How Can Genetic Studies Help Us to Understand Links Between Birth Weight and Type 2 Diabetes?

PURPOSE OF REVIEW: In observational epidemiology, both low and high birth weights are associated with later type 2 diabetes. The mechanisms underlying the associations are poorly understood. We review evidence for the roles of genetic and non-genetic factors linking both sides of the birth weight distribution to risk of type 2 diabetes, focusing on contributions made by the most recent genome-wide association studies (GWAS) of birth weight. RECENT FINDINGS: There are now nine genetic loci robustly implicated in both fetal growth and type 2 diabetes. At many of these, the same alleles are associated both with a higher risk of type 2 diabetes and a lower birth weight. This supports the Fetal Insulin Hypothesis and reflects a general pattern for type 2 diabetes susceptibility alleles: genome-wide, there is an inverse genetic correlation with birth weight, and initial estimates suggest genetic factors explain a large part of the covariance between the two traits. However, the associations at individual loci show heterogeneity; some fetal risk alleles are associated with higher birth weight. For most of these, the association reflects their correlation with the maternal risk allele which raises maternal glucose, thus increasing fetal insulin-mediated growth. SUMMARY: GWAS have improved our understanding of the mechanisms underlying associations between type 2 diabetes and birth weight but questions remain about the relative importance of genetic versus non-genetic factors and of maternal versus fetal genotypes. To answer these questions, future work will require well-powered analyses of parents and offspring

Assessing allele-specific expression across multiple tissues from RNA-seq read data

Motivation: RNA sequencing enables allele-specific expression (ASE) studies that complement standard genotype expression studies for common variants and, importantly, also allow measuring the regulatory impact of rare variants. The Genotype-Tissue Expression (GTEx) project is collecting RNA-seq data on multiple tissues of a same set of individuals and novel methods are required for the analysis of these data. Results: We present a statistical method to compare different patterns of ASE across tissues and to classify genetic variants according to their impact on the tissue-wide expression profile. We focus on strong ASE effects that we are expecting to see for protein-truncating variants, but our method can also be adjusted for other types of ASE effects. We illustrate the method with a real data example on a tissue-wide expression profile of a variant causal for lipoid proteinosis, and with a simulation study to assess our method more generally. Availability and implementation: http://www.well.ox.ac.uk/~rivas/mamba/. R-sources and data examples http://www.iki.fi/mpirinen/ Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Higher chylomicron remnants and LDL particle numbers associate with CD36 SNPs and DNA methylation sites that reduce CD36

Cluster of differentiation 36 (CD36) variants influence fasting lipids and risk of metabolic syndrome, but their impact on postprandial lipids, an independent risk factor for cardiovascular disease, is unclear. We determined the effects of SNPs within a ~410 kb region encompassing CD36 and its proximal and distal promoters on chylomicron (CM) remnants and LDL particles at fasting and at 3.5 and 6 h following a high-fat meal (Genetics of Lipid Lowering Drugs and Diet Network study, n = 1,117). Five promoter variants associated with CMs, four with delayed TG clearance and five with LDL particle number. To assess mechanisms underlying the associations, we queried expression quantitative trait loci, DNA methylation, and ChIP-seq datasets for adipose and heart tissues that function in postprandial lipid clearance. Several SNPs that associated with higher serum lipids correlated with lower adipose and heart CD36 mRNA and aligned to active motifs for PPARγ, a major CD36 regulator. The SNPs also associated with DNA methylation sites that related to reduced CD36 mRNA and higher serum lipids, but mixed-model analyses indicated that the SNPs and methylation independently influence CD36 mRNA. The findings support contributions of CD36 SNPs that reduce adipose and heart CD36 RNA expression to inter-individual variability of postprandial lipid metabolism and document changes in CD36 DNA methylation that influence both CD36 expression and lipids

Discovery of Sexual Dimorphisms in Metabolic and Genetic Biomarkers

Metabolomic profiling and the integration of whole-genome genetic association data has proven to be a powerful tool to comprehensively explore gene regulatory networks and to investigate the effects of genetic variation at the molecular level. Serum metabolite concentrations allow a direct readout of biological processes, and association of specific metabolomic signatures with complex diseases such as Alzheimer's disease and cardiovascular and metabolic disorders has been shown. There are well-known correlations between sex and the incidence, prevalence, age of onset, symptoms, and severity of a disease, as well as the reaction to drugs. However, most of the studies published so far did not consider the role of sexual dimorphism and did not analyse their data stratified by gender. This study investigated sex-specific differences of serum metabolite concentrations and their underlying genetic determination. For discovery and replication we used more than 3,300 independent individuals from KORA F3 and F4 with metabolite measurements of 131 metabolites, including amino acids, phosphatidylcholines, sphingomyelins, acylcarnitines, and C6-sugars. A linear regression approach revealed significant concentration differences between males and females for 102 out of 131 metabolites (p-values<3.8 x 10(-4); Bonferroni-corrected threshold). Sex-specific genome-wide association studies (GWAS) showed genome-wide significant differences in beta-estimates for SNPs in the CPS1 locus (carbamoyl-phosphate synthase 1, significance level: p<3.8 x 10(-10); Bonferroni-corrected threshold) for glycine. We showed that the metabolite profiles of males and females are significantly different and, furthermore, that specific genetic variants in metabolism-related genes depict sexual dimorphism. Our study provides new important insights into sex-specific differences of cell regulatory processes and underscores that studies should consider sex-specific effects in design and interpretation

Whole-genome sequencing to understand the genetic architecture of common gene expression and biomarker phenotypes.

