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Abstract
Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers average 60% or lower for most conditions. We assess epidemiologic and genetic factors contributing to risk prediction in monogenic variant carriers, demonstrating that inclusion of polygenic variation significantly improves biomarker estimation for two monogenic dyslipidemias.
Highlights
Healthcare providers and researchers are increasingly faced with interpreting genetic sequence data collected from individuals who are asymptomatic or for whom limited clinical information is available
Our analyses focused on 26 genes offered by clinical laboratories in the United States for evaluation of monogenic forms of diabetes or diabetes-related traits through autosomal dominant modes of inheritance: MODY most commonly offered in panel testing (GCK, HNF1A, HNF1B, HNF4A, PDX1), an extended set of purported MODY genes less frequently offered in panel testing (AKT2, KLF11, APPL1, ABCC8, KCNJ11, NEUROD1, CEL, INS), neonatal diabetes (ABCC8, GATA4, GATA6, HNF1B, INS, KCNJ11), lipodystrophy (AKT2, LMNA, PLIN1, PPARG), elevated LDL cholesterol (LDLR, APOB), low serum LDL cholesterol (APOB, PCSK9), elevated serum HDL cholesterol (CETP), hypertriglyceridemia (APOA5, LPL), and monogenic obesity (MC4R)
We performed stringent variant curation using the clinical gold standard ACMG/Association for Molecular Pathology (AMP) criteria, blinded to carrier phenotypic data for two classes of variants: 276 variants previously reported to be clinically significant in the ClinVar database[19] or designated as disease-causing in review articles[20,21,22]; and 218 predicted loss of function variants in genes with supported loss-of-function mechanism of action, which underwent curation including manual inspection of sequence reads by two independent reviewers
Summary
Healthcare providers and researchers are increasingly faced with interpreting genetic sequence data collected from individuals who are asymptomatic or for whom limited clinical information is available. The scope of genetic variation interpreted in current clinical genetics practice is predominantly limited to rare monogenic “Mendelian” disease variants with large predicted effect sizes, leaving the vast majority of the genome, including common variants, unassessed. Large-scale population-based and cohort studies with both sequence and phenotype data offer an opportunity to estimate penetrance and expressivity with less upward bias compared to family or case-control studies. Recent studies attempting to connect large-scale genetic and phenotypic data have noted reduced penetrance estimates compared to those previously reported; these recent studies were limited by sample size and/or application of less stringent curation of genetic variants than the current clinical standard of care ACMG/AMP guideline approach[6,13,14,15,16]. We present analyses performed in two separate datasets: 38,618 exomes from individuals ascertained as part of multiancestral type 2 diabetes (T2D) case-control studies, and 38,566 exomes from individual volunteers in the UK Biobank (UKB)
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