1. Chromosomes and Gene Expression
  2. Genetics and Genomics

Genetic and environmental determinants of variation in the plasma lipidome of older Australian twins

  1. Matthew WK Wong
  2. Anbupalam Thalamuthu
  3. Nady Braidy
  4. Karen A Mather
  5. Yue Liu
  6. Liliana Ciobanu
  7. Bernhardt T Baune
  8. Nicola J Armstrong
  9. John Kwok
  10. Peter Schofield
  11. Margaret J Wright
  12. David Ames
  13. Russell Pickford
  14. Teresa Lee
  15. Anne Poljak
  16. Perminder S Sachdev  Is a corresponding author
  1. Centre for Healthy Brain Ageing, School of Psychiatry, Faculty of Medicine, University of New South Wales, Australia
  2. Neuroscience Research Australia, Australia
  3. The University of Adelaide, Adelaide Medical School, Discipline of Psychiatry, Australia
  4. Department of Psychiatry, University of Münster, Germany
  5. Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Australia
  6. The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
  7. Mathematics and Statistics, Murdoch University, Australia
  8. Brain and Mind Centre, The University of Sydney, Australia
  9. School of Medical Sciences, University of New South Wales, Australia
  10. Queensland Brain Institute, University of Queensland, Australia
  11. Centre for Advanced Imaging, University of Queensland, Australia
  12. University of Melbourne Academic Unit for Psychiatry of Old Age, Australia
  13. National Ageing Research Institute, Australia
  14. Bioanalytical Mass Spectrometry Facility, University of New South Wales, Australia
  15. Neuropsychiatric Institute, Euroa Centre, Prince of Wales Hospital, Australia
https://doi.org/10.7554/eLife.58954
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Cite this article
  1. Matthew WK Wong
  2. Anbupalam Thalamuthu
  3. Nady Braidy
  4. Karen A Mather
  5. Yue Liu
  6. Liliana Ciobanu
  7. Bernhardt T Baune
  8. Nicola J Armstrong
  9. John Kwok
  10. Peter Schofield
  11. Margaret J Wright
  12. David Ames
  13. Russell Pickford
  14. Teresa Lee
  15. Anne Poljak
  16. Perminder S Sachdev
(2020)
Genetic and environmental determinants of variation in the plasma lipidome of older Australian twins
eLife 9:e58954.
https://doi.org/10.7554/eLife.58954
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4 figures, 6 tables and 5 additional files

Figures

Figure 1 with 1 supplement
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Heritability of lipids.

(A) Percentage distribution of heritable lipids. The central wheel represents significantly heritable lipids and their percentage distribution by lipid class. Smaller wheels emanating from each sector represent proportions of these heritable lipids compared to total measured lipids of that class, such that the sum of these smaller wheels equals the total pool of 207 individual lipids measured. For example, 45% of significantly heritable lipids belonged to the TG lipid class, and these heritable lipids represented 17% of total measured plasma TG. Orange sectors represent non-heritable percentage of each lipid class. (B) The distribution of heritability (h2), estimated from the ACE model, for each individual lipid species grouped according to class. Boxplots show median with interquartile range for each class. Dark circles represent heritable lipids, as opposed to grey circles, which represent lipids that were not significantly heritable. Minimum (significant) heritability is h2 >0.287.

Figure 1—figure supplement 1
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Genetic correlation heatmap.

Genetic correlation matrix heatmap. Values represent the median of genetic correlations taken between combinations of heritable lipid species of one lipid class with lipid species of another class (or the same class). Note SM represents the sum of SM with a single double bond, thus no correlation could be computed for SM with itself. TG_t represents triglycerides referred to as a traditional lipid measure (as opposed to individual species measured by mass spectrometry).

Figure 2 with 1 supplement
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Heritability estimate (h2a) vs total variance explained (Nagelkerke r2) by gene expression probe transcripts for heritable lipids.

Pearson correlation was calculated.

