    362. Stumvoll M
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    364. Uitterlinden AG
    365. Uusitupa M
    366. van der Harst P
    367. Veronesi G
    368. Walker M
    369. Wareham NJ
    370. Watkins H
    371. Wichmann HE
    372. Abecasis GR
    373. Assimes TL
    374. Berndt SI
    375. Boehnke M
    376. Borecki IB
    377. Deloukas P
    378. Franke L
    379. Frayling TM
    380. Groop LC
    381. Hunter DJ
    382. Kaplan RC
    383. O'Connell JR
    384. Qi L
    385. Schlessinger D
    386. Strachan DP
    387. Thorsteinsdottir U
    388. van Duijn CM
    389. Willer CJ
    390. Visscher PM
    391. Yang J
    392. Hirschhorn JN
    393. Zillikens MC
    394. McCarthy MI
    395. Speliotes EK
    396. North KE
    397. Fox CS
    398. Barroso I
    399. Franks PW
    400. Ingelsson E
    401. Heid IM
    402. Loos RJ
    403. Cupples LA
    404. Morris AP
    405. Lindgren CM
    406. Mohlke KL
    407. ADIPOGen Consortium
    408. CARDIOGRAMplusC4D Consortium
    409. CKDGen Consortium
    410. GEFOS Consortium
    411. GENIE Consortium
    412. GLGC
    413. ICBP
    414. International Endogene Consortium
    415. LifeLines Cohort Study
    416. MAGIC Investigators
    417. MuTHER Consortium
    418. PAGE Consortium
    419. ReproGen Consortium
    (2015) New genetic loci link adipose and insulin biology to body fat distribution
    Nature 518:187–196.
    https://doi.org/10.1038/nature14132
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    63. Ripatti S
    64. Rivadeneira F
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    66. Scott LJ
    67. Timpson NJ
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    72. Wiklund F
    73. Barlassina C
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    75. Cooper MN
    76. Jansson JO
    77. Lawrence RW
    78. Pellikka N
    79. Prokopenko I
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    81. Thiering E
    82. Alavere H
    83. Alibrandi MT
    84. Almgren P
    85. Arnold AM
    86. Aspelund T
    87. Atwood LD
    88. Balkau B
    89. Balmforth AJ
    90. Bennett AJ
    91. Ben-Shlomo Y
    92. Bergman RN
    93. Bergmann S
    94. Biebermann H
    95. Blakemore AI
    96. Boes T
    97. Bonnycastle LL
    98. Bornstein SR
    99. Brown MJ
    100. Buchanan TA
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    102. Campbell H
    103. Cappuccio FP
    104. Cavalcanti-Proença C
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    119. Eriksson JG
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    122. Fischer-Posovszky P
    123. Folsom AR
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    125. Freimer NB
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    81. D'Eustacchio A
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    86. Mutsert R
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    91. Hollander AI
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    93. Dennis JG
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    95. Di Angelantonio E
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    99. Dunning AM
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    105. Evangelou E
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    109. Fauser S
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    116. Franco OH
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    120. Frikke-Schmidt R
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    125. Gibson J
    126. Giedraitis V
    127. Gjesing AP
    128. Gordon-Larsen P
    129. Gorski M
    130. Grabe HJ
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    138. Hakonarson H
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    140. Hansen T
    141. Harris KM
    142. Harris TB
    143. Hattersley AT
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    148. Heath AC
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    159. Ikram MA
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    249. Nikus K
    250. Njølstad PR
    251. Nordestgaard BG
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    254. O'Donoghue ML
    255. Olde Loohuis LM
    256. Ophoff RA
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    261. Palmer ND
    262. Pasterkamp G
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    268. Pennell CE
    269. Perola M
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    274. Peters A
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    276. Peyser PA
    277. Pirie A
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    300. Sattar N
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    302. Schreiner PJ
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    304. Scott RA
    305. Segura-Lepe MP
    306. Shah SH
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    308. Sim X
    309. Slater AJ
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Decision letter

  1. Mone Zaidi
    Senior and Reviewing Editor; Icahn School of Medicine at Mount Sinai, United States
  2. David Meyre
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

The study is well done and is likely to significantly impact our understanding gene associations in large GWAS datasets. Review critique was thoughtfully and thoroughly addressed. Notably, statistical correction for multiple testing of GWAS data requires increasingly large sample sizes to establish potential associations. This retrospective study used chromatin accessibility and direct contact with gene promoters as biological constraints. The application of such constraints on otherwise sub-significant GWAS signals was shown to reveal potentially true-positive loci without the requirement to increase sample size.

