scholarly journals Global biobank analyses provide lessons for computing polygenic risk scores across diverse cohorts

2021 ◽  
Author(s):  
Ying Wang ◽  
Shinichi Namba ◽  
Esteban Lopera ◽  
Sini Kerminen ◽  
Kristin Tsuo ◽  
...  
Keyword(s):  
Best Practices ◽  
Prediction Accuracy ◽  
Disease Risk ◽  
Association Studies ◽  
Meta Analysis ◽  
European Ancestry ◽  
Risk Scores ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Polygenic Risk

SummaryWith the increasing availability of biobank-scale datasets that incorporate both genomic data and electronic health records, many associations between genetic variants and phenotypes of interest have been discovered. Polygenic risk scores (PRS), which are being widely explored in precision medicine, use the results of association studies to predict the genetic component of disease risk by accumulating risk alleles weighted by their effect sizes. However, limited studies have thoroughly investigated best practices for PRS in global populations across different diseases. In this study, we utilize data from the Global-Biobank Meta-analysis Initiative (GBMI), which consists of individuals from diverse ancestries and across continents, to explore methodological considerations and PRS prediction performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRS using heuristic (pruning and thresholding, P+T) and Bayesian (PRS-CS) methods. We found that the genetic architecture, such as SNP-based heritability and polygenicity, varied greatly among endpoints. For both PRS construction methods, using a European ancestry LD reference panel resulted in comparable or higher prediction accuracy compared to several other non-European based panels; this is largely attributable to European descent populations still comprising the majority of GBMI participants. PRS-CS overall outperformed the classic P+T method, especially for endpoints with higher SNP-based heritability. For example, substantial improvements are observed in East-Asian ancestry (EAS) using PRS-CS compared to P+T for heart failure (HF) and chronic obstructive pulmonary disease (COPD). Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma which has known variation in disease prevalence across global populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using the GBMI and highlight the importance of best practices for PRS in the biobank-scale genomics era.

scholarly journals On cross-ancestry cancer polygenic risk scores

PLoS Genetics ◽  
2021 ◽  
Vol 17 (9) ◽  
pp. e1009670
Author(s):  
Lars G. Fritsche ◽  
Ying Ma ◽  
Daiwei Zhang ◽  
Maxwell Salvatore ◽  
Seunggeun Lee ◽  
...  
Keyword(s):  
Breast Cancer ◽  
South Asian ◽  
Disease Risk ◽  
Association Studies ◽  
East Asian ◽  
European Ancestry ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
Target Sample

Polygenic risk scores (PRS) can provide useful information for personalized risk stratification and disease risk assessment, especially when combined with non-genetic risk factors. However, their construction depends on the availability of summary statistics from genome-wide association studies (GWAS) independent from the target sample. For best compatibility, it was reported that GWAS and the target sample should match in terms of ancestries. Yet, GWAS, especially in the field of cancer, often lack diversity and are predominated by European ancestry. This bias is a limiting factor in PRS research. By using electronic health records and genetic data from the UK Biobank, we contrast the utility of breast and prostate cancer PRS derived from external European-ancestry-based GWAS across African, East Asian, European, and South Asian ancestry groups. We highlight differences in the PRS distributions of these groups that are amplified when PRS methods condense hundreds of thousands of variants into a single score. While European-GWAS-derived PRS were not directly transferrable across ancestries on an absolute scale, we establish their predictive potential when considering them separately within each group. For example, the top 10% of the breast cancer PRS distributions within each ancestry group each revealed significant enrichments of breast cancer cases compared to the bottom 90% (odds ratio of 2.81 [95%CI: 2.69,2.93] in European, 2.88 [1.85, 4.48] in African, 2.60 [1.25, 5.40] in East Asian, and 2.33 [1.55, 3.51] in South Asian individuals). Our findings highlight a compromise solution for PRS research to compensate for the lack of diversity in well-powered European GWAS efforts while recruitment of diverse participants in the field catches up.

