Hanieh Yaghootkar

Systematic identification of trans eQTLs as putative drivers of known disease associations

Identifying the downstream effects of disease-associated SNPs is challenging. To help overcome this problem, we performed expression quantitative trait locus (eQTL) meta-analysis in non-transformed peripheral blood samples from 5,311 individuals with replication in 2,775 individuals. We identified and replicated trans eQTLs for 233 SNPs (reflecting 103 independent loci) that were previously associated with complex traits at genome-wide significance. Some of these SNPs affect multiple genes in trans that are known to be altered in individuals with disease: rs4917014, previously associated with systemic lupus erythematosus (SLE), altered gene expression of C1QB and five type I interferon response genes, both hallmarks of SLE. DeepSAGE RNA sequencing showed that rs4917014 strongly alters the 3′ UTR levels of IKZF1 in cis, and chromatin immunoprecipitation and sequencing analysis of the trans-regulated genes implicated IKZF1 as the causal gene. Variants associated with cholesterol metabolism and type 1 diabetes showed similar phenomena, indicating that large-scale eQTL mapping provides insight into the downstream effects of many trait-associated variants.

Human aging is characterized by focused changes in gene expression and deregulation of alternative splicing.

Aging is a major risk factor for chronic disease in the human population, but there are little human data on gene expression alterations that accompany the process. We examined human peripheral blood leukocyte in‐vivo RNA in a large‐scale transcriptomic microarray study (subjects aged 30–104 years). We tested associations between probe expression intensity and advancing age (adjusting for confounding factors), initially in a discovery set (n = 458), following‐up findings in a replication set (n = 240). We confirmed expression of key results by real‐time PCR. Of 16 571 expressed probes, only 295 (2%) were robustly associated with age. Just six probes were required for a highly efficient model for distinguishing between young and old (area under the curve in replication set; 95%). The focused nature of age‐related gene expression may therefore provide potential biomarkers of aging. Similarly, only 7 of 1065 biological or metabolic pathways were age‐associated, in gene set enrichment analysis, notably including the processing of messenger RNAs (mRNAs); [P < 0.002, false discovery rate (FDR) q < 0.05]. This is supported by our observation of age‐associated disruption to the balance of alternatively expressed isoforms for selected genes, suggesting that modification of mRNA processing may be a feature of human aging.

Integrative genomic analysis implicates limited peripheral adipose storage capacity in the pathogenesis of human insulin resistance

Insulin resistance is a key mediator of obesity-related cardiometabolic disease, yet the mechanisms underlying this link remain obscure. Using an integrative genomic approach, we identify 53 genomic regions associated with insulin resistance phenotypes (higher fasting insulin levels adjusted for BMI, lower HDL cholesterol levels and higher triglyceride levels) and provide evidence that their link with higher cardiometabolic risk is underpinned by an association with lower adipose mass in peripheral compartments. Using these 53 loci, we show a polygenic contribution to familial partial lipodystrophy type 1, a severe form of insulin resistance, and highlight shared molecular mechanisms in common/mild and rare/severe insulin resistance. Population-level genetic analyses combined with experiments in cellular models implicate CCDC92, DNAH10 and L3MBTL3 as previously unrecognized molecules influencing adipocyte differentiation. Our findings support the notion that limited storage capacity of peripheral adipose tissue is an important etiological component in insulin-resistant cardiometabolic disease and highlight genes and mechanisms underpinning this link.

Common Genetic Variants Highlight the Role of Insulin Resistance and Body Fat Distribution in Type 2 Diabetes, Independent of Obesity

We aimed to validate genetic variants as instruments for insulin resistance and secretion, to characterize their association with intermediate phenotypes, and to investigate their role in type 2 diabetes (T2D) risk among normal-weight, overweight, and obese individuals. We investigated the association of genetic scores with euglycemic-hyperinsulinemic clamp– and oral glucose tolerance test–based measures of insulin resistance and secretion and a range of metabolic measures in up to 18,565 individuals. We also studied their association with T2D risk among normal-weight, overweight, and obese individuals in up to 8,124 incident T2D cases. The insulin resistance score was associated with lower insulin sensitivity measured by M/I value (β in SDs per allele [95% CI], −0.03 [−0.04, −0.01]; P = 0.004). This score was associated with lower BMI (−0.01 [−0.01, −0.0]; P = 0.02) and gluteofemoral fat mass (−0.03 [−0.05, −0.02; P = 1.4 × 10−6) and with higher alanine transaminase (0.02 [0.01, 0.03]; P = 0.002) and γ-glutamyl transferase (0.02 [0.01, 0.03]; P = 0.001). While the secretion score had a stronger association with T2D in leaner individuals (Pinteraction = 0.001), we saw no difference in the association of the insulin resistance score with T2D among BMI or waist strata (Pinteraction > 0.31). While insulin resistance is often considered secondary to obesity, the association of the insulin resistance score with lower BMI and adiposity and with incident T2D even among individuals of normal weight highlights the role of insulin resistance and ectopic fat distribution in T2D, independently of body size.

