Identifying Crohn’s disease signal from variome analysis

Abstract

Background After years of concentrated research efforts, the exact cause of Crohn’s disease (CD) remains unknown. Its accurate diagnosis, however, helps in management and preventing the onset of disease. Genome-wide association studies have identified 241 CD loci, but these carry small log odds ratios and are thus diagnostically uninformative. Methods Here, we describe a machine learning method—AVA,Dx (Analysis of Variation for Association with Disease)—that uses exonic variants from whole exome or genome sequencing data to extract CD signal and predict CD status. Using the person-specific coding variation in genes from a panel of only 111 individuals, we built disease-prediction models informative of previously undiscovered disease genes. By additionally accounting for batch effects, we were able to accurately predict CD status for thousands of previously unseen individuals from other panels. Results AVA,Dx highlighted known CD genes including NOD2 and new potential CD genes. AVA,Dx identified 16% (at strict cutoff) of CD patients at 99% precision and 58% of the patients (at default cutoff) with 82% precision in over 3000 individuals from separately sequenced panels. Conclusions Larger training panels and additional features, including other types of genetic variants and environmental factors, e.g., human-associated microbiota, may improve model performance. However, the results presented here already position AVA,Dx as both an effective method for revealing pathogenesis pathways and as a CD risk analysis tool, which can improve clinical diagnostic time and accuracy. Links to the AVA,Dx Docker image and the BitBucket source code are at https://bromberglab.org/project/avadx/.

Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions

“…About 86.2% (50/58) of studies were published within the past 5 years (2015 and later). There were 23 studies[ 18 - 39 ] that focused on IBD diagnosis and risk prediction, 19 studies[ 40 - 58 ] which evaluated disease activity, and 17 studies[ 45 , 59 - 74 ] which predicted IBD clinical outcomes (response to therapy, colonic neoplasia, post-surgical complications, quality of life, IBD well-being and emotional content). There were 22 studies with combined IBD cohorts (CD and UC), 16 studies with UC patients only, 18 studies with CD only, and 5 pediatric IBD cohorts.…”

The variome concept: focus on CNVariome

“…CNVs affecting genes involved in the cell cycle pathway) are able to cause chromosome instability detectable by cytogenetic analysis [20, 24, 25]. Similar results have been reported in studies of variome at the sequence level in health and disease highlighting new genomic mechanisms for interindividual diversity achieved through the functional variation in molecular pathways [26, 27]. More importantly, it is suggested that such genomic variations contribute to intercellular functional diversity, as well [28–30].…”

An interpretable low-complexity machine learning framework for robust exome-based in-silico diagnosis of Crohn’s disease patients

“… a result reported from ( 11 ), b result reported from ( 26 ). CAGI results have been reported from ( 8 ).…”

“…Table 2 shows this comparison, with most results reported from ( 8 ). We also reported the performance of ( 11 ), which was the winner of the 2016 CD challenge, and from the recently published AVA,Dx ( 26 ). To reach this result, they used ML methods such as Naive Bayes or Random Forest along with inputed marker SNPs information from third-party GWAS studies to distinguish between CD cases and controls.…”