Penalized regression and model selection methods for polygenic scores on summary statistics

Pattee, Jack; Pan, Wei

doi:10.1371/journal.pcbi.1008271

Cited by 32 publications

(22 citation statements)

References 37 publications

(61 reference statements)

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“…Other methods employ high-dimensional regression analysis to jointly estimate the effect sizes of risk variants, and incorporate various penalty terms to shrink the linear coefficients. For example, PANPRS ( Chen et al , 2020 ) use L 1 penalty, TlpSum takes a truncated LASSO penalty ( Pattee and Pan, 2020 ), and LassoSum ( Mak et al. , 2017 ) and ElastSum ( Pattee and Pan, 2020 ) use a combination of L 1 and L 2 penalties.…”

Section: Introductionmentioning

confidence: 99%

“…For example, PANPRS ( Chen et al , 2020 ) use L 1 penalty, TlpSum takes a truncated LASSO penalty ( Pattee and Pan, 2020 ), and LassoSum ( Mak et al. , 2017 ) and ElastSum ( Pattee and Pan, 2020 ) use a combination of L 1 and L 2 penalties. In comparison to penalized regression approaches, Bayesian high-dimensional regression methods bring additional flexibility by allowing for a wide range of priors to model the polygenic structure of complex diseases.…”

Section: Introductionmentioning

confidence: 99%

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A data-adaptive Bayesian regression approach for polygenic risk prediction

2022

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Motivation Polygenic risk score (PRS) has been widely exploited for genetic risk prediction due to its accuracy and conceptual simplicity. We introduce a unified Bayesian regression framework, NeuPred, for PRS construction, which accommodates varying genetic architectures and improves overall prediction accuracy for complex diseases by allowing for a wide class of prior choices. To take full advantage of the framework, we propose a summary-statistics-based cross-validation strategy to automatically select suitable chromosome-level priors, which demonstrates a striking variability of the prior preference of each chromosome, for the same complex disease, and further significantly improves the prediction accuracy. Results Simulation studies and real data applications with seven disease datasets from the Wellcome Trust Case Control Consortium cohort and eight groups of large-scale genome-wide association studies demonstrate that NeuPred achieves substantial and consistent improvements in terms of predictive r2 over existing methods. In addition, NeuPred has similar or advantageous computational efficiency compared with the state-of-the-art Bayesian methods. Availability The R package implementing NeuPred is available at https://github.com/shuangsong0110/NeuPred. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A data-adaptive Bayesian regression approach for polygenic risk prediction

2022

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“…For simplicity, we only considered a continuous

boldy

. Because the effect size of individual gene expression is small, we expect similar results should largely hold for a binary outcome (Pattee & Pan, 2020).…”

Section: Simulation Studymentioning

confidence: 84%

InTACT: An adaptive and powerful framework for joint‐tissue transcriptome‐wide association studies

Bae

2021

Genetic Epidemiology

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Transcriptome‐wide association studies (TWAS) that integrate transcriptomic reference data and genome‐wide association studies (GWAS) have successfully enhanced the discovery of candidate genes for many complex traits. However, existing methods may suffer from substantial power loss because they fail to effectively consider that expression of many genes tends to be consistent across tissues. Here we propose a computationally efficient testing method, referred to as Integrative Test for Associations via Cauchy Transformation (InTACT), that effectively combines information across multiple tissues and thus improves the power of identifying associated genes. Through simulation studies, we show that InTACT maintains high power while properly controls for Type 1 error rates. We applied InTACT to the largest GWAS of Alzheimer's disease (AD) to date and identified 227 genome‐wide significant genes, of which 130 were not identified by benchmark methods, TWAS and MultiXcan. Importantly, InTACT identified five novel loci for AD. We implemented InTACT in publicly available software, “InTACT.”

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“…There are other interesting tools that can construct PRS from the summary statistics of a GWAS. These are PRS-CS [ 84 ], RSS [ 85 ], R2BGLiMS [ 86 ], penRegSum [ 87 ], ggmix [ 88 ], XPASS [ 79 ] and NPS [ 89 ].…”

Section: Enhance Your Gwas: the Different Ways To Exploit Snp Array Datamentioning

confidence: 99%

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

Balagué-Dobón

Cáceres

González

2022

Briefings in Bioinformatics

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Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.

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Penalized regression and model selection methods for polygenic scores on summary statistics

Cited by 32 publications

References 37 publications

A data-adaptive Bayesian regression approach for polygenic risk prediction

A data-adaptive Bayesian regression approach for polygenic risk prediction

InTACT: An adaptive and powerful framework for joint‐tissue transcriptome‐wide association studies

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

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