Genetic evidence for a normal-weight “metabolically obese” phenotype linking insulin resistance, hypertension, coronary artery disease and type 2 diabetes

The mechanisms that predispose to hypertension, coronary artery disease (CAD), and type 2 diabetes (T2D) in individuals of normal weight are poorly understood. In contrast, in monogenic primary lipodystrophy—a reduction in subcutaneous adipose tissue—it is clear that it is adipose dysfunction that causes severe insulin resistance (IR), hypertension, CAD, and T2D. We aimed to test the hypothesis that common alleles associated with IR also influence the wider clinical and biochemical profile of monogenic IR. We selected 19 common genetic variants associated with fasting insulin–based measures of IR. We used hierarchical clustering and results from genome-wide association studies of eight nondisease outcomes of monogenic IR to group these variants. We analyzed genetic risk scores against disease outcomes, including 12,171 T2D cases, 40,365 CAD cases, and 69,828 individuals with blood pressure measurements. Hierarchical clustering identified 11 variants associated with a metabolic profile consistent with a common, subtle form of lipodystrophy. A genetic risk score consisting of these 11 IR risk alleles was associated with higher triglycerides (β = 0.018; P = 4 × 10−29), lower HDL cholesterol (β = −0.020; P = 7 × 10−37), greater hepatic steatosis (β = 0.021; P = 3 × 10−4), higher alanine transaminase (β = 0.002; P = 3 × 10−5), lower sex-hormone-binding globulin (β = −0.010; P = 9 × 10−13), and lower adiponectin (β = −0.015; P = 2 × 10−26). The same risk alleles were associated with lower BMI (per-allele β = −0.008; P = 7 × 10−8) and increased visceral-to-subcutaneous adipose tissue ratio (β = −0.015; P = 6 × 10−7). Individuals carrying ≥17 fasting insulin–raising alleles (5.5% population) were slimmer (0.30 kg/m2) but at increased risk of T2D (odds ratio [OR] 1.46; per-allele P = 5 × 10−13), CAD (OR 1.12; per-allele P = 1 × 10−5), and increased blood pressure (systolic and diastolic blood pressure of 1.21 mmHg [per-allele P = 2 × 10−5] and 0.67 mmHg [per-allele P = 2 × 10−4], respectively) compared with individuals carrying ≤9 risk alleles (5.5% population). Our results provide genetic evidence for a link between the three diseases of the “metabolic syndrome” and point to reduced subcutaneous adiposity as a central mechanism.

Mendelian Randomization Studies Do Not Support a Causal Role for Reduced Circulating Adiponectin Levels in Insulin Resistance and Type 2 Diabetes

Adiponectin is strongly inversely associated with insulin resistance and type 2 diabetes, but its causal role remains controversial. We used a Mendelian randomization approach to test the hypothesis that adiponectin causally influences insulin resistance and type 2 diabetes. We used genetic variants at the ADIPOQ gene as instruments to calculate a regression slope between adiponectin levels and metabolic traits (up to 31,000 individuals) and a combination of instrumental variables and summary statistics–based genetic risk scores to test the associations with gold-standard measures of insulin sensitivity (2,969 individuals) and type 2 diabetes (15,960 case subjects and 64,731 control subjects). In conventional regression analyses, a 1-SD decrease in adiponectin levels was correlated with a 0.31-SD (95% CI 0.26–0.35) increase in fasting insulin, a 0.34-SD (0.30–0.38) decrease in insulin sensitivity, and a type 2 diabetes odds ratio (OR) of 1.75 (1.47–2.13). The instrumental variable analysis revealed no evidence of a causal association between genetically lower circulating adiponectin and higher fasting insulin (0.02 SD; 95% CI −0.07 to 0.11; N = 29,771), nominal evidence of a causal relationship with lower insulin sensitivity (−0.20 SD; 95% CI −0.38 to −0.02; N = 1,860), and no evidence of a relationship with type 2 diabetes (OR 0.94; 95% CI 0.75–1.19; N = 2,777 case subjects and 13,011 control subjects). Using the ADIPOQ summary statistics genetic risk scores, we found no evidence of an association between adiponectin-lowering alleles and insulin sensitivity (effect per weighted adiponectin-lowering allele: −0.03 SD; 95% CI −0.07 to 0.01; N = 2,969) or type 2 diabetes (OR per weighted adiponectin-lowering allele: 0.99; 95% CI 0.95–1.04; 15,960 case subjects vs. 64,731 control subjects). These results do not provide any consistent evidence that interventions aimed at increasing adiponectin levels will improve insulin sensitivity or risk of type 2 diabetes.

