@article {9372, title = {Genome-Wide Association Study of Apparent Treatment-Resistant Hypertension in the CHARGE Consortium: The CHARGE Pharmacogenetics Working Group.}, journal = {Am J Hypertens}, volume = {32}, year = {2019}, month = {2019 Nov 15}, pages = {1146-1153}, abstract = {

BACKGROUND: Only a handful of genetic discovery efforts in apparent treatment-resistant hypertension (aTRH) have been described.

METHODS: We conducted a case-control genome-wide association study of aTRH among persons treated for hypertension, using data from 10 cohorts of European ancestry (EA) and 5 cohorts of African ancestry (AA). Cases were treated with 3 different antihypertensive medication classes and had blood pressure (BP) above goal (systolic BP >= 140 mm Hg and/or diastolic BP >= 90 mm Hg) or 4 or more medication classes regardless of BP control (nEA = 931, nAA = 228). Both a normotensive control group and a treatment-responsive control group were considered in separate analyses. Normotensive controls were untreated (nEA = 14,210, nAA = 2,480) and had systolic BP/diastolic BP < 140/90 mm Hg. Treatment-responsive controls (nEA = 5,266, nAA = 1,817) had BP at goal (<140/90 mm Hg), while treated with one antihypertensive medication class. Individual cohorts used logistic regression with adjustment for age, sex, study site, and principal components for ancestry to examine the association of single-nucleotide polymorphisms with case-control status. Inverse variance-weighted fixed-effects meta-analyses were carried out using METAL.

RESULTS: The known hypertension locus, CASZ1, was a top finding among EAs (P = 1.1 {\texttimes} 10-8) and in the race-combined analysis (P = 1.5 {\texttimes} 10-9) using the normotensive control group (rs12046278, odds ratio = 0.71 (95\% confidence interval: 0.6-0.8)). Single-nucleotide polymorphisms in this locus were robustly replicated in the Million Veterans Program (MVP) study in consideration of a treatment-responsive control group. There were no statistically significant findings for the discovery analyses including treatment-responsive controls.

CONCLUSION: This genomic discovery effort for aTRH identified CASZ1 as an aTRH risk locus.

}, keywords = {Aged, Antihypertensive Agents, Black or African American, Blood Pressure, Case-Control Studies, DNA (Cytosine-5-)-Methyltransferases, DNA Methyltransferase 3A, DNA-Binding Proteins, Drug Resistance, Dystrophin-Associated Proteins, Europe, Female, Genetic Loci, Genome-Wide Association Study, Humans, Hypertension, Male, Middle Aged, Myosin Heavy Chains, Myosin Type V, Neuropeptides, Pharmacogenetics, Pharmacogenomic Variants, Polymorphism, Single Nucleotide, Risk Assessment, Risk Factors, Transcription Factors, United States, White People}, issn = {1941-7225}, doi = {10.1093/ajh/hpz150}, author = {Irvin, Marguerite R and Sitlani, Colleen M and Floyd, James S and Psaty, Bruce M and Bis, Joshua C and Wiggins, Kerri L and Whitsel, Eric A and St{\"u}rmer, Til and Stewart, James and Raffield, Laura and Sun, Fangui and Liu, Ching-Ti and Xu, Hanfei and Cupples, Adrienne L and Tanner, Rikki M and Rossing, Peter and Smith, Albert and Zilh{\~a}o, Nuno R and Launer, Lenore J and Noordam, Raymond and Rotter, Jerome I and Yao, Jie and Li, Xiaohui and Guo, Xiuqing and Limdi, Nita and Sundaresan, Aishwarya and Lange, Leslie and Correa, Adolfo and Stott, David J and Ford, Ian and Jukema, J Wouter and Gudnason, Vilmundur and Mook-Kanamori, Dennis O and Trompet, Stella and Palmas, Walter and Warren, Helen R and Hellwege, Jacklyn N and Giri, Ayush and O{\textquoteright}donnell, Christopher and Hung, Adriana M and Edwards, Todd L and Ahluwalia, Tarunveer S and Arnett, Donna K and Avery, Christy L} } @article {9161, title = {Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations.}, journal = {Commun Biol}, volume = {5}, year = {2022}, month = {2022 08 22}, pages = {856}, abstract = {

Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a trait, yet they fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). We address this via a machine learning approach, validated in nine complex phenotypes in a multi-ancestry population. We use an ensemble method of SNP selection followed by gradient boosted trees (XGBoost) to allow for non-linearities and interaction effects. We compare our results to the standard, linear PRS model developed using PRSice, LDpred2, and lassosum2. Combining a PRS as a feature in an XGBoost model results in a relative increase in the percentage variance explained compared to the standard linear PRS model by 22\% for height, 27\% for HDL cholesterol, 43\% for body mass index, 50\% for sleep duration, 58\% for systolic blood pressure, 64\% for total cholesterol, 66\% for triglycerides, 77\% for LDL cholesterol, and 100\% for diastolic blood pressure. Multi-ancestry trained models perform similarly to specific racial/ethnic group trained models and are consistently superior to the standard linear PRS models. This work demonstrates an effective method to account for non-linearities and interaction effects in genetics-based prediction models.

}, keywords = {Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Machine Learning, Multifactorial Inheritance, Polymorphism, Single Nucleotide}, issn = {2399-3642}, doi = {10.1038/s42003-022-03812-z}, author = {Elgart, Michael and Lyons, Genevieve and Romero-Brufau, Santiago and Kurniansyah, Nuzulul and Brody, Jennifer A and Guo, Xiuqing and Lin, Henry J and Raffield, Laura and Gao, Yan and Chen, Han and de Vries, Paul and Lloyd-Jones, Donald M and Lange, Leslie A and Peloso, Gina M and Fornage, Myriam and Rotter, Jerome I and Rich, Stephen S and Morrison, Alanna C and Psaty, Bruce M and Levy, Daniel and Redline, Susan and Sofer, Tamar} }