Figure 2—figure supplement 1
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Batch correction using inverse rank normal transform of residuals.

PCA plots showing good overlap of experimental batch lipids after (A) residuals were taken and (B) after inverse rank normal transformation was applied to these residuals.

Figure 3
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Venn diagrams showing distribution of gene transcripts associated with a majority of TG lipids.

These were subdivided into those associated with saturated vs monounsaturated vs polyunsaturated lipids for (A) significantly heritable TGs and (B) non-heritable TGs. Also shown are heritable vs non-heritable set of significant gene expression associations of TG lipids that were first subdivided based on (C) double bond group/saturation (Supplementary file 2G) and (D) total number of carbons (<49 carbons, 49–55 carbons and 56+ carbons, Supplementary file 2H). Gene transcripts included in these Venn diagrams were those significantly associated with the highest and second highest number of lipids of a particular saturation class (A and B), or among heritable and non-heritable lipids (C and D). Upwards and downwards arrows indicate positive and inverse gene expression associations with lipid levels respectively.

Figure 4
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Schematic of the combined genetic and environmental influences on the blood lipidome, and the association of this lipidome with the blood transcriptome.

Under this model, non-heritable lipids could affect gene transcription, while heritable lipids could also affect gene transcription (collectively ‘blood lipid associated transcriptome’), but are possibly modified upstream by genetic machinery such as elongases, desaturases, synthetases, receptors and binding proteins. Gene transcripts encoding these enzymes and proteins may be independent of the ‘blood lipid associated transcriptome’ noted in this study.

Tables

Table 1
Participant characteristics for heritability analyses.
MZ (n = 150)DZ (n = 110)Statisticp-value
Age75.7 (5.47)76.07 (5.31)−0.5480.584
Females100 (67%)79 (72%)0.7850.376
Education (yrs)10.99 (3.18)11.2 (3.18)−0.4750.635
BMI (kg/m2)27.934 (4.74)27.5 (4.92)0.7760.438
WHR0.89 (0.09)0.89 (0.08)0.1640.87
MMSE28.9 (1.37)28.95 (1.76)−0.0620.95
LDL-C (mmol/L)2.77 (0.97)2.78 (0.97)−0.0780.938
HDL-C (mmol/L)1.73 (0.46)1.60 (0.44)2.3410.02
Cholesterol (mmol/L)5.08 (1.01)4.98 (1.12)0.8220.412
Triglyceride (mmol/L)1.30 (0.54)1.32 (0.56)−0.2980.766
APOE ε4 carrier*35 (26%)27 (28%)0.1180.731

  1. Means (SD) are presented for continuous variables, while n (%) is presented for categorical variables. Comparisons of MZ and DZ pairs used t tests for continuous variables and χ (Quehenberger et al., 2010) tests for categorical variables.

    Abbreviations: MZ = monozygotic, DZ = dizygotic. body mass index (BMI), mini-mental state exam (MMSE), waist-hip ratio (WHR), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C).

  2. *excludes participants with missing data (n = 231 participants with APOE genotype data).

Table 2
Gene expression associations among TG lipids.
TG classNumber of associated lipidsNumber of transcript associations
Saturated TG1–2282
3–86
Monounsaturated TG1–259
3–87
Polyunsaturated TG1–2243
3–8119
>89

  1. Note. Table lists number of gene expression associations common to a maximum of 1–2, 3–8 and >8 lipids in each TG saturation class (saturated, monounsaturated, and polyunsaturated TG).

