Two‐phase designs for joint quantitative‐trait‐dependent and genotype‐dependent sampling in post‐GWAS regional sequencing

Espin‐Garcia, Osvaldo; Craiu, Radu V.; Bull, Shelley B.

doi:10.1002/gepi.22099

Cited by 6 publications

(8 citation statements)

References 38 publications

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“…We assume a data generating mechanism similar to Espin-Garcia et al [17] . Briefly, for a phase 1 sample size (N ), and given values for minor allele frequencies (MAFs), q G and q z and the linkage disequilibrium (LD), quantified through the Pearson correlation coefficient, r, we simulate two variants on the same haplotype under Hardy-Weinberg equilibrium (HWE): G 1 and Z.…”

Section: Data Generationmentioning

confidence: 99%

Two-phase sample selection strategies for design and analysis in post-genome wide association fine-mapping studies

Espin‐Garcia

Craiu

Bull

2021

Preprint

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Post-GWAS analysis, in many cases, focuses on fine-mapping targeted genetic regions discovered at GWAS-stage; that is, the aim is to pinpoint potential causal variants and susceptibility genes for complex traits and disease outcomes using next-generation sequencing (NGS) technologies. Large-scale GWAS cohorts are necessary to identify target regions given the typically modest genetic effect sizes. In this context, two-phase sampling design and analysis is a cost-reduction technique that utilizes data collected during phase 1 GWAS to select an informative subsample for phase 2 sequencing. The main goal is to make inference for genetic variants measured via NGS by efficiently combining data from phases 1 and 2. We propose two approaches for selecting a phase 2 design under a budget constraint. The first method identifies sampling fractions that select a phase 2 design yielding an asymptotic variance covariance matrix with certain optimal characteristics, e.g. smallest trace, via Lagrange multipliers (LM). The second relies on a genetic algorithm (GA) with a defined fitness function to identify exactly a phase 2 subsample. We perform comprehensive simulation studies to evaluate the empirical properties of the proposed designs for a genetic association study of a quantitative trait. We compare our methods against two ranked designs: residual-dependent sampling and a recently identified optimal design. Our findings demonstrate that the proposed designs, GA in particular, can render competitive power in combined phase 1 and 2 analysis compared to alternative designs while preserving type 1 error control. These results are especially apparent under the more practical scenario where design values need to be defined a priori and are subject to mispecification. We illustrate the proposed methods in a study of triglyceride levels in the North Finland Birth Cohort of 1966. R code to reproduce our results is available at github.com/egosv/TwoPhase_postGWAS.

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Section: Data Generationmentioning

confidence: 99%

Two-phase sample selection strategies for design and analysis in post-genome wide association fine-mapping studies

Espin‐Garcia

Craiu

Bull

2021

Preprint

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“…The formulation above has been amply studied 11,14,15,17 . Estimates can be obtained via the EM algorithm 18‐20 and the corresponding asymptotic variance covariance matrix is computed via the Louis' method 21 .…”

Section: Phase 2 Sample Selection Under Maximum Likelihoodmentioning

confidence: 99%

“…We assume a data generating mechanism similar to Espin‐Garcia et al 11 Briefly, for a phase 1 sample size ( N ), and given values for minor allele frequencies (MAFs),

q_{G}

and

q_{z}

and the linkage disequilibrium (LD), quantified through the Pearson correlation coefficient, r , we simulate two variants on the same haplotype under Hardy‐Weinberg equilibrium (HWE):

G_{1}

and Z . Here,

q_{G}

and

q_{Z}

are the frequencies of the less common allele in the population for

G_{1}

and Z , respectively, whereas LD is the level of correlation between them.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…In this report, we propose two approaches for two‐phase sample selection in post‐GWA fine‐mapping studies. Our previously described methods, 11 when paired with the resulting sample designs, preserve type I error control and are applicable for all distributions in the exponential family. The first approach, LM, extends and adapts previous work primarily developed for case‐control studies by solving a constrained optimization problem via Lagrange multipliers using numerical methods.…”

