International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways.

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5 × 10(-8)) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine-cytokine pathways, for which relevant therapies exist

The ATXN2-SH2B3 locus is associated with peripheral arterial disease: an electronic medical record-based genome-wide association study

Objectives: In contrast to coronary heart disease, genetic variants that influence susceptibility to peripheral arterial disease (PAD) remain unknown. Background: We performed a two-stage genomic association study leveraging an electronic medical record linked-biorepository to identify genetic variants that mediate susceptibility to PAD.Methods: PAD was defined as a resting/post-exercise ankle-brachial index (ABI) ≤0.9 or ≥1.4 and/or history of lower extremity revascularization. Controls were patients without history of PAD. In Stage I we performed a genome-wide association analysis adjusting for age and sex, of 561,490 SNPs in 1641 PAD cases (66±11 y, 64% men) and 1604 control subjects (61±7 y, 60% men) of European ancestry. In Stage II we genotyped the top 48 SNPs that were associated with PAD in Stage I, in a replication cohort of 740 PAD cases (70±11 y, 63% men) and 1051 controls (70±12 y, 61% men). Results: The SNP rs653178 in the ATXN2-SH2B3 locus was significantly associated with PAD in the discovery cohort (OR: 1.23; P=5.59x10-5), in the replication cohort (OR=1.22; 8.9x10-4) and in the combined cohort (OR=1.22; P-value: P=6.46x10-7). In the combined cohort this SNP remained associated with PAD after additional adjustment for cardiovascular risk factors including smoking (OR: 1.22; P=2.15x10-6) and after excluding patients with ABI >1.4 (OR: 1.237; P=3.98x10-7). The SNP is in near-complete linkage disequilibrium (r2=0.99) with a missense SNP (rs3184504) in SH2B3, a gene encoding an adapter protein that plays a key role in immune and inflammatory response pathways and vascular homeostasis. The SNP has pleiotropic effects and has been previously associated with multiple phenotypes including myocardial infarction. Conclusions: Our findings suggest that the ATXN2-SH2B3 locus influences susceptibility to PAD

Imputation and quality control steps for combining multiple genome-wide datasets

The electronic MEdical Records and GEnomics (eMERGE) network brings together DNA biobanks linked to electronic health records (EHRs) from multiple institutions. Approximately 52,000 DNA samples from distinct individuals have been genotyped using genome-wide SNP arrays across the nine sites of the network. The eMERGE Coordinating Center and the Genomics Workgroup developed a pipeline to impute and merge genomic data across the different SNP arrays to maximize sample size and power to detect associations with a variety of clinical endpoints. The 1000 Genomes cosmopolitan reference panel was used for imputation. Imputation results were evaluated using the following metrics: accuracy of imputation, allelic R2 (estimated correlation between the imputed and true genotypes), and the relationship between allelic R2 and minor allele frequency. Computation time and memory resources required by two different software packages (BEAGLE and IMPUTE2) were also evaluated. A number of challenges were encountered due to the complexity of using two different imputation software packages, multiple ancestral populations, and many different genotyping platforms. We present lessons learned and describe the pipeline implemented here to impute and merge genomic data sets. The eMERGE imputed dataset will serve as a valuable resource for discovery, leveraging the clinical data that can be mined from the EHR

Controlling for population structure and genotyping platform bias in the eMERGE multi-institutional biobank linked to Electronic Health Records

Combining samples across multiple cohorts in large-scale scientific research programs is often required to achieve the necessary power for genome-wide association studies. Controlling for genomic ancestry through principal component analysis (PCA) to address the effect of population stratification is a common practice. In addition to local genomic variation, such as copy number variation and inversions, other factors directly related to combining multiple studies, such as platform and site recruitment bias, can drive the correlation patterns in PCA. In this report, we describe combination and analysis of multi-ethnic cohort with biobanks linked to electronic health records for large-scale genomic association discovery analyses. First, we outline the observed site and platform bias, in addition to ancestry differences. Second, we outline a general protocol for selecting variants for input into the subject variance-covariance matrix, the conventional PCA approach. Finally, we introduce an alternative approach to PCA by deriving components from subject loadings calculated from a reference sample. This alternative approach of generating principal components controlled for site and platform bias, in addition to ancestry differences, with the advantage of fewer covariates and degrees of freedom.principal component analysis, ancestry, biobank, loadings, genetic association stud