Endophenotype effect sizes support variant pathogenicity in monogenic disease susceptibility genes.
Jennifer L HalfordValerie N MorrillSeung Hoan ChoiSean Joseph JurgensGiorgio MelloniNicholas A MarstonLu-Chen WangVictor NauffalAmelia W HallSophia GunnChristina A Austin-TseJames Paul PirruccelloShaan KhurshidHeidi L RehmEmelia J BenjaminEric BoerwinkleJennifer A BrodyAdolfo CorreaBrandon K FornwaltNamrata GuptaChristopher M HaggertyStephanie HarrisSusan R HeckbertCharles C HongCharles KooperbergHenry J LinRuth J F LoosBraxton D MitchellAlanna C MorrisonWendy PostBruce M PsatySusan RedlineKenneth M RiceStephen S RichJerome I RotterPeter F SchnatzElsayed Z SolimanNona SotoodehniaEugene K Wongnull nullMarc S SabatineChristian T RuffKathryn L LunettaPatrick T EllinorSteven A LubitzPublished in: Nature communications (2022)
Accurate and efficient classification of variant pathogenicity is critical for research and clinical care. Using data from three large studies, we demonstrate that population-based associations between rare variants and quantitative endophenotypes for three monogenic diseases (low-density-lipoprotein cholesterol for familial hypercholesterolemia, electrocardiographic QTc interval for long QT syndrome, and glycosylated hemoglobin for maturity-onset diabetes of the young) provide evidence for variant pathogenicity. Effect sizes are associated with pathogenic ClinVar assertions (P < 0.001 for each trait) and discriminate pathogenic from non-pathogenic variants (area under the curve 0.82-0.84 across endophenotypes). An effect size threshold of ≥ 0.5 times the endophenotype standard deviation nominates up to 35% of rare variants of uncertain significance or not in ClinVar in disease susceptibility genes with pathogenic potential. We propose that variant associations with quantitative endophenotypes for monogenic diseases can provide evidence supporting pathogenicity.
Keyphrases
- copy number
- genome wide
- biofilm formation
- type diabetes
- high resolution
- cardiovascular disease
- palliative care
- gene expression
- heart failure
- deep learning
- electronic health record
- escherichia coli
- pseudomonas aeruginosa
- skeletal muscle
- glycemic control
- climate change
- mitral valve
- chronic pain
- left atrial
- affordable care act
- insulin resistance
- middle aged
- data analysis
- human health