@article {122,
	title = {Mapping and characterization of structural variation in 17,795 human genomes.},
	journal = {Nature},
	volume = {583},
	year = {2020},
	month = {2020 07},
	pages = {83-89},
	abstract = {<p>A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0-11.2\% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2\% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90\% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2\% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing.</p>},
	keywords = {Alleles, Case-Control Studies, Continental Population Groups, Epigenesis, Genetic, Female, Gene Dosage, Genetic Variation, Genetics, Population, Genome, Human, High-Throughput Nucleotide Sequencing, Humans, Male, Molecular Sequence Annotation, Quantitative Trait Loci, Software, Whole Genome Sequencing},
	issn = {1476-4687},
	doi = {10.1038/s41586-020-2371-0},
	author = {Abel, Haley J and Larson, David E and Regier, Allison A and Chiang, Colby and Das, Indraniel and Kanchi, Krishna L and Layer, Ryan M and Neale, Benjamin M and Salerno, William J and Reeves, Catherine and Buyske, Steven and Matise, Tara C and Muzny, Donna M and Zody, Michael C and Lander, Eric S and Dutcher, Susan K and Stitziel, Nathan O and Hall, Ira M}
}
@article {29,
	title = {Genetic association studies in cardiovascular diseases: Do we have enough power?},
	journal = {Trends Cardiovasc Med},
	volume = {27},
	year = {2017},
	month = {2017 Aug},
	pages = {397-404},
	abstract = {<p>Genetic association studies have a long history of delivering insightful results for cardiovascular disease (CVD) research. Beginning with early candidate gene studies, to genome-wide association studies, and now on to newer whole-genome sequencing studies, research in human genetics has enriched our understanding of the pathobiology of CVD. As these studies continue to expand, the issue of statistical power plays an important role in study design as well as the interpretation of results. We provide an overview of the component parts that determine statistical power and preview the future of CVD genetic association studies through this lens.</p>},
	keywords = {Cardiovascular Diseases, Data Accuracy, Data Interpretation, Statistical, Genetic Association Studies, Genetic Markers, Genetic Predisposition to Disease, Genetic Variation, Humans, Phenotype, Reproducibility of Results, Research Design, Risk Assessment, Risk Factors},
	issn = {1873-2615},
	doi = {10.1016/j.tcm.2017.03.005},
	author = {Auer, Paul L and Stitziel, Nathan O}
}
