@article {7572, title = {Single-trait and multi-trait genome-wide association analyses identify novel loci for blood pressure in African-ancestry populations.}, journal = {PLoS Genet}, volume = {13}, year = {2017}, month = {2017 May}, pages = {e1006728}, abstract = {

Hypertension is a leading cause of global disease, mortality, and disability. While individuals of African descent suffer a disproportionate burden of hypertension and its complications, they have been underrepresented in genetic studies. To identify novel susceptibility loci for blood pressure and hypertension in people of African ancestry, we performed both single and multiple-trait genome-wide association analyses. We analyzed 21 genome-wide association studies comprised of 31,968 individuals of African ancestry, and validated our results with additional 54,395 individuals from multi-ethnic studies. These analyses identified nine loci with eleven independent variants which reached genome-wide significance (P < 1.25{\texttimes}10-8) for either systolic and diastolic blood pressure, hypertension, or for combined traits. Single-trait analyses identified two loci (TARID/TCF21 and LLPH/TMBIM4) and multiple-trait analyses identified one novel locus (FRMD3) for blood pressure. At these three loci, as well as at GRP20/CDH17, associated variants had alleles common only in African-ancestry populations. Functional annotation showed enrichment for genes expressed in immune and kidney cells, as well as in heart and vascular cells/tissues. Experiments driven by these findings and using angiotensin-II induced hypertension in mice showed altered kidney mRNA expression of six genes, suggesting their potential role in hypertension. Our study provides new evidence for genes related to hypertension susceptibility, and the need to study African-ancestry populations in order to identify biologic factors contributing to hypertension.

}, keywords = {African Americans, Animals, Basic Helix-Loop-Helix Transcription Factors, Blood Pressure, Cadherins, Case-Control Studies, Female, Genetic Loci, Genome-Wide Association Study, Humans, Hypertension, Male, Membrane Proteins, Mice, Multifactorial Inheritance, Polymorphism, Single Nucleotide}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1006728}, author = {Liang, Jingjing and Le, Thu H and Edwards, Digna R Velez and Tayo, Bamidele O and Gaulton, Kyle J and Smith, Jennifer A and Lu, Yingchang and Jensen, Richard A and Chen, Guanjie and Yanek, Lisa R and Schwander, Karen and Tajuddin, Salman M and Sofer, Tamar and Kim, Wonji and Kayima, James and McKenzie, Colin A and Fox, Ervin and Nalls, Michael A and Young, J Hunter and Sun, Yan V and Lane, Jacqueline M and Cechova, Sylvia and Zhou, Jie and Tang, Hua and Fornage, Myriam and Musani, Solomon K and Wang, Heming and Lee, Juyoung and Adeyemo, Adebowale and Dreisbach, Albert W and Forrester, Terrence and Chu, Pei-Lun and Cappola, Anne and Evans, Michele K and Morrison, Alanna C and Martin, Lisa W and Wiggins, Kerri L and Hui, Qin and Zhao, Wei and Jackson, Rebecca D and Ware, Erin B and Faul, Jessica D and Reiner, Alex P and Bray, Michael and Denny, Joshua C and Mosley, Thomas H and Palmas, Walter and Guo, Xiuqing and Papanicolaou, George J and Penman, Alan D and Polak, Joseph F and Rice, Kenneth and Taylor, Ken D and Boerwinkle, Eric and Bottinger, Erwin P and Liu, Kiang and Risch, Neil and Hunt, Steven C and Kooperberg, Charles and Zonderman, Alan B and Laurie, Cathy C and Becker, Diane M and Cai, Jianwen and Loos, Ruth J F and Psaty, Bruce M and Weir, David R and Kardia, Sharon L R and Arnett, Donna K and Won, Sungho and Edwards, Todd L and Redline, Susan and Cooper, Richard S and Rao, D C and Rotter, Jerome I and Rotimi, Charles and Levy, Daniel and Chakravarti, Aravinda and Zhu, Xiaofeng and Franceschini, Nora} } @article {8639, title = {Whole genome sequence analysis of pulmonary function and COPD in 19,996 multi-ethnic participants.}, journal = {Nat Commun}, volume = {11}, year = {2020}, month = {2020 10 14}, pages = {5182}, abstract = {

