Abstract

Autosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P = 8.5 × 10−72), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P = 1.7 × 10−4), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P = 1.4 × 10−5). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids.

Document Type

Article

Publication Date

4-12-2021

Notes/Citation Information

Published in Nature Communications, v. 12, issue 1, article no. 2182.

© The Author(s) 2021

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The first 20 authors and the author from the University of Kentucky are shown on the author list above. Please refer to the downloaded document for the complete author list.

Digital Object Identifier (DOI)

https://doi.org/10.1038/s41467-021-22339-1

Funding Information

This work was supported by grants from the National Heart, Lung, and Blood Institute (NHLBI): R01HL142711 (to P.N. and G.M.P.), K08HL140203 (to P.N.), R03HL141439 and K01HL125751 (to G.M.P.). P.N. is also supported by a Hassenfeld Scholar Award from the Massachusetts General Hospital, Fondation Leducq (TNE-18CVD04), and additional grants from the National Heart, Lung, and Blood Institute (R01HL148565 and R01HL148050). P.S.de.V. is supported by American Heart Association grant number 18CDA34110116. B.E.C. and J.L. are supported by R35HL135818, HL113338, and HL46380. B.E.C. is also supported by K01HL135405. S.L. is supported by NIH grant 1R01HL139731 and American Heart Association 18SFRN34250007. Whole-genome sequencing (WGS) for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI). Centralized read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Phenotype harmonization, data management, sample-identity QC, and general study coordination were provided by the TOPMed Data Coordinating Center (3R01HL-120393-02S1; contract HHSN268201800001I).

Related Content

Data availability

Controlled access of the individual-level TOPMed data is available through dbGaP, and the individual-level UK Biobank data are available upon application to the UK Biobank (https://www.ukbiobank.ac.uk/). FinnGen summary-level data are fully freely available at https://www.finngen.fi/en/access_results. Individual-level access to FinnGen and HUNT cohorts may be obtained through reasonable request and suitable institutional review board approvals. The dbGaP accessions for TOPMed cohorts are as follows: Atherosclerosis Risk in Communities (ARIC) phs001211 and phs000280; Old Order Amish phs000956 and phs000391; Mt Sinai BioMe Biobank phs001644 and phs000925; Coronary Artery Risk Development in Young Adults (CARDIA) phs001612 and phs000285; Cleveland Family Study (CFS) phs000954 and phs000284; Cardiovascular Health Study (CHS) phs001368; Diabetes Heart Study (DHS) phs001412 and phs001012; Framingham Heart Study (FHS) phs000974 and phs000007; Genetic Epidemiology Network of Arteriopathy (GENOA) phs001345 and phs001238; Genetics of Lipid-Lowering Drugs and Diet Network (GOLDN) phs001359 and phs000741; Genetic Epidemiology Network of Salt Sensitivity (GenSalt) phs001217 and phs000784; Genetic Studies of Atherosclerosis Risk (GeneSTAR) phs001218 and phs000375; Hispanic Community Health Study—Study of Latinos (HCHS/SOL) phs001395 and phs000810; Hypertension Genetic Epidemiology Network and Genetic Epidemiology Network of Arteriopathy (HyperGEN) phs001293; Jackson Heart Study (JHS) phs000964 and phs000286; Multi-Ethnic Study of Atherosclerosis (MESA) phs001416 and phs000209; Massachusetts General Hospital Atrial Fibrillation Study (MGH_AF) phs001062 and phs001001; San Antonio Family Study (SAFS) phs001215 and phs000462; Samoan Adiposity Study phs000972 and phs000914; Taiwan Study of Hypertension using Rare Variants (THRV) phs001387; Women’s Health Initiative (WHI) phs001237 and phs000200. Source data are provided with this paper.

Code availability

The variant calling software tools are under active development; updated versions can be accessed at http://github.com/atks/vt, http://github.com/hyunminkang/apigenome, and https://github.com/statgen/topmed_variant_calling.

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