A polygenic score method boosted by non-additive models.

Dominance heritability in complex traits has received increasing recognition. However, most polygenic score (PGS) approaches do not incorporate non-additive effects. Here, we present GenoBoost, a flexible PGS modeling framework capable of considering both additive and non-additive effects, specifically focusing on genetic dominance. Building on statistical boosting theory, we derive provably optimal GenoBoost scores and provide its efficient implementation for analyzing large-scale cohorts. We benchmark it against seven commonly used PGS methods and demonstrate its competitive predictive performance. GenoBoost is ranked the best for four traits and second-best for three traits among twelve tested disease outcomes in UK Biobank. We reveal that GenoBoost improves prediction for autoimmune diseases by incorporating non-additive effects localized in the MHC locus and, more broadly, works best in less polygenic traits. We further demonstrate that GenoBoost can infer the mode of genetic inheritance without requiring prior knowledge. For example, GenoBoost finds non-zero genetic dominance effects for 602 of 900 selected genetic variants, resulting in 2.5% improvements in predicting psoriasis cases. Lastly, we show that GenoBoost can prioritize genetic loci with genetic dominance not previously reported in the GWAS catalog. Our results highlight the increased accuracy and biological insights from incorporating non-additive effects in PGS models.

Genome-wide association study identifies a new susceptibility locus in PLA2G4C for Multiple System Atrophy.

To elucidate the molecular basis of multiple system atrophy (MSA), a neurodegenerative disease, we conducted a genome-wide association study (GWAS) in a Japanese MSA case/control series followed by replication studies in Japanese, Korean, Chinese, European and North American samples. In the GWAS stage rs2303744 on chromosome 19 showed a suggestive association ( P = 6.5 × 10 -7 ) that was replicated in additional Japanese samples ( P = 2.9 × 10 -6 . OR = 1.58; 95% confidence interval, 1.30 to 1.91), and then confirmed as highly significant in a meta-analysis of East Asian population data ( P = 5.0 × 10 -15 . Odds ratio= 1.49; 95% CI 1.35 to 1.72). The association of rs2303744 with MSA remained significant in combined European/North American samples ( P =0.023. Odds ratio=1.14; 95% CI 1.02 to 1.28) despite allele frequencies being quite different between these populations. rs2303744 leads to an amino acid substitution in PLA2G4C that encodes the cPLA2γ lysophospholipase/transacylase. The cPLA2γ-Ile143 isoform encoded by the MSA risk allele has significantly decreased transacylase activity compared with the alternate cPLA2γ-Val143 isoform that may perturb membrane phospholipids and α-synuclein biology.