Ghislain Rocheleau

A genome-wide association study identifies novel risk loci for type 2 diabetes

Type 2 diabetes mellitus results from the interaction of environmental factors with a combination of genetic variants, most of which were hitherto unknown. A systematic search for these variants was recently made possible by the development of high-density arrays that permit the genotyping of hundreds of thousands of polymorphisms. We tested 392,935 single-nucleotide polymorphisms in a French case–control cohort. Markers with the most significant difference in genotype frequencies between cases of type 2 diabetes and controls were fast-tracked for testing in a second cohort. This identified four loci containing variants that confer type 2 diabetes risk, in addition to confirming the known association with the TCF7L2 gene. These loci include a non-synonymous polymorphism in the zinc transporter SLC30A8, which is expressed exclusively in insulin-producing β-cells, and two linkage disequilibrium blocks that contain genes potentially involved in β-cell development or function (IDE–KIF11–HHEX and EXT2–ALX4). These associations explain a substantial portion of disease risk and constitute proof of principle for the genome-wide approach to the elucidation of complex genetic traits.

A variant near MTNR1B is associated with increased fasting plasma glucose levels and type 2 diabetes risk

In genome-wide association (GWA) data from 2,151 nondiabetic French subjects, we identified rs1387153, near MTNR1B (which encodes the melatonin receptor 2 (MT2)), as a modulator of fasting plasma glucose (FPG; P = 1.3 × 10−7). In European populations, the rs1387153 T allele is associated with increased FPG (β = 0.06 mmol/l, P = 7.6 × 10−29, N = 16,094), type 2 diabetes (T2D) risk (odds ratio (OR) = 1.15, 95% CI = 1.08–1.22, P = 6.3 × 10−5, cases N = 6,332) and risk of developing hyperglycemia or diabetes over a 9-year period (hazard ratio (HR) = 1.20, 95% CI = 1.06–1.36, P = 0.005, incident cases N = 515). RT-PCR analyses confirm the presence of MT2 transcripts in neural tissues and show MT2 expression in human pancreatic islets and beta cells. Our data suggest a possible link between circadian rhythm regulation and glucose homeostasis through the melatonin signaling pathway.

Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia

Genome-wide association studies have identified common variants that only partially explain the genetic risk for type 2 diabetes (T2D). Using genome-wide association data from 1,376 French individuals, we identified 16,360 SNPs nominally associated with T2D and studied these SNPs in an independent sample of 4,977 French individuals. We then selected the 28 best hits for replication in 7,698 Danish subjects and identified 4 SNPs showing strong association with T2D, one of which (rs2943641, P = 9.3 × 10−12, OR = 1.19) was located adjacent to the insulin receptor substrate 1 gene (IRS1). Unlike previously reported T2D risk loci, which predominantly associate with impaired beta cell function, the C allele of rs2943641 was associated with insulin resistance and hyperinsulinemia in 14,358 French, Danish and Finnish participants from population-based cohorts; this allele was also associated with reduced basal levels of IRS1 protein and decreased insulin induction of IRS1-associated phosphatidylinositol-3-OH kinase activity in human skeletal muscle biopsies.

Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes

Genome-wide association studies have revealed that common noncoding variants in MTNR1B (encoding melatonin receptor 1B, also known as MT2) increase type 2 diabetes (T2D) risk. Although the strongest association signal was highly significant (P < 1 × 10−20), its contribution to T2D risk was modest (odds ratio (OR) of ∼1.10–1.15). We performed large-scale exon resequencing in 7,632 Europeans, including 2,186 individuals with T2D, and identified 40 nonsynonymous variants, including 36 very rare variants (minor allele frequency (MAF) <0.1%), associated with T2D (OR = 3.31, 95% confidence interval (CI) = 1.78–6.18; P = 1.64 × 10−4). A four-tiered functional investigation of all 40 mutants revealed that 14 were non-functional and rare (MAF < 1%), and 4 were very rare with complete loss of melatonin binding and signaling capabilities. Among the very rare variants, the partial- or total-loss-of-function variants but not the neutral ones contributed to T2D (OR = 5.67, CI = 2.17–14.82; P = 4.09 × 10−4). Genotyping the four complete loss-of-function variants in 11,854 additional individuals revealed their association with T2D risk (8,153 individuals with T2D and 10,100 controls; OR = 3.88, CI = 1.49–10.07; P = 5.37 × 10−3). This study establishes a firm functional link between MTNR1B and T2D risk.

