A comparison of popular TDT‐generalizations for family‐based association analysis

Hecker, Julian; Laird, Nan M.; Lange, Christoph

doi:10.1002/gepi.22181

Cited by 7 publications

(15 citation statements)

References 29 publications

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“…We do not assess any indirect effects (e.g., maternal effect, parent-of-origin effect, imprinting) or any interaction effect (e.g., maternal-fetal genotype interactions, gene-environment interaction, epistasis) [Ainsworth et al, 2011]. We use the TDT-like methods exclusively as association tests [Laird and Lange, 2006], and do not explore their type I error rates separately for the other possible null hypothesis scenarios under the original “no linkage or no association” composite null hypothesis [Laird and Lange, 2008, Hecker, Laird and Lange, 2019]. We do not consider monads [Fan et al, 2013] or any other pedigree structure [Chen, Manichaikul and Rich, 2009, Hecker, Laird and Lange, 2019].…”

Section: Discussionmentioning

confidence: 99%

“…We use the TDT-like methods exclusively as association tests [Laird and Lange, 2006], and do not explore their type I error rates separately for the other possible null hypothesis scenarios under the original “no linkage or no association” composite null hypothesis [Laird and Lange, 2008, Hecker, Laird and Lange, 2019]. We do not consider monads [Fan et al, 2013] or any other pedigree structure [Chen, Manichaikul and Rich, 2009, Hecker, Laird and Lange, 2019]. We explore three different mother-child pair designs and their corresponding trio designs; other hybrid designs are certainly possible [Vermeulen et al, 2009, Gjerdevik et al, 2020] but are beyond the scope of this paper.…”

Section: Discussionmentioning

confidence: 99%

“…We consider bi-allelic SNPs only (not considering multi-allelic or haplotype effects) [Cordell, Barratt and Clayton, 2004, Gjessing and Lie, 2006]. Our simulation framework is simple and do not reflect the usual complex genetic architecture underlying many disorders; however, we use a similar framework and parameter choices as used by many others [Deng and Chen, 2001, Chen, Manichaikul and Rich, 2009, Hecker, Laird and Lange, 2019]. Our simulations do not involve any confounders since most TDT-like methods cannot accommodate covariate effects.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, methods for analyzing dyads were mostly developed 20 or more years ago when type I error performance and statistical power were evaluated at nominal significance levels instead of the more stringent genome-wide levels used now. Recently, Hecker, Laird and Lange [2019] compared type I error and power of methods for general pedigree designs under different founder genotype distribution schemes but only considered the case-parent trio design among nuclear family designs and no hybrid designs. Gjerdevik et al [2020] compared the relative efficiency of different hybrid designs with the case-control and the case-parent trio designs using direct and indirect effects under log-linear models implemented in the HAPLIN program.…”

Section: Introductionmentioning

confidence: 99%

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Benchmarking statistical methods for analyzing parent-child dyads in genetic association studies

Ray

Vergara

Taub

et al. 2021

Preprint

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Genetic association studies of child health outcomes often employ family-based designs. One of the most popular family-based designs is the case-parent trio design that considers the smallest possible nuclear family consisting of two parents and their affected child. This trio design is particularly advantageous for studying relatively rare disorders because it is less prone to type 1 error inflation due to population stratification compared to population-based study designs (e.g., case-control studies). However, obtaining genetic data from both parents is difficult, from a practical perspective, and many large studies predominantly measure genetic variants in mother-child dyads. While some statistical methods for analyzing parent-child dyad data (most commonly involving mother-child pairs) exist, it is not clear if they provide the same advantage as trio methods in protecting against population stratification, or if a specific dyad design (e.g., case-mother dyads vs. case-mother/control-mother dyads) is more advantageous. In this article, we review existing statistical methods for analyzing genome-wide data on dyads and perform extensive simulation experiments to benchmark their type I errors and statistical power under different scenarios. We extend our evaluation to existing methods for analyzing a combination of case-parent trios and dyads together. We apply these methods on genotyped and imputed data from multi-ethnic mother-child pairs only, case-parent trios only or combinations of both dyads and trios from the Gene, Environment Association Studies consortium (GENEVA), where each family was ascertained through a child affected by nonsyndromic cleft lip with or without cleft palate. Results from the GENEVA study corroborate the findings from our simulation experiments. Finally, we provide recommendations for using statistical genetic association methods for dyads.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Benchmarking statistical methods for analyzing parent-child dyads in genetic association studies

