Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges

Abstract

Precision medicine aims to predict a patient’s disease risk and best therapeutic options by using that individual’s genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome sequencing data: bipolar disorder, Crohn’s disease, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn’s disease challenge. Here, we discuss the range of techniques used for phenotype prediction and discuss the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

“…This identified similar autoimmune comorbidities for both diseases. 28,29 Identifying and assessing autoimmune disease risk Prediction of disease risk [30][31][32][33][34][35][36][37][38][39] and identification of novel risk factors through feature selection [40][41][42][43][44] was documented for IBD, type 1 diabetes (T1D), RA, systemic lupus erythematosus (SLE) and MS. Fifteen studies employed genetic data, using either sequencing arrays (GWAS) or exome data (nine studies), individual SNPs 38 within in the HLA regions 37,45 or pre-selected genes, 41 or gene expression data. 30,43 Only one study employed clinical data, 31 and two others combined clinical and genomic data.…”

“…Two reviewers completed screening independently, and where consensus could not be reached, a third reviewer assessed these articles and decided whether they were included or excluded. [20][21][22]26,27,31,32,[40][41][42][46][47][48] [33][34][35][36]43,57,69,73,79,[83][84][85][86] 58,87,90,194,195 55,113,196,197 autoimmune disease risk. By far the most prevalent type of data is the use of clinical and laboratory data.…”

RheoScale: A tool to aggregate and quantify experimentally determined substitution outcomes for multiple variants at individual protein positions

“…Indeed, numerous computer algorithms have been developed to predict the impact of specific amino acid substitutions (e.g., Adzhubei et al., ; Bao, Zhou, & Cui, ; Bendl et al., ; Capriotti, Altman, & Bromberg, ; Capriotti, Calabrese, & Casadio, ; Capriotti, Fariselli, Calabrese, & Casadio, ; Choi, Sims, Murphy, Miller, & Chan, ; Hecht, Bromberg, & Rost, ; Mathe et al., ; Ng & Henikoff, ; Niroula, Urolagin, & Vihinen, ; Pejaver, Mooney, & Radivojac, ; Petukh, Dai, & Alexov, ; Petukh, Kucukkal, & Alexov, ; Ramensky, Bork, & Sunyaev, ; Reva, Antipin, & Sander, ; Schymkowitz et al., ; Stone & Sidow, ; Tang & Thomas, ). However, several studies have noted the need to improve predictions (e.g., Dong et al., ; Gray, Kukurba, & Kumar, ; Miller, Bromberg, & Swint‐Kruse, ), and this is a key goal of CAGI (the ongoing “Critical Assessment of Genome Interpretation”) (e.g., Daneshjou et al., ).…”

Reports from CAGI: The Critical Assessment of Genome Interpretation

“…For single base variants, there are challenges that address the problem of interpreting the impact of missense mutations on protein activity using a variety of molecular and cellular phenotypes, challenges that test the ability to predict the effect of mutations in cancer driver genes on cell growth, and challenges on the effect of single‐base variants on RNA expression levels and splicing (including Beer, ; Capriotti, Martelli, Fariselli, & Casadio, ; Carraro et al., ; Katsonis & Lichtarge, ; Kreimer et al., ; Niroula & Vihinen ; Pejaver et al., ; Tang et al., 2017; Tang & Fenton, ; Xu et al., ; Yin et al., ; Zeng, Edwards, Guo, & Gifford, ; Zhang et al., ). At the level of full exome and genome sequence, there are challenges that assess methods for assigning complex traits phenotypes and that evaluate the ability to associate genome sequence and an extensive profile of phenotypic traits (including Cai et al., 2017; Daneshjou et al., ; Daneshjou et al., ; Giollo et al., ; Laksshman, Bhat, Viswanath, & Li, ; Pal, Kundu, Yin, & Moult, ; Wang et al., ). CAGI has also included challenges in which participants were asked to identify causative variants for rare diseases in gene panel, exome, and whole‐genome sequence data (including Chandonia et al., ; Kundu, Pal, Yin, & Moult, ; Pal, Kundu, Yin, & Moult, ).…”