Chromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices

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.

Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association

“…Genetic studies for CHD have focused primarily on the autosome; a study of X chromosome genetic variation in >500 000 individuals found common alleles on chromosome Xq23 to be strongly associated with lower TC, LDL-C, and triglycerides in both females and males and associated with a reduced odds for CHD and type 2 diabetes. 36 ORs for CHD and type 2 diabetes for each rs5942634-T allele, the lead cholesterol-lowering variant in chromosome Xq23, were 0.98 (95% CI, 0.96–0.99) and 0.97 (95% CI, 0.96–0.99), respectively.…”

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

“…The Amish cohort had a higher-than-average concentration of LDL-C (140 [SD 43] mg/dL) and HDL-C (56 [SD 16] mg/dL) as well as lower TG (median 63 [IQR 50] mg/dL) consistent with the known founder mutations in APOB and APOC3 7 , 8 , 14 . In the Women’s Health Initiative (WHI) cohort, the TC (230 [SD 41] mg/dL) and TG (median 129 [IQR 87] mg/dL) concentrations were higher than for other cohorts as previously described 12 . We accounted for lipid-lowering medications and fasting status and inverse rank normalized the phenotypes as before 12 , 14 which are further detailed in the Methods.…”

“…In the Women’s Health Initiative (WHI) cohort, the TC (230 [SD 41] mg/dL) and TG (median 129 [IQR 87] mg/dL) concentrations were higher than for other cohorts as previously described 12 . We accounted for lipid-lowering medications and fasting status and inverse rank normalized the phenotypes as before 12 , 14 which are further detailed in the Methods. The adjusted normalized lipid concentrations for the four lipids were similar across the cohorts.…”

“…We harmonized the phenotypes across each cohort 18 and inverse rank normalization of the residuals of each race within each cohort scaled by the standard deviation of the trait and adjusted for covariates 12 . We included covariates such as age, age 2 , sex, PC1–11, study-groups as well as Mendelian founder lipid variants APOB p.R3527Q and APOC3 p.R19X for the Amish cohort 7 , 8 , 71 .…”

“…Due to purifying selection, causal variants conferring large effects tend to occur relatively more recently, and are thus rare and often specific to families or communities 6 . Most discovery analyses for large-effect rare alleles have focused on the analysis of disruptive protein-coding variants given (1) well-recognized constraint in coding regions, (2) incomplete genotyping of rare non-coding sequence given relative sparsity of deep-coverage (i.e., >30X) whole genome sequencing (WGS), and (3) better prediction of coding versus non-coding sequence variation consequence 1 , 7 – 12 . We recently described a statistical framework incorporating multi-dimensional reference datasets paired with genomic data to improve rare coding and non-coding variant analyses for WGS analysis of lipids and other complex traits 13 , 14 .…”