scholarly journals Polygenic scores via penalized regression on summary statistics

2017 ◽  
Vol 41 (6) ◽  
pp. 469-480 ◽  
Author(s):  
Timothy Shin Heng Mak ◽  
Robert Milan Porsch ◽  
Shing Wan Choi ◽  
Xueya Zhou ◽  
Pak Chung Sham
Keyword(s):  
Penalized Regression ◽  
Summary Statistics ◽  
Polygenic Scores

scholarly journals Polygenic scores via penalized regression on summary statistics

2016 ◽  
Author(s):  
Timothy Shin Heng Mak ◽  
Robert Milan Porsch ◽  
Shing Wan Choi ◽  
Xueya Zhou ◽  
Pak Chung Sham
Keyword(s):  
Prediction Accuracy ◽  
Penalized Regression ◽  
Tuning Parameter ◽  
Summary Statistics ◽  
Validation Data ◽  
Polygenic Scores ◽  
Pertinent Question ◽  
Almost All ◽  
Risk Categories ◽  
General Method

AbstractPolygenic scores (PGS) summarize the genetic contribution of a person’s genotype to a disease or phenotype. They can be used to group participants into different risk categories for diseases, and are also used as covariates in epidemiological analyses. A number of possible ways of calculating polygenic scores have been proposed, and recently there is much interest in methods that incorporate information available in published summary statistics. As there is no inherent information on linkage disequilibrium (LD) in summary statistics, a pertinent question is how we can make use of LD information available elsewhere to supplement such analyses. To answer this question we propose a method for constructing PGS using summary statistics and a reference panel in a penalized regression framework, which we call lassosum. We also propose a general method for choosing the value of the tuning parameter in the absence of validation data. In our simulations, we showed that pseudovalidation often resulted in prediction accuracy that is comparable to using a dataset with validation phenotype and was clearly superior to the conservative option of setting the tuning parameter of lassosum to its lowest value. We also showed that lassosum achieved better prediction accuracy than simple clumping and p-value thresholding in almost all scenarios. It was also substantially faster and more accurate than the recently proposed LDpred.

Contrasting Broad- and Clinically- defined Polygenic Indicators of Depression and Depression-related Phenotypes in Adults and Children

2020 ◽  
Author(s):  
John E. McGeary ◽  
Chelsie Benca-Bachman ◽  
Victoria Risner ◽  
Christopher G Beevers ◽  
Brandon Gibb ◽  
...  
Keyword(s):  
Suicidal Ideation ◽  
Cognitive Reappraisal ◽  
Twin Studies ◽  
European Ancestry ◽  
Summary Statistics ◽  
Depression Severity ◽  
Uk Biobank ◽  
Polygenic Scores ◽  
Adults And Children ◽  
The Uk

Twin studies indicate that 30-40% of the disease liability for depression can be attributed to genetic differences. Here, we assess the explanatory ability of polygenic scores (PGS) based on broad- (PGSBD) and clinical- (PGSMDD) depression summary statistics from the UK Biobank using independent cohorts of adults (N=210; 100% European Ancestry) and children (N=728; 70% European Ancestry) who have been extensively phenotyped for depression and related neurocognitive phenotypes. PGS associations with depression severity and diagnosis were generally modest, and larger in adults than children. Polygenic prediction of depression-related phenotypes was mixed and varied by PGS. Higher PGSBD, in adults, was associated with a higher likelihood of having suicidal ideation, increased brooding and anhedonia, and lower levels of cognitive reappraisal; PGSMDD was positively associated with brooding and negatively related to cognitive reappraisal. Overall, PGS based on both broad and clinical depression phenotypes have modest utility in adult and child samples of depression.

scholarly journals Polygenic scores for UK Biobank scale data

2018 ◽  
Author(s):  
Timothy Shin Heng Mak ◽  
Robert Milan Porsch ◽  
Shing Wan Choi ◽  
Pak Chung Sham
Keyword(s):  
External Source ◽  
Summary Statistics ◽  
Uk Biobank ◽  
Validation Data ◽  
Raw Data ◽  
Cross Prediction ◽  
Polygenic Scores ◽  
The Difference ◽  
The Uk ◽  
Meta Analyses

