@article {138, title = {Comprehensive cell type decomposition of circulating cell-free DNA with CelFiE.}, journal = {Nat Commun}, volume = {12}, year = {2021}, month = {2021 05 11}, pages = {2717}, abstract = {

Circulating cell-free DNA (cfDNA) in the bloodstream originates from dying cells and is a promising noninvasive biomarker for cell death. Here, we propose an algorithm, CelFiE, to accurately estimate the relative abundances of cell types and tissues contributing to cfDNA from epigenetic cfDNA sequencing. In contrast to previous work, CelFiE accommodates low coverage data, does not require CpG site curation, and estimates contributions from multiple unknown cell types that are not available in external reference data. In simulations, CelFiE accurately estimates known and unknown cell type proportions from low coverage and noisy cfDNA mixtures, including from cell types composing less than 1\% of the total mixture. When used in two clinically-relevant situations, CelFiE correctly estimates a large placenta component in pregnant women, and an elevated skeletal muscle component in amyotrophic lateral sclerosis (ALS) patients, consistent with the occurrence of muscle wasting typical in these patients. Together, these results show how CelFiE could be a useful tool for biomarker discovery and monitoring the progression of degenerative disease.

}, keywords = {Adult, Algorithms, Amyotrophic Lateral Sclerosis, B-Lymphocytes, Biomarkers, Case-Control Studies, Cell-Free Nucleic Acids, DNA Methylation, Epigenesis, Genetic, Female, Humans, Macrophages, Male, Monocytes, Muscle, Skeletal, Neutrophils, Organ Specificity, Pregnancy, Pregnancy Trimesters, T-Lymphocytes}, issn = {2041-1723}, doi = {10.1038/s41467-021-22901-x}, author = {Caggiano, Christa and Celona, Barbara and Garton, Fleur and Mefford, Joel and Black, Brian L and Henderson, Robert and Lomen-Hoerth, Catherine and Dahl, Andrew and Zaitlen, Noah} } @article {144, title = {A cross-population atlas of genetic associations for 220 human phenotypes.}, journal = {Nat Genet}, volume = {53}, year = {2021}, month = {2021 10}, pages = {1415-1424}, abstract = {

Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (n = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (n = 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics.

}, keywords = {ABO Blood-Group System, Biological Specimen Banks, Genetic Association Studies, Genetic Loci, Genetic Pleiotropy, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Major Histocompatibility Complex, Meta-Analysis as Topic, Mutation, Phenotype}, issn = {1546-1718}, doi = {10.1038/s41588-021-00931-x}, author = {Sakaue, Saori and Kanai, Masahiro and Tanigawa, Yosuke and Karjalainen, Juha and Kurki, Mitja and Koshiba, Seizo and Narita, Akira and Konuma, Takahiro and Yamamoto, Kenichi and Akiyama, Masato and Ishigaki, Kazuyoshi and Suzuki, Akari and Suzuki, Ken and Obara, Wataru and Yamaji, Ken and Takahashi, Kazuhisa and Asai, Satoshi and Takahashi, Yasuo and Suzuki, Takao and Shinozaki, Nobuaki and Yamaguchi, Hiroki and Minami, Shiro and Murayama, Shigeo and Yoshimori, Kozo and Nagayama, Satoshi and Obata, Daisuke and Higashiyama, Masahiko and Masumoto, Akihide and Koretsune, Yukihiro and Ito, Kaoru and Terao, Chikashi and Yamauchi, Toshimasa and Komuro, Issei and Kadowaki, Takashi and Tamiya, Gen and Yamamoto, Masayuki and Nakamura, Yusuke and Kubo, Michiaki and Murakami, Yoshinori and Yamamoto, Kazuhiko and Kamatani, Yoichiro and Palotie, Aarno and Rivas, Manuel A and Daly, Mark J and Matsuda, Koichi and Okada, Yukinori} } @article {126, title = {A common variant in PNPLA3 is associated with age at diagnosis of NAFLD in patients from a multi-ethnic biobank.}, journal = {J Hepatol}, volume = {72}, year = {2020}, month = {2020 06}, pages = {1070-1081}, abstract = {

BACKGROUND \& AIMS: The Ile138Met variant (rs738409) in the PNPLA3 gene has the largest effect on non-alcoholic fatty liver disease (NAFLD), increasing the risk of progression to severe forms of liver disease. It remains unknown if the variant plays a role in age of NAFLD onset. We aimed to determine if rs738409 impacts on the age of NAFLD diagnosis.

