@article {152, title = {Genetic and non-genetic factors affecting the expression of COVID-19-relevant genes in the large airway epithelium.}, journal = {Genome Med}, volume = {13}, year = {2021}, month = {2021 04 21}, pages = {66}, abstract = {

BACKGROUND: The large airway epithelial barrier provides one of the first lines of defense against respiratory viruses, including SARS-CoV-2 that causes COVID-19. Substantial inter-individual variability in individual disease courses is hypothesized to be partially mediated by the differential regulation of the genes that interact with the SARS-CoV-2 virus or are involved in the subsequent host response. Here, we comprehensively investigated non-genetic and genetic factors influencing COVID-19-relevant bronchial epithelial gene expression.

METHODS: We analyzed RNA-sequencing data from bronchial epithelial brushings obtained from uninfected individuals. We related ACE2 gene expression to host and environmental factors in the SPIROMICS cohort of smokers with and without chronic obstructive pulmonary disease (COPD) and replicated these associations in two asthma cohorts, SARP and MAST. To identify airway biology beyond ACE2 binding that may contribute to increased susceptibility, we used gene set enrichment analyses to determine if gene expression changes indicative of a suppressed airway immune response observed early in SARS-CoV-2 infection are also observed in association with host factors. To identify host genetic variants affecting COVID-19 susceptibility in SPIROMICS, we performed expression quantitative trait (eQTL) mapping and investigated the phenotypic associations of the eQTL variants.

RESULTS: We found that ACE2 expression was higher in relation to active smoking, obesity, and hypertension that are known risk factors of COVID-19 severity, while an association with interferon-related inflammation was driven by the truncated, non-binding ACE2 isoform. We discovered that expression patterns of a suppressed airway immune response to early SARS-CoV-2 infection, compared to other viruses, are similar to patterns associated with obesity, hypertension, and cardiovascular disease, which may thus contribute to a COVID-19-susceptible airway environment. eQTL mapping identified regulatory variants for genes implicated in COVID-19, some of which had pheWAS evidence for their potential role in respiratory infections.

CONCLUSIONS: These data provide evidence that clinically relevant variation in the expression of COVID-19-related genes is associated with host factors, environmental exposures, and likely host genetic variation.

}, keywords = {Adult, Aged, Aged, 80 and over, Angiotensin-Converting Enzyme 2, Asthma, Bronchi, Cardiovascular Diseases, COVID-19, Gene Expression, Genetic Variation, Humans, Middle Aged, Obesity, Pulmonary Disease, Chronic Obstructive, Quantitative Trait Loci, Respiratory Mucosa, Risk Factors, SARS-CoV-2, Smoking}, issn = {1756-994X}, doi = {10.1186/s13073-021-00866-2}, author = {Kasela, Silva and Ortega, Victor E and Martorella, Molly and Garudadri, Suresh and Nguyen, Jenna and Ampleford, Elizabeth and Pasanen, Anu and Nerella, Srilaxmi and Buschur, Kristina L and Barjaktarevic, Igor Z and Barr, R Graham and Bleecker, Eugene R and Bowler, Russell P and Comellas, Alejandro P and Cooper, Christopher B and Couper, David J and Criner, Gerard J and Curtis, Jeffrey L and Han, MeiLan K and Hansel, Nadia N and Hoffman, Eric A and Kaner, Robert J and Krishnan, Jerry A and Martinez, Fernando J and McDonald, Merry-Lynn N and Meyers, Deborah A and Paine, Robert and Peters, Stephen P and Castro, Mario and Denlinger, Loren C and Erzurum, Serpil C and Fahy, John V and Israel, Elliot and Jarjour, Nizar N and Levy, Bruce D and Li, Xingnan and Moore, Wendy C and Wenzel, Sally E and Zein, Joe and Langelier, Charles and Woodruff, Prescott G and Lappalainen, Tuuli and Christenson, Stephanie A} } @article {156, title = {Haplotype-resolved diverse human genomes and integrated analysis of structural variation.}, journal = {Science}, volume = {372}, year = {2021}, month = {2021 04 02}, abstract = {