Chronic obstructive pulmonary disease (COPD), diagnosed by reduced lung function, is a leading cause of morbidity and mortality. We performed whole genome sequence (WGS) analysis of lung function and COPD in a multi-ethnic sample of 11,497 participants from population- and family-based studies, and 8499 individuals from COPD-enriched studies in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. We identify at genome-wide significance 10 known GWAS loci and 22 distinct, previously unreported loci, including two common variant signals from stratified analysis of African Americans. Four novel common variants within the regions of PIAS1, RGN (two variants) and FTO show evidence of replication in the UK Biobank (European ancestry n ~ 320,000), while colocalization analyses leveraging multi-omic data from GTEx and TOPMed identify potential molecular mechanisms underlying four of the 22 novel loci. Our study demonstrates the value of performing WGS analyses and multi-omic follow-up in cohorts of diverse ancestry.

}, keywords = {Adult, African Americans, Aged, Aged, 80 and over, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Calcium-Binding Proteins, Feasibility Studies, Female, Follow-Up Studies, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Intracellular Signaling Peptides and Proteins, Lung, Male, Middle Aged, Polymorphism, Single Nucleotide, Protein Inhibitors of Activated STAT, Pulmonary Disease, Chronic Obstructive, Respiratory Physiological Phenomena, Small Ubiquitin-Related Modifier Proteins, Whole Genome Sequencing}, issn = {2041-1723}, doi = {10.1038/s41467-020-18334-7}, author = {Zhao, Xutong and Qiao, Dandi and Yang, Chaojie and Kasela, Silva and Kim, Wonji and Ma, Yanlin and Shrine, Nick and Batini, Chiara and Sofer, Tamar and Taliun, Sarah A Gagliano and Sakornsakolpat, Phuwanat and Balte, Pallavi P and Prokopenko, Dmitry and Yu, Bing and Lange, Leslie A and Dupuis, Jos{\'e}e and Cade, Brian E and Lee, Jiwon and Gharib, Sina A and Daya, Michelle and Laurie, Cecelia A and Ruczinski, Ingo and Cupples, L Adrienne and Loehr, Laura R and Bartz, Traci M and Morrison, Alanna C and Psaty, Bruce M and Vasan, Ramachandran S and Wilson, James G and Taylor, Kent D and Durda, Peter and Johnson, W Craig and Cornell, Elaine and Guo, Xiuqing and Liu, Yongmei and Tracy, Russell P and Ardlie, Kristin G and Aguet, Francois and VanDenBerg, David J and Papanicolaou, George J and Rotter, Jerome I and Barnes, Kathleen C and Jain, Deepti and Nickerson, Deborah A and Muzny, Donna M and Metcalf, Ginger A and Doddapaneni, Harshavardhan and Dugan-Perez, Shannon and Gupta, Namrata and Gabriel, Stacey and Rich, Stephen S and O{\textquoteright}Connor, George T and Redline, Susan and Reed, Robert M and Laurie, Cathy C and Daviglus, Martha L and Preudhomme, Liana K and Burkart, Kristin M and Kaplan, Robert C and Wain, Louise V and Tobin, Martin D and London, Stephanie J and Lappalainen, Tuuli and Oelsner, Elizabeth C and Abecasis, Goncalo R and Silverman, Edwin K and Barr, R Graham and Cho, Michael H and Manichaikul, Ani} } @article {9109, title = {The Value of Rare Genetic Variation in the Prediction of Common Obesity in European Ancestry Populations.}, journal = {Front Endocrinol (Lausanne)}, volume = {13}, year = {2022}, month = {2022}, pages = {863893}, abstract = {

