Pedersen, E. M. | bioRxiv Science

bioRxiv2022

Publicly Available Privacy-preserving Benchmarks for Polygenic Prediction

Recently, several new approaches for creating polygenic scores (PGS) have been developed and this trend shows no sign of abating. However, it has thus far been challenging to determine which approaches are superior, as different studies report seemingly conflicting benchmark results. This heterogeneity in benchmark results is in part due to different outcomes being used, but also due to differences in the genetic variants being used, data preprocessing, and other quality control steps. As a solution, a publicly available benchmark for polygenic prediction is presented here, which allows researchers to both train and test polygenic prediction methods using only summary-level information, thus preserving privacy. Using simulations and real data, we show that model performance can be estimated with accuracy, using only linkage disequilibrium (LD) information and genome-wide association summary statistics for target outcomes. Finally, we make this PGS benchmark - consisting of 8 outcomes, including somatic and psychiatric disorders - publicly available for researchers to download on our PGS benchmark platform (http://www.pgsbenchmark.org). We believe this benchmark can help establish a clear and unbiased standard for future polygenic score methods to compare against.

genetics↗

bioRxiv2021

Accounting for age-of-onset and family history improves power in genome-wide association studies

Genome-wide association studies (GWAS) have revolutionized human genetics, allowing researchers to identify thousands of disease-related genes and possible drug targets. However, case-control status does not account for the fact that not all controls may have lived through their period of risk for the disorder of interest. This can be quantified by examining the age-of-onset distribution and the age of the controls or the age-of-onset for cases. The age-of-onset distribution may also depend on information such as sex and birth year. In addition, family history is not routinely included in the assessment of control status. Here we present LT-FH++, an extension of the liability threshold model conditioned on family history (LT-FH), that jointly accounts for age-of-onset and sex, as well as family history. Using simulations, we show that, when family history and the age-of-onset distribution are available, the proposed approach yields large power gains over both LT-FH and genome-wide association study by proxy (GWAX). We applied our method to four psychiatric disorders available in the iPSYCH data, and to mortality in the UK Biobank, finding 20 genome-wide significant associations with LT-FH++, compared to 10 for LT-FH and 8 for a standard case-control GWAS. As more genetic data with linked electronic health records become available to researchers, we expect methods that account for additional health information, such as LT-FH++, to become even more beneficial.

bioinformatics↗