Kilpelainen, E. | Search bioRxiv

bioRxiv2017

Phenome-wide association studies (PheWAS) across large "real-world data" population cohorts support drug target validation

Phenome-wide association studies (PheWAS), which assess whether a genetic variant is associated with multiple phenotypes across a phenotypic spectrum, have been proposed as a possible aid to drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we evaluate whether PheWAS can inform target validation during drug development. We selected 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease therapeutic indications. We independently interrogated these SNPs through PheWAS in four large \"real-world data\" cohorts (23andMe, UK Biobank, FINRISK, CHOP) for association with a total of 1,892 binary endpoints. We then conducted meta-analyses for 145 harmonized disease endpoints in up to 697,815 individuals and joined results with summary statistics from 57 published GWAS. Our analyses replicate 70% of known GWAS associations and identify 10 novel associations with study-wide significance after multiple test correction (P<1.8x10-6; out of 72 novel associations with FDR<0.1). By leveraging directionality and point estimate of the effect sizes, we describe new associations that may predict ADEs, e.g., acne, high cholesterol, gout and gallstones for rs738409 (p.I148M) in PNPLA3; or asthma for rs1990760 (p.T946A) in IFIH1. We further propose how quantitative estimates of genetic safety/efficacy profiles can be used to help prioritize candidate targets for a specific indication. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery.\n\nOne Sentence SummaryMatching genetics with phenotypes in 800,000 individuals predicts efficacy and on-target safety of future drugs.

genetics↗

bioRxiv2017

Accuracy of programs for the determination of HLA alleles from NGS data

The human leukocyte antigen (HLA) genes code for proteins that play a central role in the function of the immune system by presenting peptide antigens to T cells. As HLA genes show extremely high genetic polymorphism, HLA typing on the allele level is demanding and is based on DNA sequencing. Determination of HLA alleles is warranted as many HLA alleles are major genetic factors that confer susceptibility to autoimmune diseases and is important for the matching of HLA alleles in transplantation. Here, we compared the accuracy of several published HLA-typing algorithms that are based on next generation sequencing (NGS) data. As genome screens are becoming increasingly routine in research, we wanted to test how well HLA alleles can be deduced from genome screens not designed for HLA typing. The accuracies were assessed using datasets consisting of NGS data produced using the ImmunoSEQ platform, including the full 4 Mbp HLA segment, from 94 stem cell transplantation patients and exome sequences from the 1000 Genomes collection. When used with the default settings none of the methods gave perfect results for all the genes and samples. However, we found that ensemble prediction of the results or modifications of the settings could be used to improve accuracy. Most of the algorithms did not perform very well for the exome-only data. The results indicate that the use of these algorithms for accurate HLA allele determination based on NGS data is not straightforward.

immunology↗