Anubha Mahajan

Loss-of-function mutations in SLC30A8 protect against type 2 diabetes

Loss-of-function mutations protective against human disease provide in vivo validation of therapeutic targets, but none have yet been described for type 2 diabetes (T2D). Through sequencing or genotyping of ∼150,000 individuals across 5 ancestry groups, we identified 12 rare protein-truncating variants in SLC30A8, which encodes an islet zinc transporter (ZnT8) and harbors a common variant (p.Trp325Arg) associated with T2D risk and glucose and proinsulin levels. Collectively, carriers of protein-truncating variants had 65% reduced T2D risk (P = 1.7 × 10−6), and non-diabetic Icelandic carriers of a frameshift variant (p.Lys34Serfs*50) demonstrated reduced glucose levels (−0.17 s.d., P = 4.6 × 10−4). The two most common protein-truncating variants (p.Arg138* and p.Lys34Serfs*50) individually associate with T2D protection and encode unstable ZnT8 proteins. Previous functional study of SLC30A8 suggested that reduced zinc transport increases T2D risk, and phenotypic heterogeneity was observed in mouse Slc30a8 knockouts. In contrast, loss-of-function mutations in humans provide strong evidence that SLC30A8 haploinsufficiency protects against T2D, suggesting ZnT8 inhibition as a therapeutic strategy in T2D prevention.

Impact of Common Variants of PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, and CDKAL1 on the Risk of Type 2 Diabetes in 5,164 Indians

OBJECTIVE Common variants in PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, and CDKAL1 genes have been shown to be associated with type 2 diabetes in European populations by genome-wide association studies. We have studied the association of common variants in these eight genes with type 2 diabetes and related traits in Indians by combining the data from two independent case–control studies. RESEARCH DESIGN AND METHODS We genotyped eight single nucleotide polymorphisms (PPARG-rs1801282, KCNJ11-rs5219, TCF7L2-rs7903146, SLC30A8-rs13266634, HHEX-rs1111875, CDKN2A-rs10811661, IGF2BP2-rs4402960, and CDKAL1-rs10946398) in 5,164 unrelated Indians of Indo-European ethnicity, including 2,486 type 2 diabetic patients and 2,678 ethnically matched control subjects. RESULTS We confirmed the association of all eight loci with type 2 diabetes with odds ratio (OR) ranging from 1.18 to 1.89 (P = 1.6 × 10−3 to 4.6 × 10−34). The strongest association with the highest effect size was observed for TCF7L2 (OR 1.89 [95% CI 1.71–2.09], P = 4.6 × 10−34). We also found significant association of PPARG and TCF7L2 with homeostasis model assessment of β-cell function (P = 6.9 × 10−8 and 3 × 10−4, respectively), which looked consistent with recessive and under-dominant models, respectively. CONCLUSIONS Our study replicates the association of well-established common variants with type 2 diabetes in Indians and shows larger effect size for most of them than those reported in Europeans.

The impact of low-frequency and rare variants on lipid levels

Using a genome-wide screen of 9.6 million genetic variants achieved through 1000 Genomes Project imputation in 62,166 samples, we identify association to lipid traits in 93 loci, including 79 previously identified loci with new lead SNPs and 10 new loci, 15 loci with a low-frequency lead SNP and 10 loci with a missense lead SNP, and 2 loci with an accumulation of rare variants. In six loci, SNPs with established function in lipid genetics (CELSR2, GCKR, LIPC and APOE) or candidate missense mutations with predicted damaging function (CD300LG and TM6SF2) explained the locus associations. The low-frequency variants increased the proportion of variance explained, particularly for low-density lipoprotein cholesterol and total cholesterol. Altogether, our results highlight the impact of low-frequency variants in complex traits and show that imputation offers a cost-effective alternative to resequencing.

Identification and Functional Characterization of G6PC2 Coding Variants Influencing Glycemic Traits Define an Effector Transcript at the G6PC2-ABCB11 Locus

Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights.

Genome wide association identifies common variants at the SERPINA6/SERPINA1 locus influencing plasma cortisol and corticosteroid binding globulin

Variation in plasma levels of cortisol, an essential hormone in the stress response, is associated in population-based studies with cardio-metabolic, inflammatory and neuro-cognitive traits and diseases. Heritability of plasma cortisol is estimated at 30–60% but no common genetic contribution has been identified. The CORtisol NETwork (CORNET) consortium undertook genome wide association meta-analysis for plasma cortisol in 12,597 Caucasian participants, replicated in 2,795 participants. The results indicate that <1% of variance in plasma cortisol is accounted for by genetic variation in a single region of chromosome 14. This locus spans SERPINA6, encoding corticosteroid binding globulin (CBG, the major cortisol-binding protein in plasma), and SERPINA1, encoding α1-antitrypsin (which inhibits cleavage of the reactive centre loop that releases cortisol from CBG). Three partially independent signals were identified within the region, represented by common SNPs; detailed biochemical investigation in a nested sub-cohort showed all these SNPs were associated with variation in total cortisol binding activity in plasma, but some variants influenced total CBG concentrations while the top hit (rs12589136) influenced the immunoreactivity of the reactive centre loop of CBG. Exome chip and 1000 Genomes imputation analysis of this locus in the CROATIA-Korcula cohort identified missense mutations in SERPINA6 and SERPINA1 that did not account for the effects of common variants. These findings reveal a novel common genetic source of variation in binding of cortisol by CBG, and reinforce the key role of CBG in determining plasma cortisol levels. In turn this genetic variation may contribute to cortisol-associated degenerative diseases.

