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. 2022 Oct 11;13(1):5995.
doi: 10.1038/s41467-022-33510-7.

Whole genome sequence analysis of blood lipid levels in >66,000 individuals

Margaret Sunitha Selvaraj  1   2   3 Xihao Li  4 Zilin Li  4 Akhil Pampana  2 David Y Zhang  5   6 Joseph Park  5   6 Stella Aslibekyan  7 Joshua C Bis  8 Jennifer A Brody  8 Brian E Cade  9 Lee-Ming Chuang  10 Ren-Hua Chung  11 Joanne E Curran  12 Lisa de las Fuentes  13   14 Paul S de Vries  15 Ravindranath Duggirala  12 Barry I Freedman  16 Mariaelisa Graff  17 Xiuqing Guo  18 Nancy Heard-Costa  19 Bertha Hidalgo  7 Chii-Min Hwu  20 Marguerite R Irvin  7 Tanika N Kelly  21   22 Brian G Kral  23 Leslie Lange  24 Xiaohui Li  18 Martin Lisa  25 Steven A Lubitz  1   26 Ani W Manichaikul  27 Preuss Michael  28 May E Montasser  29 Alanna C Morrison  15 Take Naseri  30 Jeffrey R O'Connell  29 Nicholette D Palmer  31 Patricia A Peyser  32 Muagututia S Reupena  33 Jennifer A Smith  32 Xiao Sun  21 Kent D Taylor  18 Russell P Tracy  34 Michael Y Tsai  35 Zhe Wang  28 Yuxuan Wang  36 Wei Bao  37 John T Wilkins  38 Lisa R Yanek  23 Wei Zhao  32 Donna K Arnett  39 John Blangero  12 Eric Boerwinkle  15 Donald W Bowden  31 Yii-Der Ida Chen  40 Adolfo Correa  41 L Adrienne Cupples  36 Susan K Dutcher  42 Patrick T Ellinor  1   26 Myriam Fornage  43 Stacey Gabriel  44 Soren Germer  45 Richard Gibbs  46 Jiang He  21   22 Robert C Kaplan  47   48 Sharon L R Kardia  32 Ryan Kim  49 Charles Kooperberg  48 Ruth J F Loos  28   50 Karine A Viaud-Martinez  51 Rasika A Mathias  23 Stephen T McGarvey  52 Braxton D Mitchell  29   53 Deborah Nickerson  54 Kari E North  17 Bruce M Psaty  8   55   56 Susan Redline  9 Alexander P Reiner  48   55 Ramachandran S Vasan  57   58   59 Stephen S Rich  27 Cristen Willer  60 Jerome I Rotter  18 Daniel J Rader  5   6   61 Xihong Lin  2   4   62 NHLBI Trans-Omics for Precision Medicine (TOPMed) ConsortiumGina M Peloso  63 Pradeep Natarajan  64   65   66
Collaborators, Affiliations

Whole genome sequence analysis of blood lipid levels in >66,000 individuals

Margaret Sunitha Selvaraj et al. Nat Commun. .

Abstract

Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

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Conflict of interest statement

