Biology subjects

Bodian, D. L.

Publications and source records attributed to Bodian, D. L..

3 recordsLinked to original sources

bioRxiv2017

Mendelian inheritance errors in whole genome sequenced trios are enriched in repeats and cluster within copy number losses

Trio-based whole genome sequencing (WGS) data can contribute significantly towards the development of quality control methods that can be applied to non-family WGS. Mendelian inheritance errors (MIEs) in parent-offspring trios are commonly attributed to erroneous sequencing calls, as the rate of true de novo mutations is extremely low compared to the incidence of MIEs. Here, we analyzed WGS data from 1,314 trios across diverse human populations with the goal of studying the characteristics of MIEs. We applied filters based on genotype call quality and observed that filtering has a greater impact on frequent MIEs. Our results indicate that MIEs are enriched in repeats and MIE density correlates with short interspersed nuclear elements (SINEs) density. We also observed clustered MIEs in regions overlapping large deletions. We created population-specific MIE profiles and discovered regions that represent different MIE distributions across populations. Finally, we have provided population-specific MIE tracks that can be loaded in UCSC Genome Browser. These profiles can be used for flagging calls in proximity of clustered MIEs before allele frequency and admixture calculations, annotating candidate de novo mutations, discovering population-specific putative deletions, and for distinguishing between regions that have errors due to sequence quality vs. chromosomal anomalies.

genomics↗

bioRxiv2017

Germline De Novo Mutation Clusters Arise During Oocyte Aging In Genomic Regions With Increased Double-Strand Break Incidence

Clustering of mutations has been found both in somatic mutations from cancer genomes and in germline de novo mutations (DNMs). We identified 1,755 clustered DNMs (cDNMs) within whole-genome sequencing data from 1,291 parent-offspring trios and investigated the underlying mutational mechanisms. We found that the number of clusters on the maternalallele was positively correlated with maternal age and that these consist of more individual mutations with larger intra-mutational distances compared to paternal clusters. More than 50% of maternal clusters were located on chromosomes 8, 9 and 16, in regions with an overall increased maternal mutation rate. Maternal clusters in these regions showed a distinct mutation signature characterized by C>G mutations. Finally, we found that maternal clusters associate with processes involving double-stranded-breaks (DSBs) such as meiotic gene conversions and de novo deletions events. These findings suggest accumulation of DSB-induced mutations throughout oocyte aging as an underlying mechanism leading to maternal mutation clusters.

genomics↗

bioRxiv2017

Moving Beyond Clinical Risk Scores with a Mobile App for the Genomic Risk of Coronary Artery Disease

Primary prevention of coronary artery disease (CAD) is important for individuals at increased risk, and largely consists of healthy lifestyle modifications and initiation of medications when appropriate - including statins. Defining the inherent risk for any given individual typically relies on traditional risk factors established decades ago by the Framingham Heart Study. Unfortunately, recent studies have indicated that these traditional clinical risk factors systematically overestimate the risk of CAD across all major ancestries. This has increased the number of patients that would be eligible for statin therapy for the primary prevention of CAD but would likely receive little benefit and potentially incur negative consequences. On the other hand, researchers have demonstrated that genetic factors can effectively identify a subset of high risk individuals, and that the benefit from statin therapy is greatest among individuals with the highest genetic risk score (GRS). These individuals also receive the greatest absolute benefit from healthy lifestyle choices, being able to titrate their risk to normal levels despite high genetic predisposition. However, it is not yet possible for the average individual to discover their genetic risk because no tools are currently available to make such a determination. Here, we present a free mobile app - MyGeneRank - that can provide this information. Individuals may choose to use this knowledge to complement traditional risk assessments, and make critical decisions regarding lifelong statin therapy and lifestyle changes. As of 1/25/2017, MyGeneRank is currently in closed beta and will soon be available to the public.

genomics↗