Genetic association studies of alterations in protein function expose recessive effects on cancer predisposition

Abstract

The characterization of germline genetic variation affecting cancer risk, known as cancer predisposition, is fundamental to preventive and personalized medicine. Studies of genetic cancer predisposition typically identify significant genomic regions based on family-based cohorts or genome-wide association studies (GWAS). However, the results of such studies rarely provide biological insight or functional interpretation. In this study, we conducted a comprehensive analysis of cancer predisposition in the UK Biobank cohort using a new gene-based method for detecting protein-coding genes that are functionally interpretable. Specifically, we conducted proteome-wide association studies (PWAS) to identify genetic associations mediated by alterations to protein function. With PWAS, we identified 110 significant gene-cancer associations in 70 unique genomic regions across nine cancer types and pan-cancer. In 48 of the 110 PWAS associations (44%), estimated gene damage is associated with reduced rather than elevated cancer risk, suggesting a protective effect. Together with standard GWAS, we implicated 145 unique genomic loci with cancer risk. While most of these genomic regions are supported by external evidence, our results also highlight many novel loci. Based on the capacity of PWAS to detect non-additive genetic effects, we found that 46% of the PWAS-significant cancer regions exhibited exclusive recessive inheritance. These results highlight the importance of recessive genetic effects, without relying on familial studies. Finally, we show that many of the detected genes exert substantial cancer risk in the studied cohort determined by a quantitative functional description, suggesting their relevance for diagnosis and genetic consulting.

Recessive and sex-dependent genetic effects in primary hypertension

“…In this study, we used both GWAS and PWAS association methodologies and extensively compared them [25, 26]. We showed that PWAS as a gene-based method can detect non-additive effects (Figure 3) .…”

“…We examined the entire distribution of PWAS effect scores by considering all possible cutoffs for the relevant cohort, i.e., all subsets with effect scores that are either below or above a certain threshold. We describe the relevant sub-populations by percentiles (e.g., "bottom 10%" refers to the 10% of the cohort with the lowest PWAS scores) [26].…”

“…PWAS aggregates the signal from all variants affecting each protein-coding gene with the assumption of dominant and recessive inheritance. While routine applications of GWAS assume additive heritability for genetic effects, ignoring alternative heritability modes [17], recessive effects account for a substantial genetic signal in complex traits (e.g., cancer) [26]. We identified a gene-association predisposition for hypertension that is sex-dependent.…”

Open problems in human trait genetics

“…If the two copies of a gene are affected by different variants, a case known as compound heterozygosity, then a variant-level recessive model would be completely blind to it. Due to the inherent blindness of GWAS to compound heterozygosity, the study of recessive genetic effects in complex human traits is highly neglected, but some recent works show that they are in fact common in complex traits [ 95 , 96 ]. Gene-based methods (as opposed to variant-based methods) seem especially promising for detecting recessive effects [ 45 ].…”

Genome-wide prediction of disease variants with a deep protein language model

“…Determining the phenotypic consequences of genetic variants is a key challenge in human genetics [1][2][3][4]. While most genetic variants associated with common human disease occur in non-coding regions of the genome, variants that affect the amino-acid (AA) sequence of proteins are enriched amongst variants associated with common human disease and cause many rare Mendelian and somatic disorders [5][6][7][8]. Coding variants are also of special interest because the mechanisms by which they act are better understood, and they are more therapeutically actionable.…”