Mannakee, B. K. | bioRxiv Science

bioRxiv2019

BATCAVE: Calling somatic mutations with a tumor- and site-specific prior

Detecting somatic mutations withins tumors is key to understanding treatment resistance, patient prognosis, and tumor evolution. Mutations at low allelic frequency, those present in only a small portion of tumor cells, are particularly difficult to detect. Many algorithms have been developed to detect such mutations, but none models a key aspect of tumor biology. Namely, every tumor has its own profile of mutation types that it tends to generate. We present BATCAVE (Bayesian Analysis Tools for Context-Aware Variant Evaluation), an algorithm that first learns the individual tumor mutational profile and mutation rate then uses them in a prior for evaluating potential mutations. We also present an R implementation of the algorithm, built on the popular caller MuTect. Using simulations, we show that adding the BATCAVE algorithm to MuTect improves variant detection. It also improves the calibration of posterior probabilities, enabling more principled tradeoff between precision and recall. We also show that BATCAVE performs well on real data. Our implementation is computationally inexpensive and straightforward to incorporate into existing MuTect pipelines. More broadly, the algorithm can be added to other variant callers, and it can be extended to include additional biological features that affect mutation generation.

bioinformatics↗

bioRxiv2017

How much research is spurred when a gene is associated with a complex disease?

The past decade has seen major investment in genome-wide association studies (GWAS), with the goal of identifying and motivating research on novel genes involved in complex human disease. To assess whether this goal is being met, we quantified the effect of GWAS on the overall distribution of biomedical research publications and on the subsequent publication history of genes newly associated with complex disease. We found that the historical skew of publications toward genes involved in Mendelian disease has not changed since the advent of GWAS. Genes newly implicated by GWAS in complex disease do experience additional publications compared to control genes, and they are more likely to become exceptionally studied. But the magnitude of both effects has declined dramatically over the past decade. Our results suggest that reforms to encourage follow-up studies may be needed for GWAS to most successfully guide biomedical research toward the molecular mechanisms underlying complex human disease.\n\nAuthor summaryOver the past decade, thousands of genome-wide association studies (GWAS) have been performed to link genetic variation with complex human disease. A major goal of such studies is to identify novel disease genes, so they can be further studied. We tested whether this goal is being met, by studying patterns of scientific research publications on human genes. We found that publications are still concentrated on genes involved in simple Mendelian disease, even after the advent of GWAS. Compared to other genes, disease genes discovered by GWAS do experience additional publications, but that effect has declined dramatically since GWAS were first performed. Our results suggest that the ability of GWAS to stimulate research into novel disease genes is declining. To realize the full potential of GWAS to reveal the molecular mechanisms driving human disease, this decline and the reasons for it must be understood, so that it can be reversed.

genetics↗