@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 = {<p>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.</p>},
	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 {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 = {<p>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.</p>},
	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}
}
