Abstract
The prevalence of childhood obesity is increasing worldwide, along with the associated common comorbidities of type 2 diabetes and cardiovascular disease in later life. Motivated by evidence for a strong genetic component, our prior genome-wide association study (GWAS) efforts for childhood obesity revealed 19 independent signals for the trait; however, the mechanism of action of these loci remains to be elucidated. To molecularly characterize these childhood obesity loci we sought to determine the underlying causal variants and the corresponding effector genes within diverse cellular contexts. Integrating childhood obesity GWAS summary statistics with our existing 3D genomic datasets for 57 human cell types, consisting of high-resolution promoter-focused Capture-C/Hi-C, ATAC-seq, and RNA-seq, we applied stratified LD score regression and calculated the proportion of genome-wide SNP heritability attributable to cell type-specific features, revealing pancreatic alpha cell enrichment as the most statistically significant. Subsequent chromatin contact-based fine-mapping was carried out for genome-wide significant childhood obesity loci and their linkage disequilibrium proxies to implicate effector genes, yielded the most abundant number of candidate variants and target genes at the BDNF, ADCY3, TMEM18 and FTO loci in skeletal muscle myotubes and the pancreatic beta-cell line, EndoC-BH1. One novel implicated effector gene, ALKAL2 – an inflammation-responsive gene in nerve nociceptors – was observed at the key TMEM18 locus across multiple immune cell types. Interestingly, this observation was also supported through colocalization analysis using expression quantitative trait loci (eQTL) derived from the Genotype-Tissue Expression (GTEx) dataset, supporting an inflammatory and neurologic component to the pathogenesis of childhood obesity. Our comprehensive appraisal of 3D genomic datasets generated in a myriad of different cell types provides genomic insights into pediatric obesity pathogenesis.
Key points
Question
What are the causal variants and corresponding effector genes conferring pediatric obesity susceptibility in different cellular contexts?
Findings
Our method of assessing 3D genomic data across a range of cell types revealed heritability enrichment of childhood obesity variants, particularly within pancreatic alpha cells. The mapping of putative causal variants to cis-regulatory elements revealed candidate effector genes for cell types spanning metabolic, neural, and immune systems.
Meaning
We gain a systemic view of childhood obesity genomics by leveraging 3D techniques that implicate regulatory regions harboring causal variants, providing insights into the disease pathogenesis across different cellular systems.
Introduction
The prevalence of obesity has risen significantly worldwide1, especially among children and adolescents2. Obesity is associated with chronic diseases, such as diabetes, cardiovascular diseases, and certain cancers3–6, along with mechanical issues including osteoarthritis and sleep apnea7.
Modern lifestyle factors, including physical inactivity, excessive caloric intake, and socioeconomic inequity, along with disrupted sleep and microbiome, represent environmental risk factors for obesity pathogenesis. However, genetics also play a significant role, with the estimated heritability ranging from 40% to 70%8–10. Studies show that body weight and obesity remain stable from infancy to adulthood11–14, but variation between individuals does exist15. Genome-wide association studies (GWAS) have improved our understanding of the genetic contribution to childhood obesity16–21. However, the functional consequences and molecular mechanisms of identified genetic variants in such GWAS efforts are yet to be fully elucidated. Efforts are now being made to predict target effector genes and explore potential drug targets using various computational and experimental approaches22–26, which subsequently warrant functional follow-up efforts.
With our extensive datasets generated on a range of different cell types, by combining 3D chromatin maps (Hi-C, Capture-C) with matched transcriptome (RNA-seq) and chromatin accessibility data (ATAC-seq), we investigated heritability patterns of pediatric obesity-associated variants and their gene-regulatory functions in a cell type-specific manner. This approach yielded 94 candidate causal variants mapped to their putative effector gene(s) and corresponding cell type(s) setting. In addition, using methods comparable to our prior efforts in other disease contexts27–34, we also uncovered new variant-to-gene combinations within specific novel cellular settings, most notably in immune cell types, which further confirmed the involvement of the immune system in the pathogenesis of obesity in the early stages of life.
Methods
Data and resource
Datasets used in prior studies are listed in eTable 1. ATAC-seq, RNA-seq, Hi-C, and Capture-C library generation for each cell type is provided in their original published study and their pre-processing pipelines and tools can be found in eMethods.
Definition of cis-Regulatory Elements (cREs)
We intersected ATAC-seq open chromatin regions (OCRs) of each cell type with chromatin conformation capture data determined by Hi-C/Capture-C of the same cell type, and with promoters (-1,500/+500bp of TSS, which were referenced by GENCODE v30.
Childhood obesity GWAS summary statistics
Data on childhood obesity from the EGG consortium was downloaded from www.egg-consortium.org. We used 8,566,179 European ancestry variants (consisting of 8,613 cases and 12,696 controls in stage I; of 921 cases and 1,930 controls in stage II), representing ∼55% of the total 15,504,218 variants observed across all ancestries in the original study35. The sumstats file was reformatted by munge_sumstats.py to standardize with the weighted variants from HapMap v3 within the LDSC baseline, which reduced the variants to 1,217,311 (7.8% of total).
Cell type specific partitioned heritability
We used LDSC (http://www.github.com/bulik/ldsc) v.1.0.1 with --h2 flag to estimate SNP-based heritability of childhood obesity within 4 defined sets of input genomic regions: (1) OCRs, (2) OCRs at gene promoters, (3) cREs, and (4) cREs with an expanded window of ±500 bp. The baseline model LD scores, plink filesets, allele frequencies and variants weights files for the European 1000 genomes project phase 3 in hg38 were downloaded from the provided link (https://alkesgroup.broadinstitute.org/LDSCORE/GRCh38/). The cREs of each cell type were used to create the annotation, which in turn were used to compute annotation-specific LD scores for each cell types cREs set.