Polygenic risk scores (PRSs) aggregate the effects of genetic variants across the genome and are used to predict risk of complex diseases, such as obesity. Current PRSs only include common variants (minor allele frequency (MAF) >=1\%), whereas the contribution of rare variants in PRSs to predict disease remains unknown. Here, we examine whether augmenting the standard common variant PRS (PRS) with a rare variant PRS (PRS) improves prediction of obesity. We used genome-wide genotyped and imputed data on 451,145 European-ancestry participants of the UK Biobank, as well as whole exome sequencing (WES) data on 184,385 participants. We performed single variant analyses (for both common and rare variants) and gene-based analyses (for rare variants) for association with BMI (kg/m), obesity (BMI >= 30 kg/m), and extreme obesity (BMI >= 40 kg/m). We built PRSs and PRSs using a range of methods (Clumping+Thresholding [C+T], PRS-CS, lassosum, gene-burden test). We selected the best-performing PRSs and assessed their performance in 36,757 European-ancestry unrelated participants with whole genome sequencing (WGS) data from the Trans-Omics for Precision Medicine (TOPMed) program. The best-performing PRS explained 10.1\% of variation in BMI, and 18.3\% and 22.5\% of the susceptibility to obesity and extreme obesity, respectively, whereas the best-performing PRS explained 1.49\%, and 2.97\% and 3.68\%, respectively. The PRS was associated with an increased risk of obesity and extreme obesity (OR = 1.37 per SD, = 1.7x10; OR = 1.55 per SD, = 3.8x10), which was attenuated, after adjusting for PRS (OR = 1.08 per SD, = 9.8x10; OR= 1.09 per SD, = 0.02). When PRS and PRS are combined, the increase in explained variance attributed to PRS was small (incremental Nagelkerke R = 0.24\% for obesity and 0.51\% for extreme obesity). Consistently, combining PRS to PRS provided little improvement to the prediction of obesity (PRS AUC = 0.591; PRS AUC = 0.708; PRS AUC = 0.710). In summary, while rare variants show convincing association with BMI, obesity and extreme obesity, the PRS provides limited improvement over PRS in the prediction of obesity risk, based on these large populations.

}, keywords = {Gene Frequency, Genetic Variation, Genome-Wide Association Study, Humans, Obesity, Whole Genome Sequencing}, issn = {1664-2392}, doi = {10.3389/fendo.2022.863893}, author = {Wang, Zhe and Choi, Shing Wan and Chami, Nathalie and Boerwinkle, Eric and Fornage, Myriam and Redline, Susan and Bis, Joshua C and Brody, Jennifer A and Psaty, Bruce M and Kim, Wonji and McDonald, Merry-Lynn N and Regan, Elizabeth A and Silverman, Edwin K and Liu, Ching-Ti and Vasan, Ramachandran S and Kalyani, Rita R and Mathias, Rasika A and Yanek, Lisa R and Arnett, Donna K and Justice, Anne E and North, Kari E and Kaplan, Robert and Heckbert, Susan R and de Andrade, Mariza and Guo, Xiuqing and Lange, Leslie A and Rich, Stephen S and Rotter, Jerome I and Ellinor, Patrick T and Lubitz, Steven A and Blangero, John and Shoemaker, M Benjamin and Darbar, Dawood and Gladwin, Mark T and Albert, Christine M and Chasman, Daniel I and Jackson, Rebecca D and Kooperberg, Charles and Reiner, Alexander P and O{\textquoteright}Reilly, Paul F and Loos, Ruth J F} } @article {9261, title = {Whole genome sequencing identifies structural variants contributing to hematologic traits in the NHLBI TOPMed program.}, journal = {Nat Commun}, volume = {13}, year = {2022}, month = {2022 Dec 08}, pages = {7592}, abstract = {

Genome-wide association studies have identified thousands of single nucleotide variants and small indels that contribute to variation in hematologic traits. While structural variants are known to cause rare blood or hematopoietic disorders, the genome-wide contribution of structural variants to quantitative blood cell trait variation is unknown. Here we utilized whole genome sequencing data in ancestrally diverse participants of the NHLBI Trans Omics for Precision Medicine program (N = 50,675) to detect structural variants associated with hematologic traits. Using single variant tests, we assessed the association of common and rare structural variants with red cell-, white cell-, and platelet-related quantitative traits and observed 21 independent signals (12 common and 9 rare) reaching genome-wide significance. The majority of these associations (N = 18) replicated in independent datasets. In genome-editing experiments, we provide evidence that a deletion associated with lower monocyte counts leads to disruption of an S1PR3 monocyte enhancer and decreased S1PR3 expression.

