Goncalo R Abecasis | Search bioRxiv

bioRxiv2016

Population and individual effects of non-coding variants inform genetic risk factors

Identifying functional non-coding variants can enhance genome interpretation and inform novel genetic risk factors. We used whole genomes and peripheral white blood cell transcriptomes from 624 Sardinian individuals to identify non-coding variants that contribute to population, family, and individual differences in transcript abundance. We identified 21,183 independent expression quantitative trait loci (eQTLs) and 6,768 independent splicing quantitative trait loci (sQTLs) influencing 73 and 41% of all tested genes. When we compared Sardinian eQTLs to those previously identified in Europe, we identified differentiated eQTLs at genes involved in malarial resistance and multiple sclerosis, reflecting the long-term epidemiological history of the islands population. Taking advantage of pedigree data for the population sample, we identify segregating patterns of outlier gene expression and allelic imbalance in 61 Sardinian trios. We identified 809 expression outliers (median z-score of 2.97) averaging 13.3 genes with outlier expression per individual. We then connected these outlier expression events to rare non-coding variants. Our results provide new insight into the effects of non-coding variants and their relationship to population history, traits and individual genetic risk.

Genomics↗

bioRxiv2016

Reference-based phasing using the Haplotype Reference Consortium panel

Haplotype phasing is a fundamental problem in medical and population genetics. Phasing is generally performed via statistical phasing within a genotyped cohort, an approach that can attain high accuracy in very large cohorts but attains lower accuracy in smaller cohorts. Here, we instead explore the paradigm of reference-based phasing. We introduce a new phasing algorithm, Eagle2, that attains high accuracy across a broad range of cohort sizes by efficiently leveraging information from large external reference panels (such as the Haplotype Reference Consortium, HRC) using a new data structure based on the positional BurrowsWheeler transform. We demonstrate that Eagle2 attains a {approx}20x speedup and {approx}10% increase in accuracy compared to reference-based phasing using SHAPEIT2. On European-ancestry samples, Eagle2 with the HRC panel achieves >2x the accuracy of 1000 Genomes-based phasing. Eagle2 is open source and freely available for HRC-based phasing via the Sanger Imputation Service and the Michigan Imputation Server.

Genetics↗