Genetic loci included in variant-to-genes mapping
19 sentinel signals that achieved genome-wide significance in the trans-ancestral meta-analysis study35 were leveraged for our analyses. Proxies for each sentinel SNP were queried using TopLD36 and LDlinkR tool37 with the GRCh38 Genome assembly, 1000 Genomes phase 3 v5 variant set, European population, and LD threshold of r2>0.8, which resulted in 771 proxies, including the 21 SNPs from the 99% credible set of the original study (eTable 2).
GWAS-eQTL colocalization
The summary statistics for the European ancestry subset from the EGG consortium GWAS for childhood obesity was used. Common variants (MAF ≥ 0.01) from the 1000 Genomes Project v3 samples were used as a reference panel. We used non-overlapped genomic windows of ±250,000 bases extended in both directions from the median genomic position of each of 19 sentinel loci as input. We used ColocQuiaL38 to test genome-wide colocalization of all possible variants included in each inputted window against GTEx v.8 eQTLs associations for all 49 tissues available from https://www.gtexportal.org/home/datasets. A conditional posterior probability of colocalization of 0.8 or greater was imposed.
Results
Enrichment assessment of childhood obesity variants across cell types
To explore the enrichment of childhood obesity GWAS variants across cell types, we carried out Partitioned Linkage Disequilibrium Score Regression (LDSR)39 on all ATAC-seq-defined OCRs for each cell type. We assessed cell-type specific enrichment of GWAS signals in four main categories of genomic regions (Fig. 1A): (1) Total OCRs: open chromatin regions defined by ATAC-seq; (2) Promoter OCRs: the subset of OCRs overlapping a gene promoter; (3) cREs: the subset of OCRs that form chromatin loops (as determined by Hi-C/Promoter Capture-C) with a gene promoter, and are therefore considered putative enhancers or suppressors regulating gene expression; (4) cREs ± 500bases: extended cREs by 500 bases in both directions. The rationale behind this approach is that different GWAS variants can influence phenotypes by regulating gene expression in a cell-type specific manner through various regulatory mechanisms. For example, they may alter enhancer function (cREs category) or affect the binding of a transcription factor at a gene’s promoter (Promoter OCRs category).
We observed that 41 of 57 cell types – including 22 metabolic, 21 immune, 7 neural cell types and 7 independent cell lines (eTable 1) – showed at least a degree of directional enrichment with the total set of OCRs (Fig. 1B – Total OCRs). However, only four cell types – two pancreatic alpha and two pancreatic beta cell-based datasets – had statistically significant enrichments (P<0.05). These enrichments were less pronounced when focusing on promoter OCRs only (Fig. 1B – Promoter OCRs). To further limit the LD enrichment assessment to just those OCRs that can putatively regulate gene expression via chromatin contacts with gene promoters, we used the putative cREs27,29. This reduced the number of cell types showing at least nominal enrichment (31 of 57), enlarged the dispersion of enrichment ranges across different cell types, increased the 95% confidence intervals (CI) of enrichments, and hence increased the P-value of the resulting regression score. cREs from pancreatic alpha cells derived from single-cell ATAC-seq were the only dataset that remained statically significant (Fig. 1B – putative cREs).
The original reported LDSR method analyzed enrichment in the 500bp flanking regions of their regulatory categories39. However, when we expanded our analysis to the ±500bp window for our cREs, albeit incorporating more weighted variants into the enrichment (represented by larger dots in Fig. 1B – cREs ±500 bases), this resulted in a decrease in the number of cell types yielding at least nominal enrichment (26 cell types), the enrichment range across cell types, the 95% CI, and level of significance. The pancreatic alpha cell observation also dropped below the bar for significance with this expanded window definition.
Consistency and diversity of childhood obesity proxy variants mapped to cREs
Despite the enrichments above only being limited to just a small number of cell types, it is likely that individual loci have differing levels of contributions in various cellular contexts and could not be detected at the genome wide assessment scale. As such we elected to further explore the candidate effector genes that are directly affected by cREs harboring childhood obesity-associated variants by systematically mapping the genomic positions of the LD proxies onto each cell type’s cREs. Most proxies fall within chromatin contact regions (blue area in Venn diagram Fig. 2A) or OCRs (yellow area) or open chromatin contact regions (red area), or completely outside (white area) any defined region. Only 94 proxies fall within our defined cREs (overlapped area with dotted green border in Fig. 2A), they clustered at 13 original loci (eTable 3). eFig. 1A outlines the number of signals at each locus included or excluded based on the criteria we defined for our regions of interest. The TMEM18 locus yielded the most variants through cREs mapping, with 46 proxies for the two lead independent variants, rs7579427 and rs62104180. The second most abundant locus was ADCY3, with 21 proxies for lead variant rs4077678 (Fig. 2B). The higher number of variants at one locus did not correlate with implicating more genes or cell types through mapping. The mapping frequency of various variants within a specific locus exhibited substantial differences.
Inspecting individual variants regardless of their locus, we found that 28 of 94 proxies appeared in cREs across multiple cell types, with another 66 observed in just one cell type (Fig. 2D). 45 variants of these 66 just contacted one gene promoter, such as at the GPR1 and TFAP2B loci (eFig. 2).