P.N. reports investigator-initiated grant support from Amgen, Apple, AstraZeneca, and Boston Scientific, personal fees from Apple, AstraZeneca, Blackstone Life Sciences, Foresite Labs, Genentech, TenSixteen Bio, and Novartis, scientific advisory board membership of geneXwell and TenSixteen Bio, and spousal employment at Vertex, all unrelated to the present work. B.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. M.E.M. receives funding from Regeneron Pharmaceutical Inc. unrelated to this work. S.A. has employment and equity in 23andMe, Inc. The spouse of C.J.W. works at Regeneron. S.A.L. is a full-time employee of Novartis as of July 18, 2022. S.A.L. has received sponsored research support from Bristol Myers Squibb, Pfizer, Boehringer Ingelheim, Fitbit, Medtronic, Premier, and IBM, and has consulted for Bristol Myers Squibb, Pfizer, Blackstone Life Sciences, and Invitae. X. Lin is a consultant of AbbVie Pharmaceuticals and Verily Life Sciences. The remaining authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Overall study schematic.
The analyses were conducted using the multi-ancestral TOPMed freeze8 data to associate whole genome sequence variation with lipid phenotypes (i.e., LDL-C, HDL-C, TC, and TG). A total of 66,329 samples with lipids quantified data from five ancestry groups were analyzed. Single variant GWAS were carried out using SAIGE on the Encore platform using SNPs with MAC >20. Both trans-ancestry and ancestry-specific GWAS were conducted. Genome-wide rare variant (MAF <1%) gene-centric and region-based aggregate tests were grouped and analyzed using STAARpipeline. Finally, single variant and rare variant associations at Mendelian dyslipidemia genes were investigated in further detail. TOPMed Trans-Omics for Precision Medicine, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, TC total cholesterol, TG triglycerides, GWAS genome wide association study, SAIGE Scalable and Accurate Implementation of GEneralized mixed model, MAC minor allele count, MAF minor allele frequency, SNPs single nucleotide polymorphisms.
Fig. 2
Fig. 2. Summary of single variant genome-wide association.
Representation of the single variant GWAS results from TOPMed Freeze 8 whole genome sequenced data of 66,329 samples. Each quarter represents a different lipid phenotype, and dots extending in clock-wise fashion represent variants with increasing evidence of association as noted by −log10(p-value), which was truncated at 200. The outer three circles show the GWAS data from TOPMed freeze8 where variants binned to nominally significant (p-value 0.05–5 × 10−07), suggestive significant (p-value 5 × 10−07–5 × 10−09) and genome wide significant (p-value < 5 × 10−09). The inner three circles compare our TOPMed results with known significantly associated lipid loci and variants from the MVP summary statistics and GWAS catalog to the identified novel variants and loci that are genome-wide significant from the current study, respectively. The figure represents the outputs from two-sided genetic association testing preformed using SAIGE-QT model, where the model was adjusted for all the covariates; see Methods. TOPMed Trans-Omics for Precision Medicine, GWAS genome wide association study, MVP million veteran program.
Fig. 3
Fig. 3. Comparison of effects estimates for HDL-C and LDL-C among variants in the CETP locus.
The color scale of the data points was based on −log10 p-values from HDL-C association and the size of each data point was based on −log10 p-values of LDL-C association. Variants which are genome wide significant with LDL-C are represented as chromosome:position:reference allele:alternate allele. The effect estimates and p-values were calculated from two-sided genetic association testing preformed using SAIGE-QT model, where the model was adjusted for all the covariates; see Methods. HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol.
Fig. 4
Fig. 4. Conditional analysis of coding rare-variants from the same gene and a near-by gene.
Non-coding rare variant sets significantly associated with TC and TG after the conditional analysis on known variants are shown with additional adjustment on rare-coding variants. The additional adjustment for rare-coding variants were carried out for the same gene of the aggregate set and for certain gene aggregates (SPC24) the conditional analysis was carried out with a nearby Mendelian gene. After adjusting for rare-coding variants and known variants, EHD3 signal drops minimally, whereas signal from PCSK9 (promoter-DHS, enhancer-DHS), LDLR-loci (enhancer-DHS, SPC24 enhancer-DHS) enhances significantly. APOB1, SPC24 (enhancer-CAGE), HBB and APOE signal drops after the conditional analysis on rare-coding variants. The different colored dots on the plot represents the conditional STAAR-O p-values when adjusting for known variants (Set1) and rare-coding variants of the same or near-by gene. The p-values were calculated from two-sided aggregate testing preformed using STAAR gene-centric model, where the model was adjusted for all the covariates; see Methods. STAAR variant-Set Test for Association using annotation information, TC total cholesterol, TG triglycerides, CAGE cap analysis of gene expression, DHS DNase hypersensitivity.
Fig. 5
Fig. 5. Influence of common and rare variants with hypercholesterolemia.
In addition to monogenic contributions from rare variants in Mendelian hypercholesterolemia genes, multiple genome-wide significant LDL-C-associated common variants also yield a polygenic basis for hypercholesterolemia. In the present work, we now identify rare non-coding variants in proximity of Mendelian hypercholesterolemia genes, specifically LDLR and PCSK9, that also contribute to the genetic basis of hypercholesterolemia. Parts of the figure were generated using pictures from Servier Medical Art. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License (https://creativecommons.org/licenses/by/3.0/). LDL-C low-density lipoprotein cholesterol.

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