Long-read and strand-specific sequencing technologies together facilitate the de novo assembly of high-quality haplotype-resolved human genomes without parent-child trio data. We present 64 assembled haplotypes from 32 diverse human genomes. These highly contiguous haplotype assemblies (average minimum contig length needed to cover 50\% of the genome: 26 million base pairs) integrate all forms of genetic variation, even across complex loci. We identified 107,590 structural variants (SVs), of which 68\% were not discovered with short-read sequencing, and 278 SV hotspots (spanning megabases of gene-rich sequence). We characterized 130 of the most active mobile element source elements and found that 63\% of all SVs arise through homology-mediated mechanisms. This resource enables reliable graph-based genotyping from short reads of up to 50,340 SVs, resulting in the identification of 1526 expression quantitative trait loci as well as SV candidates for adaptive selection within the human population.

}, keywords = {Female, Genetic Variation, Genome, Human, Genotype, Haplotypes, High-Throughput Nucleotide Sequencing, Humans, INDEL Mutation, Interspersed Repetitive Sequences, Male, Population Groups, Quantitative Trait Loci, Retroelements, Sequence Analysis, DNA, Sequence Inversion, Whole Genome Sequencing}, issn = {1095-9203}, doi = {10.1126/science.abf7117}, author = {Ebert, Peter and Audano, Peter A and Zhu, Qihui and Rodriguez-Martin, Bernardo and Porubsky, David and Bonder, Marc Jan and Sulovari, Arvis and Ebler, Jana and Zhou, Weichen and Serra Mari, Rebecca and Yilmaz, Feyza and Zhao, Xuefang and Hsieh, PingHsun and Lee, Joyce and Kumar, Sushant and Lin, Jiadong and Rausch, Tobias and Chen, Yu and Ren, Jingwen and Santamarina, Martin and H{\"o}ps, Wolfram and Ashraf, Hufsah and Chuang, Nelson T and Yang, Xiaofei and Munson, Katherine M and Lewis, Alexandra P and Fairley, Susan and Tallon, Luke J and Clarke, Wayne E and Basile, Anna O and Byrska-Bishop, Marta and Corvelo, Andr{\'e} and Evani, Uday S and Lu, Tsung-Yu and Chaisson, Mark J P and Chen, Junjie and Li, Chong and Brand, Harrison and Wenger, Aaron M and Ghareghani, Maryam and Harvey, William T and Raeder, Benjamin and Hasenfeld, Patrick and Regier, Allison A and Abel, Haley J and Hall, Ira M and Flicek, Paul and Stegle, Oliver and Gerstein, Mark B and Tubio, Jose M C and Mu, Zepeng and Li, Yang I and Shi, Xinghua and Hastie, Alex R and Ye, Kai and Chong, Zechen and Sanders, Ashley D and Zody, Michael C and Talkowski, Michael E and Mills, Ryan E and Devine, Scott E and Lee, Charles and Korbel, Jan O and Marschall, Tobias and Eichler, Evan E} } @article {99, title = {Cell type-specific genetic regulation of gene expression across human tissues.}, journal = {Science}, volume = {369}, year = {2020}, month = {2020 09 11}, abstract = {

The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci in cis (QTLs) for the majority of genes across a wide range of human tissues. However, the functional characterization of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. We mapped interactions between computational estimates of cell type abundance and genotype to identify cell type-interaction QTLs for seven cell types and show that cell type-interaction expression QTLs (eQTLs) provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations with 87 complex traits show a contribution from cell type-interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.