Overall, the number of cell types in which a variant was observed in open chromatin correlated with the number of genes contacted via chromatin loops (eFig. 3A). However, we also observed that some variants found in cREs in multiple cell types were more selective with respect to their candidate effector genes (eFig. 3B-red arrow), or conversely, more selective across given cell types but implicated multiple genes (eFig. 3B-blue arrow). eFig. 4 outlines our observations at the TMEM18 locus – an example locus involved in both scenarios.
Implicated genes cluster at loci strongly associated with childhood obesity consistently across multiple cell types
Mapping the variants across all the cell types resulted in a total of 111 implicated childhood obesity candidate effector genes (Table 1). Among these, 45 genes were specific to just one cell type (eFig. 5A), including 13 in myotubes and 7 in natural killer cells. Conversely and notably, BDNF appeared across 42 different cell types. Across the metabolic, neural, and immune systems and seven other cell lines, there were 9 genes consistently implicated in all four categories (top panel Fig. 3 – red stars, eFig. 5B: “all”), while 5 genes were consistently implicated in metabolic, neural, and immune systems (top panel Fig. 3 – blue stars, eFig. 5B: “all_main”). Two genes, ADCY3 and BDNF, had variants both at their promoters and contacted variants in cREs via chromatin loops (eFig. 6).
At the TMEM18 locus on chr 2p25.3, a highly significant human obesity locus that has long been associated with both adult and childhood obesity, we obeserved differing degrees of evidence for 16 genes, but noted that rs6548240, rs35796073, and rs35142762 consistently contacted the SH3YL1, ACP1, and ALKAL2 promoters across multiple cell types (Fig. 2D-third and fourth column).
At the chr 2p23 locus, ADCY3 yielded the most contacts (i.e. many proxies contacting the same gene via chromatin loops), suggesting this locus acts as a regulatory hub. However, we observed a similar composition in cell types for four other genes: DNAJC27, DNAJC27-AS1 (both previously implicated in obesity and/or diabetes traits40), AC013267.1, and SNORD14 (RF00016). ITSN2, NCOA1, and EFR3B were three genes within this locus that were only implicated in immune cell types. NCOA1 encodes a prominent meta-inflammation factor41 known to reduce adipogenesis and shift the energy balance between white and brown fat, and its absence known to induce obesity42.
CALCR was the most frequently implicated gene at its locus, supported by 20 cell types across all systems. While within the BDNF locus, METTL15 and KIF18A – two non-cell-type-specific genes - plus some lncRNA genes, were contacted by childhood obesity-associated proxies within the same multiple cell types as BNDF, again suggesting the presence of a regulatory hub.
At the FAIM2 locus on chr 12q13.12, we observed known genes associated with obesity, eating patterns, and diabetes-related traits, including ASIC1, AQP2, AQP5, AQP6, RACGAP1, and AC025154.2 (AQP5-AS1) along with FAIM2 (Table 1). These genes were harbored within cREs of astrocytes, neural progenitors, hypothalamic neurons, and multiple metabolic cell types. Plasmacytoid and CD1c+ conventional dendritic cells were the only two immune cell types that harbored such proxies within their cREs, implicating ASIC1, PRPF40B, RPL35AP28, TMBIM6, and LSM6P2 at the FAIM2 locus.
The independent ADCY9 and FTO loci are both located on chromosome 16. Genes at the ADCY9 locus were only implicated in a subset of immune cell types. Interestingly, genes at the FTO locus were only implicated in Hi-C datasets (as opposed to Capture C), including 6 metabolic cell types and astrocytes. Most genes at the FTO locus were implicated in skeletal myotubes, differentiated osteoblasts, and astrocytes, namely FTO and IRX3; while IRX5, CRNDE, and AC106738.1 were also implicated in adipocytes and hepatocytes.
The most implicated cell types by two sets of analyses
EndoC-BH1 and myotubes are the two cell types in which we implicated the most effector genes, with 38 and 42, respectively – Fig. 3 side panel. This phenomenon is likely proportional in the case of myotubes, given the large number of cREs identified by overlapped Hi-C contact data and ATAC-seq open regions (Fig. 1A), but not for EndoC-BH1. Albeit harboring an average number of cREs compared to other cell types, EndoC-BH1 cells were consistently among the top-ranked heritability estimates for the childhood obesity variants resulting from the EGG consortium GWAS (Fig. 1) and harbored a significant number of implicated genes by the mapping of proxies. Interestingly, the pancreatic alpha cell type – shown above to be the most significant for heritability estimate by LDSC – revealed only 6 implicated genes contacted by the defined proxies, namely BDNF and five lncRNA genes.
Pathway analysis
Of the 111 implicated genes in total, PubMed query revealed functional studies for 66 genes. The remaining were principally lncRNA and miRNA genes with currently undefined functions (Table 1). To investigate how our implicated genes could confer obesity risk, we performed several pathway analyses keeping them either separated for each cell type or pooling into the respective metabolic, neural, or immune system sets. eFig. 7 shows simple Gene Ontology (GO) biological process terms enrichment results.