DL-PRS: a novel deep learning approach to polygenic risk scores

2021 ◽  
Author(s):  
Sijia Huang ◽  
Xiao Ji ◽  
Michael Cho ◽  
Jaehyun Joo ◽  
Jason Moore
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Disease Risk ◽  
Association Studies ◽  
Genetic Effects ◽  
Risk Scores ◽  
Chronic Obstructive ◽  
Genome Wide Association Studies ◽  
Polygenic Risk

Abstract Background COPD is a complex heterogeneous disease influenced by both environmental and genetic risk factors. Traditional genome wide association studies (GWAS) have been successful in identifying many reproducible risk variants of moderate to small effect. Polygenic risk scores (PRS) were developed as way to aggregate risk alleles weighted by their effect size to produce a score which could be used in clinical practice to identify individuals at high risk of disease. A limitation of both GWAS and PRS is that they make the important assumption that the effect of each allele is independent and not modified by other genetic or environmental factors. Machine learning methods such as deep learning (DL) neural networks complement the GWAS and PRS paradigm by making fewer assumptions about the nature of the genetic effects being modeled. For example, the hidden layers of a DL model have the potential to model gene-gene interactions with non-additive effects on disease risk. The goal of the present study was to develop a DL neural network approach to GWAS and PRS and to compare it to the prevailing paradigm based on modeling independent effects. We applied our DL-PRS method to genetic association data from several GWAS studies of chronic obstructive pulmonary disease (COPD).Results We developed a DL learning algorithm for modeling the relationship between genetic variation from GWAS and risk of COPD in several population-based studies. We then developed a DL-PRS based on nodes and associated weights from the first and second layer of the DL neural network. Our DL-PRS framework has overall satisfactory performance in the prediction of COPD and provides significant contribution to prediction in addition to the current PRS methods. Moreover, regarding the clinical relevance of COPD, our DL-PRS has a consistent and closer relationship regarding individual deciles and lung functions such as FEV1/FVC and predicted FEV1%. Conclusions Not only does DL-PRS show favorable predictive performance with current benchmark PRS methods, but it also extends the ranges of PRS deciles in predicting different stages of COPD. Moreover, our DL-PRS results were replicated in an independent cohort. This study opens the door to the use of machine learning for developing risk scores from models developed using fewer assumptions about the nature of the genetic effects.

scholarly journals Polygenic transcriptome risk scores improve portability of polygenic risk scores across ancestries

2020 ◽  
Author(s):  
Yanyu Liang ◽  
Milton Pividori ◽  
Ani Manichaikul ◽  
Abraham A. Palmer ◽  
Nancy J. Cox ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Association Studies ◽  
Transcript Abundance ◽  
Effect Sizes ◽  
European Ancestry ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
European Populations

AbstractPolygenic risk scores (PRS) are on course to translate the results of genome-wide association studies (GWAS) into clinical practice. To date, most GWAS have been based on individuals of European-ancestry, meaning that the utility of PRS for non-European populations is limited because SNP effects and LD patterns may not be conserved across populations. We hypothesized that cross population prediction at the level of genes rather than SNPs would be more effective, since the effect of genes on traits is likely to be more highly conserved. Therefore, we developed a framework to convert effect sizes at SNPs into effect sizes for genetically predicted transcript abundance, which we used for prediction in non-European populations. We compared this approach, which we call polygenic transcriptome risk scores (PTRS), to PRS, using data from 17 quantitative traits that were measured in multiple ancestries (European, African, East Asian, and South Asian) by UK Biobank. On average, PTRS using whole blood predicted transcriptome had lower absolute prediction accuracy than PRS, as we expected since not all regulatory processes were captured by a single tissue. However, as hypothesized, we found that in the African target set, the portability (prediction accuracy relative to the European reference set) was significantly higher for PTRS than PRS (p=0.03) with additional gain when transcriptomic prediction models ancestry matched the target population (p=0.021). Taken together, our results suggest that using PTRS can improve prediction in underrepresented populations and that increasing the diversity of transcriptomic data may be an effective way to improve portability of GWAS results between populations and help reduce health disparities.