Ray

Vergara

Taub

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…As noted in Chen et al 26 , if there is no variant where all three observed genotypes (mother, father, offspring) are heterozygous, the phase information can be recaptured from the observed unphased genotype data. However, as described in Hecker et al 14 , treating inferred haplotypes as observed haplotypes can lead to misspecification. Nevertheless, more specifically, if there is no variant for which both parental genotypes are heterozygous, haplotypes can be phased, and the resulting conditional genotype distribution obtained by the FBAT haplotype algorithm equals the conditional distribution where we treat the haplotypes as observed. If we restrict the genetic data to rare variants, this is true for most nuclear families.…”

mentioning

confidence: 99%

An exact, unifying framework for region-based association testing in family-based designs, including higher criticism approaches, SKATs, multivariate and burden tests

Hecker

Townes

Kachroo

et al. 2019

Preprint

Self Cite

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Analysis of rare variants in family-based studies remains a challenge. To perform a region/set-based association analysis of rare variants in family-based studies, we propose a general methodological framework that integrates higher criticism, maximum, SKATs, and burden approaches into the family-based association testing (FBAT) framework. Using the haplotype algorithm for FBATs to compute the conditional genotype distribution under the null hypothesis of Mendelian transmissions, virtually any association test statistics can be implemented in our approach and simulation-based or exact p-values can be computed without the need for asymptotic settings. Using simulations, we compare the features of the proposed test statistics in our framework with the existing region-based methodology for family-based studies under various scenarios. The tests of our framework outperform the existing approaches. We provide general guidelines for which scenarios, e.g., sparseness of the signals or local LD structure, which test statistic will have distinct power advantages over the others. We also illustrate our approach in an application to a whole-genome sequencing dataset with 897 asthmatic trios.

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Benchmarking statistical methods for analyzing parent–child dyads in genetic association studies

Ray

Vergara

Taub

et al. 2022

Genetic Epidemiology

View full text Add to dashboard Cite

Genetic association studies of child health outcomes often employ family‐based study designs. One of the most popular family‐based designs is the case–parent trio design that considers the smallest possible nuclear family consisting of two parents and their affected child. This trio design is particularly advantageous for studying relatively rare disorders because it is less prone to type 1 error inflation due to population stratification compared to population‐based study designs (e.g., case–control studies). However, obtaining genetic data from both parents is difficult, from a practical perspective, and many large studies predominantly measure genetic variants in mother–child dyads. While some statistical methods for analyzing parent‐child dyad data (most commonly involving mother–child pairs) exist, it is not clear if they provide the same advantage as trio methods in protecting against population stratification, or if a specific dyad design (e.g., case–mother dyads vs. case–mother/control–mother dyads) is more advantageous. In this article, we review existing statistical methods for analyzing genome‐wide marker data on dyads and perform extensive simulation experiments to benchmark their type I errors and statistical power under different scenarios. We extend our evaluation to existing methods for analyzing a combination of case–parent trios and dyads together. We apply these methods on genotyped and imputed data from multiethnic mother–child pairs only, case–parent trios only or combinations of both dyads and trios from the Gene, Environment Association Studies consortium (GENEVA), where each family was ascertained through a child affected by nonsyndromic cleft lip with or without cleft palate. Results from the GENEVA study corroborate the findings from our simulation experiments. Finally, we provide recommendations for using statistical genetic association methods for dyads.

show abstract

A comparison of popular TDT‐generalizations for family‐based association analysis

Cited by 7 publications

References 29 publications

Benchmarking statistical methods for analyzing parent-child dyads in genetic association studies

Benchmarking statistical methods for analyzing parent-child dyads in genetic association studies

An exact, unifying framework for region-based association testing in family-based designs, including higher criticism approaches, SKATs, multivariate and burden tests

Benchmarking statistical methods for analyzing parent–child dyads in genetic association studies

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