@article {8400, title = {Chronic obstructive pulmonary disease and related phenotypes: polygenic risk scores in population-based and case-control cohorts.}, journal = {Lancet Respir Med}, volume = {8}, year = {2020}, month = {2020 07}, pages = {696-708}, abstract = {

BACKGROUND: Genetic factors influence chronic obstructive pulmonary disease (COPD) risk, but the individual variants that have been identified have small effects. We hypothesised that a polygenic risk score using additional variants would predict COPD and associated phenotypes.

METHODS: We constructed a polygenic risk score using a genome-wide association study of lung function (FEV and FEV/forced vital capacity [FVC]) from the UK Biobank and SpiroMeta. We tested this polygenic risk score in nine cohorts of multiple ethnicities for an association with moderate-to-severe COPD (defined as FEV/FVC <0{\textperiodcentered}7 and FEV <80\% of predicted). Associations were tested using logistic regression models, adjusting for age, sex, height, smoking pack-years, and principal components of genetic ancestry. We assessed predictive performance of models by area under the curve. In a subset of studies, we also studied quantitative and qualitative CT imaging phenotypes that reflect parenchymal and airway pathology, and patterns of reduced lung growth.

FINDINGS: The polygenic risk score was associated with COPD in European (odds ratio [OR] per SD 1{\textperiodcentered}81 [95\% CI 1{\textperiodcentered}74-1{\textperiodcentered}88] and non-European (1{\textperiodcentered}42 [1{\textperiodcentered}34-1{\textperiodcentered}51]) populations. Compared with the first decile, the tenth decile of the polygenic risk score was associated with COPD, with an OR of 7{\textperiodcentered}99 (6{\textperiodcentered}56-9{\textperiodcentered}72) in European ancestry and 4{\textperiodcentered}83 (3{\textperiodcentered}45-6{\textperiodcentered}77) in non-European ancestry cohorts. The polygenic risk score was superior to previously described genetic risk scores and, when combined with clinical risk factors (ie, age, sex, and smoking pack-years), showed improved prediction for COPD compared with a model comprising clinical risk factors alone (AUC 0{\textperiodcentered}80 [0{\textperiodcentered}79-0{\textperiodcentered}81] vs 0{\textperiodcentered}76 [0{\textperiodcentered}75-0{\textperiodcentered}76]). The polygenic risk score was associated with CT imaging phenotypes, including wall area percent, quantitative and qualitative measures of emphysema, local histogram emphysema patterns, and destructive emphysema subtypes. The polygenic risk score was associated with a reduced lung growth pattern.

INTERPRETATION: A risk score comprised of genetic variants can identify a small subset of individuals at markedly increased risk for moderate-to-severe COPD, emphysema subtypes associated with cigarette smoking, and patterns of reduced lung growth.

FUNDING: US National Institutes of Health, Wellcome Trust.

}, keywords = {Adult, Case-Control Studies, Cohort Studies, Female, Forced Expiratory Volume, Genome-Wide Association Study, Humans, Male, Middle Aged, Phenotype, Pulmonary Disease, Chronic Obstructive, Risk Factors, Vital Capacity}, issn = {2213-2619}, doi = {10.1016/S2213-2600(20)30101-6}, author = {Moll, Matthew and Sakornsakolpat, Phuwanat and Shrine, Nick and Hobbs, Brian D and DeMeo, Dawn L and John, Catherine and Guyatt, Anna L and McGeachie, Michael J and Gharib, Sina A and Obeidat, Ma{\textquoteright}en and Lahousse, Lies and Wijnant, Sara R A and Brusselle, Guy and Meyers, Deborah A and Bleecker, Eugene R and Li, Xingnan and Tal-Singer, Ruth and Manichaikul, Ani and Rich, Stephen S and Won, Sungho and Kim, Woo Jin and Do, Ah Ra and Washko, George R and Barr, R Graham and Psaty, Bruce M and Bartz, Traci M and Hansel, Nadia N and Barnes, Kathleen and Hokanson, John E and Crapo, James D and Lynch, David and Bakke, Per and Gulsvik, Amund and Hall, Ian P and Wain, Louise and Weiss, Scott T and Silverman, Edwin K and Dudbridge, Frank and Tobin, Martin D and Cho, Michael H} } @article {9037, title = {Polygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program.}, journal = {Am J Hum Genet}, year = {2022}, month = {2022 Mar 31}, abstract = {

While polygenic risk scores (PRSs) enable early identification of genetic risk for chronic obstructive pulmonary disease (COPD), predictive performance is limited when the discovery and target populations are not well matched. Hypothesizing that the biological mechanisms of disease are shared across ancestry groups, we introduce a PrediXcan-derived polygenic transcriptome risk score (PTRS) to improve cross-ethnic portability of risk prediction. We constructed the PTRS using summary statistics from application of PrediXcan on large-scale GWASs of lung function (forced expiratory volume in 1~s [FEV] and its ratio to forced vital capacity [FEV/FVC]) in the UK Biobank. We examined prediction performance and cross-ethnic portability of PTRS through smoking-stratified analyses both on 29,381 multi-ethnic participants from TOPMed population/family-based cohorts and on 11,771 multi-ethnic participants from TOPMed COPD-enriched studies. Analyses were carried out for two dichotomous COPD traits (moderate-to-severe and severe COPD) and two quantitative lung function traits (FEV and FEV/FVC). While the proposed PTRS showed weaker associations with disease than PRS for European ancestry, the PTRS showed stronger association with COPD than PRS for African Americans (e.g., odds ratio [OR]~= 1.24 [95\% confidence interval [CI]: 1.08-1.43] for PTRS versus 1.10 [0.96-1.26] for PRS among heavy smokers with >= 40 pack-years of smoking) for moderate-to-severe COPD. Cross-ethnic portability of the PTRS was significantly higher than the PRS (paired t test p~<~2.2~{\texttimes}~10 with portability gains ranging from 5\% to 28\%) for both dichotomous COPD traits and across all smoking strata. Our study demonstrates the value of PTRS for improved cross-ethnic portability compared to PRS in predicting COPD risk.