Common variants of FTO and the risk of obesity and type 2 diabetes in Indians

Common variants of fat mass and obesity-associated gene (FTO, fat mass- and obesity-associated gene) have been shown to be associated with obesity and type 2 diabetes in population of European and non-European ethnicity. However, studies in Indian population have provided inconsistent results. Here, we examined association of eight FTO variants (rs1421085, rs8050136, rs9939609, rs9930506, rs1861867, rs9926180, rs2540769 and rs708277) with obesity and type 2 diabetes in 5364 North Indians (2474 type 2 diabetes patients and 2890 non-diabetic controls) in two stages. None of the variants including previously reported intron 1 variants (rs1421085, rs8050136, rs9939609 and rs9930506) showed body mass index (BMI)-dependent/independent association with type 2 diabetes. However, rs1421085, rs8050136 and rs9939609 were associated with obesity status and measures of obesity (BMI, waist circumference and waist-to-hip ratio) in stage 2 and combined study population. Meta-analysis of the two study population results also revealed that rs1421085, rs8050136 and rs9939609 were significantly associated with BMI both under the random- and fixed-effect models (P (random/fixed)=0.02/0.0001, 0.004/0.0006 and 0.01/0.01, respectively). In conclusion, common variants of FTO were associated with obesity, but not with type 2 diabetes in North Indian population.

Discovery and fine-mapping of glycaemic and obesity-related trait loci using high-density imputation

Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated.

Sixteen new lung function signals identified through 1000 Genomes Project reference panel imputation.

Lung function measures are used in the diagnosis of chronic obstructive pulmonary disease. In 38,199 European ancestry individuals, we studied genome-wide association of forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and FEV1/FVC with 1000 Genomes Project (phase 1)-imputed genotypes and followed up top associations in 54,550 Europeans. We identify 14 novel loci (P<5 × 10−8) in or near ENSA, RNU5F-1, KCNS3, AK097794, ASTN2, LHX3, CCDC91, TBX3, TRIP11, RIN3, TEKT5, LTBP4, MN1 and AP1S2, and two novel signals at known loci NPNT and GPR126, providing a basis for new understanding of the genetic determinants of these traits and pulmonary diseases in which they are altered.

Clinical and Genetic Correlates of Growth Differentiation Factor 15 in the Community

BACKGROUND Growth differentiation factor 15 (GDF15), a stress-responsive cytokine produced in cardiovascular cells under conditions of inflammation and oxidative stress, is emerging as an important prognostic marker in individuals with and without existing cardiovascular disease (CVD). We therefore examined the clinical and genetic correlates of circulating GDF15 concentrations, which have not been investigated collectively. METHODS Plasma GDF15 concentrations were measured in 2991 participants in the Framingham Offspring Study who were free of clinically overt CVD (mean age, 59 years; 56% women). Clinical correlates of GDF15 were examined in multivariable analyses. We then conducted a genomewide association study of the GDF15 concentration that included participants in the Framingham Offspring Study and participants in the PIVUS (Prospective Investigation of the Vasculature in Uppsala Seniors) study. RESULTS GDF15 was positively associated with age, smoking, antihypertensive treatment, diabetes, worse kidney function, and use of nonsteroidal antiinflammatory drugs (NSAIDs), but it was negatively associated with total cholesterol and HDL cholesterol. Clinical correlates accounted for 38% of interindividual variation in the circulating GDF15 concentration, whereas genetic factors accounted for up to 38% of the residual variability (h(2) = 0.38; P = 2.5 × 10(-11)). We identified 1 locus of genomewide significance. This locus, which is on chromosome 19p13.11 and includes the GDF15 gene, is associated with GDF15 concentration (smallest P = 2.74 × 10(-32) for rs888663). Conditional analyses revealed 2 independent association signals at this locus (rs888663 and rs1054564), which were associated with altered cis gene expression in blood cell lines. CONCLUSIONS In ambulatory individuals, both cardiometabolic risk factors and genetic factors play important roles in determining circulating GDF15 concentrations and contribute similarly to the overall variation.

Genome-wide association study of toxic metals and trace elements reveals novel associations

The accumulation of toxic metals in the human body is influenced by exposure and mechanisms involved in metabolism, some of which may be under genetic control. This is the first genome-wide association study to investigate variants associated with whole blood levels of a range of toxic metals. Eleven toxic metals and trace elements (aluminium, cadmium, cobalt, copper, chromium, mercury, manganese, molybdenum, nickel, lead and zinc) were assayed in a cohort of 949 individuals using mass spectrometry. DNA samples were genotyped on the Infinium Omni Express bead microarray and imputed up to reference panels from the 1000 Genomes Project. Analyses revealed two regions associated with manganese level at genome-wide significance, mapping to 4q24 and 1q41. The lead single nucleotide polymorphism (SNP) in the 4q24 locus was rs13107325 (P-value = 5.1 × 10−11, β = −0.77), located in an exon of SLC39A8, which encodes a protein involved in manganese and zinc transport. The lead SNP in the 1q41 locus is rs1776029 (P-value = 2.2 × 10−14, β = −0.46). The SNP lies within the intronic region of SLC30A10, another transporter protein. Among other metals, the loci 6q14.1 and 3q26.32 were associated with cadmium and mercury levels (P = 1.4 × 10−10, β = −1.2 and P = 1.8 × 10−9, β = −1.8, respectively). Whole blood measurements of toxic metals are associated with genetic variants in metal transporter genes and others. This is relevant in inferring metabolic pathways of metals and identifying subsets of individuals who may be more susceptible to metal toxicity.