An Excess of Risk-Increasing Low-Frequency Variants Can Be a Signal of Polygenic Inheritance in Complex Diseases

Abstract

In most complex diseases, much of the heritability remains unaccounted for by common variants. It has been postulated that lower-frequency variants contribute to the remaining heritability. Here, we describe a method to test for polygenic inheritance from lower-frequency variants by using GWAS summary association statistics. We explored scenarios with many causal low-frequency variants and showed that there is more power to detect risk variants than to detect protective variants, resulting in an increase in the ratio of detected risk to protective variants (R/P ratio). Such an excess can also occur if risk variants are present and kept at lower frequencies because of negative selection. The R/P ratio can be falsely elevated because of reasons unrelated to polygenic inheritance, such as uneven sample sizes or asymmetric population stratification, so precautions to correct for these confounders are essential. We tested our method on published GWAS results and observed a strong signal in some diseases (schizophrenia and type 2 diabetes) but not others. We also explored the shared genetic component in overlapping phenotypes related to inflammatory bowel disease (Crohn disease [CD] and ulcerative colitis [UC]) and diabetic nephropathy (macroalbuminuria and end-stage renal disease [ESRD]). Although the signal was still present when both CD and UC were jointly analyzed, the signal was lost when macroalbuminuria and ESRD were jointly analyzed, suggesting that these phenotypes should best be studied separately. Thus, our method may also help guide the design of future genetic studies of various traits and diseases.

Whole-Genome Sequencing and Integrative Genomic Analysis Approach on Two 22q11.2 Deletion Syndrome Family Trios for Genotype to Phenotype Correlations

“…As a whole, we identified an enrichment of SCZD candidate genes in P1 compared with P2, which is in line with recent studies on SCZD, showing that affected individuals have increased burden of genetic variants [Ahn et al., ; Chan, et al. ; Kong et al., ]. These findings highlight the possible importance of these genes as modifiers of the mental illness phenotype seen in P1.…”

Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

“…Because of allele frequency differences, number of SNPs, and inferred effect size differences along the frequency spectrum, the scale is orders of magnitude different between the true and inferred raw, unstandardized scores, cautioning that while they are informative on a relative scale ( Figures 5C and S11), their absolute scale should not be over interpreted. The inferred risk difference between populations is driven by the increased power to detect minor risk alleles rather than protective alleles in the study population, 93 given the differential selection of case and control subjects in the liability threshold model. We demonstrate this empirically in these neutral simulations within the European population ( Figure S14A), indicating that this phenomenon occurs even in the absence of population structure and when case and control cohort sizes are equal.…”

Homogeneous case subgroups increase power in genetic association studies

“…Indeed, in some diseases, splitting the cases into groups may not prove beneficial. Interrogation of GWAS data sets with GREML methods, 27 polygenic scoring, 30 and polygenic rare variant association methods, 34 as discussed above, may help to shed some light on this.…”

“…33 Finally, polygenic rare variant analysis approaches can be used to identify disease groups that have a polygenic contribution from rare variants and can be used to identify disease subgroups. 34 These methods will be valuable for identifying the genetically distinct groups that would benefit from further association analysis.…”