Decision letter after peer review:

Thank you for submitting your article "Biological constraints on GWAS SNPs at suggestive significance thresholds reveals true BMI loci" for consideration by eLife. Your article has been reviewed by two peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Clifford Rosen as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: David Meyre (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

As the editors have judged that your manuscript is of interest, but as described below that substantial revisions are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary:

The authors sought to determine whether SNP's located in chromatin that physically contacts GWAS hits provide evidence that boost test scores from suggestive to significant. If successful, this approach might obviate the need for larger, more expensive GWAS studies. The strategy is to first identify relevant open chromatin sites, focusing on genes that were suggestive in early studies. Then in parallel ask which suggestive loci in these early studies became significant in later and larger studies. They claim that positive results support the argument that epigenetic evidence would have boosted scores and precluded the need for larger and more expensive studies. The manuscript has potential, but work is needed. Critical evidence is not provided, or provided in a way that makes critical reading a challenge.

Essential revisions:

1) This is not the first study to demonstrate that biological annotations combined to more relax thresholds of statistical associations from GWAS “rescue” true associations in obesity field. As an illustration, Meyre et al., 2009, rescued a modest stage 1 GWAS association signal for extreme obesity in the NPC1 gene using a candidate gene strategy, and the association was replicated in stage 2. The GWAS obesity hit in NPC1 was recently confirmed as a genome-wide significant signal for BMI in a large meta-analysis (Turcot et al., 2018). Wang et al. (Diabetes 2019) demonstrated a strong enrichment in positive associations with BMI for SNPs located in/near syndromic obesity genes. The authors may like to discuss these and other reports in literature.

2) A strengths and limitation section would add a lot in the Discussion.

3) The biological criteria (chromatin accessibility / direct contact with gene promoters) used in this study are very original, but to be more exhaustive additional biological criteria may have been used to select more SNPs (listed in Li and Meyre, Int J Obes 2013 in the “hypothesis-driven GWAS analysis” section). This may be acknowledged as a limitation of the study.

4) Tissues targeted in the study (adipocyte and hypothalamus) are extremely relevant in the context of genetic susceptibility to obesity. However, genetic association studies have also highlighted the important role of other tissues in energy balance (e.g. beta-cells, liver, muscle, stomach… see Locke et al., 2015, and Pigeyre et al., Clin Sci 2016). Having not explored all tissues potentially relevant for obesity may be acknowledged as another limitation of the study.

5) While Speliotes et al., 2010 and Yengo et al., 2018 GWAS for BMI have been performed in populations of European ancestry, the Locke et al., 2015 study included a multi-ethnic population. Did the authors analyze the GWAS summary statistics in the European population in the Locke et al., 2015 study. If yes, they may provide more details on what they did in the Materials and methods section. If no, I think using the multi-ethnic GWAS summary statistics may add some heterogeneity, and I recommend to focus the analysis in the European population in the Locke et al., 2015 study.

6) Organization of data and analysis. Curiously, the epigenetic data, evidence and analysis of the Capture-C and ATAC-seq data is in the Materials and methods rather than the Results. The combination of methods and data are somewhat incomplete perhaps because of their unusual location in the manuscript. These are key to the overall study and should be in the Results section. As I read the manuscript, my curiosity grew about the nature of the evidence for connecting flanking and regulatory SNPs with the previously reported target SNPs – what is the source of the data, what is the nature of the evidence, – only to learn that they are in the Materials and methods, albeit incompletely. What is the evidence for open chromatin at their targets and in the sentinel genes that were suggestive in early GWAS studies?

7) Study design, analysis, and presentation. A flow chart would help readers understand the sequence of tasks and help the authors with the organization of the results.

8) Bottom-line. The key question is what is the benefit of the proposed method? What's missing – a simple statement about how many suggestive loci became significant later, and how many didn't, in the regular course of work, and in parallel how many epigenetic hits at suggestive loci become significant later, and how many didn't? That is, what's the quantitative benefit of the new assay. Remarkably they don't show the numbers for this seemingly simple and central question. Surprisingly, this point is not even in the Abstract; the 4th and 5th sentences, which address the key results, do not make these points clearly.

