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.

Keywords: SNP-chip; biobank; genetic; genotyping; pathogenicity; penetrance; rare variant; variant.

Figure 1

Correlation between Minor Allele Frequency and Analytical Validity Quality Score (A and B) Density plot (A) and boxplot (B) of manual quality scores (from 1–5, see Figure S1) of genotype data in UKB versus minor allele frequency (MAF) for 4,585 putatively clinically important variants, where MAF < 1%, Hardy–Weinberg equilibrium (HWE) > 0.05, and missingness < 0.01. (C) Histogram of the number of variants at each quality score versus presence or absence of the variant in gnomAD (exome data) or the 1000 Genomes Project. Red = score 1; gold = score 2; green = score 3; blue = score 4; purple = score 5. (D) Estimation of the false-positive rate (FPR) versus MAF for variants assayed with the UKB genotyping arrays, calculated by the grouping of quality scores into low (score = 1 or 2) and high (score = 4 or 5) and use of the rocreg command in Stata for fitting a ROC curve.

Figure 2

Comparison of Penetrance Estimate for HNF4A p.Arg114Trp in UK Biobank versus Previously Published Estimates from MODY Cohort Studies A Kaplan-Meier plot of the proportion of individuals who are diabetes free at various ages for 379,768 individuals from UK Biobank (red line), 122 UK Biobank individuals who are heterozygous for HNF4A p.Arg114Trp (green line), 26 MODY referral probands (blue line), and 24 family members of the probands (yellow line) from Laver et al.