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We conducted a genome-wide association analysis of 7 subfractions of low density lipoproteins (LDLs) and 3 subfractions of intermediate density lipoproteins (IDLs) measured by gradient gel electrophoresis, and their response to statin treatment, in 1868 individuals of European ancestry from the Pharmacogenomics and Risk of Cardiovascular Disease study. Our analyses identified four previously-implicated loci (SORT1, APOE, LPA, and CETP) as containing variants that are very strongly associated with lipoprotein subfractions (log(10)Bayes Factor > 15). Subsequent conditional analyses suggest that three of these (APOE, LPA and CETP) likely harbor multiple independently associated SNPs. Further, while different variants typically showed different characteristic patterns of association with combinations of subfractions, the two SNPs in CETP show strikingly similar patterns--both in our original data and in a replication cohort--consistent with a common underlying molecular mechanism. Notably, the CETP variants are very strongly associated with LDL subfractions, despite showing no association with total LDLs in our study, illustrating the potential value of the more detailed phenotypic measurements. In contrast with these strong subfraction associations, genetic association analysis of subfraction response to statins showed much weaker signals (none exceeding log(10)Bayes Factor of 6). However, two SNPs (in APOE and LPA) previously-reported to be associated with LDL statin response do show some modest evidence for association in our data, and the subfraction response proles at the LPA SNP are consistent with the LPA association, with response likely being due primarily to resistance of Lp(a) particles to statin therapy. An additional important feature of our analysis is that, unlike most previous analyses of multiple related phenotypes, we analyzed the subfractions jointly, rather than one at a time. Comparisons of our multivariate analyses with standard univariate analyses demonstrate that multivariate analyses can substantially increase power to detect associations. Software implementing our multivariate analysis methods is available at http://stephenslab.uchicago.edu/software.html.

Chronic low-grade inflammation is linked to a multitude of chronic diseases. We report the largest genome-wide association study (GWAS) on C-reactive protein (CRP), a marker of systemic inflammation, in UK Biobank participants (N = 427,367, European descent) and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium (total N = 575,531 European descent). We identify 266 independent loci, of which 211 are not previously reported. Gene-set analysis highlighted 42 gene sets associated with CRP levels ( p  ≤ 3.2 ×10 −6 ) and tissue expression analysis indicated a strong association of CRP related genes with liver and whole blood gene expression. Phenome-wide association study identified 27 clinical outcomes associated with genetically determined CRP and subsequent Mendelian randomisation analyses supported a causal association with schizophrenia, chronic airway obstruction and prostate cancer. Our findings identified genetic loci and functional properties of chronic low-grade inflammation and provided evidence for causal associations with a range of diseases. Inflammation is associated with a variety of diseases. Here, the authors identify 266 genetic loci associated with C-reactive protein levels, a marker of inflammation, in >500,000 Europeans, along with associated pathways, clinical outcomes and potential causal associations with disease.

Using a polygenic score of DNA sequence polymorphisms, the authors of this study quantified genetic risk and assessed four healthy lifestyle factors. Among participants at high genetic risk, a healthy lifestyle was associated with a reduced risk of coronary disease. Both genetic and lifestyle factors are key drivers of coronary artery disease, a complex disorder that is the leading cause of death worldwide. 1 A familial pattern in the risk of coronary artery disease was first described in 1938 and was subsequently confirmed in large studies involving twins and prospective cohorts. 2 – 6 Since 2007, genomewide association analyses have identified more than 50 independent loci associated with the risk of coronary artery disease. 7 – 15 These risk alleles, when aggregated into a polygenic risk score, are predictive of incident coronary events and provide a continuous and quantitative measure of genetic susceptibility. 16 – 24 Much . . .

Gene-environment interactions represent the modification of genetic effects by environmental exposures and are critical for understanding disease and informing personalized medicine. These often induce differential phenotypic variance across genotypes; these variance-quantitative trait loci can be prioritized in a two-stage interaction detection strategy to greatly reduce the computational and statistical burden and enable testing of a broader range of exposures. We perform genome-wide variance-quantitative trait locus analysis for 20 serum cardiometabolic biomarkers by multi-ancestry meta-analysis of 350,016 unrelated participants in the UK Biobank, identifying 182 independent locus-biomarker pairs ( p  < 4.5×10 −9 ). Most are concentrated in a small subset (4%) of loci with genome-wide significant main effects, and 44% replicate ( p  < 0.05) in the Women’s Genome Health Study ( N  = 23,294). Next, we test each locus-biomarker pair for interaction across 2380 exposures, identifying 847 significant interactions ( p  < 2.4×10 −7 ), of which 132 are independent ( p  < 0.05) after accounting for correlation between exposures. Specific examples demonstrate interaction of triglyceride-associated variants with distinct body mass- versus body fat-related exposures as well as genotype-specific associations between alcohol consumption and liver stress at the ADH1B gene. Our catalog of variance-quantitative trait loci and gene-environment interactions is publicly available in an online portal. Understanding how our genes interact with the environment is critical to improving health. Using a large-scale discovery pipeline, here the authors investigate synergies between genetic variants and a broad range of environmental factors impacting cardiometabolic health.

