@article {136, title = {Incorporating European GWAS findings improve polygenic risk prediction accuracy of breast cancer among East Asians.}, journal = {Genet Epidemiol}, year = {2021}, month = {2021 Mar 19}, abstract = {

Previous genome-wide association studies (GWASs) have been largely focused on European (EUR) populations. However, polygenic risk scores (PRSs) derived from EUR have been shown to perform worse in non-EURs compared with EURs. In this study, we aim to improve PRS prediction in East Asians (EASs). We introduce a rescaled meta-analysis framework to combine both EUR (N = 122,175) and EAS (N = 30,801) GWAS summary statistics. To improve PRS prediction in EASs, we use a scaling factor to up-weight the EAS data, such that the resulting effect size estimates are more relevant to EASs. We then derive PRSs for EAS from the rescaled meta-analysis results of EAS and EUR data. Evaluated in an independent EAS validation data set, this approach increases the prediction liability-adjusted Nagelkerke{\textquoteright}s pseudo R by 40\%, 41\%, and 5\%, respectively, compared with PRSs derived from an EAS GWAS only, EUR GWAS only, and conventional fixed-effects meta-analysis of EAS and EUR data. The PRS derived from the rescaled meta-analysis approach achieved an area under the receiver operating characteristic curve (AUC)~of 0.6059, higher than AUC = 0.5782, 0.5809, 0.6008 for EAS, EUR, and conventional meta-analysis of EAS and EUR. We further compare PRSs constructed by single-nucleotide polymorphisms that have different linkage disequilibrium (LD) scores and minor allele frequencies (MAFs) between EUR and EAS, and observe that lower LD scores or MAF in EAS correspond to poorer PRS performance (AUC = 0.5677,~0.5530, respectively) than higher LD scores or MAF (AUC = 0.589, 0.5993, respectively). We finally build a PRS stratified by LD score differences in EUR and EAS using rescaled meta-analysis, and obtain an AUC of 0.6096, with improvement over other strategies investigated.

}, issn = {1098-2272}, doi = {10.1002/gepi.22382}, author = {Ji, Ying and Long, Jirong and Kweon, Sun-Seog and Kang, Daehee and Kubo, Michiaki and Park, Boyoung and Shu, Xiao-Ou and Zheng, Wei and Tao, Ran and Li, Bingshan} } @article {82, title = {Electronic health record phenotypes associated with genetically regulated expression of CFTR and application to cystic fibrosis.}, journal = {Genet Med}, volume = {22}, year = {2020}, month = {2020 07}, pages = {1191-1200}, abstract = {

PURPOSE: The increasing use of electronic health records (EHRs) and biobanks offers unique opportunities to study Mendelian diseases. We described a novel approach to summarize clinical manifestations from patient EHRs into phenotypic evidence for cystic fibrosis (CF) with potential to alert unrecognized patients of the disease.

METHODS: We estimated genetically predicted expression (GReX) of cystic fibrosis transmembrane conductance regulator (CFTR) and tested for association with clinical diagnoses in the Vanderbilt University biobank (N = 9142 persons of European descent with 71 cases of CF). The top associated EHR phenotypes were assessed in combination as a phenotype risk score (PheRS) for discriminating CF case status in an additional 2.8 million patients from Vanderbilt University Medical Center (VUMC) and 125,305 adult patients including 25,314 CF cases from MarketScan, an independent external cohort.

RESULTS: GReX of CFTR was associated with EHR phenotypes consistent with CF. PheRS constructed using the EHR phenotypes and weights discovered by the genetic associations improved discriminative power for CF over the initially proposed PheRS in both VUMC and MarketScan.

CONCLUSION: Our study demonstrates the power of EHRs for clinical description of CF and the benefits of using a genetics-informed weighing scheme in construction of a phenotype risk score. This research may find broad applications for phenomic studies of Mendelian disease genes.

}, keywords = {Adult, Cystic Fibrosis, Cystic Fibrosis Transmembrane Conductance Regulator, Electronic Health Records, Humans, Mutation, Phenotype}, issn = {1530-0366}, doi = {10.1038/s41436-020-0786-5}, author = {Zhong, Xue and Yin, Zhijun and Jia, Gengjie and Zhou, Dan and Wei, Qiang and Faucon, Annika and Evans, Patrick and Gamazon, Eric R and Li, Bingshan and Tao, Ran and Rzhetsky, Andrey and Bastarache, Lisa and Cox, Nancy J} } @article {103, title = {Multi-Ethnic Genome-Wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits.}, journal = {Circ Genom Precis Med}, volume = {13}, year = {2020}, month = {2020 08}, pages = {e002680}, abstract = {

BACKGROUND: We examined how expanding electrocardiographic trait genome-wide association studies to include ancestrally diverse populations, prioritize more precise phenotypic measures, and evaluate evidence for shared genetic effects enabled the detection and characterization of loci.