Section: Introductionmentioning

confidence: 99%

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Two‐phase sample selection strategies for design and analysis in post‐genome‐wide association fine‐mapping studies

Espin‐Garcia

Craiu

Bull

2021

Statistics in Medicine

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Post‐GWAS analysis, in many cases, focuses on fine‐mapping targeted genetic regions discovered at GWAS‐stage; that is, the aim is to pinpoint potential causal variants and susceptibility genes for complex traits and disease outcomes using next‐generation sequencing (NGS) technologies. Large‐scale GWAS cohorts are necessary to identify target regions given the typically modest genetic effect sizes. In this context, two‐phase sampling design and analysis is a cost‐reduction technique that utilizes data collected during phase 1 GWAS to select an informative subsample for phase 2 sequencing. The main goal is to make inference for genetic variants measured via NGS by efficiently combining data from phases 1 and 2. We propose two approaches for selecting a phase 2 design under a budget constraint. The first method identifies sampling fractions that select a phase 2 design yielding an asymptotic variance covariance matrix with certain optimal characteristics, for example, smallest trace, via Lagrange multipliers (LM). The second relies on a genetic algorithm (GA) with a defined fitness function to identify exactly a phase 2 subsample. We perform comprehensive simulation studies to evaluate the empirical properties of the proposed designs for a genetic association study of a quantitative trait. We compare our methods against two ranked designs: residual‐dependent sampling and a recently identified optimal design. Our findings demonstrate that the proposed designs, GA in particular, can render competitive power in combined phase 1 and 2 analysis compared with alternative designs while preserving type 1 error control. These results are especially evident under the more practical scenario where design values need to be defined a priori and are subject to misspecification. We illustrate the proposed methods in a study of triglyceride levels in the North Finland Birth Cohort of 1966. R code to reproduce our results is available at https://github.com/egosv/TwoPhase_postGWAS.

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“…Compared to simple random sampling, which ignores genetic information from GWAS, tag‐SNP‐based stratified sample allocation reduces the number of variants in the credible interval and is more likely to promote the causal sequence variant into confirmation studies (Chen, Craiu, & Bull, ). For studies of quantitative traits the use of trait‐dependent sampling, alone or in combination with genotype‐dependent sampling, can also improve cost‐efficiency but inference is complicated by ascertainment on the outcome (Yilmaz & Bull, 2001; Lin, Zeng, & Rang, ; Derkach, Lawless, & Sun, ; Espin‐Garcia, Craiu, & Bull, ).…”

Section: Design and Analysis—two Phase Sampling Studiesmentioning

confidence: 99%

Statistical challenges in high‐dimensional molecular and genetic epidemiology

2017

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Molecular and genetic association studies conducted in well‐characterized longitudinal cohorts offer a powerful approach to investigate factors influencing disease course or complex trait expression. As measurement technologies continue to develop and evolve, studies based on existing cohorts raise methodological challenges. Five such challenges are illustrated in two long‐term inter‐disciplinary collaborations. In one, molecular genetic prognostic factors in the natural history of node‐negative breast cancer are investigated using a combination of hypothesis‐testing and hypothesis‐generating molecular approaches. In the other, genome‐wide association methods are applied to identify genes for multiple traits in extended follow‐up data from participants of a therapeutic RCT in type 1 diabetes. The Canadian Journal of Statistics 46: 24–40; 2018 © 2017 Statistical Society of Canada

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Two‐phase designs for joint quantitative‐trait‐dependent and genotype‐dependent sampling in post‐GWAS regional sequencing

Cited by 6 publications

References 38 publications

Two-phase sample selection strategies for design and analysis in post-genome wide association fine-mapping studies

Two-phase sample selection strategies for design and analysis in post-genome wide association fine-mapping studies

Two‐phase sample selection strategies for design and analysis in post‐genome‐wide association fine‐mapping studies

Statistical challenges in high‐dimensional molecular and genetic epidemiology

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