}, keywords = {Cells, Gene Expression Regulation, Humans, Organ Specificity, Quantitative Trait Loci, RNA, Long Noncoding, Transcriptome}, issn = {1095-9203}, doi = {10.1126/science.aaz8528}, author = {Kim-Hellmuth, Sarah and Aguet, Fran{\c c}ois and Oliva, Meritxell and Mu{\~n}oz-Aguirre, Manuel and Kasela, Silva and Wucher, Valentin and Castel, Stephane E and Hamel, Andrew R and Vi{\~n}uela, Ana and Roberts, Amy L and Mangul, Serghei and Wen, Xiaoquan and Wang, Gao and Barbeira, Alvaro N and Garrido-Mart{\'\i}n, Diego and Nadel, Brian B and Zou, Yuxin and Bonazzola, Rodrigo and Quan, Jie and Brown, Andrew and Martinez-Perez, Angel and Soria, Jos{\'e} Manuel and Getz, Gad and Dermitzakis, Emmanouil T and Small, Kerrin S and Stephens, Matthew and Xi, Hualin S and Im, Hae Kyung and Guigo, Roderic and Segr{\`e}, Ayellet V and Stranger, Barbara E and Ardlie, Kristin G and Lappalainen, Tuuli} } @article {97, title = {The GTEx Consortium atlas of genetic regulatory effects across human tissues.}, journal = {Science}, volume = {369}, year = {2020}, month = {2020 09 11}, pages = {1318-1330}, abstract = {

The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

}, keywords = {Datasets as Topic, Disease, Female, Gene Expression Regulation, Genome-Wide Association Study, Humans, Male, Organ Specificity, Quantitative Trait Loci, Sequence Analysis, RNA}, issn = {1095-9203}, doi = {10.1126/science.aaz1776} } @article {102, title = {The impact of sex on gene expression across human tissues.}, journal = {Science}, volume = {369}, year = {2020}, month = {2020 09 11}, abstract = {

Many complex human phenotypes exhibit sex-differentiated characteristics. However, the molecular mechanisms underlying these differences remain largely unknown. We generated a catalog of sex differences in gene expression and in the genetic regulation of gene expression across 44 human tissue sources surveyed by the Genotype-Tissue Expression project (GTEx, v8 release). We demonstrate that sex influences gene expression levels and cellular composition of tissue samples across the human body. A total of 37\% of all genes exhibit sex-biased expression in at least one tissue. We identify cis expression quantitative trait loci (eQTLs) with sex-differentiated effects and characterize their cellular origin. By integrating sex-biased eQTLs with genome-wide association study data, we identify 58 gene-trait associations that are driven by genetic regulation of gene expression in a single sex. These findings provide an extensive characterization of sex differences in the human transcriptome and its genetic regulation.

}, keywords = {Chromosomes, Human, X, Disease, Epigenesis, Genetic, Female, Gene Expression, Gene Expression Regulation, Genetic Variation, Genome-Wide Association Study, Humans, Male, Organ Specificity, Promoter Regions, Genetic, Quantitative Trait Loci, Sex Characteristics, Sex Factors}, issn = {1095-9203}, doi = {10.1126/science.aba3066}, author = {Oliva, Meritxell and Mu{\~n}oz-Aguirre, Manuel and Kim-Hellmuth, Sarah and Wucher, Valentin and Gewirtz, Ariel D H and Cotter, Daniel J and Parsana, Princy and Kasela, Silva and Balliu, Brunilda and Vi{\~n}uela, Ana and Castel, Stephane E and Mohammadi, Pejman and Aguet, Fran{\c c}ois and Zou, Yuxin and Khramtsova, Ekaterina A and Skol, Andrew D and Garrido-Mart{\'\i}n, Diego and Reverter, Ferran and Brown, Andrew and Evans, Patrick and Gamazon, Eric R and Payne, Anthony and Bonazzola, Rodrigo and Barbeira, Alvaro N and Hamel, Andrew R and Martinez-Perez, Angel and Soria, Jos{\'e} Manuel and Pierce, Brandon L and Stephens, Matthew and Eskin, Eleazar and Dermitzakis, Emmanouil T and Segr{\`e}, Ayellet V and Im, Hae Kyung and Engelhardt, Barbara E and Ardlie, Kristin G and Montgomery, Stephen B and Battle, Alexis J and Lappalainen, Tuuli and Guigo, Roderic and Stranger, Barbara E} } @article {112, title = {Major Impacts of Widespread Structural Variation on Gene Expression and Crop Improvement in Tomato.}, journal = {Cell}, volume = {182}, year = {2020}, month = {2020 07 09}, pages = {145-161.e23}, abstract = {

