Predicting Polygenic Risk of Psychiatric Disorders.

Proportion of DNA shared influences risk of heritable disease.Relationship with schizophrenia patients predicts lifetime risk of schizophrenia in family members (adapted from Gottesman, 1991).

Normal genetic risk in a population with an additive genetic architectureA) Definition and illustration of polygenic risk score calculation. Using a set of existing GWAS summary statistics, the polygenic risk score is computed in a target cohort as Y=∑j=1mgjβj, where j is a SNP in m independent SNPs associated with the phenotype of interest, g is the number of trait-increasing alleles for a particular SNP, and β is the corresponding GWAS effect size estimate. An LD clump is an associated locus with one or few causal loci but a linkage peak of associated variants due to LD correlation in the region. The signal-to-noise ratio can be tuned to maximize prediction accuracy in a target cohort by modifying the maximum p-value threshold for SNP inclusion. B) Large numbers of SNPs contributing to complex traits can be modeled accurately with genetic liability as a normal distribution. Here, we demonstrate this by showing the genetic risk distribution for increasing numbers of SNPs with an allele frequency of 0.5 (although normality is expected regardless of allele frequency when larger numbers of SNPs are causal). The best-powered GWAS of complex traits such as height, schizophrenia, and educational attainment have identified hundreds to thousands of independent, genome-wide significant loci. This phenomenon can be explained by the central limit theorem as demonstrated previously (107). C) Additive GWAS regression models tend to work well for genetic associations across a range of allele frequencies, even in the presence of dominance. D) Previous work in the UK Biobank has demonstrated that across 25 complex traits and diseases, most of the heritable variation in complex traits can be explained by common variants (e.g., ≥ 5% allele frequency). While the exact proportion can vary, the curve illustrates h2 = 2*p*(1-p)α, where α = −0.38 ± 0.02 across complex traits (108). E) A previous GWAS of schizophrenia identified 128 independent genome-wide significant loci, shown here (11). These loci illustrate a relationship between frequency and corresponding odds ratios, in which lower frequency variants can have larger effect sizes, reflecting the impact of natural selection on genetic architecture.

Correlated epidemiological and genetic factors can be causally dissected with Mendelian randomization.GRS = genetic risk scores from independent genome-wide significant SNPs. BMI = body mass index, LDL-C = LDL cholesterol, HDL-C = HDL cholesterol, CHD = coronary heart disease. A) Epidemiological factors from FINRISK and their associations with risk of CHD after all evaluated factors have been normalized, and age and sex have been regressed out. Test statistics for each of the panel comparisons are written in plot corners and are as follows (t-tests for the top 3 panels, ANOVA for the bottom 3 panels): BMI: p=3.5e-18; LDL: p=0.79; HDL: p=5.3e-62; LDL and BMI: p=2.7e-73, HDL and BMI: p<1e-100, HDL and LDL: p=2.0e-7. B) Genetic factors associated with LDL-C, HDL-C, and BMI enable causal inference for CHD. Whereas genetic risk of increased LDL-C and BMI are causally associated with increased risk of CHD, HDL-C is genetically anti-correlated with CHD but is not causal. Genetic correlations (ρg) are from LD Hub (12).

Predictive accuracy of polygenic risk scores for height at intervals along the measured height distribution in the UK Biobank.Using summary statistics from the GIANT Consortium, we computed polygenic risk scores for height and compared them to the distribution of standardized height in the UK Biobank after adjusting for sex and the first 10 principal components. However, prediction accuracy is not distributed evenly; it performs particularly poorly at the extreme short end of the height distribution, indicating a larger contribution of environmental factors, large-effect rare variants, and/or other factors in these individuals. Numbers in the plot indicate observed versus expected polygenic risk scores within corresponding breakpoints along the adjusted height distribution. Expected polygenic risk scores comes from multivariate normal simulations assuming the same correlation between adjusted height and observed polygenic risk scores.

Genetic correlation between psychiatric disorders and cognitive/behavioral phenotypes.Measures are from LD Hub (12). ASD = autism spectrum disorders, BIP = bipolar disorder, SCZ = schizophrenia. Legend indicates genetic correlation, ρg.