@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 {101, title = {Transcriptomic signatures across human tissues identify functional rare genetic variation.}, journal = {Science}, volume = {369}, year = {2020}, month = {2020 09 11}, abstract = {

Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.

}, keywords = {Genetic Variation, Genome, Human, Humans, Multifactorial Inheritance, Organ Specificity, Transcriptome}, issn = {1095-9203}, doi = {10.1126/science.aaz5900}, author = {Ferraro, Nicole M and Strober, Benjamin J and Einson, Jonah and Abell, Nathan S and Aguet, Fran{\c c}ois and Barbeira, Alvaro N and Brandt, Margot and Bucan, Maja and Castel, Stephane E and Davis, Joe R and Greenwald, Emily and Hess, Gaelen T and Hilliard, Austin T and Kember, Rachel L and Kotis, Bence and Park, YoSon and Peloso, Gina and Ramdas, Shweta and Scott, Alexandra J and Smail, Craig and Tsang, Emily K and Zekavat, Seyedeh M and Ziosi, Marcello and Ardlie, Kristin G and Assimes, Themistocles L and Bassik, Michael C and Brown, Christopher D and Correa, Adolfo and Hall, Ira and Im, Hae Kyung and Li, Xin and Natarajan, Pradeep and Lappalainen, Tuuli and Mohammadi, Pejman and Montgomery, Stephen B and Battle, Alexis} } @article {64, title = {Genomic Analysis in the Age of Human Genome Sequencing.}, journal = {Cell}, volume = {177}, year = {2019}, month = {2019 Mar 21}, pages = {70-84}, abstract = {

Affordable genome sequencing technologies promise to revolutionize the field of human genetics by enabling comprehensive studies that interrogate all classes of genome variation, genome-wide, across the entire allele frequency spectrum. Ongoing projects worldwide are sequencing many thousands-and soon millions-of human genomes as part of various gene mapping studies, biobanking efforts, and clinical programs. However, while genome sequencing data production has become routine, genome analysis and interpretation remain challenging endeavors with many limitations and caveats. Here, we review the current state of technologies for genetic variant discovery, genotyping, and functional interpretation and discuss the prospects for future advances. We focus on germline variants discovered by whole-genome sequencing, genome-wide functional genomic approaches for predicting and measuring variant functional effects, and implications for studies of common and rare human disease.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2019.02.032}, author = {Lappalainen, Tuuli and Scott, Alexandra J and Brandt, Margot 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} }