Genome-Wide Association Analyses in 128,266 Individuals Identifies New Morningness and Sleep Duration Loci

Disrupted circadian rhythms and reduced sleep duration are associated with several human diseases, particularly obesity and type 2 diabetes, but until recently, little was known about the genetic factors influencing these heritable traits. We performed genome-wide association studies of self-reported chronotype (morning/evening person) and self-reported sleep duration in 128,266 white British individuals from the UK Biobank study. Sixteen variants were associated with chronotype (P<5x10-8), including variants near the known circadian rhythm genes RGS16 (1.21 odds of morningness, 95% CI [1.15, 1.27], P = 3x10-12) and PER2 (1.09 odds of morningness, 95% CI [1.06, 1.12], P = 4x10-10). The PER2 signal has previously been associated with iris function. We sought replication using self-reported data from 89,283 23andMe participants; thirteen of the chronotype signals remained associated at P<5x10-8 on meta-analysis and eleven of these reached P<0.05 in the same direction in the 23andMe study. We also replicated 9 additional variants identified when the 23andMe study was used as a discovery GWAS of chronotype (all P<0.05 and meta-analysis P<5x10-8). For sleep duration, we replicated one known signal in PAX8 (2.6 minutes per allele, 95% CI [1.9, 3.2], P = 5.7x10-16) and identified and replicated two novel associations at VRK2 (2.0 minutes per allele, 95% CI [1.3, 2.7], P = 1.2x10-9; and 1.6 minutes per allele, 95% CI [1.1, 2.2], P = 7.6x10-9). Although we found genetic correlation between chronotype and BMI (rG = 0.056, P = 0.05); undersleeping and BMI (rG = 0.147, P = 1x10-5) and oversleeping and BMI (rG = 0.097, P = 0.04), Mendelian Randomisation analyses, with limited power, provided no consistent evidence of causal associations between BMI or type 2 diabetes and chronotype or sleep duration. Our study brings the total number of loci associated with chronotype to 22 and with sleep duration to three, and provides new insights into the biology of sleep and circadian rhythms in humans.

Height, body mass index, and socioeconomic status: mendelian randomisation study in UK Biobank

Objective To determine whether height and body mass index (BMI) have a causal role in five measures of socioeconomic status. Design Mendelian randomisation study to test for causal effects of differences in stature and BMI on five measures of socioeconomic status. Mendelian randomisation exploits the fact that genotypes are randomly assigned at conception and thus not confounded by non-genetic factors. Setting UK Biobank. Participants 119 669 men and women of British ancestry, aged between 37 and 73 years. Main outcome measures Age completed full time education, degree level education, job class, annual household income, and Townsend deprivation index. Results In the UK Biobank study, shorter stature and higher BMI were observationally associated with several measures of lower socioeconomic status. The associations between shorter stature and lower socioeconomic status tended to be stronger in men, and the associations between higher BMI and lower socioeconomic status tended to be stronger in women. For example, a 1 standard deviation (SD) higher BMI was associated with a £210 (€276;

Allelic heterogeneity and more detailed analyses of known loci explain additional phenotypic variation and reveal complex patterns of association

300; 95% confidence interval £84 to £420; P=6×10−3) lower annual household income in men and a £1890 (£1680 to £2100; P=6×10−15) lower annual household income in women. Genetic analysis provided evidence that these associations were partly causal. A genetically determined 1 SD (6.3 cm) taller stature caused a 0.06 (0.02 to 0.09) year older age of completing full time education (P=0.01), a 1.12 (1.07 to 1.18) times higher odds of working in a skilled profession (P=6×10−7), and a £1130 (£680 to £1580) higher annual household income (P=4×10−8). Associations were stronger in men. A genetically determined 1 SD higher BMI (4.6 kg/m2) caused a £2940 (£1680 to £4200; P=1×10−5) lower annual household income and a 0.10 (0.04 to 0.16) SD (P=0.001) higher level of deprivation in women only. Conclusions These data support evidence that height and BMI play an important partial role in determining several aspects of a person’s socioeconomic status, especially women’s BMI for income and deprivation and men’s height for education, income, and job class. These findings have important social and health implications, supporting evidence that overweight people, especially women, are at a disadvantage and that taller people, especially men, are at an advantage.

Using Genetic Variants to Assess the Relationship Between Circulating Lipids and Type 2 Diabetes

The identification of multiple signals at individual loci could explain additional phenotypic variance (‘missing heritability’) of common traits, and help identify causal genes. We examined gene expression levels as a model trait because of the large number of strong genetic effects acting in cis. Using expression profiles from 613 individuals, we performed genome-wide single nucleotide polymorphism (SNP) analyses to identify cis-expression quantitative trait loci (eQTLs), and conditional analysis to identify second signals. We examined patterns of association when accounting for multiple SNPs at a locus and when including additional SNPs from the 1000 Genomes Project. We identified 1298 cis-eQTLs at an approximate false discovery rate 0.01, of which 118 (9%) showed evidence of a second independent signal. For this subset of 118 traits, accounting for two signals resulted in an average 31% increase in phenotypic variance explained (Wilcoxon P< 0.0001). The association of SNPs with cis gene expression could increase, stay similar or decrease in significance when accounting for linkage disequilibrium with second signals at the same locus. Pairs of SNPs increasing in significance tended to have gene expression increasing alleles on opposite haplotypes, whereas pairs of SNPs decreasing in significance tended to have gene expression increasing alleles on the same haplotypes. Adding data from the 1000 Genomes Project showed that apparently independent signals could be potentially explained by a single association signal. Our results show that accounting for multiple variants at a locus will increase the variance explained in a substantial fraction of loci, but that allelic heterogeneity will be difficult to define without resequencing loci and functional work.