METHODS: We applied a novel natural language processing (NLP) algorithm to a longitudinal electronic health records (EHR) dataset of >27,000 individuals with genetic data from a multi-ethnic biobank, defining NAFLD cases (n~= 1,703) and confirming controls (n~= 8,119). We conducted i) a survival analysis to determine if age at diagnosis differed by rs738409 genotype, ii) a receiver operating characteristics analysis to assess the utility of the rs738409 genotype in discriminating NAFLD cases from controls, and iii) a phenome-wide association study (PheWAS) between rs738409 and 10,095 EHR-derived disease diagnoses.

RESULTS: The PNPLA3 G risk allele was associated with: i) earlier age of NAFLD diagnosis, with the strongest effect in Hispanics (hazard ratio 1.33; 95\% CI 1.15-1.53; p <0.0001) among whom a NAFLD diagnosis was 15\% more likely in risk allele carriers vs. non-carriers; ii) increased NAFLD risk (odds ratio 1.61; 95\% CI 1.349-1.73; p <0.0001), with the strongest effect among Hispanics (odds ratio 1.43; 95\% CI 1.28-1.59; p <0.0001); iii) additional liver diseases in a PheWAS (p <4.95~{\texttimes} 10) where the risk variant also associated with earlier age of diagnosis.

CONCLUSION: Given the role of the rs738409 in NAFLD diagnosis age, our results suggest that stratifying risk within populations known to have an enhanced risk of liver disease, such as Hispanic carriers of the rs738409 variant, would be effective in earlier identification of those who would benefit most from early NAFLD prevention and treatment strategies.

LAY SUMMARY: Despite clear associations between the PNPLA3 rs738409 variant and elevated risk of progression from non-alcoholic fatty liver disease (NAFLD) to more severe forms of liver disease, it remains unknown if PNPLA3 rs738409 plays a role in the age of NAFLD onset. Herein, we found that this risk variant is associated with an earlier age of NAFLD and other liver disease diagnoses; an observation most pronounced in Hispanic Americans. We conclude that PNPLA3 rs738409 could be used to better understand liver disease risk within vulnerable populations and identify patients that may benefit from early prevention strategies.

}, issn = {1600-0641}, doi = {10.1016/j.jhep.2020.01.029}, author = {Walker, Ryan W and Belbin, Gillian M and Sorokin, Elena P and Van Vleck, Tielman and Wojcik, Genevieve L and Moscati, Arden and Gignoux, Christopher R and Cho, Judy and Abul-Husn, Noura S and Nadkarni, Girish and Kenny, Eimear E and Loos, Ruth J F} } @article {100, title = {On the cross-population generalizability of gene expression prediction models.}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 08}, pages = {e1008927}, abstract = {

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction.

}, keywords = {African Americans, Gene Expression Profiling, Genome-Wide Association Study, Humans, Models, Genetic, Quantitative Trait Loci, Reference Standards, RNA-Seq, Transcriptome}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008927}, author = {Keys, Kevin L and Mak, Angel C Y and White, Marquitta J and Eckalbar, Walter L and Dahl, Andrew W and Mefford, Joel and Mikhaylova, Anna V and Contreras, Mar{\'\i}a G and Elhawary, Jennifer R and Eng, Celeste and Hu, Donglei and Huntsman, Scott and Oh, Sam S and Salazar, Sandra and LeNoir, Michael A and Ye, Jimmie C and Thornton, Timothy A and Zaitlen, Noah and Burchard, Esteban G and Gignoux, Christopher R} } @article {66, title = {Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology.}, journal = {Nat Commun}, volume = {10}, year = {2019}, month = {2019 Sep 06}, pages = {4064}, abstract = {

Population-based biobanks with genomic and dense phenotype data provide opportunities for generating effective therapeutic hypotheses and understanding the genomic role in disease predisposition. To characterize latent components of genetic associations, we apply truncated singular value decomposition (DeGAs) to matrices of summary statistics derived from genome-wide association analyses across 2,138 phenotypes measured in 337,199 White British individuals in the UK Biobank study. We systematically identify key components of genetic associations and the contributions of variants, genes, and phenotypes to each component. As an illustration of the utility of the approach to inform downstream experiments, we report putative loss of function variants, rs114285050 (GPR151) and rs150090666 (PDE3B), that substantially contribute to obesity-related traits and experimentally demonstrate the role of these genes in adipocyte biology. Our approach to dissect components of genetic associations across the human phenome will accelerate biomedical hypothesis generation by providing insights on previously unexplored latent structures.

