Prospective functional classification of all possible missense variants in PPARG

Abstract

Clinical exome sequencing routinely identifies missense variants in disease-related genes, but functional characterization is rarely undertaken, leading to diagnostic uncertainty1,2. For example, mutations in PPARG cause Mendelian lipodystrophy3,4 and increase risk of type 2 diabetes (T2D)5. While approximately one in 500 people harbor missense variants in PPARG, most are of unknown consequence. To prospectively characterize PPARγ variants we used highly parallel oligonucleotide synthesis to construct a library encoding all 9,595 possible single amino acid substitutions. We developed a pooled functional assay in human macrophages, experimentally evaluated all protein variants, and used the experimental data to train a variant classifier by supervised machine learning (http://miter.broadinstitute.org). When applied to 55 novel missense variants identified in population-based and clinical sequencing, the classifier annotated six as pathogenic; these were subsequently validated by single-variant assays. Saturation mutagenesis and prospective experimental characterization can support immediate diagnostic interpretation of newly discovered missense variants in disease-related genes.

Genetic association study of exfoliation syndrome identifies a protective rare variant at LOXL1 and five new susceptibility loci

“…We observed a broad enrichment of rare LOXL1 non-synonymous variants in the normal controls compared to the XFS patients (OR = 0.46, P = 4.2 x 10 -7 ; Table 1). As the vast majority of non-synonymous variants do not exert functional effects [31][32][33][34] , we performed a second test restricting the analysis to aggregate only rare, non-synonymous variants conservatively predicted to be deleterious by all five functional effect prediction algorithms (SIFT, Polyphen 2-HumDiv, LRT score, MutationTaster, and Condel) 33 . In so doing, we observed a substantially larger protective effect size conferred by rare variant burden (OR = 0.18, P = 4.23 x 10 -11 ; Table 2).…”

Variant Interpretation: Functional Assays to the Rescue

“…For example, a PPARg-specific MAVE was used to discriminate between pathogenic lipodystrophy variants and high-frequency, most likely benign variants (FPLD3 [MIM: 604367]). 21 Stimulation with PPARg agonists leads to enhanced uptake of oxidized low-density lipoprotein via transcriptional induction of CD36 expression. 34 The effect of all possible PPARg SNVs on the expression of CD36 in response to multiple agonists was measured in macrophages.…”

“…For example, PPARg MAVE data were used for training a classifier that maximized discrimination between a set of pathogenic lipodystrophy variants and a set of highfrequency, most likely benign variants. 21 The classifier was then used for predicting the probability of variant pathogenicity for 42 rare, previously unseen PPARg variants. The probability of pathogenicity, determined from the MAVE data, was combined with lipodystrophy prevalence for estimation of a pathogenicity odds ratio for each variant.…”

“…For example, measurement of the effect of nearly all possible missense variants of the RING domain of BRCA1 and the full open reading frame of PPARG (MIM: 601487) generated functional data that were used for accurately predicting pathogenicity. 20,21 Assessment of tens of thousands of eQTLadjacent, and therefore possibly functional, transcriptional regulatory variants revealed a few hundred that influenced expression. 22 These examples illustrate how the rich and comprehensive datasets that MAVEs produce can generate predictions that are much more accurate than those of currently available variant-effect-prediction algorithms.…”

A framework for exhaustively mapping functional missense variants

“…Recent DMS studies, for example, covered the critical RING domain of BRCA1 (Starita et al , 2015) associated with breast cancer risk, and the PPARG protein associated with Mendelian lipodystrophy and increased risk of type 2 diabetes (Majithia et al , 2016). Such maps can accurately identify functionality of a clinical variant in advance of that variant's first clinical presentation.…”