scholarly journals On Cross-ancestry Cancer Polygenic Risk Scores

2021 ◽  
Author(s):  
Lars G. Fritsche ◽  
Ying Ma ◽  
Daiwei Zhang ◽  
Maxwell Salvatore ◽  
Seunggeun Lee ◽  
...  
Keyword(s):  
Breast Cancer ◽  
South Asian ◽  
Disease Risk ◽  
Association Studies ◽  
East Asian ◽  
European Ancestry ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
Target Sample

AbstractPolygenic risk scores (PRS) can provide useful information for personalized risk stratification and disease risk assessment, especially when combined with non-genetic risk factors. However, their construction depends on the availability of summary statistics from genome-wide association studies (GWAS) independent from the target sample. For best compatibility, it was reported that GWAS and the target sample should match in terms of ancestries. Yet, GWAS, especially in the field of cancer, often lack diversity and are predominated by European ancestry. This bias is a limiting factor in PRS research. By using electronic health records and genetic data from the UK Biobank, we contrast the utility of breast and prostate cancer PRS derived from external European-ancestry-based GWAS across African, East Asian, European, and South Asian ancestry groups. We highlight differences in the PRS distributions of these groups that are amplified when PRS methods condense hundreds of thousands of variants into a single score. While European-GWAS-derived PRS were not directly transferrable across ancestries on an absolute scale, we establish their predictive potential when considering them separately within each group. For example, the top 10% of the breast cancer PRS distributions within each ancestry group each revealed significant enrichments of breast cancer cases compared to the bottom 90% (odds ratio of 2.81 [95%CI: 2.69,2.93] in European, 2.88 [1.85, 4.48] in African, 2.60 [1.25, 5.40] in East Asian, and 2.33 [1.55, 3.51] in South Asian individuals). Our findings highlight a compromise solution for PRS research to compensate for the lack of diversity in well-powered European GWAS efforts while recruitment of diverse participants in the field catches up.

Polymorphisms of the ADAM33 gene and chronic obstructive pulmonary disease risk: a meta-analysis

2013 ◽  
Vol 8 (1) ◽  
pp. 108-115 ◽  
Author(s):  
Haidiya Aierken ◽  
Jing Wang ◽  
Qimanguli Wushouer ◽  
Elnur Shayhidin ◽  
Xing Hu ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Disease Risk ◽  
Meta Analysis ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease

scholarly journals Sleep Deficits and Cannabis Use Behaviors: An Analysis of Shared Genetics Using Linkage Disequilibrium Score Regression and Polygenic Risk Prediction

2020 ◽  
Author(s):  
Evan A. Winiger ◽  
Jarrod M. Ellingson ◽  
Claire L. Morrison ◽  
Robin P. Corley ◽  
Joëlle A. Pasman ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Sleep Duration ◽  
Association Studies ◽  
Genetic Correlations ◽  
Cannabis Use ◽  
European Ancestry ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Short Sleep Duration ◽  
Polygenic Risk

AbstractStudy ObjectivesEstimate the genetic relationship of cannabis use with sleep deficits and eveningness chronotype.MethodsWe used linkage disequilibrium score regression (LDSC) to analyze genetic correlations between sleep deficits and cannabis use behaviors. Secondly, we generated sleep deficit polygenic risk scores (PRSs) and estimated their ability to predict cannabis use behaviors using logistic regression. Summary statistics came from existing genome wide association studies (GWASs) of European ancestry that were focused on sleep duration, insomnia, chronotype, lifetime cannabis use, and cannabis use disorder (CUD). A target sample for PRS prediction consisted of high-risk participants and participants from twin/family community-based studies (n = 796, male = 66%; mean age = 26.81). Target data consisted of self-reported sleep (sleep duration, feeling tired, and taking naps) and cannabis use behaviors (lifetime use, number of lifetime uses, past 180-day use, age of first use, and lifetime CUD symptoms).ResultsSignificant genetic correlation between lifetime cannabis use and eveningness chronotype (rG = 0.24, p < 0.01), as well as between CUD and both short sleep duration (<7 h) (rG = 0.23, p = 0.02) and insomnia (rG = 0.20, p = 0.02). Insomnia PRS predicted earlier age of first cannabis use (β = −0.09, p = 0.02) and increased lifetime CUD symptom count use (β = 0.07, p = 0.03).ConclusionCannabis use is genetically associated with both sleep deficits and an eveningness chronotype, suggesting that there are genes that predispose individuals to both cannabis use and sleep deficits.