}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2022.03.007}, author = {Hu, Xiaowei and Qiao, Dandi and Kim, Wonji and Moll, Matthew and Balte, Pallavi P and Lange, Leslie A and Bartz, Traci M and Kumar, Rajesh and Li, Xingnan and Yu, Bing and Cade, Brian E and Laurie, Cecelia A and Sofer, Tamar and Ruczinski, Ingo and Nickerson, Deborah A and Muzny, Donna M and Metcalf, Ginger A and Doddapaneni, Harshavardhan and Gabriel, Stacy and Gupta, Namrata and Dugan-Perez, Shannon and Cupples, L Adrienne and Loehr, Laura R and Jain, Deepti and Rotter, Jerome I and Wilson, James G and Psaty, Bruce M and Fornage, Myriam and Morrison, Alanna C and Vasan, Ramachandran S and Washko, George and Rich, Stephen S and O{\textquoteright}Connor, George T and Bleecker, Eugene and Kaplan, Robert C and Kalhan, Ravi and Redline, Susan and Gharib, Sina A and Meyers, Deborah and Ortega, Victor and Dupuis, Jos{\'e}e and London, Stephanie J and Lappalainen, Tuuli and Oelsner, Elizabeth C and Silverman, Edwin K and Barr, R Graham and Thornton, Timothy A and Wheeler, Heather E and Cho, Michael H and Im, Hae Kyung and Manichaikul, Ani} } @article {9586, title = {Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores.}, journal = {medRxiv}, year = {2023}, month = {2023 Dec 14}, 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.

}, doi = {10.1101/2023.12.13.23299909}, 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 Mitchel, 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 Der Ida Chen, Yii and Sofer, Tamar} } @article {9412, title = {Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.}, journal = {Nat Genet}, volume = {55}, year = {2023}, month = {2023 Feb}, pages = {291-300}, abstract = {

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55\% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

}, keywords = {Biology, Drug Repositioning, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Tobacco Use, Transcriptome}, issn = {1546-1718}, doi = {10.1038/s41588-022-01282-x}, author = {Chen, Fang and Wang, Xingyan and Jang, Seon-Kyeong and Quach, Bryan C and Weissenkampen, J Dylan and Khunsriraksakul, Chachrit and Yang, Lina and Sauteraud, Renan and Albert, Christine M and Allred, Nicholette D D and Arnett, Donna K and Ashley-Koch, Allison E and Barnes, Kathleen C and Barr, R Graham and Becker, Diane M and Bielak, Lawrence F and Bis, Joshua C and Blangero, John and Boorgula, Meher Preethi and Chasman, Daniel I and Chavan, Sameer and Chen, Yii-der I and Chuang, Lee-Ming and Correa, Adolfo and Curran, Joanne E and David, Sean P and Fuentes, Lisa de Las and Deka, Ranjan and Duggirala, Ravindranath and Faul, Jessica D and Garrett, Melanie E and Gharib, Sina A and Guo, Xiuqing and Hall, Michael E and Hawley, Nicola L and He, Jiang and Hobbs, Brian D and Hokanson, John E and Hsiung, Chao A and Hwang, Shih-Jen and Hyde, Thomas M and Irvin, Marguerite R and Jaffe, Andrew E and Johnson, Eric O and Kaplan, Robert and Kardia, Sharon L R and Kaufman, Joel D and Kelly, Tanika N and Kleinman, Joel E and Kooperberg, Charles and Lee, I-Te and Levy, Daniel and Lutz, Sharon M and Manichaikul, Ani W and Martin, Lisa W and Marx, Olivia and McGarvey, Stephen T and Minster, Ryan L and Moll, Matthew and Moussa, Karine A and Naseri, Take and North, Kari E and Oelsner, Elizabeth C and Peralta, Juan M and Peyser, Patricia A and Psaty, Bruce M and Rafaels, Nicholas and Raffield, Laura M and Reupena, Muagututi{\textquoteright}a Sefuiva and Rich, Stephen S and Rotter, Jerome I and Schwartz, David A and Shadyab, Aladdin H and Sheu, Wayne H-H and Sims, Mario and Smith, Jennifer A and Sun, Xiao and Taylor, Kent D and Telen, Marilyn J and Watson, Harold and Weeks, Daniel E and Weir, David R and Yanek, Lisa R and Young, Kendra A and Young, Kristin L and Zhao, Wei and Hancock, Dana B and Jiang, Bibo and Vrieze, Scott and Liu, Dajiang J} } @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} }