Structural variants (SVs) underlie important crop improvement and domestication traits. However, resolving the extent, diversity, and quantitative impact of SVs has been challenging. We used long-read nanopore sequencing to capture 238,490 SVs in 100 diverse tomato lines. This panSV genome, along with 14 new reference assemblies, revealed large-scale intermixing of diverse genotypes, as well as thousands of SVs intersecting genes and cis-regulatory regions. Hundreds of SV-gene pairs exhibit subtle and significant expression changes, which could broadly influence quantitative trait variation. By combining quantitative genetics with genome editing, we show how multiple SVs that changed gene dosage and expression levels modified fruit flavor, size, and production. In the last example, higher order epistasis among four SVs affecting three related transcription factors allowed introduction of an important harvesting trait in modern tomato. Our findings highlight the underexplored role of SVs in genotype-to-phenotype relationships and their widespread importance and utility in crop improvement.

}, keywords = {Alleles, Crops, Agricultural, Cytochrome P-450 Enzyme System, Ecotype, Epistasis, Genetic, Fruit, Gene Duplication, Gene Expression Regulation, Plant, Genome, Plant, Genomic Structural Variation, Genotype, Inbreeding, Lycopersicon esculentum, Molecular Sequence Annotation, Phenotype, Plant Breeding, Quantitative Trait Loci}, issn = {1097-4172}, doi = {10.1016/j.cell.2020.05.021}, author = {Alonge, Michael and Wang, Xingang and Benoit, Matthias and Soyk, Sebastian and Pereira, Lara and Zhang, Lei and Suresh, Hamsini and Ramakrishnan, Srividya and Maumus, Florian and Ciren, Danielle and Levy, Yuval and Harel, Tom Hai and Shalev-Schlosser, Gili and Amsellem, Ziva and Razifard, Hamid and Caicedo, Ana L and Tieman, Denise M and Klee, Harry and Kirsche, Melanie and Aganezov, Sergey and Ranallo-Benavidez, T Rhyker and Lemmon, Zachary H and Kim, Jennifer and Robitaille, Gina and Kramer, Melissa and Goodwin, Sara and McCombie, W Richard and Hutton, Samuel and Van Eck, Joyce and Gillis, Jesse and Eshed, Yuval and Sedlazeck, Fritz J and van der Knaap, Esther and Schatz, Michael C and Lippman, Zachary B} } @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 = {

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.

}, 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 {141, title = {Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics.}, journal = {Am J Hum Genet}, volume = {107}, year = {2020}, month = {2020 07 02}, pages = {46-59}, abstract = {

In complex trait genetics, the ability to predict phenotype from genotype is the ultimate measure of our understanding of genetic architecture underlying the heritability of a trait. A complete understanding of the genetic basis of a trait should allow for predictive methods with accuracies approaching the trait{\textquoteright}s heritability. The highly polygenic nature of quantitative traits and most common phenotypes has motivated the development of statistical strategies focused on combining myriad individually non-significant genetic effects. Now that predictive accuracies are improving, there is a growing interest in the practical utility of such methods for predicting risk of common diseases responsive to early therapeutic intervention. However, existing methods require individual-level genotypes or depend on accurately specifying the genetic architecture underlying each disease to be predicted. Here, we propose a polygenic risk prediction method that does not require explicitly modeling any underlying genetic architecture. We start with summary statistics in the form of SNP effect sizes from a large GWAS cohort. We then remove the correlation structure across summary statistics arising due to linkage disequilibrium and apply a piecewise linear interpolation on conditional mean effects. In both simulated and real datasets, this new non-parametric shrinkage (NPS) method can reliably allow for linkage disequilibrium in summary statistics of 5 million dense genome-wide markers and consistently improves prediction accuracy. We show that NPS improves the identification of groups at high risk for breast cancer, type 2 diabetes, inflammatory bowel disease, and coronary heart disease, all of which have available early intervention or prevention treatments.