Leveraging the availability of our expression data generated via RNA-seq (available for 46 of 57 cell types), we performed pathway analysis. Given that our gene sets from the variant-to-gene process was stringently mapped, the sparse enrichment from normal direct analyses is not ideal for exploring obesity genetic etiology. Thus, we incorporated two methods from the pathfindR package43 and our customized SPIA (details in eMethods). The result of 60 enriched KEGG terms is shown in eFig. 8 (eTable 4), with 13 genes in 14 cell types for pathfindR and 39 enriched KEGG terms shown in eFig. 9 (eTable 5), with 10 genes in 42 cell types for customized SPIA. There were 20 overlapping pathways between the two approaches (yellow rows in eTable 4&5) including many signaling pathways such as the GnRH (hsa04912), cAMP (hsa04024), HIF-1 (hsa04066), Glucagon (hsa04922), Relaxin (hsa04926), Apelin (hsa04371), and Phospholipase D (hsa04072) signaling pathways. They were all driven by one or more of these 5 genes: ADCY3, ADCY9, CREBBP, MMP2, and NCOA1. Interestingly, we observed the involvement of natural killer cells in nearly all the enriched KEGG terms from pathfindR due to the high expression of the two adenylyl cyclase encoded genes, ADCY3 and ADCY9, along with CREBBP. The SPIA approach disregarded the aquaporin genes (given they appear so frequently in so many pathways that involve cellular channels) but highlighted the central role of BDNF which single-handedly drove four signaling pathways: the Ras, Neurotrophin, PI3K-Akt, and MAPK signaling pathways. This also revealed the role of TRAP1 in neurodegeneration.
These two approaches did not discount the role of FAIM2 and CALCR. However, their absence was mainly due to the content of the current KEGG database. On the other hand, these approaches accentuated the role of the MMP2 gene at the FTO locus in skeletal myotubes, given its consistency within the GnRH signaling pathway (eFig. 10), which is in line with previous studies linking its expression with obesity44–46.
Supportive evidence by colocalization of target effector genes with eQTLs
The GTEx consortium has characterized thousands of eQTLs, albeit in heterogeneous bulk tissues47. To assess how many observed gene-SNP pairs agreed with our physical variant-to-gene mapping approach in our multiple separate cellular settings, we performed colocalization analysis using ColocQuiaL38.
282 genes were reported to be associated with the variants within 13 loci from our variant-to-genes analysis. We found 114 colocalizations for ten of our loci that had high conditioned posterior probabilities (cond.PP.H4.abf ≥ 0.8), involving 44 genes and 41 tissues among the eQTLs. We extracted the posterior probabilities for each SNP within each colocalization and selected the 95% credible set as the likely causal variants (complete list in eTable 6). Despite sensitivity differences and varying cellular settings, when compared with our variant-to-gene mapping results, colocalization analysis yielded consistent identification for 21 pairs of SNP-gene interactions when considering the analyses across all our cell types, composed of 20 SNPs and 7 genes. Details of these SNP-gene pairs are shown in Fig. 4A and B.
Of these 20 SNPs, 15 were at the ADCY3 locus, in LD with sentinel variant rs4077678, and all implicated ADCY3 as the effector gene in 29 cell types – 15 metabolic, 6 immune, 4 neural cell types and 4 independent cell lines (Fig. 4C). Indeed, missense mutations have been previously reported for this gene in the context of obesity48,49 while another member of this gene family, ADCY5, has also been extensively implicated in metabolic traits50.
Predicting transcription factors (TFs) binding disruption at implicated genes contributing to obesity risk
TFs regulate gene expression by binding to DNA motifs at enhancers and silencers, where any disruption by a SNP can potentially cause dysregulation of a target gene. Thus, we used motifbreakR (R package) to predict such possible events at the loci identified by our variant-to-gene mapping. Each variant was predicted to disrupt the binding of several different TFs, thus requiring further literature cross-examination to select the most probable effects. For example, rs7132908 (consistently contacting FAIM2 in 25 cell types) was predicted to disrupt the binding of 12 different transcription factors. Among them, SREBF1 (eFig. 11A) was the only TF that concurred with evidence that it regulates AQP2 and FAIM2 at the same enhancer51. The full prediction list can be found in eTable 7.
To narrow down the list of putative TF binding sites at each variant position, we leveraged the ATAC-seq footprint analysis using the RGT suite52. The final set of Motif-Predicted Binding Sites (MPBS) within each cell type ATAC-seq footprints was used to overlap with the genomic locations of the OCRs, and then overlapped with our obesity variants, resulting in annotated 29 variants. Mosaic plot in eFig. 11B shows the number and proportions of variants predicted by motifbreakR and/or overlapped with MPBS. Insignificant P-value from Fisher’s exact test indicated the independence of the two analyses. Only seven variants were found within the cREs for the same TF motifs predicted to be disrupted by motifbreakR (eFig. 11C). eFig. 12 outlines the seven variants that motifbreakR and ATAC-seq footprint analysis agreed on the TF bindings they might disrupt.
Discussion
Given the challenge of uncovering the underlying molecular mechanisms driving such a multifactorial disease as obesity, our approach leveraging GWAS summary statistics, RNA-seq, ATAC-seq, and promoter Capture C / Hi-C offers new insights. This is particularly true as it is becoming increasingly evident that multiple effector genes can operate in a temporal fashion at a given locus depending on cell state, including at the FTO locus53. Our approach offers an opportunity to implicate relevant cis-regulatory regions across different cell types contributing to the genetic etiology of the disease. By assigning GWAS signals to candidate causal variants and corresponding putative effector genes via open chromatin and chromatin contact information, we enhanced the fine-mapping process with an experimental genomic perspective to yield new insights into the biological pathways influencing childhood obesity.
LD score regression is a valuable method that estimates the relationship between linkage disequilibrium score and the summary statistics of GWAS SNPs to quantify the separate contributions of polygenic effects and various confounding factors that produce SNP-based heritability of disease. The general positive heritability enrichment across our open chromatin features spanning multiple cell types (Fig. 1B.a) reinforces the notion that obesity etiology involves many systems in our body.