METHODS: We decomposed 10 seconds, 12-lead electrocardiograms from 34 668 multi-ethnic participants (15\% Black; 30\% Hispanic/Latino) into 6 contiguous, physiologically distinct (P wave, PR segment, QRS interval, ST segment, T wave, and TP segment) and 2 composite, conventional (PR interval and QT interval) interval scale traits and conducted multivariable-adjusted, trait-specific univariate genome-wide association studies using 1000-G imputed single-nucleotide polymorphisms. Evidence of shared genetic effects was evaluated by aggregating meta-analyzed univariate results across the 6 continuous electrocardiographic traits using the combined phenotype adaptive sum of powered scores test.

RESULTS: We identified 6 novels (, and ) and 87 known loci (adaptive sum of powered score test <5{\texttimes}10). Lead single-nucleotide polymorphism rs3211938 at was common in Blacks (minor allele frequency=10\%), near monomorphic in European Americans, and had effects on the QT interval and TP segment that ranked among the largest reported to date for common variants. The other 5 novel loci were observed when evaluating the contiguous but not the composite electrocardiographic traits. Combined phenotype testing did not identify novel electrocardiographic loci unapparent using traditional univariate approaches, although this approach did assist with the characterization of known loci.

CONCLUSIONS: Despite including one-third as many participants as published electrocardiographic trait genome-wide association studies, our study identified 6 novel loci, emphasizing the importance of ancestral diversity and phenotype resolution in this era of ever-growing genome-wide association studies.

}, issn = {2574-8300}, doi = {10.1161/CIRCGEN.119.002680}, author = {Baldassari, Antoine R and Sitlani, Colleen M and Highland, Heather M and Arking, Dan E and Buyske, Steve and Darbar, Dawood and Gondalia, Rahul and Graff, Misa and Guo, Xiuqing and Heckbert, Susan R and Hindorff, Lucia A and Hodonsky, Chani J and Ida Chen, Yii-Der and Kaplan, Robert C and Peters, Ulrike and Post, Wendy and Reiner, Alex P and Rotter, Jerome I and Shohet, Ralph V and Seyerle, Amanda A and Sotoodehnia, Nona and Tao, Ran and Taylor, Kent D and Wojcik, Genevieve L and Yao, Jie and Kenny, Eimear E and Lin, Henry J and Soliman, Elsayed Z and Whitsel, Eric A and North, Kari E and Kooperberg, Charles and Avery, Christy L} } @article {64, title = {A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data.}, journal = {Nat Neurosci}, volume = {22}, year = {2019}, month = {2019 05}, pages = {691-699}, abstract = {

Genome-wide association studies (GWAS) have identified more than 100 schizophrenia (SCZ)-associated loci, but using these findings to illuminate disease biology remains a challenge. Here we present integrative risk gene selector (iRIGS), a Bayesian framework that integrates multi-omics data and gene networks to infer risk genes in GWAS loci. By applying iRIGS to SCZ GWAS data, we predicted a set of high-confidence risk genes, most of which are not the nearest genes to the GWAS index variants. High-confidence risk genes account for a significantly enriched heritability, as estimated by stratified linkage disequilibrium score regression. Moreover, high-confidence risk genes are predominantly expressed in brain tissues, especially prenatally, and are enriched for targets of approved drugs, suggesting opportunities to reposition existing drugs for SCZ. Thus, iRIGS can leverage accumulating functional genomics and GWAS data to advance our understanding of SCZ etiology and potential therapeutics.

}, keywords = {Animals, Bayes Theorem, Disease Models, Animal, Gene Regulatory Networks, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Humans, Mice, Risk Factors, Schizophrenia}, issn = {1546-1726}, doi = {10.1038/s41593-019-0382-7}, author = {Wang, Quan and Chen, Rui and Cheng, Feixiong and Wei, Qiang and Ji, Ying and Yang, Hai and Zhong, Xue and Tao, Ran and Wen, Zhexing and Sutcliffe, James S and Liu, Chunyu and Cook, Edwin H and Cox, Nancy J and Li, Bingshan} }