A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts

Abstract

Background: Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies (GWASs). PGS methods differ in which DNA variants are included and the weights assigned

Handedness and its genetic influences are associated with structural asymmetries of the cerebral cortex in 31,864 individuals

“…SNP-wise effects on left-handedness disposition from this sample were then applied in mass combination to the 28,802 right-handers and 3,062 left-handers with post-quality-control imaging and genetic data (see Imaging genetics dataset ) to estimate their polygenic dispositions to left-handedness, using the PRS-CS software ( 42 ). This method uses a high-dimensional Bayesian regression framework to estimate posterior effect sizes of SNPs and has shown superior prediction statistics compared to approaches based on clumping by linkage disequilibrium and P value thresholding ( 79 ). In total, effect sizes of 1,103,636 SNPs spanning the autosomes were used based also on their presence in the 1,000 Genomes project European-descent dataset from where PRS-CS obtains its linkagedisequilibrium information ( 80 ).…”

Cumulative Effects of Resting-state Connectivity Across All Brain Networks Significantly Correlate with ADHD Symptoms

“…We used a method for calculating polygenic scores that has demonstrated good performance in a variety of data sets, as well as significantly improved performance compared to more traditional methods (Vilhjálmsson et al 2015;Ni et al 2021;Pain et al 2021). We also constructed PRS based on both the full and European-ancestry-only PGC+iPSYCH ADHD GWAS meta-analyses, to examine whether the discovery sample used significantly impacted our results.…”

Polygenic burden could explain high rates of affective disorders in a community with restricted founder population

“…Our PGS analyses showed that distributions of PGS of BMI, T2D, coronary artery disease, and CNT are similar between the community samples and all three sets of UKB control samples, while the mean PGS is significantly higher in the community samples for psychiatric traits including major depression (0.56 PGS SD units, p = 2.1 Â 10 À19 ), BIP (0.56 PGS SD units, p = 2.5 Â 10 À15 ), and SCZ (0.64 PGS SD units for SCZ, p = 3.8 Â 10 À21 ). These values can be benchmarked against case/control comparisons from the Psychiatric Genomics Consortia (Ni et al, 2021).Using the same method and discovery samples to generate PGS, the mean difference (across 30 cohorts) in PGS between SCZ cases and controls was 0.90 SCZ PGS SD units; the mean difference (across 26 cohorts) in PGS for major depression cases and controls was 0.36 major depression PGS SD units (under the reasonable assumption that control cohorts generate the same SD units); the maximum across the 26 cohorts was 0.58 SD units. Moreover, the Psychiatric Genomics Consortia control data are mostly screened to exclude anyone who has reported an affective disorder, whereas the UKB controls here were randomly sampled.…”