scholarly journals A Systematic Analysis of Protein-altering Exonic Variants in Chronic Obstructive Pulmonary Disease

2021 ◽  
Author(s):  
Matthew Moll ◽  
Victoria E. Jackson ◽  
Bing Yu ◽  
Megan L. Grove ◽  
Stephanie J. London ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Cell Proliferation ◽  
Pulmonary Disease ◽  
Association Studies ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Chronic Obstructive ◽  
Genome Wide Association Studies ◽  
Obstructive Pulmonary Disease ◽  
Coding Variants

Genome-wide association studies (GWASs) have identified regions associated with chronic obstructive pulmonary disease (COPD). GWASs of other diseases have shown an approximately 10-fold overrepresentation of nonsynonymous variants, despite limited exonic coverage on genotyping arrays. We hypothesized that a large-scale analysis of coding variants could discover novel genetic associations with COPD, including rare variants with large effect sizes. We performed a meta-analysis of exome arrays from 218,399 controls and 33,851 moderate-to-severe COPD cases. All exome-wide significant associations were present in regions previously identified by GWAS. We did not identify any novel rare coding variants with large effect sizes. Within GWAS regions on chromosomes 5q, 6p, and 15q, four coding variants were conditionally significant (p < 0.00015) when adjusting for lead GWAS SNPs. A common GSDMB splice variant (rs11078928) previously associated with decreased risk for asthma, was nominally associated with decreased risk for COPD (MAF = 0.46, p=1.8e-4). Two stop variants in CCHCR1, a gene involved in regulating cell proliferation, were associated with COPD (both p < 0.0001). The SERPINA1 Z allele was associated with a random effects odds ratio of 1.43 for COPD (95% CI: 1.17-1.74), though with marked heterogeneity across studies. Overall, COPD-associated exonic variants were identified in genes involved in DNA methylation, cell-matrix interactions, cell proliferation, and cell death. In conclusion, we performed the largest exome array meta-analysis of COPD to date and identified potential functional coding variants. Future studies are needed to identify rarer variants, and further define the role of coding variants in COPD pathogenesis.

scholarly journals Polygenic transcriptome risk scores (PTRS) can improve portability of polygenic risk scores across ancestries

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Yanyu Liang ◽  
Milton Pividori ◽  
Ani Manichaikul ◽  
Abraham A. Palmer ◽  
Nancy J. Cox ◽  
...  
Keyword(s):  
Association Studies ◽  
Poor Performance ◽  
Genome Wide Association ◽  
European Ancestry ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Transcript Levels ◽  
Polygenic Risk ◽  
Genome Wide

Abstract Background Polygenic risk scores (PRS) are valuable to translate the results of genome-wide association studies (GWAS) into clinical practice. To date, most GWAS have been based on individuals of European-ancestry leading to poor performance in populations of non-European ancestry. Results We introduce the polygenic transcriptome risk score (PTRS), which is based on predicted transcript levels (rather than SNPs), and explore the portability of PTRS across populations using UK Biobank data. Conclusions We show that PTRS has a significantly higher portability (Wilcoxon p=0.013) in the African-descent samples where the loss of performance is most acute with better performance than PRS when used in combination.

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