9) Chromatin evidence. The evidence is based on two cell lines – MSC-derived adipocytes and ESC-derived hypothalamic-like neurons. Both are relevant cell types in vivo. But beyond that functional connection, no other rationale is provided, and no discussion is provided to critically evaluate the reliability of the evidence. Chromatin states are dynamic properties of cells both in vivo and in vitro. Physiological conditions and disease state can impact these profiles. How stable are these profiles in vitro, and how consistent are these features with their in vivo counter-parts? How do these profiles vary among individuals in health or disease? What about single-cell heterogeneity? Presumably, these factors would contribute noise in the assays, creating false positives, false negatives. All of this is fine, in principle; every approach has limitations. But remarkably little consideration to these issues, either in study design or in discussion of the results and analysis. These issues need careful, thorough and critical consideration.

10) The authors should discuss how the model might work. The authors are correctly concerned about linkage disequilibrium, with an emphasis on independent evidence (subsection “Variant-to-gene mapping pipeline”). But no data are formally presented; if these SNPs are independent (no LD), what is the argument that these data can be combined rather than additive? Are positive results for this variant-to-gene mapping simply a reflection of additive effects? Or do they argue that SNP-interaction (epistasis) is involved? The text is unclear about these issues.

11) Approach. These are important questions for any proposed method:

A) The authors use a frequentist approach. Perhaps they justify that approach versus Bayes, which the logic of their approach nicely fits.

B) The authors should be able to give clear basic statistics – number/percentage of hits that validate in general, and then when applying their approach, essentially they need to give the false positive and false negative rates, i.e. how many does their approach put forward that end up being negative, and how many do they miss with their approach (presumably loci with non-epigenetic mechanisms would obviously be missed). Obviously, they need to be clear that a comparable threshold is being applied so that readers can assess the relative performance of the proposed and standard methods. Importantly, are they preserving the type I error rate with their new method, and what is their power? Unfortunately, the Abstract, Results and Discussion are not clear on these points – these are the essence of the paper.

https://doi.org/10.7554/eLife.62206.sa1

Author response

Essential revisions:

1) This is not the first study to demonstrate that biological annotations combined to more relax thresholds of statistical associations from GWAS “rescue” true associations in obesity field. As an illustration, Meyre et al., 2009, rescued a modest stage 1 GWAS association signal for extreme obesity in the NPC1 gene using a candidate gene strategy, and the association was replicated in stage 2. The GWAS obesity hit in NPC1 was recently confirmed as a genome-wide significant signal for BMI in a large meta-analysis (Turcot et al., 2018). Wang et al. (Diabetes 2019) demonstrated a strong enrichment in positive associations with BMI for SNPs located in/near syndromic obesity genes. The authors may like to discuss these and other reports in literature.

The reviewer raises a good point and speaks to the feasibility of the uniform genome wide approach we have employed. In response to these points, we now refer to previous work in this context of using biological annotations to salvage sub-threshold signals. We’ve taken the suggestion of including the key example of the Meyre et al., 2009 study as an introduction to this idea of sub-threshold SNP mining, along with citing the Turcot et al. paper which replicated this association. We have also added an additional study by Wang et al., 2016, as it relates greatly to this paper in its use of epigenomic data to mine biologically relevant sub-threshold signals from GWAS.

2) A strengths and limitation section would add a lot in the Discussion.

We agree that a strengths and limitation section is an excellent addition to the manuscript. While we previously included a number of points regarding the limitations of this study, we recognize that a more complete breakdown is warranted. We have added a far more comprehensive discussion concerning the classification rates, noting the relatively poor sensitivity while also highlighting the high precision and specificity. We believe this trade-off is an acceptable outcome given the positive predictions went on to largely achieve genome-wide significance. As per comment #4, we have also noted in our limitations the lack of a more diverse set of cell types, although adipocytes and hypothalamic neurons are prime candidate, it is clear there are other relevant cell types that could be used in such a study; indeed they could be the subject of a future study in this regard.

3) The biological criteria (chromatin accessibility / direct contact with gene promoters) used in this study are very original, but to be more exhaustive additional biological criteria may have been used to select more SNPs (listed in Li and Meyre, Int J Obes 2013 in the “hypothesis-driven GWAS analysis” section). This may be acknowledged as a limitation of the study.

