Biology subjects

Euan A Ashley

Publications and source records attributed to Euan A Ashley.

3 recordsLinked to original sources

bioRxiv2016

A precision metric for clinical genome sequencing

A requisite precondition for the application of next-generation sequencing to clinical medicine is the ability to confidently call genotype at each coding/splicing position of every gene of interest. Current gold standard technologies, such as Sanger sequencing and microarrays, allow confident identification of the genomic origin of the DNA of interest. A commonly used minimum standard for the adoption of new technology in medicine is non-inferiority. We developed a metric to quantify the extent to which current sequencing technologies reach this clinical grade reporting standard. This metric, the rationale for which we present here, is defined as the absolute number of base pairs per gene not callable with confidence, as specified by the presence of 20 high quality (Q20) bases from uniquely mapped (mapq>0) reads per locus. To illustrate the utility of this metric, we apply it across data from several commercially available clinical sequencing products. We present specific examples of coverage for genes known to be important for clinical medicine. We derive data from a variety of platforms including whole genome sequencing (Illumina Hiseq and X chemistry) and exome capture (including medically optimized capture from Agilent, Baylor Clinical Lab, and Personalis). We observe that compared to whole genomes (with {small tilde}30x average coverage), augmented exomes perform far better for known disease causing genes, but less well for other genes and in untranslated regions. Increasing whole genome coverage improves this discrepancy with an average coverage of {small tilde}45x representing the cross over point where performance equals that of exome capture for disease causing genes. A combination of some genome-wide coverage and augmented exon coverage may offer the most cost effective solution for clinical grade genome sequencing today. In summary, this coverage metric provides transparency regarding the current state of next-generation sequencing for clinical medicine and will inform genotype interpretation, technology improvement, and sequencing platform choices for physicians and laboratories. We provide an application on precision.fda.gov (Coverage of Key Genes app) to calculate this metric.

Genomics↗

bioRxiv2016

Multi-dimensional structure function relationships in human β-cardiac myosin from population scale genetic variation.

Myosin motors are the fundamental force-generating element of muscle contraction. Variation in the human {beta}-cardiac myosin gene (MYH7) can lead to hypertrophic cardiomyopathy (HCM), a heritable disease characterized by cardiac hypertrophy, heart failure, and sudden cardiac death. How specific myosin variants alter motor function or clinical expression of disease remains incompletely understood. Here, we combine structural models of myosin from multiple stages of its chemomechanical cycle, exome sequencing data from population cohorts of 60,706 and 42,930 individuals, and genetic and phenotypic data from 2,913 HCM patients to elucidate novel structure-function relationships within {beta}-cardiac myosin. We first developed computational models of the human {beta}-cardiac myosin protein before and after the myosin power stroke. Then, using a spatial scan statistic modified to analyze genetic variation in protein three-dimensional space, we found significant enrichment of disease-associated variants in the converter, a kinetic domain that transduces force from the catalytic domain to the lever arm to accomplish the power stroke. Focusing our analysis on surface-exposed residues, we identified another region enriched for disease-associated variants that contains both the converter domain and residues on a single flat surface on the myosin head described as a myosin mesa. This surface is prominent in the pre-stroke model, but substantially reduced in size following the power stroke. Notably, HCM patients with variants in the enriched regions have earlier presentation and worse outcome than those with variants in other regions. In summary, this study provides a model for the combination of protein structure, large-scale genetic sequencing and detailed phenotypic data to reveal insight into time-shifted protein structures and genetic disease.

Genomics↗

bioRxiv2016

A community overlap strategy reveals central genes and networks in heart failure

Heart failure is a leading cause of mortality, yet our understanding of the genetic interactions underlying this disease remains incomplete. Here, we harvested 1352 healthy and failing human hearts directly from transplant center operating rooms, and obtained genome-wide genotyping and gene expression measurements for a subset of 313. We built failing and non-failing cardiac regulatory gene networks, revealing important regulators and cardiac expression quantitative trait loci (eQTLs). PPP1R3A emerged as a novel regulator whose network connectivity changed significantly between health and disease. Time-course RNA sequencing after PPP1R3A knock-down validated network-based predictions of metabolic pathway expression, increased cardiomyocyte size, and perturbed respiratory metabolism. Mice lacking PPP1R3A were protected against pressure-overload heart failure. We present a global gene interaction map of the human heart failure transition, identify new cardiac eQTLs, and demonstrate the discovery potential of disease-specific networks through the description of PPP1R3A as a novel central protective regulator in heart failure.

Genomics↗