Table 3
Functions of genes with significant lipid-gene transcriptome associations.
Biological PathwaysGene Transcripts*Relevance to the CNS
Inflammation
Innate immunity↓LILRB3, ↓MGAM
Adaptive immune response↓LILRA6
Host Defense↓FPR1, ↓TRIM51FPR1 found in neural glial cells, astrocytes and neuroblastoma (Cussell et al., 2019).
Allergic Response↓ADAM8, ↓HDC, ↓CPA3ADAM8 may regulate cell adhesion during neurodegeneration (Schlomann et al., 2000).
HDC as a histidine decarboxylase, produces histamine, which in the CNS is a neurotransmitter (Yoshikawa et al., 2014).
Class I MHC antigen binding↓LILRA6, ↓LILRB3
B-Cell response/receptor signalling↓GAB2, ↓LILRB3, ↓PRKCDGAB2 is associated with Alzheimer's disease. By activating PI3K, increases amyloid production and microglia-mediated inflammation. Several GAB2 SNPs are associated with late-onset Alzheimer’s disease (Chen et al., 2018).
Mast Cell Degranulation↓CPA3, ↓HDC,
Vasoactive Actions
Regulation of vasoactive peptides (e.g., endothelin, angiotensin 1, snake toxins, etc)↓GATA2, ↓CPA3,
Epithelial Cell Integrity↓KRT23, ↓PRKCD
Cell Adhesion↓APMAPAPMAP supresses brain Aβ production (Mosser et al., 2015).
DNA Regulation↑ RPSA, ↑ SNORA62, ↑ SNHG1
Vesicle/Endosome Regulation/Transport↑ VAMP8, ↓REPS2, ↓SLC45A3SLC45A3 regulates oligodendrocyte differentiation (Shin et al., 2012).
Pseudogenes/non-protein coding↓S100A11P1, ↓RPSAP15,
↑ RP11-179G5.1,
↑ RP11-350G8.3,
↑ RPL35P5, ↑ RPL4P2,
↑ RPS10P14, ↑ RPSAP15,
↑ RPSAP58, ↑ SNHG1,
↑ SNORA62		Regulatory roles. Gene silencing, affects mRNA stability.
Table 4
Regression of lipid residuals significantly associated with genome wide average DNA methylation levels.
LipidBetaSEtp-valueh2p-value for h2
CE(20:3)0.210.092.342.31E-020.310.30
LPC(15:0)−0.220.09−2.541.39E-026.51E-161
LPC(16:0)−0.270.09−3.122.90E-033.82E-141
LPC(17:0)−0.210.09−2.342.30E-022.82E-141
LPC(18:1e)−0.210.09−2.441.81E-023.52E-171
LPC(26:0)−0.270.09−3.103.07E-030.0560.87
PC(39:3)0.180.092.123.84E-020.390.14
TG(18:1_17:1_22:6)−0.180.09−2.054.51E-020.310.05
TG(18:1_18:1_22:5)−0.230.09−2.699.58E-033.42E-151
TG(18:1_20:4_22:6)−0.210.09−2.411.96E-022.98E-151
TG(19:0_18:1_18:1)−0.180.09−2.143.73E-020.3120.29
GroupLPC−0.240.09−2.748.32E-031.88E-151

  1. Notes. Associations of GWAM with lipid residuals (adjusted for age, sex, education, BMI, lipid lowering medication, smoking status, experimental batch and APOE ε4 carrier status).

