Fred Wright, Patrick Sullivan and colleagues present the results of a large expression QTL study of peripheral blood using a classic twin design with follow-up replication in independent samples. Their results enable a more precise estimate of the heritability of gene expression and provide a useful resource for exploring the genetic control of transcription. We assessed gene expression profiles in 2,752 twins, using a classic twin design to quantify expression heritability and quantitative trait loci (eQTLs) in peripheral blood. The most highly heritable genes (∼777) were grouped into distinct expression clusters, enriched in gene-poor regions, associated with specific gene function or ontology classes, and strongly associated with disease designation. The design enabled a comparison of twin-based heritability to estimates based on dizygotic identity-by-descent sharing and distant genetic relatedness. Consideration of sampling variation suggests that previous heritability estimates have been upwardly biased. Genotyping of 2,494 twins enabled powerful identification of eQTLs, which we further examined in a replication set of 1,895 unrelated subjects. A large number of non-redundant local eQTLs (6,756) met replication criteria, whereas a relatively small number of distant eQTLs (165) met quality control and replication standards. Our results provide a new resource toward understanding the genetic control of transcription.

Obesity is an important component of the pathophysiology of chronic diseases. Identifying epigenetic modifications associated with elevated adiposity, including DNA methylation variation, may point to genomic pathways that are dysregulated in numerous conditions. The Illumina 450K Bead Chip array was used to assay DNA methylation in leukocyte DNA obtained from 2097 African American adults in the Atherosclerosis Risk in Communities (ARIC) study. Mixed-effects regression models were used to test the association of methylation beta value with concurrent body mass index (BMI) and waist circumference (WC), and BMI change, adjusting for batch effects and potential confounders. Replication using whole-blood DNA from 2377 White adults in the Framingham Heart Study and CD4+ T cell DNA from 991 Whites in the Genetics of Lipid Lowering Drugs and Diet Network Study was followed by testing using adipose tissue DNA from 648 women in the Multiple Tissue Human Expression Resource cohort. Seventy-six BMI-related probes, 164 WC-related probes and 8 BMI change-related probes passed the threshold for significance in ARIC (P < 1 × 10⁻⁷; Bonferroni), including probes in the recently reported HIF3A, CPT1A and ABCG1 regions. Replication using blood DNA was achieved for 37 BMI probes and 1 additional WC probe. Sixteen of these also replicated in adipose tissue, including 15 novel methylation findings near genes involved in lipid metabolism, immune response/cytokine signaling and other diverse pathways, including LGALS3BP, KDM2B, PBX1 and BBS2, among others. Adiposity traits are associated with DNA methylation at numerous CpG sites that replicate across studies despite variation in tissue type, ethnicity and analytic approaches.

The identification of small molecules that target specific CFTR variants has ushered in a new era of treatment for cystic fibrosis (CF), yet optimal, individualized treatment of CF will require identification and targeting of disease modifiers. Here we use genome-wide association analysis to identify genetic modifiers of CF lung disease, the primary cause of mortality. Meta-analysis of 6,365 CF patients identifies five loci that display significant association with variation in lung disease. Regions on chr3q29 (MUC4/MUC20; P=3.3 × 10⁻¹¹), chr5p15.3 (SLC9A3; P=6.8 × 10⁻¹²), chr6p21.3 (HLA Class II; P=1.2 × 10⁻⁸) and chrXq22-q23 (AGTR2/SLC6A14; P=1.8 × 10⁻⁹) contain genes of high biological relevance to CF pathophysiology. The fifth locus, on chr11p12-p13 (EHF/APIP; P=1.9 × 10⁻¹⁰), was previously shown to be associated with lung disease. These results provide new insights into potential targets for modulating lung disease severity in CF.

With the growing importance of microbiome research, there is increasing evidence that host variation in microbial communities is associated with overall host health. Advancement in genetic sequencing methods for microbiomes has coincided with improvements in machine learning, with important implications for disease risk prediction in humans. One aspect specific to microbiome prediction is the use of taxonomy-informed feature selection. In this review for non-experts, we explore the most commonly used machine learning methods, and evaluate their prediction accuracy as applied to microbiome host trait prediction. Methods are described at an introductory level, and R/Python code for the analyses is provided.

Cadmium (Cd) is prevalent in the environment yet understudied as a developmental toxicant. Cd partially crosses the placental barrier from mother to fetus and is linked to detrimental effects in ne...

Summary: seeQTL is a comprehensive and versatile eQTL database, including various eQTL studies and a meta-analysis of HapMap eQTL information. The database presents eQTL association results in a convenient browser, using both segmented local-association plots and genome-wide Manhattan plots. Availability and implementation: seeQTL is freely available for non-commercial use at http://www.bios.unc.edu/research/genomic_software/seeQTL/. Contact: fred_wright@unc.edu; kxia@bios.unc.edu Supplementary information: Supplementary data are available at Bioinformatics online.