While obesity has long been known to be a risk factor for pancreatitis and pancreatic cancer, the significant enrichment of pancreatic alpha and beta cell related 3D genomic features for childhood obesity GWAS signals demonstrates the bidirectional relationship between obesity and the pancreas; indeed, it is well established that insulin has obesogenic properties. Moreover, the comorbidity of obesity and diabetes (either causal or a result of the overlap between SNPs associated with these two diseases) is tangible. When focusing on genetic annotation of the cREs only, the association with obesity became more diverse across cell types, especially in metabolic cells. Interestingly, the lack of enrichment (only 8 of 57 cell types yielded no degree of enrichment) of obesity SNPs heritability in open gene promoters (Fig. 1B.b) reveals that cRE regions harboring obesity SNPs are more involved in gene regulation than disruption, and therefore potentially contributing more weight to the manifestation of the disease.
Of course, we should factor in the effective sample sizes of the GWAS efforts that are wide-ranging (2,000-24,000 – given that the N for each variant is different within a single dataset, thus contributing to the weights and P-value of each SNP when the algorithm calculates the genome-wide heritability), which could result in noise and negative enrichment observed in the analysis – a methodology limitation of partial linkage regression that has been extensively discussed in the field54. Thus, it is crucial to interpret the enrichment (or lack thereof) of disease variants in a certain cellular setting with an ad hoc biological context.
From mapping the common proxies of 19 independent sentinel SNPs that were genome-wide significantly associated with childhood obesity to putative effector genes through chromatin contacting cREs, one striking finding was the several potential “hubs” of putatively core effector genes, whose occurrence spread across three human physiological systems. With the data available from so many cell types, our approach connected new candidate causal variants to known obesity-related genes and new implications of cell modality for previously known associations.
A potential application of this association could be to fine-tune the effect of a drug toward controlling appetite. An example of bringing new aspects to the old is for the signal within the FTO locus that contacted IRX3 and IRX5: previous studies have suggested these obesogenic effects operate in adipocytes55, brain56, or pancreas57; here we confirmed this association in adipocytes and uncover the presence of distal chromatin contacts in myotubes for the first time.
Besides the above-mentioned genes with known associations with obesity, we discovered newly implicated genes. For example, the LRRIQ3 gene at the TNNI3K locus had its open promoter contacted by two SNPs, rs1040070 and rs10493544, in NTERA2 cells only. The published studies58,59 that associated LRRIQ3 with major depressive disorder and opioid usage acknowledged the overlapping promoter of this gene, albeit in the opposite direction, with a run-through transcript of FPGT-TNNI3K – previously shown to be associated with BMI in European60 and Korean populations61.
It is apparent that not all the implicated genes we report would contribute equally to the susceptibility of obesity pathogenesis. Each locus comprises genes whose functions are obviously related to obesity or similar traits like BMI, fat weight, etc., while other genes are not so directly obvious in their relation to these traits.
It is encouraging that for implicated genes within these multi-cell-type loci across different physiological systems we could find previous associations to the corresponding cell types or systems. Examples are the two aforementioned genes at the TMEM18 locus (SH3YL1 and ACP1)62–66 with the broad spectrum of their functions, HEPACAM2 implicated in the NCIH716 cell line at the CALCR locus67,68, and LRRIQ3 in the NTERA2 cell line at theTNNI3K locus69.
Chronic inflammation is an essential characteristic of obesity pathogenesis. Adipose tissue-resident immune cells have been observed, leading to an increased focus in recent years on their potential contribution to metabolic dysfunction. On the other hand, neurological or psychological conditions, such as stress, induce the secretion of both glucocorticoids (increase motivation for food) and insulin (promotes food intake and obesity). Pleasure feeding then reduces activity in the stress-response network, reinforcing the feeding habit. It has been shown that voluntary behaviors, stimulated by external or internal stressors or pleasurable feelings, memories, and habits, can override the basic homeostatic controls of energy balance70. The potential link between the immune system and metabolic disease, and moreover, through the neural system, was tangible in our findings.
Two of the three SNPs which ranked the third most consistent in our variant-to-gene mapping (Fig. 2C) – rs35796073 and rs35142762 – contacted the ALKAL2 promoter (supported by GTEx evidence to colocalize with ALKAL2 expression). The anaplastic lymphoma kinase (encoded by ALK gene) is a receptor tyrosine kinase, belongs to the insulin receptor family, and has been reported to promote nerve cell growth and differentiation71,72. Despite ALKAL2 (ALK and LTK ligand 2) being studied principally in the context of immunity, a recent study using the EGCUT biobank GWAS identified ALK as a candidate thinness gene and genetic deletion showed that its expression in hypothalamic neurons acts as a negative regulator in controlling energy expenditure via sympathetic control of adipose tissue lipolysis73. ALKAL2 – encoding a high-affinity agonist of ALK/LTK receptors – which has been reported to enhance expression in response to inflammatory pain in nociceptors74,75 - has been recently implicated as a novel candidate gene for childhood BMI by transcriptome-wide association study76, and achieved genome-wide significance in a GWAS study contrasting persistent healthy thinness with severe early-onset obesity using the STILTS and SCOOP cohorts77. The finding that overexpression of ALKAL2 could potentiate neuroblastoma progression in the absence of ALK mutation78 echoes the relationship between ADCY3 and MC4R79, where a peripheral gene, ADCY3, can regulate/impair the function of a core gene, i.e. MC4R, within the energy-regulating melanocortin signaling pathway80.
Our approach implicates putative target genes based on a mechanism of regulation for these variants to alter gene expression – through regulator TF(s) that bind to these contact sites. A potential limitation of the predictions from motifbreakR and matching TF motifs to ATAC-seq footprint by the RGT toolkit is that they were both based on the position probability matrixes of Jaspar and Hocomoco, which come from public motif databases. The ATAC-seq footprint analysis also carries sequence bias that can lead to false positive discovery. Thus, our attempt to call such regulators by predicting TF binding disruption can only serve as nominations – but warrant further functional follow up.