We agree that more criteria and constraints could always be employed. Our intention was to observe if this unique, and relatively straightforward, combination of chromatin state and physical contacts between implicated proxy SNPs and gene promoters alone would suffice to salvage sub-threshold SNPs. We do not doubt that additional filters could improve predictions to a degree, especially at more relaxed p-value thresholds, so we have highlighted this point in our Discussion while presenting the possibility that additional criteria could be used in a future study to improve the quality of the predictions.

4) Tissues targeted in the study (adipocyte and hypothalamus) are extremely relevant in the context of genetic susceptibility to obesity. However, genetic association studies have also highlighted the important role of other tissues in energy balance (e.g. beta-cells, liver, muscle, stomach… see Locke et al., 2015, and Pigeyre et al., Clin Sci 2016). Having not explored all tissues potentially relevant for obesity may be acknowledged as another limitation of the study.

We agree that more criteria and constraints could always be employed. Our intention was to observe if this unique, and relatively straightforward, combination of chromatin state and physical contacts between implicated proxy SNPs and gene promoters alone would suffice to salvage sub-threshold SNPs. We do not doubt that additional filters could improve predictions to a degree, especially at more relaxed p-value thresholds, so we have highlighted this point in our Discussion while presenting the possibility that additional criteria could be used in a future study to improve the quality of the predictions.

5) While Speliotes et al., 2010 and Yengo et al., 2018 GWAS for BMI have been performed in populations of European ancestry, the Locke et al., 2015 study included a multi-ethnic population. Did the authors analyze the GWAS summary statistics in the European population in the Locke et al., 2015 study. If yes, they may provide more details on what they did in the Materials and methods section. If no, I think using the multi-ethnic GWAS summary statistics may add some heterogeneity, and I recommend to focus the analysis in the European population in the Locke et al., 2015 study.

Thank you for catching this omission. We leveraged European ancestry results for all the studies analyzed. We have updated the Materials and methods accordingly to reflect this point.

6) Organization of data and analysis. Curiously, the epigenetic data, evidence and analysis of the Capture-C and ATAC-seq data is in the Materials and methods rather than the Results. The combination of methods and data are somewhat incomplete perhaps because of their unusual location in the manuscript. These are key to the overall study and should be in the Results section. As I read the manuscript, my curiosity grew about the nature of the evidence for connecting flanking and regulatory SNPs with the previously reported target SNPs – what is the source of the data, what is the nature of the evidence, – only to learn that they are in the Materials and methods, albeit incompletely. What is the evidence for open chromatin at their targets and in the sentinel genes that were suggestive in early GWAS studies?

Our intention with this form of presentation was to facilitate narrative flow and to dedicate largely the Results section to the process involving the retrospective analyses employed, while placing the processing of the epigenetic data in the Materials and methods section. However, given the reviewer’s comment, we agree that additional details should be provided in the Results section. We have therefore added additional information on these datasets in the Results section, while leaving the extensive details of how these data were generated and processed within the Materials and methods. The hypothalamic neuron related data were previously generated and analyzed in a separate study of ours – which we now cited. In addition, we include specific ATAC-seq peak numbers, as well as the interaction counts, for our three adipocyte libraries.

7) Study design, analysis, and presentation. A flow chart would help readers understand the sequence of tasks and help the authors with the organization of the results.

Given the complexity of these analyses, we strongly agree with this suggestion. To aid comprehension, we have now added a flowchart outlining the pipelines used (see Figure 3). The flowchart we include uses the BMI 2010-2015 data to exemplify our approach, and includes numbers to aid description of each computational step and how many SNPs and loci occur at each step of these analyses.

8) Bottom-line. The key question is what is the benefit of the proposed method? What's missing – a simple statement about how many suggestive loci became significant later, and how many didn't, in the regular course of work, and in parallel how many epigenetic hits at suggestive loci become significant later, and how many didn't? That is, what's the quantitative benefit of the new assay. Remarkably they don't show the numbers for this seemingly simple and central question. Surprisingly, this point is not even in the Abstract; the 4th and 5th sentences, which address the key results, do not make these points clearly.