}, issn = {2041-1723}, doi = {10.1038/s41467-019-11953-9}, author = {Tanigawa, Yosuke and Li, Jiehan and Justesen, Johanne M and Horn, Heiko and Aguirre, Matthew and DeBoever, Christopher and Chang, Chris and Narasimhan, Balasubramanian and Lage, Kasper and Hastie, Trevor and Park, Chong Y and Bejerano, Gill and Ingelsson, Erik and Rivas, Manuel A} } @article {35, title = {A common loss-of-function variant is associated with lower vitamin B concentration in African Americans.}, journal = {Blood}, volume = {131}, year = {2018}, month = {2018 Jun 21}, pages = {2859-2863}, keywords = {Black or African American, Female, Genome-Wide Association Study, Humans, Loss of Function Mutation, Male, Polymorphism, Single Nucleotide, Transcobalamins, Vitamin B 12, Vitamin B Deficiency}, issn = {1528-0020}, doi = {10.1182/blood-2018-03-841023}, author = {Hu, Yao and Raffield, Laura M and Polfus, Linda M and Moscati, Arden and Nadkarni, Girish and Preuss, Michael H and Zhong, Xue and Wei, Qiang and Rich, Stephen S and Li, Yun and Wilson, James G and Correa, Adolfo and Loos, Ruth J F and Li, Bingshan and Auer, Paul L and Reiner, Alex P} } @article {15, title = {Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.}, journal = {Genome Med}, volume = {9}, year = {2017}, month = {2017 Nov 24}, pages = {98}, abstract = {

BACKGROUND: Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects.

METHODS: Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals.

RESULTS: We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations.

CONCLUSIONS: Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

}, keywords = {Anticoagulants, Black or African American, Female, Gene Expression, Genome-Wide Association Study, Humans, Male, Tissue Distribution, Warfarin}, issn = {1756-994X}, doi = {10.1186/s13073-017-0495-0}, author = {Gottlieb, Assaf and Daneshjou, Roxana and DeGorter, Marianne and Bourgeois, Stephane and Svensson, Peter J and Wadelius, Mia and Deloukas, Panos and Montgomery, Stephen B and Altman, Russ B} } @article {21, title = {Covariate selection for association screening in multiphenotype genetic studies.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 Dec}, pages = {1789-1795}, abstract = {

Testing for associations in big data faces the problem of multiple comparisons, wherein true signals are difficult to detect on the background of all associations queried. This difficulty is particularly salient in human genetic association studies, in which phenotypic variation is often driven by numerous variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. Although successful, this approach does not leverage the environmental and genetic factors shared among the multiple phenotypes collected in contemporary cohorts. Here we developed covariates for multiphenotype studies (CMS), an approach that improves power when correlated phenotypes are measured on the same samples. Our analyses of real and simulated data provide direct evidence that correlated phenotypes can be used to achieve increases in power to levels often surpassing the power gained by a twofold increase in sample size.

}, keywords = {Algorithms, Genetic Association Studies, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Models, Genetic, Multivariate Analysis, Phenotype, Reproducibility of Results, Sample Size}, issn = {1546-1718}, doi = {10.1038/ng.3975}, author = {Aschard, Hugues and Guillemot, Vincent and Vilhjalmsson, Bjarni and Patel, Chirag J and Skurnik, David and Ye, Chun J and Wolpin, Brian and Kraft, Peter and Zaitlen, Noah} } @article {22, title = {Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases.}, journal = {Am J Epidemiol}, volume = {186}, year = {2017}, month = {2017 Oct 01}, pages = {753-761}, abstract = {

Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G{\texttimes}Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G{\texttimes}E discovery. This paper highlights current and critical issues and themes in G{\texttimes}E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.

}, keywords = {Disease, Gene-Environment Interaction, Genetic Predisposition to Disease, Genome-Wide Association Study, High-Throughput Nucleotide Sequencing, Humans, Software}, issn = {1476-6256}, doi = {10.1093/aje/kwx227}, author = {McAllister, Kimberly and Mechanic, Leah E and Amos, Christopher and Aschard, Hugues and Blair, Ian A and Chatterjee, Nilanjan and Conti, David and Gauderman, W James and Hsu, Li and Hutter, Carolyn M and Jankowska, Marta M and Kerr, Jacqueline and Kraft, Peter and Montgomery, Stephen B and Mukherjee, Bhramar and Papanicolaou, George J and Patel, Chirag J and Ritchie, Marylyn D and Ritz, Beate R and Thomas, Duncan C and Wei, Peng and Witte, John S} }