Another limitation of this work is the diversity in data quality among different samples, since different datasets were sampled and collected at different time points, from different patients, using different protocols, with libraries sequenced at different depths and qualities, and initially preprocessed with different pipelines and parameters. Thus, it is crucial to keep in mind that the discrepancy in data points might have resulted from variations in data quality. Importantly, any association discovered must be validated functionally before effector genes of the genetic variants can be leveraged to develop new therapies. Their putative function(s) must be characterized, together with the mechanism whereby the given variant’s alleles differentially affect the expression of the targeted genes. The next step is to explore how the target genes affect the trait of interest more directly.
Our results have provided a set of leads for future exploratory experiments in specific cellular settings in order to further expand our knowledge of childhood obesity genomics and hence equip us with more effective means to overcome the burden of this systematic disease.
Conclusion
Our approach of combining RNA-seq, ATAC-seq, and promoter Capture C/Hi-C datasets with GWAS summary statistics offers a systemic view of the multi-cellular nature of childhood obesity, shedding light on potential regulatory regions and effector genes. By leveraging physical properties, such as open chromatin status and chromatin contacts, we enhanced the fine-mapping process and gained new insights into the biological pathways influencing the disease. Although further functional validation is required, our findings provide valuable leads together with their cellular contexts for future research and the development of more effective strategies to address the burden of childhood obesity.
Data Availability
All data produced in the present study are available upon reasonable request to the authors
Acknowledgements
This work was supported by National Institutes of Health awards R01 HD056465, R01 DK122586 and UM1 DK126194, and the Daniel B. Burke Endowed Chair for Diabetes Research.
Given the use of de-identified datasets and biospecimens was not considered human subjects research, ethical oversight was waived by the Institutional Review Board of the Children’s Hospital of Philadelphia
References
- 1.Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adultsLancet 390:2627–2642https://doi.org/10.1016/s0140-6736(17)32129-3
- 2.National Health and Nutrition Examination Survey 2017–March 2020 Prepandemic Data Files Development of Files and Prevalence Estimates for Selected Health OutcomesNational Center for Health Statistics (U.S.) https://doi.org/10.15620/cdc:106273
- 3.Health Effects of Overweight and Obesity in 195 Countries over 25 YearsNew England Journal of Medicine 377:13–27https://doi.org/10.1056/NEJMoa1614362
- 4.The Age-Specific Quantitative Effects of Metabolic Risk Factors on Cardiovascular Diseases and Diabetes: A Pooled AnalysisPLOS One 8https://doi.org/10.1371/journal.pone.0065174
- 5.Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studiesThe Lancet 377:1085–1095https://doi.org/10.1016/S0140-6736(11)60105-0
- 6.Body Fatness and Cancer — Viewpoint of the IARC Working GroupNew England Journal of Medicine 375:794–798https://doi.org/10.1056/NEJMsr1606602
- 7.Obesity and health-related quality of lifeObes Rev 2:173–82https://doi.org/10.1046/j.1467-789x.2001.00032.x
- 8.The genetics of obesity: from discovery to biologyNature Reviews Genetics 23:120–133https://doi.org/10.1038/s41576-021-00414-z
- 9.Genetic and environmental factors in relative body weight and human adiposityBehavior genetics 27:325–51
- 10.Variability in the heritability of body mass index: a systematic review and meta-regressionFrontiers in endocrinology 3
- 11.Genetic and environmental influences on infant weight and weight change: the Fels Longitudinal StudyAm J Hum Biol. Sep 19:692–702https://doi.org/10.1002/ajhb.20660
- 12.Genetic and environmental influences on body size in early childhood: a twin birth-cohort studyTwin Res Hum Genet 10:479–85https://doi.org/10.1375/twin.10.3.479
- 13.Evidence for a strong genetic influence on childhood adiposity despite the force of the obesogenic environmentAm J Clin Nutr 87:398–404https://doi.org/10.1093/ajcn/87.2.398
- 14.Childhood obesity: are genetic differences involved?The American Journal of Clinical Nutrition 89:1494–1501https://doi.org/10.3945/ajcn.2009.27113C
- 15.Genetic Determinants of Childhood ObesityMol Diagn Ther 24:653–663https://doi.org/10.1007/s40291-020-00496-1
- 16.Novel loci for childhood body mass index and shared heritability with adult cardiometabolic traitsPLOS Genetics 16https://doi.org/10.1371/journal.pgen.1008718
- 17.Genetic Studies of Leptin Concentrations Implicate Leptin in the Regulation of Early AdiposityDiabetes 69:2806–2818https://doi.org/10.2337/db20-0070
- 18.GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMISci Adv 5https://doi.org/10.1126/sciadv.aaw3095
- 19.Childhood sleep duration modifies the polygenic risk for obesity in youth through leptin pathway: the Beijing Child and Adolescent Metabolic Syndrome cohort studyInt J Obes (Lond) 43:1556–1567https://doi.org/10.1038/s41366-019-0405-1