It is uncertain whether measurement of circulating total atherogenic lipoprotein particle cholesterol mass [non-HDL cholesterol (nonHDLc)] or particle concentration [apolipoprotein B (apo B) and LDL particle concentration (LDLp)] more accurately reflects risk of incident coronary heart disease (CHD). We evaluated CHD risk among women in whom these markers where discordant. Among 27533 initially healthy women in the Women's Health Study (NCT00000479), using residuals from linear regression models, we compared risk among women with higher or lower observed particle concentration relative to nonHDLc (highest and lowest residual quartiles, respectively) to individuals with agreement between markers (middle quartiles) using Cox proportional hazards models. Although all 3 biomarkers were correlated ( ≥ 0.77), discordance occurred in up to 20.2% of women. Women with discordant high particle concentration were more likely to have metabolic syndrome (MetS) and diabetes (both < 0.001). Over a median follow-up of 20.4 years, 1246 CHD events occurred (514725 person-years). Women with high particle concentration relative to nonHDLc had increased CHD risk: age-adjusted hazard ratio (95% CI) = 1.77 (1.56-2.00) for apo B and 1.70 (1.50-1.92) for LDLp. After adjustment for clinical risk factors including MetS, these risks attenuated to 1.22 (1.07-1.39) for apo B and 1.13 (0.99-1.29) for LDLp. Discordant low apo B or LDLp relative to nonHDLc was not associated with lower risk. Discordance between atherogenic particle cholesterol mass and particle concentration occurs in a sizeable proportion of apparently healthy women and should be suspected clinically among women with cardiometabolic traits. In such women, direct measurement of lipoprotein particle concentration might better inform CHD risk assessment.

Testing for genetic effects on mean values of a quantitative trait has been a very successful strategy. However, most studies to date have not explored genetic effects on the variance of quantitative traits as a relevant consequence of genetic variation. In this report, we demonstrate that, under plausible scenarios of genetic interaction, the variance of a quantitative trait is expected to differ among the three possible genotypes of a biallelic SNP. Leveraging this observation with Levene's test of equality of variance, we propose a novel method to prioritize SNPs for subsequent gene-gene and gene-environment testing. This method has the advantageous characteristic that the interacting covariate need not be known or measured for a SNP to be prioritized. Using simulations, we show that this method has increased power over exhaustive search under certain conditions. We further investigate the utility of variance per genotype by examining data from the Women's Genome Health Study. Using this dataset, we identify new interactions between the LEPR SNP rs12753193 and body mass index in the prediction of C-reactive protein levels, between the ICAM1 SNP rs1799969 and smoking in the prediction of soluble ICAM-1 levels, and between the PNPLA3 SNP rs738409 and body mass index in the prediction of soluble ICAM-1 levels. These results demonstrate the utility of our approach and provide novel genetic insight into the relationship among obesity, smoking, and inflammation.

Obesity is a risk factor for > 13 cancer sites, although it is unknown whether there is a common mechanism across sites. Evidence suggests a role for impaired branched-chain amino acid (BCAAs; isoleucine, leucine, valine) metabolism in obesity, insulin resistance, and immunity; thus, we hypothesized circulating BCAAs may be associated with incident obesity-related cancers. We analyzed participants in the prospective Women’s Health Study without a history of cancer at baseline blood collection (N = 26,711, mean age = 54.6 years [SD = 7.1]). BCAAs were quantified via NMR spectroscopy, log-transformed, and standardized. We used Cox proportional regression models adjusted for age, race, smoking, diet, alcohol, physical activity, menopausal hormone use, Body Mass Index (BMI), diabetes, and other risk factors. The endpoint was a composite of obesity-related cancers, defined per the International Agency for Research on Cancer 2016 report, over a median 24 years follow-up. Baseline BMI ≥ 30 kg/m 2 compared with BMI 18.5–25.0 kg/m 2 was associated with 23% greater risk of obesity-related cancers (n = 2751 events; multivariable HR 1.23, 95% CI 1.11–1.37). However, BCAAs were not associated with obesity-related cancers (multivariable HR per SD = 1.01 [0.97–1.05]). Results for individual BCAA metabolites suggested a modest association for leucine with obesity-related cancers (1.04 [1.00–1.08]), and no association for isoleucine or valine (0.99 [0.95–1.03] and 1.00 [0.96–1.04], respectively). Exploratory analyses of BCAAs with individual sites included positive associations between leucine and postmenopausal breast cancer, and isoleucine with pancreatic cancer. Total circulating BCAAs were unrelated to obesity-related cancer incidence although an association was observed for leucine with incident obesity-related cancer.