}, keywords = {Aged, Cohort Studies, Diabetes Mellitus, Type 2, Female, Genome-Wide Association Study, Genotype, Humans, Linkage Disequilibrium, Male, Middle Aged, Models, Genetic, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2020.05.004}, author = {Chun, Sung and Imakaev, Maxim and Hui, Daniel and Patsopoulos, Nikolaos A and Neale, Benjamin M and Kathiresan, Sekar and Stitziel, Nathan O and Sunyaev, Shamil R} } @article {32, title = {Genetic effects on gene expression across human tissues.}, journal = {Nature}, volume = {550}, year = {2017}, month = {2017 Oct 11}, pages = {204-213}, abstract = {

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

}, keywords = {Alleles, Chromosomes, Human, Disease, Female, Gene Expression Profiling, Gene Expression Regulation, Genetic Variation, Genome, Human, Genotype, Humans, Male, Organ Specificity, Quantitative Trait Loci}, issn = {1476-4687}, doi = {10.1038/nature24277}, author = {Battle, Alexis and Brown, Christopher D and Engelhardt, Barbara E and Montgomery, Stephen B} } @article {25, title = {Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations.}, journal = {Nat Commun}, volume = {8}, year = {2017}, month = {2017 Aug 16}, pages = {266}, abstract = {

The immune system plays a major role in human health and disease, and understanding genetic causes of interindividual variability of immune responses is vital. Here, we isolate monocytes from 134 genotyped individuals, stimulate these cells with three defined microbe-associated molecular patterns (LPS, MDP, and 5{\textquoteright}-ppp-dsRNA), and profile the transcriptomes at three time points. Mapping expression quantitative trait loci (eQTL), we identify 417 response eQTLs (reQTLs) with varying effects between conditions. We characterize the dynamics of genetic regulation on early and late immune response and observe an enrichment of reQTLs in distal cis-regulatory elements. In addition, reQTLs are enriched for recent positive selection with an evolutionary trend towards enhanced immune response. Finally, we uncover reQTL effects in multiple GWAS loci and show a stronger enrichment for response than constant eQTLs in GWAS signals of several autoimmune diseases. This demonstrates the importance of infectious stimuli in modifying genetic predisposition to disease.Insight into the genetic influence on the immune response is important for the understanding of interindividual variability in human pathologies. Here, the authors generate transcriptome data from human blood monocytes stimulated with various immune stimuli and provide a time-resolved response eQTL map.

}, keywords = {Acetylmuramyl-Alanyl-Isoglutamine, Adjuvants, Immunologic, Adolescent, Adult, Autoimmune Diseases, Gene Expression, Gene Expression Profiling, Gene Expression Regulation, Genetic Predisposition to Disease, Healthy Volunteers, Humans, Indicators and Reagents, Lipids, Lipopolysaccharides, Male, Monocytes, Quantitative Trait Loci, Regulatory Sequences, Nucleic Acid, RNA, Double-Stranded, RNA, Messenger, Young Adult}, issn = {2041-1723}, doi = {10.1038/s41467-017-00366-1}, author = {Kim-Hellmuth, Sarah and Bechheim, Matthias and P{\"u}tz, Benno and Mohammadi, Pejman and N{\'e}d{\'e}lec, Yohann and Giangreco, Nicholas and Becker, Jessica and Kaiser, Vera and Fricker, Nadine and Beier, Esther and Boor, Peter and Castel, Stephane E and N{\"o}then, Markus M and Barreiro, Luis B and Pickrell, Joseph K and M{\"u}ller-Myhsok, Bertram and Lappalainen, Tuuli and Schumacher, Johannes and Hornung, Veit} } @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 {7, title = {Practical Approaches for Whole-Genome Sequence Analysis of Heart- and Blood-Related Traits.}, journal = {Am J Hum Genet}, volume = {100}, year = {2017}, month = {2017 Feb 02}, pages = {205-215}, abstract = {