We opted principally not to highlight these numbers outside of the various figures due to the many conditions that we were employed. This was to avoid the perception that we were somehow cherry-picking the best trait, year, and P-value bin given the variance in these numbers, potentially confusing or misleading the reader from the start. However, we do acknowledge that such numbers in text would be beneficial to keep the reader from consistently having to refer back to the figures and tables. Therefore, we have added such numbers at the end of each respective section using counts from within the consistent bin of 5 x 10-8P < 5 x 10-5.

9) Chromatin evidence. The evidence is based on two cell lines – MSC-derived adipocytes and ESC-derived hypothalamic-like neurons. Both are relevant cell types in vivo. But beyond that functional connection, no other rationale is provided, and no discussion is provided to critically evaluate the reliability of the evidence. Chromatin states are dynamic properties of cells both in vivo and in vitro. Physiological conditions and disease state can impact these profiles. How stable are these profiles in vitro, and how consistent are these features with their in vivo counter-parts? How do these profiles vary among individuals in health or disease? What about single-cell heterogeneity? Presumably, these factors would contribute noise in the assays, creating false positives, false negatives. All of this is fine, in principle; every approach has limitations. But remarkably little consideration to these issues, either in study design or in discussion of the results and analysis. These issues need careful, thorough and critical consideration.

Our data are generated from three replicates and/or donors, and we focus on regions that are accessible across all replicates; indeed, we do note that there is high consistency between replicates. It should be noted that most chromatin differences across individuals are quantitative, not qualitative. A study by the Regev group (Gate et al., Nature Genetics 2018) showed that open chromatin regions in T cells from >100 individuals were highly consistent, but that 15% of OCR varied quantitatively in a manner associated with common genetic variation. Combined with the additional constraint of insisting on only SNPs coinciding with open chromatin via ATAC-seq, our Capture C based approach avoids a high degree of false positives; however, it is challenging to determine the false negative rate, given a lack of reference data. However, given the statistical significance of our observations, we strongly believe our data should be shared with the complex disease genetics field. Furthermore, we ensure we only work with cells that are derived from healthy individuals, given we are focused on characterizing the susceptibility conferred by these loci before onset of disease, i.e. we are investigating the genetic build up to disease pathogenesis, or in other words “before the car crash”. The data generated here are consistent with the datasets we describe in recent papers we published in Nature Communications for lupus and bone mineral density. Finally, we have not conducted chromatin conformation capture in a single cell setting, an approach which is still in its infancy and does represent some drawbacks, including important multiple amplification steps which can itself a high degree of false positives. We have highlighted these points in the Discussion.

10) The authors should discuss how the model might work. The authors are correctly concerned about linkage disequilibrium, with an emphasis on independent evidence (subsection “Variant-to-gene mapping pipeline”). But no data are formally presented; if these SNPs are independent (no LD), what is the argument that these data can be combined rather than additive? Are positive results for this variant-to-gene mapping simply a reflection of additive effects? Or do they argue that SNP-interaction (epistasis) is involved? The text is unclear about these issues.

We are less clear on the issues raised by the reviewer in this regard. We are in fact not concerned about LD, rather we are leveraging it to integrate with ATAC-seq data in order to shortlist putative causal variants, which are further shortlisted by the further integration of Capture C data. We are treating all the independent BMI and WHR loci as independent signals, and making inferences as such. Our study does not address or observe any form of epistasis.

11) Approach. These are important questions for any proposed method:

A) The authors use a frequentist approach. Perhaps they justify that approach versus Bayes, which the logic of their approach nicely fits.

We appreciate this suggestion. We have updated the statistical analysis with a Bayesian model to identify the probability that our constrained method outperforms no constraints. We note few changes to the results, though the situations where changes occurred resulted in >95% probability of our biological constraint outperforming no constraints. And overall, the changes do not affect our interpretation of these results.

B) The authors should be able to give clear basic statistics – number/percentage of hits that validate in general, and then when applying their approach, essentially they need to give the false positive and false negative rates, i.e. how many does their approach put forward that end up being negative, and how many do they miss with their approach (presumably loci with non-epigenetic mechanisms would obviously be missed). Obviously, they need to be clear that a comparable threshold is being applied so that readers can assess the relative performance of the proposed and standard methods. Importantly, are they preserving the type I error rate with their new method, and what is their power? Unfortunately, the Abstract, Results and Discussion are not clear on these points – these are the essence of the paper.