Table 5
Comparison of heritability estimates for traditional lipids and specific lipid classes/species summarising the current work and other published studies.
Study and cohort detailsFindingsReference
Traditional lipids
Present study
75 MZ pairs, 55 DZ pairs
69–93 years		Range h2: 0.404–0.427
HDL-C had substantial C-component 0.27
Qingdao Twin Registry
382 MZ pairs and 139 DZ pairs, mean age 51 ± 7		Total Cholesterol and LDL-C 0.614, 0.655
HDL-C h2 = 0.26, C-component = 0.478		Liu et al., 2018
National Heart Lung and Blood Institute Veteran Twin Study;
235 MZ, 260 DZ pairs
48–63 years		Longitudinal increases in heritability across three time pts
Total Cholesterol (from 0.46 to 0.57), LDL-C (from 0.49 to 0.64), and HDL-C (from 0.50 to 0.62)
TG: h2 = 0.40		Goode et al., 2007
San Antonio Family Heart Study
N = 569, mean age 39.4 years		h2HDL-C = 0.55, h2TG = 0.53Goode et al., 2007; Mahaney et al., 1995
Lipid Species/Classes
Present studyRange h2: 0–0.59
Range heritable lipids: 0.287–0.59, median: 0.433
Heritable lipids: some Cer, TG, DG. Fewer PE, PC
Non-heritable lipids: LPC, SM, PI, CE
Wisconsin Registry for Alzheimer’s Prevention n = 1212, mean age 60.8Range 0.2–84.9%, median h2 = 0.354
Median h2ceramides = 0.48
Median h2DG = 0.38		Darst et al., 2019
San Antonio Family Heart study, n = 1212
mean age 39.52		h2range = 0.09–0.60
Median = 0.37
Heritable: almost all lipids, including Cer, TG, DG
Least heritable: LPC, alkyl-PE		Bellis et al., 2014
NUGAT Twin Study
34 MZ, 12 DZ twin pairs
18–70 years, median age 25		Range h2: 0–0.62 (19/152 lipids had h2 > 0.40)
Heritable lipids: LPC, PE, SM
Non-heritable: Cer		Frahnow et al., 2017; Tabassum et al., 2019
FINRISK n = 2181
25–74 years
SNP based heritability		SNP based range h2: 0.10–0.54
Heritable lipids: Cer, LPC, SM, TG
Non-heritable: PI		Tabassum et al., 2019
n = 203 plasma samples from 31 familiesCer heritability range: 0.10–0.63McGurk et al., 2017
n = 999, 196 British families, mean age 45SNP-based Cer heritability range: 0.18–0.87McGurk et al., 2019
Key resources table
Reagent type
(species) or
resource		DesignationSource or
reference		IdentifiersAdditional
information
Biological sample (Homo sapiens)Fasting human EDTA plasma
OATS
Wave 3		Sachdev, P.S., et al (2011). Cognitive functioning in older twins: the Older Australian Twins Study. Australasian journal on ageing 30 Suppl 2, 17–23.Subject Cohort used:
Wave three from the Older Australian
Twins Study (OATS)
Age range: 69–93 years		Plasma used for lipidomics analysis.
Cohort also has genetics (SNPs) data and gene methylation data
Chemical compound, drugSPLASH Lipidomix Mass Spec StandardAvanti (Alabaster, Alabama, United States)SKU 330707-1EAStable isotope labelled internal lipid standards
OtherQExactive Plus mass spectrometer and associated software: Xcalibur (3.1.66.10) and MS Tune (2.8 SP1 Build 2806))Thermo Fischer Scientific (Waltham MA United States)MSMSMass spectrometer and controller software
OtherDIONEX UltiMate 3000 LC System and associated Chromeleon softwareThermo Fischer Scientific (Waltham MA United States)LC and controller softwareThe LC system is comprised of an RS pump, RS column compartment and RS autosampler
Software, algorithmLipidsearch software v4.2.2Thermo Fischer Scientific (Waltham MA United States)ThermoFisher Scientific softwareLipid identification and peak area integration
Chemical compound, drugActeonitrile
