The characterization of twenty sequenced human genomes.

Abstract

We present the analysis of twenty human genomes to evaluate the prospects for identifying rare functional variants that contribute to a phenotype of interest. We sequenced at high coverage ten "case" genomes from individuals with severe hemophilia A and ten "control" genomes. We summarize the number of genetic variants emerging from a study of this magnitude, and provide a proof of concept for the identification of rare and highly-penetrant functional variants by confirming that the cause of hemophilia A is easily recognizable in this data set. We also show that the number of novel single nucleotide variants (SNVs) discovered per genome seems to stabilize at about 144,000 new variants per genome, after the first 15 individuals have been sequenced. Finally, we find that, on average, each genome carries 165 homozygous protein-truncating or stop loss variants in genes representing a diverse set of pathways.

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Citation

Published Version (Please cite this version)

10.1371/journal.pgen.1001111

Publication Info

Pelak, Kimberly, Kevin V Shianna, Dongliang Ge, Jessica M Maia, Mingfu Zhu, Jason P Smith, Elizabeth T Cirulli, Jacques Fellay, et al. (2010). The characterization of twenty sequenced human genomes. PLoS Genet, 6(9). p. e1001111. 10.1371/journal.pgen.1001111 Retrieved from https://hdl.handle.net/10161/4478.

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Scholars@Duke

Singh

Abanish Singh

Assistant Professor in Psychiatry and Behavioral Sciences

With a unique skill set resulting from outstanding training, my sole aim was to help improve human health through cutting-edge translational research. Specifically, I have been interested in illuminating the mechanisms responsible for the causes and progression of the leading public health conditions, which may help with the development and enhancement of precision medicine.  As part of this endeavor, I also became interested in studying the measurement of biobehavioral risk factors and environmental stressors and their interactions with genes that may influence cardiovascular disease (CVD) risk factors and endophenotypes, adversely affecting the CVD pathways.

I joined medical research with my early research training on computational biology, high-throughput genomics, next-gen DNA sequencing, genome-wide studies, and big data analytics, which resulted in some of prominent findings on human genome (PMID: 18048317, PMID: 20223737, PMID: 20598109, PMID: 21703177). These findings included a significant contribution to the scientific community’s understanding that I made during my postdoctoral fellowship with Dr. David Goldstein at Duke Center for Human Genome Variation that how well RNA-Seq can identify human coding variants just using a small fraction of genome (transcriptome) as compared to whole genome (PMID: 20598109). This work was important not only scientifically, but also in pragmatic terms, given the high cost of sequencing.

In relatively recent work I discovered a novel CVD risk gene EBF1, where  a common genetic variant contributed to inter-individual differences in human central obesity, fasting blood glucose, diabetes, and CVD risk factors in the presence of chronic psychosocial stress (PMID: 25271088). This work demonstrated the genetic variant-specific significant path from chronic psychosocial stress to common carotid intimal–media thickness (CCIMT), a surrogate marker for atherosclerosis, via central obesity and fasting glucose. I also developed an algorithm to create a synthetic measure of stress using the proxy indicators of its components (PMID: 26202568).  Other more recent work has elucidated the race, sex, and age related differences in the EBF1 gene-by-stress interaction (PMID: 33077726), which suggests the need for careful evaluation of environmental measures in different ethnicities in cross-ethnic gene-by-stress interaction studies.

More recently, I have expanded my research interest in studying the genetic architecture of Alzheimer’s disease (AD) and the role of psychosocial stress in modifying the effect of genetic variants on the disease risks.


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