Whole-genome sequencing (WGS) allows for a comprehensive view of the sequence of the human genome. We present and apply integrated methodologic steps for interrogating WGS data to characterize the genetic architecture of 10 heart- and blood-related traits in a sample of 1,860 African Americans. In order to evaluate the contribution of regulatory and non-protein coding regions of the genome, we conducted aggregate tests of rare variation across the entire genomic landscape using a sliding window, complemented by an annotation-based assessment of the genome using predefined regulatory elements and within the first intron of all genes. These tests were performed treating all variants equally as well as with individual variants weighted by a measure of predicted functional consequence. Significant findings were assessed in 1,705 individuals of European ancestry. After these steps, we identified and replicated components of the genomic landscape significantly associated with heart- and blood-related traits. For two traits, lipoprotein(a) levels and neutrophil count, aggregate tests of low-frequency and rare variation were significantly associated across multiple motifs. For a third trait, cardiac troponin T, investigation of regulatory domains identified a locus on chromosome 9. These practical approaches for WGS analysis led to the identification of informative genomic regions and also showed that defined non-coding regions, such as first introns of genes and regulatory domains, are associated with important risk factor phenotypes. This study illustrates the tractable nature of WGS data and outlines an approach for characterizing the genetic architecture of complex traits.

}, keywords = {Black or African American, C-Reactive Protein, Cholesterol, HDL, Cholesterol, LDL, Chromosomes, Human, Pair 9, Gene Frequency, Genome, Human, Genome-Wide Association Study, Genomics, Hemoglobins, Humans, Introns, Leukocyte Count, Lipoprotein(a), Magnesium, Natriuretic Peptide, Brain, Neutrophils, Peptide Fragments, Phosphorus, Platelet Count, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Troponin T, White People}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2016.12.009}, author = {Morrison, Alanna C and Huang, Zhuoyi and Yu, Bing and Metcalf, Ginger and Liu, Xiaoming and Ballantyne, Christie and Coresh, Josef and Yu, Fuli and Muzny, Donna and Feofanova, Elena and Rustagi, Navin and Gibbs, Richard and Boerwinkle, Eric} } @article {30, title = {Quantifying the regulatory effect size of -acting genetic variation using allelic fold change.}, journal = {Genome Res}, volume = {27}, year = {2017}, month = {2017 Nov}, pages = {1872-1884}, abstract = {

Mapping -acting expression quantitative trait loci (-eQTL) has become a popular approach for characterizing proximal genetic regulatory variants. In this paper, we describe and characterize log allelic fold change (aFC), the magnitude of expression change associated with a given genetic variant, as a biologically interpretable unit for quantifying the effect size of -eQTLs and a mathematically convenient approach for systematic modeling of -regulation. This measure is mathematically independent from expression level and allele frequency, additive, applicable to multiallelic variants, and generalizable to multiple independent variants. We provide efficient tools and guidelines for estimating aFC from both eQTL and allelic expression data sets and apply it to Genotype Tissue Expression (GTEx) data. We show that aFC estimates independently derived from eQTL and allelic expression data are highly consistent, and identify technical and biological correlates of eQTL effect size. We generalize aFC to analyze genes with two eQTLs in GTEx and show that in nearly all cases the two eQTLs act independently in regulating gene expression. In summary, aFC is a solid measure of -regulatory effect size that allows quantitative interpretation of cellular regulatory events from population data, and it is a valuable approach for investigating novel aspects of eQTL data sets.

}, keywords = {Alleles, Databases, Genetic, Gene Expression, Gene Expression Profiling, Gene Regulatory Networks, Genetic Variation, Humans, Models, Theoretical, Quantitative Trait Loci}, issn = {1549-5469}, doi = {10.1101/gr.216747.116}, author = {Mohammadi, Pejman and Castel, Stephane E and Brown, Andrew A and Lappalainen, Tuuli} }