UN 1648		Honeywell Burdick and JacksonHPLC grade solvent
CAS 75-05-08		Solvent used for preparing LC-MS Buffers
Country of manufacture: Korea
Chemical compound, drugAmmonium formateHoneywell FlukaHPLC grade reagent
CAS 540-69-2		Reagent used for preparing LC-MS Buffers UNIVAR analytical reagent
Country of manufacture: Germany
Chemical compound, drugFormic Acid (99%)
UN 1779		AJAX Finechem
(Nuplex Industries, Australia)		AR Grade
CAS 64-18-6		Solvent used for preparing LC-MS Buffers
Chemical compound, drugMilli-Q IQ 7000
purified Water		Merck MilliporePurity monitored to a minimum of 18 MΩ resistivityPurified water for preparing buffers and general laboratory use
Chemical compound, drugIsopropanolHoneywell Burdick and Jackson Material No. 10626668
Manufactured: USA		LC-MS grade
CAS 67-63-0		Solvent used for preparing LC-MS
It is important to use LC-MS grade isopropanol in buffer B, to maintain low background signal for LCMSMS
OtherAcquity LC column
LC-MS reverse phase column		Waters CorporationAcquity UPLC CSHC18, 1.7 mm, 2.1 × 100 mm column
SKU 186005297		Includes Vanguard pre-column attachment.
Chemical compound, drugButanol for lipid extractionAsia Pacific Specialty Chemicals,
Thermo Fisher Scientific		CAS 71-36-3Extraction described: https://doi.org/www.frontiersin.org/articles/10.3389/fneur.2019.00879/full https://www.mdpi.com/2218-1989/5/2/389
Chemical compound, drugMethanol HPLC grade solvent for lipid extractionAJAX Finechem
(Nuplex Industries, Australia)		CAS 67-56-1
Commercial assay or kitPAXgene blood RNA systemPreAnalytix, QiagenCAS 762165
CAS 762164		RNA blood tube and extraction kit.
Used as per manufacturer’s protocol
Commercial assay or kitAgilent Technologies 2100 BioanalyzerAgilentG2939BARNA integrity number (RIN) assessment
Commercial assay or kitIllumina Whole-Genome Gene Expression direct Hybridization Assay System HumanHT-12 v4Illumina, San Diego, CABD-103–0604Used as per manufacturer’s protocol
BD-901–1002
Commercial assay or kitIllumina Infinium HumanMethylation 450 BeadChipIllumina, San Diego, CAWG-314–1002Used as per manufacturer’s protocol
OtherBeckman LX20 Analyser
(clinical chemistry analysis of LDL-C, HDL-C, triglyerides)		Beckman Coulter, AustraliaDone at Prince of Wales hospital, Sydney. Timed endpoint method used for calculation of LDL-C.
Commercial assay or kitAPOE genotyping: Taqman genotyping assays
Assays:
C__3084793_20 (rs429358) &
C_904973_10 (rs7412)		Poljak, A., et al. The Relationship Between Plasma Abeta Levels, Cognitive Function and Brain Volumetrics: Sydney Memory and Ageing Study. Curr Alzheimer Res 2016;13:243–55Applied Biosystems Inc, Foster city, CA
Software, algorithmROpenMx 2.12.2Neale, M.C., et al. (2016). OpenMx 2.0: Extended Structural Equation and Statistical Modeling. Psychometrika 81, 535–549.SEM heritability analysis R package
Software, algorithmOther R packages:
Minfi, RNOmni,
nlme, rcompanion, caret		Aryee MJ, et al. Minfi: a flexible and comprehensive bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics
30(10), 1363–1369 (2014).
McCaw, 2019. RNOmni: Rank Normal Transformation Omnibus Test. In. (R package
Pinheiro et al., 2019. nlme: Linear and Nonlinear Mixed Effects Models. In. (R package
Mangiafico, 2019. rcompanion: Functions to Support Extension Education Program Evaluation. In. (R package
Kuhn, M., et al. (2018). caret: Classification and Regression Training. In. (R package