}, keywords = {Blood Cells, Genome-Wide Association Study, Humans, Whole Genome Sequencing}, issn = {2041-1723}, doi = {10.1038/s41467-022-35354-7}, author = {Wheeler, Marsha M and Stilp, Adrienne M and Rao, Shuquan and Halldorsson, Bjarni V and Beyter, Doruk and Wen, Jia and Mihkaylova, Anna V and McHugh, Caitlin P and Lane, John and Jiang, Min-Zhi and Raffield, Laura M and Jun, Goo and Sedlazeck, Fritz J and Metcalf, Ginger and Yao, Yao and Bis, Joshua B and Chami, Nathalie and de Vries, Paul S and Desai, Pinkal and Floyd, James S and Gao, Yan and Kammers, Kai and Kim, Wonji and Moon, Jee-Young and Ratan, Aakrosh and Yanek, Lisa R and Almasy, Laura and Becker, Lewis C and Blangero, John and Cho, Michael H and Curran, Joanne E and Fornage, Myriam and Kaplan, Robert C and Lewis, Joshua P and Loos, Ruth J F and Mitchell, Braxton D and Morrison, Alanna C and Preuss, Michael and Psaty, Bruce M and Rich, Stephen S and Rotter, Jerome I and Tang, Hua and Tracy, Russell P and Boerwinkle, Eric and Abecasis, Goncalo R and Blackwell, Thomas W and Smith, Albert V and Johnson, Andrew D and Mathias, Rasika A and Nickerson, Deborah A and Conomos, Matthew P and Li, Yun and {\TH}orsteinsdottir, Unnur and Magn{\'u}sson, Magn{\'u}s K and Stefansson, Kari and Pankratz, Nathan D and Bauer, Daniel E and Auer, Paul L and Reiner, Alex P} } @article {9702, title = {Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.}, journal = {Sci Rep}, volume = {14}, year = {2024}, month = {2024 May 30}, pages = {12436}, abstract = {

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model{\textquoteright}s performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1\% (SBP) and 14.3\% to 17.4\% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1\% (SBP) and 4.7 to 5\% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3\% (SBP) and 5.7\% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

}, keywords = {Blood Pressure, Female, Genetic Predisposition to Disease, Genetic Risk Score, Genome-Wide Association Study, Humans, Hypertension, Machine Learning, Male, Middle Aged, Models, Genetic, Multifactorial Inheritance, Phenotype, Risk Factors}, issn = {2045-2322}, doi = {10.1038/s41598-024-62945-9}, author = {Hrytsenko, Yana and Shea, Benjamin and Elgart, Michael and Kurniansyah, Nuzulul and Lyons, Genevieve and Morrison, Alanna C and Carson, April P and Haring, Bernhard and Mitchell, Braxton D and Psaty, Bruce M and Jaeger, Byron C and Gu, C Charles and Kooperberg, Charles and Levy, Daniel and Lloyd-Jones, Donald and Choi, Eunhee and Brody, Jennifer A and Smith, Jennifer A and Rotter, Jerome I and Moll, Matthew and Fornage, Myriam and Simon, Noah and Castaldi, Peter and Casanova, Ramon and Chung, Ren-Hua and Kaplan, Robert and Loos, Ruth J F and Kardia, Sharon L R and Rich, Stephen S and Redline, Susan and Kelly, Tanika and O{\textquoteright}Connor, Timothy and Zhao, Wei and Kim, Wonji and Guo, Xiuqing and Ida Chen, Yii-Der and Sofer, Tamar} }