- 20.Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesityNat Genet 50:26–41https://doi.org/10.1038/s41588-017-0011-x
- 21.Strategies to identify causal common genetic variants and corresponding effector genes for paediatric obesityPediatr Obes 17https://doi.org/10.1111/ijpo.12968
- 22.Variant to function mapping at single-cell resolution through network propagationNature Biotechnology https://doi.org/10.1038/s41587-022-01341-y
- 23.Effective gene expression prediction from sequence by integrating long-range interactionsNature Methods 18:1196–1203https://doi.org/10.1038/s41592-021-01252-x
- 24.Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicityNature Genetics 54:827–836https://doi.org/10.1038/s41588-022-01087-y
- 25.Predicting effects of noncoding variants with deep learning– based sequence modelNature Methods 12:931–934https://doi.org/10.1038/nmeth.3547
- 26.Genome-wide enhancer maps link risk variants to disease genesNature 593:238–243https://doi.org/10.1038/s41586-021-03446-x
- 27.Genome-scale Capture C promoter interactions implicate effector genes at GWAS loci for bone mineral density. OriginalPaperNature Communications 10:1–11https://doi.org/10.1038/s41467-019-09302-x
- 28.Mapping effector genes at lupus GWAS loci using promoter Capture-C in follicular helper T cellsNature Communications 11https://doi.org/10.1038/s41467-020-17089-5
- 29.Cis-regulatory architecture of human ESC-derived hypothalamic neuron differentiation aids in variant-to-gene mapping of relevant complex traits. OriginalPaperNature Communications 12:1–12https://doi.org/10.1038/s41467-021-27001-4
- 30.Genome-wide association study implicates novel loci and reveals candidate effector genes for longitudinal pediatric bone accrualGenome Biol 22https://doi.org/10.1186/s13059-020-02207-9
- 31.A multiancestry genome-wide association study of unexplained chronic ALT elevation as a proxy for nonalcoholic fatty liver disease with histological and radiological validationNat Genet 54:761–771https://doi.org/10.1038/s41588-022-01078-z
- 32.Implicating effector genes at COVID-19 GWAS loci using promoter-focused Capture-C in disease-relevant immune cell typesGenome Biology 23https://doi.org/10.1186/s13059-022-02691-1
- 33.3D chromatin maps of the human pancreas reveal lineage-specific regulatory architecture of T2D riskCell Metabolism 34https://doi.org/10.1016/j.cmet.2022.08.014
- 34.Variant-to-gene mapping followed by cross-species genetic screening identifies GPI-anchor biosynthesis as a regulator of sleepSci Adv 9https://doi.org/10.1126/sciadv.abq0844
- 35.A trans-ancestral meta-analysis of genome-wide association studies reveals loci associated with childhood obesityHuman Molecular Genetics 28:3327–3338https://doi.org/10.1093/hmg/ddz161
- 36.TOP-LD: A tool to explore linkage disequilibrium with TOPMed whole-genome sequence dataThe American Journal of Human Genetics 109:1175–1181https://doi.org/10.1016/j.ajhg.2022.04.006
- 37.LDlinkR: An R Package for Rapidly Calculating Linkage Disequilibrium Statistics in Diverse PopulationsFront Genet 11https://doi.org/10.3389/fgene.2020.00157
- 38.ColocQuiaL: a QTL-GWAS colocalization pipelineBioinformatics 38:4409–4411https://doi.org/10.1093/bioinformatics/btac512
- 39.Partitioning heritability by functional annotation using genome-wide association summary statistics. OriginalPaperNature Genetics 47:1228–1235https://doi.org/10.1038/ng.3404
- 40.Increased Circulation and Adipose Tissue Levels of DNAJC27/RBJ in Obesity and Type 2-DiabetesFront Endocrinol (Lausanne) 9https://doi.org/10.3389/fendo.2018.00423
- 41.Minireview: nuclear receptor coregulators of the p160 family: insights into inflammation and metabolismMol Endocrinol 29:502–17https://doi.org/10.1210/me.2015-1005
- 42.Betulinic acid lowers lipid accumulation in adipocytes through enhanced NCoA1–PPARγ interactionJournal of Infection and Public Health 12:726–732https://doi.org/10.1016/j.jiph.2019.05.011
- 43.pathfindR: An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks. MethodsFrontiers in Genetics 10https://doi.org/10.3389/fgene.2019.00858
- 44.Matrix metalloproteinase-2 and -9 levels in obese patientsEndothelium 15:219–24https://doi.org/10.1080/10623320802228815
- 45.Downregulation of MMP-2 and MMP-9 genes in obesity patients and their relation with obesity-related phenotypesTurkish Journal of Biochemistry 47:425–433https://doi.org/10.1515/tjb-2021-0124
- 46.Differential Expression of MMP2 and TIMP2 in Peripheral Blood Mononuclear Cells After Roux-en-Y Gastric Bypass. Brief Research ReportFrontiers in Nutrition https://doi.org/10.3389/fnut.2021.628759
- 47.Genetic effects on gene expression across human tissuesNature 550:204–213
- 48.Loss-of-function variants in ADCY3 increase risk of obesity and type 2 diabetesNature genetics 50:172–174https://doi.org/10.1038/s41588-017-0022-7
- 49.Genome-wide association study of height-adjusted BMI in childhood identifies functional variant in ADCY3Obesity (Silver Spring) 22:2252–9https://doi.org/10.1002/oby.20840
- 50.A regulatory variant at 3q21.1 confers an increased pleiotropic risk for hyperglycemia and altered bone mineral densityCell Metabolism 33:615–628https://doi.org/10.1016/j.cmet.2021.01.001
- 51.Bayesian identification of candidate transcription factors for the regulation of Aqp2 gene expressionAm J Physiol Renal Physiol 321:F389–f401https://doi.org/10.1152/ajprenal.00204.2021