John Perry, Ken Ong and colleagues perform a genome-wide association study for reproductive ability, behavior and success to determine underlying genetic factors. They find 38 variants associated with age of first sexual intercourse and show that both physical and neurobehavioral traits influence the onset of reproductive activity. The ages of puberty, first sexual intercourse and first birth signify the onset of reproductive ability, behavior and success, respectively. In a genome-wide association study of 125,667 UK Biobank participants, we identify 38 loci associated ( P < 5 × 10 −8 ) with age at first sexual intercourse. These findings were taken forward in 241,910 men and women from Iceland and 20,187 women from the Women's Genome Health Study. Several of the identified loci also exhibit associations ( P < 5 × 10 −8 ) with other reproductive and behavioral traits, including age at first birth (variants in or near ESR1 and RBM6 – SEMA3F ), number of children ( CADM2 and ESR1 ), irritable temperament ( MSRA ) and risk-taking propensity ( CADM2 ). Mendelian randomization analyses infer causal influences of earlier puberty timing on earlier first sexual intercourse, earlier first birth and lower educational attainment. In turn, likely causal consequences of earlier first sexual intercourse include reproductive, educational, psychiatric and cardiometabolic outcomes.

Observational epidemiological studies indicate that endometriosis and migraine co-occur within individuals more than expected by chance. However, the aetiology and biological mechanisms underlying their comorbidity remain unknown. Here we examined the relationship between endometriosis and migraine using genome-wide association study (GWAS) data. Single nucleotide polymorphism (SNP) effect concordance analysis found a significant concordance of SNP risk effects across endometriosis and migraine GWAS. Linkage disequilibrium score regression analysis found a positive and highly significant genetic correlation (rG = 0.38, P = 2.30 × 10−25) between endometriosis and migraine. A meta-analysis of endometriosis and migraine GWAS data did not reveal novel genome-wide significant SNPs, and Mendelian randomisation analysis found no evidence for a causal relationship between the two traits. However, gene-based analyses identified two novel loci for migraine. Also, we found significant enrichment of genes nominally associated (Pgene < 0.05) with both traits (Pbinomial-test = 9.83 × 10−6). Combining gene-based p-values across endometriosis and migraine, three genes, two (TRIM32 and SLC35G6) of which are at novel loci, were genome-wide significant. Genes having Pgene < 0.1 for both endometriosis and migraine (Pbinomial-test = 1.85 ×10−°3) were significantly enriched for biological pathways, including interleukin-1 receptor binding, focal adhesion-PI3K-Akt-mTOR-signaling, MAPK and TNF-α signalling. Our findings further confirm the comorbidity of endometriosis and migraine and indicate a non-causal relationship between the two traits, with shared genetically-controlled biological mechanisms underlying the co-occurrence of the two disorders.

While conventional LDL-C, HDL-C, and triglyceride measurements reflect aggregate properties of plasma lipoprotein fractions, NMR-based measurements more accurately reflect lipoprotein particle concentrations according to class (LDL, HDL, and VLDL) and particle size (small, medium, and large). The concentrations of these lipoprotein sub-fractions may be related to risk of cardiovascular disease and related metabolic disorders. We performed a genome-wide association study of 17 lipoprotein measures determined by NMR together with LDL-C, HDL-C, triglycerides, ApoA1, and ApoB in 17,296 women from the Women's Genome Health Study (WGHS). Among 36 loci with genome-wide significance (P<5x10(-8)) in primary and secondary analysis, ten (PCCB/STAG1 (3q22.3), GMPR/MYLIP (6p22.3), BTNL2 (6p21.32), KLF14 (7q32.2), 8p23.1, JMJD1C (10q21.3), SBF2 (11p15.4), 12q23.2, CCDC92/DNAH10/ZNF664 (12q24.31.B), and WIPI1 (17q24.2)) have not been reported in prior genome-wide association studies for plasma lipid concentration. Associations with mean lipoprotein particle size but not cholesterol content were found for LDL at four loci (7q11.23, LPL (8p21.3), 12q24.31.B, and LIPG (18q21.1)) and for HDL at one locus (GCKR (2p23.3)). In addition, genetic determinants of total IDL and total VLDL concentration were found at many loci, most strongly at LIPC (15q22.1) and APOC-APOE complex (19q13.32), respectively. Associations at seven more loci previously known for effects on conventional plasma lipid measures reveal additional genetic influences on lipoprotein profiles and bring the total number of loci to 43. Thus, genome-wide associations identified novel loci involved with lipoprotein metabolism-including loci that affect the NMR-based measures of concentration or size of LDL, HDL, and VLDL particles-all characteristics of lipoprotein profiles that may impact disease risk but are not available by conventional assay.