@article {151, title = {Association of structural variation with cardiometabolic traits in Finns.}, journal = {Am J Hum Genet}, volume = {108}, year = {2021}, month = {2021 04 01}, pages = {583-596}, abstract = {

The contribution of genome structural variation (SV) to quantitative traits associated with cardiometabolic diseases remains largely unknown. Here, we present the results of a study examining genetic association between SVs and cardiometabolic traits in the Finnish population. We used sensitive methods to identify and genotype 129,166 high-confidence SVs from deep whole-genome sequencing (WGS) data of 4,848 individuals. We tested the 64,572 common and low-frequency SVs for association with 116 quantitative traits and tested candidate associations using exome sequencing and array genotype data from an additional 15,205 individuals. We discovered 31 genome-wide significant associations at 15 loci, including 2 loci at which SVs have strong phenotypic effects: (1) a deletion of the ALB promoter that is greatly enriched in the Finnish population and causes decreased serum albumin level in carriers (p = 1.47~{\texttimes} 10) and is also associated with increased levels of total cholesterol (p = 1.22~{\texttimes} 10) and 14 additional cholesterol-related traits, and (2) a multi-allelic copy number variant (CNV) at PDPR that is strongly associated with pyruvate (p = 4.81~{\texttimes} 10) and alanine (p = 6.14~{\texttimes} 10) levels and resides within a structurally complex genomic region that has accumulated many rearrangements over evolutionary time. We also confirmed six previously reported associations, including five led by stronger signals in single nucleotide variants (SNVs) and one linking recurrent HP gene deletion and cholesterol levels (p = 6.24~{\texttimes} 10), which was also found to be strongly associated with increased glycoprotein level (p = 3.53~{\texttimes} 10). Our study confirms that integrating SVs in trait-mapping studies will expand our knowledge of genetic factors underlying disease risk.

}, keywords = {Alleles, Cardiovascular Diseases, Cholesterol, DNA Copy Number Variations, Female, Finland, Genome, Human, Genomic Structural Variation, Genotype, High-Throughput Nucleotide Sequencing, Humans, Male, Mitochondrial Proteins, Promoter Regions, Genetic, Pyruvate Dehydrogenase (Lipoamide)-Phosphatase, Pyruvic Acid, Serum Albumin, Human}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2021.03.008}, author = {Chen, Lei and Abel, Haley J and Das, Indraniel and Larson, David E and Ganel, Liron and Kanchi, Krishna L and Regier, Allison A and Young, Erica P and Kang, Chul Joo and Scott, Alexandra J and Chiang, Colby and Wang, Xinxin and Lu, Shuangjia and Christ, Ryan and Service, Susan K and Chiang, Charleston W K and Havulinna, Aki S and Kuusisto, Johanna and Boehnke, Michael and Laakso, Markku and Palotie, Aarno and Ripatti, Samuli and Freimer, Nelson B and Locke, Adam E and Stitziel, Nathan O and Hall, Ira M} } @article {16, title = {The impact of structural variation on human gene expression.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 May}, pages = {692-699}, abstract = {

Structural variants (SVs) are an important source of human genetic diversity, but their contribution to traits, disease and gene regulation remains unclear. We mapped cis expression quantitative trait loci (eQTLs) in 13 tissues via joint analysis of SVs, single-nucleotide variants (SNVs) and short insertion/deletion (indel) variants from deep whole-genome sequencing (WGS). We estimated that SVs are causal at 3.5-6.8\% of eQTLs-a substantially higher fraction than prior estimates-and that expression-altering SVs have larger effect sizes than do SNVs and indels. We identified 789 putative causal SVs predicted to directly alter gene expression: most (88.3\%) were noncoding variants enriched at enhancers and other regulatory elements, and 52 were linked to genome-wide association study loci. We observed a notable abundance of rare high-impact SVs associated with aberrant expression of nearby genes. These results suggest that comprehensive WGS-based SV analyses will increase the power of common- and rare-variant association studies.

}, keywords = {Algorithms, Chromosome Mapping, Gene Expression Regulation, Genetic Variation, Genome, Human, Genome-Wide Association Study, Humans, INDEL Mutation, Linear Models, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Sequence Analysis, DNA}, issn = {1546-1718}, doi = {10.1038/ng.3834}, author = {Chiang, Colby and Scott, Alexandra J and Davis, Joe R and Tsang, Emily K and Li, Xin and Kim, Yungil and Hadzic, Tarik and Damani, Farhan N and Ganel, Liron and Montgomery, Stephen B and Battle, Alexis and Conrad, Donald F and Hall, Ira M} } @article {20, title = {SVScore: an impact prediction tool for structural variation.}, journal = {Bioinformatics}, volume = {33}, year = {2017}, month = {2017 Apr 01}, pages = {1083-1085}, abstract = {

SUMMARY: Here we present SVScore, a tool for in silico structural variation (SV) impact prediction. SVScore aggregates per-base single nucleotide polymorphism (SNP) pathogenicity scores across relevant genomic intervals for each SV in a manner that considers variant type, gene features and positional uncertainty. We show that the allele frequency spectrum of high-scoring SVs is strongly skewed toward lower frequencies, suggesting that they are under purifying selection, and that SVScore identifies deleterious variants more effectively than alternative methods. Notably, our results also suggest that duplications are under surprisingly strong selection relative to deletions, and that there are a similar number of strongly pathogenic SVs and SNPs in the human population.

AVAILABILITY AND IMPLEMENTATION: SVScore is implemented in Perl and available freely at {{ http://www.github.com/lganel/SVScore }} for use under the MIT license.

CONTACT: ihall@wustl.edu.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, keywords = {Gene Frequency, Genomic Structural Variation, Genomics, Humans, Polymorphism, Single Nucleotide, Sequence Deletion, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btw789}, author = {Ganel, Liron and Abel, Haley J and Hall, Ira M} }