- 52.Identification of transcription factor binding sites using ATAC-seqGenome Biology 20https://doi.org/10.1186/s13059-019-1642-2
- 53.Extensive pleiotropism and allelic heterogeneity mediate metabolic effects of IRX3 and IRX5Science (New York, NY) 372:1085–1091https://doi.org/10.1126/science.abf1008
- 54.On Negative Heritability and Negative Estimates of HeritabilityGenetics 215:343–357https://doi.org/10.1534/genetics.120.303161
- 55.FTO Obesity Variant and Adipocyte Browning in HumansN Engl J Med 374:192–3https://doi.org/10.1056/NEJMc1513316
- 56.Obesity-associated variants within FTO form long-range functional connections with IRX3Nature 507:371–375https://doi.org/10.1038/nature13138
- 57.Long-range gene regulation links genomic type 2 diabetes and obesity risk regions to HHEX, SOX4, and IRX3Proc Natl Acad Sci U S A 107:775–80https://doi.org/10.1073/pnas.0911591107
- 58.Identification of novel common variants associated with chronic pain using conditional false discovery rate analysis with major depressive disorder and assessment of pleiotropic effects of LRFN5Transl Psychiatry 9https://doi.org/10.1038/s41398-019-0613-4
- 59.Genome-wide association study of problematic opioid prescription use in 132,113 23andMe research participants of European ancestryMol Psychiatry 26:6209–6217https://doi.org/10.1038/s41380-021-01335-3
- 60.Genome-wide analysis of BMI in adolescents and young adults reveals additional insight into the effects of genetic loci over the life courseHum Mol Genet 22:3597–607https://doi.org/10.1093/hmg/ddt205
- 61.BMI prediction within a Korean populationPeerJ 5https://doi.org/10.7717/peerj.3510
- 62.Non-parametric combination analysis of multiple data types enables detection of novel regulatory mechanisms in T cells of multiple sclerosis patientsSci Rep 9https://doi.org/10.1038/s41598-019-48493-7
- 63.Identification of a Novel Coregulator, SH3YL1, That Interacts With the Androgen Receptor N-TerminusMol Endocrinol 29:1426–39https://doi.org/10.1210/me.2015-1079
- 64.Dock4 forms a complex with SH3YL1 and regulates cancer cell migrationCell Signal 26:1082–8https://doi.org/10.1016/j.cellsig.2014.01.027
- 65.SH3YL1 protein as a novel biomarker for diabetic nephropathy in type 2 diabetes mellitusNutr Metab Cardiovasc Dis 31:498–505https://doi.org/10.1016/j.numecd.2020.09.024
- 66.Male-specific association of the 2p25 region with suicide attempt in bipolar disorderJ Psychiatr Res 121:151–158https://doi.org/10.1016/j.jpsychires.2019.11.009
- 67.Analysis of potential genes and pathways associated with the colorectal normal mucosa-adenoma-carcinoma sequenceCancer Med 7:2555–2566https://doi.org/10.1002/cam4.1484
- 68.Identification of Critical Genes and Five Prognostic Biomarkers Associated with Colorectal CancerMed Sci Monit 24:4625–4633https://doi.org/10.12659/msm.907224
- 69.NTera 2 cells: a human cell line which displays characteristics expected of a human committed neuronal progenitor cellJ Neurosci Res. 35:585–602https://doi.org/10.1002/jnr.490350603
- 70.Stress-induced obesity and the emotional nervous systemTrends Endocrinol Metab 21:159–65https://doi.org/10.1016/j.tem.2009.10.004
- 71.Molecular characterization of ALK, a receptor tyrosine kinase expressed specifically in the nervous systemOncogene 14:439–49https://doi.org/10.1038/sj.onc.1200849
- 72.ALK receptor tyrosine kinase promotes cell growth and neurite outgrowthJ Cell Sci 117:3319–29https://doi.org/10.1242/jcs.01183
- 73.Identification of ALK in ThinnessCell 181:1246–1262https://doi.org/10.1016/j.cell.2020.04.034
- 74.The neuronal tyrosine kinase receptor ligand ALKAL2 mediates persistent painThe Journal of Clinical Investigation 6:132–12https://doi.org/10.1172/JCI154317
- 75.Transcriptome analysis reveals dysregulation of inflammatory and neuronal function in dorsal root ganglion of paclitaxel-induced peripheral neuropathy ratsMol Pain https://doi.org/10.1177/17448069221106167
- 76.Transcriptome-wide association study identifies multiple genes associated with childhood body mass indexInternational Journal of Obesity 45:1105–1113https://doi.org/10.1038/s41366-021-00780-y
- 77.Genetic architecture of human thinness compared to severe obesityPLoS Genet.
- 78.ALK ligand ALKAL2 potentiates MYCN-driven neuroblastoma in the absence of ALK mutationThe EMBO Journal 40https://doi.org/10.15252/embj.2020105784
- 79.Subcellular localization of MC4R with ADCY3 at neuronal primary cilia underlies a common pathway for genetic predisposition to obesityNat Genet 50:180–185https://doi.org/10.1038/s41588-017-0020-9
- 80.Genetic mapping of etiologic brain cell types for obesityeLife 9https://doi.org/10.7554/eLife.55851
- 81.Novel sequence variations in the brain-derived neurotrophic factor gene and association with major depression and antidepressant treatment responseArch Gen Psychiatry 66:488–497
- 82.BDNF/TRKB/P75NTR polymorphisms and their consequences on antidepressant efficacy in depressed patientsPharmacogenomics 16:997–1013
Article and author information
Author information
Version history
- Sent for peer review:
- Preprint posted:
- Reviewed Preprint version 1:
- Reviewed Preprint version 2:
Copyright
© 2024, Trang 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.
Metrics
- views
- 358
- downloads
- 4
- citations
- 0
Views, downloads and citations are aggregated across all versions of this paper published by eLife.