While we reported the sensitivity and specificity values in the previous version of the manuscript, we acknowledge that the false positive and false negative rates were not directly included. We did reference the general trend of these values, given their relationship to sensitivity and specificity, and the overall pattern of these rates across all analyses within the Results section entitled “Predictive power of negative control does not differ from the unconstrained set”, as well as in the Discussion; however, we acknowledge that such information relating to the preservation of the type I error rate was not included. To accommodate this comment, we have made our supplementary data file more comprehensive, which includes the classification metrics for each condition while also now including FPR and FNR. This file has been subsequently renamed Source data 1. Additionally, we now include a more direct discussion of these values and their trends within the Discussion section.

https://doi.org/10.7554/eLife.62206.sa2

Article and author information

Author details

  1. Reza K Hammond

    1. Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, United States
    2. Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, United States
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  2. Matthew C Pahl

    1. Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, United States
    2. Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, United States
    Contribution
    Formal analysis, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  3. Chun Su

    1. Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, United States
    2. Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, United States
    Contribution
    Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  4. Diana L Cousminer

    1. Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, United States
    2. Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, United States
    Contribution
    Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  5. Michelle E Leonard

    1. Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, United States
    2. Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, United States
    Contribution
    Data curation, Writing - review and editing
    Competing interests
    No competing interests declared
  6. Sumei Lu

    1. Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, United States
    2. Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, United States
    Contribution
    Data curation, Writing - review and editing
    Competing interests
    No competing interests declared
  7. Claudia A Doege

    1. Naomi Berrie Diabetes Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, United States
    2. Department of Pathology and Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University, New York, United States
    3. Columbia Stem Cell Initiative, Vagelos College of Physicians and Surgeons, Columbia University, New York, United States
    Contribution
    Resources, Writing - review and editing
    Competing interests
    No competing interests declared
  8. Yadav Wagley

    Department of Orthopaedic Surgery, University of Michigan Medical School, Ann Arbor, United States
    Contribution
    Writing - review and editing
    Competing interests
    No competing interests declared
  9. Kenyaita M Hodge

    1. Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, United States
    2. Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, United States
    Contribution
    Data curation, Writing - review and editing
    Competing interests
    No competing interests declared
  10. Chiara Lasconi

    1. Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, United States
    2. Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, United States
    Contribution
    Data curation, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4684-704X
  11. Matthew E Johnson

    1. Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, United States
    2. Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, United States
    Contribution
    Data curation, Writing - review and editing
    Competing interests
    No competing interests declared
  12. James A Pippin

    1. Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, United States
    2. Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, United States
    Contribution
    Data curation, Writing - review and editing
    Competing interests
    No competing interests declared
  13. Kurt D Hankenson

    Department of Orthopaedic Surgery, University of Michigan Medical School, Ann Arbor, United States
    Contribution
    Writing - review and editing
    Competing interests
    No competing interests declared
  14. Rudolph L Leibel

    Division of Molecular Genetics (Pediatrics) and the Naomi Berrie Diabetes Center, Columbia University Vagelos College of Physicians and Surgeons, New York, United States
    Contribution
    Writing - review and editing
    Competing interests
    No competing interests declared
  15. Alessandra Chesi

    1. Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, United States
    2. Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, United States
    Contribution
    Formal analysis, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  16. Andrew D Wells

    1. Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, United States
    2. Department of Pathology, The Children’s Hospital of Philadelphia, Philadelphia, United States
    3. Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Contribution
    Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  17. Struan FA Grant

    1. Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, United States
    2. Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, United States
    3. Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    4. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    5. Division of Diabetes and Endocrinology, The Children’s Hospital of Philadelphia, Philadelphia, United States
    Contribution
    Conceptualization, Supervision, Investigation, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    grants@email.chop.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2025-5302

Funding

Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01-HD056465)

  • Struan FA Grant

National Human Genome Research Institute (R01-HG010067)

  • Struan FA Grant

Children's Hospital of Philadelphia (Daniel B. Burke Endowed Chair for Diabetes Research)

  • Struan FA Grant

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Senior and Reviewing Editor

  1. Mone Zaidi, Icahn School of Medicine at Mount Sinai, United States

Reviewer

  1. David Meyre

Publication history

  1. Received: August 18, 2020
  2. Accepted: January 8, 2021
  3. Accepted Manuscript published: January 18, 2021 (version 1)
  4. Version of Record published: January 18, 2021 (version 2)

Copyright

© 2021, Hammond et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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