Additional files

Source code 1

R script for analyses.

https://cdn.elifesciences.org/articles/58954/elife-58954-code1-v2.zip
Supplementary file 1

(A) Heritable lipid species. Standardized additive genetic (h2 = heritability), shared environment (h2c) and unique environment (h2E) variance components (95% CI) of lipids were obtained using the ACE model. The columns p-AE, p-CE, and p-E, respectively, denote the p-values from the likelihood ratio test comparing ACE model vs AE, CE, and E models. p-CE is also the p value for heritability because testing the component A = 0 is equivalent to testing that the heritability is zero. C.I. indicates confidence interval; DZ, dizygotic; ICC, intraclass correlation coefficient; MZ, monozygotic. (B) Heritability of summed lipid groups. Standardized additive genetic (A = heritability), shared environment (C) and unique environment (E) variance components (95% CI) of lipids were obtained using the ACE model. The columns p-AE, p-CE, and p-E, respectively, denote the p-values from the likelihood ratio test comparing ACE model vs AE, CE, and E models. p-CE is also the p value for heritability because testing the component A = 0 is equivalent to testing that the heritability is zero. C.I. indicates confidence interval; DZ, dizygotic; ICC, intraclass correlation coefficient; MZ, monozygotic. CL_TG49 and CL_TG62 represent sum of triglycerides with 44–49 total carbons, and 56–62 total carbons respectively while Cer(d18:1/X) represents sum of all ceramides with an 18:1 acyl chain in the sn-1 position.

https://cdn.elifesciences.org/articles/58954/elife-58954-supp1-v2.docx
Supplementary file 2

(A) Full heritability list for all lipids. HA - Heritabiltiy; HC - Shared envrionmental component; HE - Unique environmental component; MZICC - Intraclass correlation in MZ twins; DZICC - Intraclass correlation in DZ twin; LT and UT denote lower and upper confidence limits; SignificantlyHeritable = 1 if declared heritable and 0 if not significantly heritable; SigProbAssoc = 1 if at least one gene expression probe is associated with the lipid; 0 if none is associated. (B) Sex heterogeneity test. Only significantly heritable lipids from (B) included. HA - Heritabiltiy; HC - Shared envrionmental component; HE - Unique environmental component; MZICC - Intraclass correlation in MZ twins; DZICC - Intraclass correlation in DZ twins; LT and UT denote lower and upper confidence limits; f denotes females, m denotes males; Hom_Pval is the p-value for the sex heterogenity test (p<0.05 indicates significant sex differences in heritability for that lipid); ICCmf - Intraclass correlation coefficient between opposite sex pairs. (C) ACE Age-related changes. Listed are heritability estimates for lipids at various ages (65 to 95). (D) Significant probe associations. Nprobes = Number of significantly associated gene expression probes with the lipid. NusedInModel = number of probes used for the calculation of variance explained after fitting the penalised regression model. (E) Significant transcript association with lipids. (F) Lipid-gene table. Entry one in the table indicates a significantly associated gene probe in the column with the lipid in the row and 0 indicates no association. (G) Lipid-gene by saturation index. RowSum is the sum of lipids significantly associated with a probe in each saturation level. RowSum_sigherit is the sum of heritable lipids significantly associated with a probe. Rowsum_nonsig is the sum of non-heritable lipids significantly associated with a probe. (H) Lipid-gene by total carbons. (I) Lipid expression associations with DNA methylation at CpG sites close to gene transcripts significantly associated with lipids. (J) Gene expression associations with DNA methylation at CpG sites close to gene transcripts significantly associated with lipids.

https://cdn.elifesciences.org/articles/58954/elife-58954-supp2-v2.xlsx
Supplementary file 3

Lipid (triglyceride)-gene expression associations listed by heritability and degree of saturation.

https://cdn.elifesciences.org/articles/58954/elife-58954-supp3-v2.docx
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https://cdn.elifesciences.org/articles/58954/elife-58954-transrepform-v2.docx

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  1. Matthew WK Wong
  2. Anbupalam Thalamuthu
  3. Nady Braidy
  4. Karen A Mather
  5. Yue Liu
  6. Liliana Ciobanu
  7. Bernhardt T Baune
  8. Nicola J Armstrong
  9. John Kwok
  10. Peter Schofield
  11. Margaret J Wright
  12. David Ames
  13. Russell Pickford
  14. Teresa Lee
  15. Anne Poljak
  16. Perminder S Sachdev
(2020)
Genetic and environmental determinants of variation in the plasma lipidome of older Australian twins
eLife 9:e58954.
https://doi.org/10.7554/eLife.58954