Evidence map›Paper›PMID 34596256›Full record

ArticleStatistics in medicine2021

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

Osvaldo Espin-Garcia, Radu V Craiu, Shelley B Bull

Abstract read
In one paragraph

Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Osvaldo Espin-GarciaDepartment of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.ORCID 0000-0003-2052-2626
Radu V CraiuDepartment of Statistical Sciences, Faculty of Arts and Sciences, University of Toronto, Toronto, Ontario, Canada.
Shelley B BullDivision of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.

Funding

CIHR GET-101831CIHR MOP-84287CIHR PJT-159463
6 · The paper itself

Abstract

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 github.com/egosv/TwoPhase_postGWAS.

Indexed as

Genome-Wide Association StudyPolymorphism, Single NucleotideGenetic Association StudiesGenotypeHumansPhenotypegenetic algorithmpost-GWAS targeted sequencingpractical two-phase studiesstatistical fine-mappingtwo-phase study design and analysis

Identifiers

PMID34596256
PMCPMC9293221

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.