AbstractPolygenic scores (PGS) are estimated scores representing the genetic tendency of an individual for a disease or trait and have become an indispensible tool in a variety of analyses. Typically they are linear combination of the genotypes of a large number of SNPs, with the weights calculated from an external source, such as summary statistics from large meta-analyses. Recently cohorts with genetic data have become very large, such that it would be a waste if the raw data were not made use of in constructing PGS. Making use of raw data in calculating PGS, however, presents us with problems of overfitting. Here we discuss the essence of overfitting as applied in PGS calculations and highlight the difference between overfitting due to the overlap between the target and the discovery data (OTD), and overfitting due to the overlap between the target the the validation data (OTV). We propose two methods — cross prediction and split validation — to overcome OTD and OTV respectively. Using these two methods, PGS can be calculated using raw data without overfitting. We show that PGSs thus calculated have better predictive power than those using summary statistics alone for six phenotypes in the UK Biobank data.

scholarly journals MTAG: Multi-Trait Analysis of GWAS

2017 ◽  
Author(s):  
Patrick Turley ◽  
Raymond K. Walters ◽  
Omeed Maghzian ◽  
Aysu Okbay ◽  
James J. Lee ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Well Being ◽  
Joint Analysis ◽  
Summary Statistics ◽  
Subjective Well Being ◽  
Bioinformatics Analyses ◽  
Trait Analysis ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Variance Explained

ABSTRACTWe introduce Multi-Trait Analysis of GWAS (MTAG), a method for joint analysis of summary statistics from GWASs of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (Neff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). Compared to 32, 9, and 13 genome-wide significant loci in the single-trait GWASs (most of which are themselves novel), MTAG increases the number of loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase variance explained by polygenic scores by approximately 25%, matching theoretical expectations.

scholarly journals Making the most of Clumping and Thresholding for polygenic scores

2019 ◽  
Author(s):  
Florian Privé ◽  
Bjarni J. Vilhjálmsson ◽  
Hugues Aschard ◽  
Michael G.B. Blum
Keyword(s):  
Predictive Ability ◽  
Predictive Performance ◽  
Real Data ◽  
Penalized Regression ◽  
P Value ◽  
Single Node ◽  
Depression Status ◽  
Polygenic Scores ◽  
Optimal Linear ◽  
The Uk

AbstractPolygenic prediction has the potential to contribute to precision medicine. Clumping and Thresh-olding (C+T) is a widely used method to derive polygenic scores. When using C+T, it is common to test several p-value thresholds to maximize predictive ability of the derived polygenic scores. Along with this p-value threshold, we propose to tune three other hyper-parameters for C+T. We implement an efficient way to derive thousands of different C+T polygenic scores corresponding to a grid over four hyper-parameters. For example, it takes a few hours to derive 123,200 different C+T scores for 300K individuals and 1M variants on a single node with 16 cores.We find that optimizing over these four hyper-parameters improves the predictive performance of C+T in both simulations and real data applications as compared to tuning only the p-value threshold. A particularly large increase can be noted when predicting depression status, from an AUC of 0.557 (95% CI: [0.544-0.569]) when tuning only the p-value threshold in C+T to an AUC of 0.592 (95% CI: [0.580-0.604]) when tuning all four hyper-parameters we propose for C+T.We further propose Stacked Clumping and Thresholding (SCT), a polygenic score that results from stacking all derived C+T scores. Instead of choosing one set of hyper-parameters that maximizes prediction in some training set, SCT learns an optimal linear combination of all C+T scores by using an efficient penalized regression. We apply SCT to 8 different case-control diseases in the UK biobank data and find that SCT substantially improves prediction accuracy with an average AUC increase of 0.035 over standard C+T.

scholarly journals Genomic Prediction of Complex Disease Risk

2018 ◽  
Author(s):  
Louis Lello ◽  
Timothy G. Raben ◽  
Soke Yuen Yong ◽  
Laurent CAM Tellier ◽  
Stephen D.H. Hsu
Keyword(s):  
Genomic Prediction ◽  
Complex Disease ◽  
Disease Risk ◽  
Penalized Regression ◽  
Case Control ◽  
Nucleotide Polymorphisms ◽  
Uk Biobank ◽  
Rapid Improvement ◽  
Control Data ◽  
Polygenic Scores