A polymorphism within the G6PC2 gene is associated with fasting plasma glucose levels

Several studies have shown that healthy individuals with fasting plasma glucose (FPG) levels at the high end of the normal range have an increased risk of mortality. To identify genetic determinants that contribute to interindividual variation in FPG, we tested 392,935 single-nucleotide polymorphisms (SNPs) in 654 normoglycemic participants for association with FPG, and we replicated the most strongly associated SNP (rs560887, P = 4 × 10–7) in 9353 participants. SNP rs560887 maps to intron 3 of the G6PC2 gene, which encodes glucose-6-phosphatase catalytic subunit–related protein (also known as IGRP), a protein selectively expressed in pancreatic islets. This SNP was associated with FPG (linear regression coefficient β = –0.06 millimoles per liter per A allele, combined P = 4 × 10–23) and with pancreatic β cell function (Homa-B model, combined P = 3 × 10–13) in three populations; however, it was not associated with type 2 diabetes risk. We speculate that G6PC2 regulates FPG by modulating the set point for glucose-stimulated insulin secretion in pancreatic β cells.

Detection of human adaptation during the past 2000 years

Identifying genes under recent selection Evolutionary analyses aim to identify recent genetic changes that are likely to have been subject to selection. Field et al. present a method to identify such changes, the singleton density score, which they applied to over 3000 human genomes. Over the past ∼100 generations (2000 to 3000 years), Europeans are likely to have experienced selection for genetic variants, including those that affect skin and hair pigmentation, as well as height. Science, this issue p. 760 The singleton density score identifies evidence of recent selection in Europeans. Detection of recent natural selection is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences. Applied to data from the UK10K Project, SDS reflects allele frequency changes in the ancestors of modern Britons during the past ~2000 to 3000 years. We see strong signals of selection at lactase and the major histocompatibility complex, and in favor of blond hair and blue eyes. For polygenic adaptation, we find that recent selection for increased height has driven allele frequency shifts across most of the genome. Moreover, we identify shifts associated with other complex traits, suggesting that polygenic adaptation has played a pervasive role in shaping genotypic and phenotypic variation in modern humans.

A survey about methods dedicated to epistasis detection.

During the past decade, findings of genome-wide association studies (GWAS) improved our knowledge and understanding of disease genetics. To date, thousands of SNPs have been associated with diseases and other complex traits. Statistical analysis typically looks for association between a phenotype and a SNP taken individually via single-locus tests. However, geneticists admit this is an oversimplified approach to tackle the complexity of underlying biological mechanisms. Interaction between SNPs, namely epistasis, must be considered. Unfortunately, epistasis detection gives rise to analytic challenges since analyzing every SNP combination is at present impractical at a genome-wide scale. In this review, we will present the main strategies recently proposed to detect epistatic interactions, along with their operating principle. Some of these methods are exhaustive, such as multifactor dimensionality reduction, likelihood ratio-based tests or receiver operating characteristic curve analysis; some are non-exhaustive, such as machine learning techniques (random forests, Bayesian networks) or combinatorial optimization approaches (ant colony optimization, computational evolution system).

Jointly Modelling Single Nucleotide Polymorphisms With Longitudinal and Time-to-Event Trait: An Application to Type 2 Diabetes and Fasting Plasma Glucose

In observational cohorts, longitudinal data are collected with repeated measurements at predetermined time points for many biomarkers, along with other variables measured at baseline. In these cohorts, time until a certain event of interest occurs is reported and very often, a relationship will be observed between some biomarker repeatedly measured over time and that event. Joint models were designed to efficiently estimate statistical parameters describing this relationship by combining a mixed model for the longitudinal biomarker trajectory and a survival model for the time until occurrence of the event, using a set of random effects to account for the relationship between the two types of data. In this paper, we discuss the implementation of joint models in genetic association studies. First, we check model consistency based on different simulation scenarios, by varying sample sizes, minor allele frequencies and number of repeated measurements. Second, using genotypes assayed with the Metabochip DNA arrays (Illumina) from about 4,500 individuals recruited in the French cohort D.E.S.I.R. (Data from an Epidemiological Study on the Insulin Resistance syndrome), we assess the feasibility of implementing the joint modelling approach in a real high-throughput genomic dataset. An alternative model approximating the joint model, called the Two-Step approach (TS), is also presented. Although the joint model shows more precise and less biased estimators than its alternative counterpart, the TS approach results in much reduced computational times, and could thus be used for testing millions of SNPs at the genome-wide scale.