Detecting Rare Variants for Quantitative Traits Using Nuclear Families

Guo, Wei; Shugart, Yin Yao

doi:10.1159/000338439

Cited by 9 publications

(9 citation statements)

References 36 publications

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“…The potential enrichment of the RVs that segregate with phenotypes of interest in pedigrees makes family studies particularly attractive for uncovering phenotype-associated RVs [Curtis 2011;De et al, 2013;Fang et al, 2012;Feng et al, 2012;Guo and Shugart 2012;Ionita-Laza et al, 2013;Kazma and Bailey 2011;Yip et al, 2011;Zhu et al, 2009] and a few family-based conditional tests have been developed [De et al, 2013;Fang et al, 2012;Ionita-Laza et al, 2013]. Because these methods require conditioning on parental genotypes (or sufficient statistic for parental genotypes), when the observed genotypes in a pedigree cannot be sufficiently nuclearized, the total informative sample size of a given dataset could be substantially reduced, resulting in a significant loss of power.…”

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confidence: 99%

Adjusting Family Relatedness in Data-driven Burden Test of Rare Variants

et al. 2014

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Family data represents a rich resource for detecting association between rare variants (RVs) and human traits. However, most RV association analysis methods developed in recent years are data-driven burden tests which can adaptively learn weights from data but require permutation to evaluate significance, thus are not readily applicable to family data, because random permutation will destroy family structure. Direct application of these methods to family data may result in a significant inflation of false positives. To overcome this issue, we have developed a generalized, weighted sum mixed model (WSMM) and corresponding computational techniques that can incorporate family information into data-driven burden tests, and allow adaptive and efficient permutation test in family data. Using simulated and real datasets, we demonstrate that the WSMM method can be used to appropriately adjust for genetic relatedness among family members and has a good control of type I errors. We compare WSMM with a non-data-driven, family-based Sequence Kernel Association Test (famSKAT), showing that WSMM has significantly higher power in some cases. WSMM provides a generalized, flexible framework for adapting different data-driven burden tests to analyze data with any family structures, and it can be extended to binary and time-to-onset traits, with or without covariates.

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Adjusting Family Relatedness in Data-driven Burden Test of Rare Variants

et al. 2014

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“…Our future studies will focus on comparing the FBAT-v0/v1 with alternative methods such as Betafam, proposed by Guo and Shugart [6], as well as developing novel methods that preserve the structure and thus the information contained in complex pedigrees.…”

Section: Discussionmentioning

confidence: 99%

Family-based tests applied to extended pedigrees identify rare variants related to hypertension

Wang

Guo

et al. 2014

BMC Proc

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The application of family-based tests to whole-genome sequenced data provides a new window on the role of rare variant alleles in the etiology of disease. By applying family-based tests to these data, we can now identify rare variants associated with disease. Approaches for common variants, by contrast, require large sample sizes for power, and are powerless when faced with rare variants. When we tested Yip et al's 2011 family-based association tests for rare variants on pedigrees from the Genetic Analysis Workshop 18, we found that weighted collapsing methods generally have more power than unweighted methods, but are more prone to type I errors. We then evaluated a sliding window modification of the weighted family-based association tests for rare variants method. Although this modification inflates the rate of false positives, it significantly increases the power of family-based association tests for rare variants to identify causal rare variants.

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“…Methods have been proposed to extend the current collapsing tests to rare variants in family data. Guo and Shugart [] and De et al. [], extended the family‐based association test (FBAT) [Laird and Lange, ] to rare variants in the style of a collapsing test.…”

Section: Introductionmentioning

confidence: 99%

Robust and Powerful Affected Sibpair Test for Rare Variant Association

Lin

Zöllner

2015

Genetic Epidemiology

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Advances in DNA sequencing technology facilitate investigating the impact of rare variants on complex diseases. However, using a conventional case-control design, large samples are needed to capture enough rare variants to achieve sufficient power for testing the association between suspected loci and complex diseases. In such large samples, population stratification may easily cause spurious signals. One approach to overcome stratification is to use a family-based design. For rare variants, this strategy is especially appropriate, as power can be increased considerably by analyzing cases with affected relatives. We propose a novel framework for association testing in affected sibpairs by comparing the allele count of rare variants on chromosome regions shared identical by descent to the allele count of rare variants on non-shared chromosome regions, referred to as test for rare-variant association with family-based internal control (TRAFIC). This design is generally robust to population stratification as cases and controls are matched within each sibpair. We evaluate the power analytically using general model for effect size of rare variants. For the same number of genotyped people, TRAFIC shows superior power over the conventional case-control study for variants with summed risk allele frequency f < 0.05; this power advantage is even more substantial when considering allelic heterogeneity. For complex models of gene-gene interaction, this power advantage depends on the direction of interaction and overall heritability. In sum, we introduce a new method for analyzing rare variants in affected sibpairs that is robust to population stratification, and provide freely available software.

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Detecting Rare Variants for Quantitative Traits Using Nuclear Families

Cited by 9 publications

References 36 publications

Adjusting Family Relatedness in Data-driven Burden Test of Rare Variants

Adjusting Family Relatedness in Data-driven Burden Test of Rare Variants

Family-based tests applied to extended pedigrees identify rare variants related to hypertension

Robust and Powerful Affected Sibpair Test for Rare Variant Association

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