AbstractWe construct risk predictors using polygenic scores (PGS) computed from common Single Nucleotide Polymorphisms (SNPs) for a number of complex disease conditions, using L1-penalized regression (also known as LASSO) on case-control data from UK Biobank. Among the disease conditions studied are Hypothyroidism, (Resistant) Hypertension, Type 1 and 2 Diabetes, Breast Cancer, Prostate Cancer, Testicular Cancer, Gallstones, Glaucoma, Gout, Atrial Fibrillation, High Cholesterol, Asthma, Basal Cell Carcinoma, Malignant Melanoma, and Heart Attack. We obtain values for the area under the receiver operating characteristic curves (AUC) in the range ~ 0.58 – 0.71 using SNP data alone. Substantially higher predictor AUCs are obtained when incorporating additional variables such as age and sex. Some SNP predictors alone are sufficient to identify outliers (e.g., in the 99th percentile of PGS) with 3 – 8 times higher risk than typical individuals. We validate predictors out-of-sample using the eMERGE dataset, and also with different ancestry subgroups within the UK Biobank population. Our results indicate that substantial improvements in predictive power are attainable using training sets with larger case populations. We anticipate rapid improvement in genomic prediction as more case-control data become available for analysis.

scholarly journals Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits

2018 ◽  
Author(s):  
Andrew D. Grotzinger ◽  
Mijke Rhemtulla ◽  
Ronald de Vlaming ◽  
Stuart J. Ritchie ◽  
Travis T. Mallard ◽  
...  
Keyword(s):  
Complex Traits ◽  
Structural Equation ◽  
Genetic Architecture ◽  
Genetic Correlations ◽  
Covariance Structure ◽  
Equation Modeling ◽  
Joint Analysis ◽  
Summary Statistics ◽  
Individual Traits ◽  
Polygenic Scores

AbstractMethods for using GWAS to estimate genetic correlations between pairwise combinations of traits have produced “atlases” of genetic architecture. Genetic atlases reveal pervasive pleiotropy, and genome-wide significant loci are often shared across different phenotypes. We introduce genomic structural equation modeling (Genomic SEM), a multivariate method for analyzing the joint genetic architectures of complex traits. Using formal methods for modeling covariance structure, Genomic SEM synthesizes genetic correlations and SNP-heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to identify variants with effects on general dimensions of cross-trait liability, boost power for discovery, and calculate more predictive polygenic scores. Finally, Genomic SEM can be used to identify loci that cause divergence between traits, aiding the search for what uniquely differentiates highly correlated phenotypes. We demonstrate several applications of Genomic SEM, including a joint analysis of GWAS summary statistics from five genetically correlated psychiatric traits. We identify 27 independent SNPs not previously identified in the univariate GWASs, 5 of which have been reported in other published GWASs of the included traits. Polygenic scores derived from Genomic SEM consistently outperform polygenic scores derived from GWASs of the individual traits. Genomic SEM is flexible, open ended, and allows for continuous innovations in how multivariate genetic architecture is modeled.

scholarly journals Multivariable G-E interplay in the prediction of educational achievement

2019 ◽  
Author(s):  
A.G. Allegrini ◽  
V. Karhunen ◽  
J. R. I. Coleman ◽  
S. Selzam ◽  
K. Rimfeld ◽  
...  
Keyword(s):  
Life Events ◽  
Home Environment ◽  
Environmental Effects ◽  
Prediction Models ◽  
Educational Achievement ◽  
Model Performance ◽  
Penalized Regression ◽  
Joint Analysis ◽  
Polygenic Scores ◽  
Environmental Measures

AbstractPolygenic scores are increasingly powerful predictors of educational achievement. It is unclear, however, how sets of polygenic scores, which partly capture environmental effects, perform jointly with sets of environmental measures, which are themselves heritable, in prediction models of educational achievement.Here, for the first time, we systematically investigate gene-environment correlation (rGE) and interaction (GxE) in the joint analysis of multiple genome-wide polygenic scores (GPS) and multiple environmental measures as they predict tested educational achievement (EA). We predict EA in a representative sample of 7,026 16-year-olds, with 20 GPS for psychiatric, cognitive and anthropometric traits, and 13 environments (including life events, home environment, and SES) measured earlier in life. Environmental and GPS predictors were modelled, separately and jointly, in penalized regression models with out-of-sample comparisons of prediction accuracy, considering the implications that their interplay had on model performance.Jointly modelling multiple GPS and environmental factors significantly improved prediction of EA, with cognitive-related GPS adding unique independent information beyond SES, home environment and life events. We found evidence for rGE underlying variation in EA (rGE = .36; 95% CIs = .29, .43). We estimated that 38% (95% CIs = 29%, 49%) of the GPS effects on EA were mediated by environmental effects, and in turn that 18% (95% CIs =12%, 25%) of environmental effects were accounted for by the GPS model. Lastly, we did not find evidence that GxE effects collectively contributed to multivariable prediction.Our multivariable polygenic and environmental prediction model suggests widespread rGE and unsystematic GxE contributions to EA in adolescence.

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