Initial results from sequencing studies suggest that there are relatively few low-frequency (&lt;5%) variants associated with large effects on common phenotypes. We performed low-pass whole-genome sequencing in 680 individuals from the InCHIANTI study to test two primary hypotheses: (i) that sequencing would detect single low-frequency-large effect variants that explained similar amounts of phenotypic variance as single common variants, and (ii) that some common variant associations could be explained by low-frequency variants. We tested two sets of disease-related common phenotypes for which we had statistical power to detect large numbers of common variant-common phenotype associations-11 132 cis-gene expression traits in 450 individuals and 93 circulating biomarkers in all 680 individuals. From a total of 11 657 229 high-quality variants of which 6 129 221 and 5 528 008 were common and low frequency (&lt;5%), respectively, low frequency-large effect associations comprised 7% of detectable cis-gene expression traits [89 of 1314 cis-eQTLs at P &lt; 1 × 10(-06) (false discovery rate ∼5%)] and one of eight biomarker associations at P &lt; 8 × 10(-10). Very few (30 of 1232; 2%) common variant associations were fully explained by low-frequency variants. Our data show that whole-genome sequencing can identify low-frequency variants undetected by genotyping based approaches when sample sizes are sufficiently large to detect substantial numbers of common variant associations, and that common variant associations are rarely explained by single low-frequency variants of large effect

The Sloan Digital Sky Survey-II Supernova Survey: Search Algorithm and Follow-up Observations

The Sloan Digital Sky Survey-II Supernova Survey has identified a large number of new transient sources in a 300 sq. deg. region along the celestial equator during its first two seasons of a three-season campaign. Multi-band (ugriz) light curves were measured for most of the sources, which include solar system objects, Galactic variable stars, active galactic nuclei, supernovae (SNe), and other astronomical transients. The imaging survey is augmented by an extensive spectroscopic follow-up program to identify SNe, measure their redshifts, and study the physical conditions of the explosions and their environment through spectroscopic diagnostics. During the survey, light curves are rapidly evaluated to provide an initial photometric type of the SNe, and a selected sample of sources are targeted for spectroscopic observations. In the first two seasons, 476 sources were selected for spectroscopic observations, of which 403 were identified as SNe. For the Type Ia SNe, the main driver for the Survey, our photometric typing and targeting efficiency is 90%. Only 6% of the photometric SN Ia candidates were spectroscopically classified as non-SN Ia instead, and the remaining 4% resulted in low signal-to-noise, unclassified spectra. This paper describes the search algorithm and the software, and the real-time processing of the SDSS imaging data. We also present the details of the supernova candidate selection procedures and strategies for follow-up spectroscopic and imaging observations of the discovered sources.Comment: Accepted for publication in The Astronomical Journal (66 pages, 13 figures); typos correcte

The HST/ACS Coma Cluster Survey. II. Data Description and Source Catalogs

The Coma cluster was the target of a HST-ACS Treasury program designed for deep imaging in the F475W and F814W passbands. Although our survey was interrupted by the ACS instrument failure in 2007, the partially completed survey still covers ~50% of the core high-density region in Coma. Observations were performed for 25 fields that extend over a wide range of cluster-centric radii (~1.75 Mpc) with a total coverage area of 274 arcmin^2. The majority of the fields are located near the core region of Coma (19/25 pointings) with six additional fields in the south-west region of the cluster. In this paper we present reprocessed images and SExtractor source catalogs for our survey fields, including a detailed description of the methodology used for object detection and photometry, the subtraction of bright galaxies to measure faint underlying objects, and the use of simulations to assess the photometric accuracy and completeness of our catalogs. We also use simulations to perform aperture corrections for the SExtractor Kron magnitudes based only on the measured source flux and half-light radius. We have performed photometry for ~73,000 unique objects; one-half of our detections are brighter than the 10-sigma point-source detection limit at F814W=25.8 mag (AB). The slight majority of objects (60%) are unresolved or only marginally resolved by ACS. We estimate that Coma members are 5-10% of all source detections, which consist of a large population of unresolved objects (primarily GCs but also UCDs) and a wide variety of extended galaxies from a cD galaxy to dwarf LSB galaxies. The red sequence of Coma member galaxies has a constant slope and dispersion across 9 magnitudes (-21<M_F814W<-13). The initial data release for the HST-ACS Coma Treasury program was made available to the public in 2008 August. The images and catalogs described in this study relate to our second data release.Comment: Accepted for publication in ApJS. A high-resolution version is available at http://archdev.stsci.edu/pub/hlsp/coma/release2/PaperII.pd