Assessing the Pathogenicity, Penetrance, and Expressivity of Putative Disease-Causing Variants in a Population Setting

Abstract

More than 100,000 genetic variants are classified as disease causing in public databases. However, the true penetrance of many of these rare alleles is uncertain and might be over-estimated by clinical ascertainment. Here, we use data from 379,768 UK Biobank (UKB) participants of European ancestry to assess the pathogenicity and penetrance of putatively clinically important rare variants. Although rare variants are harder to genotype accurately than common variants, we were able to classify as high quality 1,244 of 4,585 (27%) putatively clinically relevant rare (MAF < 1%) variants genotyped on the UKB microarray. We defined as “clinically relevant” variants that were classified as either pathogenic or likely pathogenic in ClinVar or are in genes known to cause two specific monogenic diseases: maturity-onset diabetes of the young (MODY) and severe developmental disorders (DDs). We assessed the penetrance and pathogenicity of these high-quality variants by testing their association with 401 clinically relevant traits. 27 of the variants were associated with a UKB trait, and we were able to refine the penetrance estimate for some of the variants. For example, the HNF4A c.340C>T (p.Arg114Trp) (GenBank: NM_175914.4 ) variant associated with diabetes is <10% penetrant by the time an individual is 40 years old. We also observed associations with relevant traits for heterozygous carriers of some rare recessive conditions, e.g., heterozygous carriers of the ERCC4 c.2395C>T (p.Arg799Trp) variant that causes Xeroderma pigmentosum were more susceptible to sunburn. Finally, we refute the previous disease association of RNF135 in developmental disorders. In conclusion, this study shows that very large population-based studies will help refine our understanding of the pathogenicity of rare genetic variants.

Variability in Gene Expression is Associated with Incomplete Penetrance in Inherited Eye Disorders

“…A study by Hanany and Sharon, for example, analyzed the allele frequencies (in the full gnomAD dataset) of variants reported to cause autosomal dominant retinal disease (according to HGMD Public and RetNet (https://sph.uth.edu/retnet/)); the key finding was that 19% of genes and 10% of variants were spurious based on a carrier frequency threshold set by the authors [27]. Additionally, a recent study by Wright et al performed a large-scale analysis of UK Biobank genotyping array data to assess the penetrance of ClinVar-reported variants in genes known to cause maturity-onset diabetes of the young (MODY) and severe developmental disorders; this led to a more refined penetrance estimate for some of the studied variants and to the refutation of a few previous disease associations [3]. Our work focused on a different group of disorders, IED, and had a number of differences in terms of study design.…”

“…These phenomena are widespread in human genetics, even in the context of Mendelian disorders, i.e., conditions that are driven by monoallelic or biallelic variants with strong effects [1]. Importantly, a firm understanding of variable penetrance is required to improve the predictive power of genomic data and to enable accurate variant interpretation [2,3].…”

Assessing the analytical validity of SNP-chips for detecting very rare pathogenic variants: implications for direct-to-consumer genetic testing

“…We have shown that SNP-chips are extremely poor for correctly genotyping very rare variants compared with sequencing data. It is widely recognised that SNP-chips are not good at genotyping very rare variants [9][10][11] so some recent SNP-chips were designed only to assay low and intermediate frequency coding variants (>1 in 5000), for which the SNP-chips perform relatively well. However, increasingly SNP-chips are being augmented with very rare pathogenic variants which, as we show, are not well genotyped.…”

“…SNP-chips have proven to be excellent for studying common genetic variation, which can be used to assess ancestry [6] as well as predisposition to many complex multifactorial diseases such as Type 2 diabetes [7,8]. Amongst the genetics community, it is generally well recognised that SNP-chips perform poorly for genotyping rare genetic variants [9][10][11] due to their reliance upon data clustering ( Figure 1). Clustering data from multiple individuals with similar genotypes works very well when variants are common, as there are large numbers of datapoints to cluster ( Figure 1a).…”

A brief history of human disease genetics

“…Population screening for monogenic disorders is most likely to be initiated for conditions for which risk estimates are well-understood and there are actionable interventions (for example, Lynch syndrome and familial hypercholesterolaemia). Expansion to other disorders requires better understanding of the penetrance of pathogenic alleles in unselected populations 152 and caution before extending screening to longer lists of genes that are less securely implicated in disease causation 153 . As certain countries consider universal capture of genome-wide genetic data at birth or later in life, key questions concern the strategies for releasing this information to citizens and their medical teams to support individual healthcare.…”