Determination of disease phenotypes and pathogenic variants from exome sequence data in the CAGI 4 gene panel challenge

Abstract

The use of gene panel sequence for diagnostic and prognostic testing is now widespread, but there are so far few objective tests of methods to interpret these data. We describe the design and implementation of a gene panel sequencing data analysis pipeline (VarP) and its assessment in a CAGI4 community experiment. The method was applied to clinical gene panel sequencing data of 106 patients, with the goal of determining which of 14 disease classes each patient has and the corresponding causative variant(s). The disease class was correctly identified for 36 cases, including 10 where the original clinical pipeline did not find causative variants. For a further seven cases, we found strong evidence of an alternative disease to that tested. Many of the potentially causative variants are missense, with no previous association with disease, and these proved the hardest to correctly assign pathogenicity or otherwise. Post analysis showed that three-dimensional structure data could have helped for up to half of these cases. Over-reliance on HGMD annotation led to a number of incorrect disease assignments. We used a largely ad hoc method to assign probabilities of pathogenicity for each variant, and there is much work still to be done in this area.

Reduced penetrance of pathogenic ACMG variants in a deeply phenotyped cohort study and evaluation of ClinVar classification over time

“…These most likely stem from overestimation of pathogenicity of HGMD, as has been described before. 22,23 This disagreement illustrates the challenge of clinically interpreting genetic variants, especially in a research setting, and how different individuals, laboratories, or databases might reach different conclusions for the same variant. Even when restricting to variants classified as class 5 in both databases, it appears that such variants can be carried without obvious phenotypic consequence.…”

Reports from CAGI: The Critical Assessment of Genome Interpretation

“…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, ). Many challenges have focused on cancer, given its prevalence and the impact of genetics.…”

Clinically Responsive Genomic Analysis Pipelines

“…The extraction of a gene panel from exome sequencing data reduces analysis time and the chance of identifying secondary findings. Others have published issues with their gene panel pipeline; for example, Kundu et al 33 state that overreliance on Human Gene Mutation Database annotation led to missed diagnoses. It is essential that the panel gene nomenclature is recognized by the analytical pipeline, and GAIA's HGNC gene nomenclature checker assesses this systematically.…”