Pooled Association Tests for Rare Variants in Exon-Resequencing Studies

Price, Alkes L.; Kryukov, Gregory V.; Bakker, Paul I. W. de; Purcell, Shaun; Staples, Jeff; Wei, Lee‐Jen; Sunyaev, Shamil

doi:10.1016/j.ajhg.2010.05.014

Cited by 201 publications

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“…For common variant scenarios, we included the principle component regression test (PCR) (Guaderman et al, 2007); the MinSNP test (Ballard et al, 2010), and the F -test in linear regression model including only main effects. For scenarios involving rare variants, the variable-threshold (VT) test (Price et al, 2010) was included. Table 1 shows results for the first set of simulations with different MAF and sample sizes.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…This is not surprising as the effect of the common variant is relatively large but down-weighted in the weighted GenRF and SKAT. As the rare causal proportion increases Price et al (2010); other entries as in Table 2. and the number of common variants is fixed at one, the results change dramatically.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…Application to Dallas Heart Study for non-synonymous variantsGenRF, the unweighted genetic random field test; SKAT, the unweighted sequential kernel association test ofWu et al (2011); PCR, the princeple component regression test ofGuaderman et al (2007); MinSNP, the MinSNP test considered byBallard et al (2010); F -test, the F -test in linear regression; GenRF.w, the genetic random field test with Beta (1, 25) weight ofWu et al (2011); SKAT.w, the sequential kernel association test with Beta (1, 25) weight; VT, the variable-threshold test ofPrice et al (2010).…”

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confidence: 99%

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Modeling and testing for joint association using a genetic random field model

Zhang

Zhan

et al. 2014

Biometrics

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Substantial progress has been made in identifying single genetic variants predisposing to common complex diseases. Nonetheless, the genetic etiology of human diseases remains largely unknown. Human complex diseases are likely influenced by the joint effect of a large number of genetic variants instead of a single variant. The joint analysis of multiple genetic variants considering linkage disequilibrium (LD) and potential interactions can further enhance the discovery process, leading to the identification of new disease-susceptibility genetic variants. Motivated by development in spatial statistics, we propose a new statistical model based on the random field theory, referred to as a genetic random field model (GenRF), for joint association analysis with the consideration of possible gene-gene interactions and LD. Using a pseudo-likelihood approach, a GenRF test for the joint association of multiple genetic variants is developed, which has the following advantages: (1) accommodating complex interactions for improved performance; (2) natural dimension reduction; (3) boosting power in the presence of LD; and (4) computationally efficient. Simulation studies are conducted under various scenarios. The development has been focused on quantitative traits and robustness of the GenRF test to other traits, for example, binary traits, is also discussed. Compared with a commonly adopted kernel machine approach, SKAT, as well as other more standard methods, GenRF shows overall comparable performance and better performance in the presence of complex interactions. The method is further illustrated by an application to the Dallas Heart Study.

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Section: Simulation Studiesmentioning

confidence: 99%

Section: Simulation Studiesmentioning

confidence: 99%

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confidence: 99%

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Modeling and testing for joint association using a genetic random field model

Zhang

Zhan

et al. 2014

Biometrics

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“…As an example, for a given rare variant one can calculate the standard deviation of the number of variants observed among controls, and then let w i equal the inverse of this value . The data can also be used to determine the optimal MAF cutpoint at which rare variants should be combined together . Here one cycles through different potential MAF thresholds to determine that which provides the optimal grouping of rare variants .…”

Section: Rare Variants: Analysis Approaches and Powermentioning

confidence: 99%

“…The data can also be used to determine the optimal MAF cutpoint at which rare variants should be combined together . Here one cycles through different potential MAF thresholds to determine that which provides the optimal grouping of rare variants .…”

Section: Rare Variants: Analysis Approaches and Powermentioning

confidence: 99%

Rare genetic variants and treatment response: sample size and analysis issues

Witte

2012

Statistics in Medicine

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Incorporating information about common genetic variants may help improve the design and analysis of clinical trials. For example, if genes impact response to treatment, one can pregenotype potential participants to screen out genetically determined non-responders and substantially reduce the sample size and duration of a trial. Genetic associations with response to treatment are generally much larger than those observed for development of common diseases, as highlighted here by findings from genome-wide association studies. With the development and decreasing cost of next generation sequencing, more extensive genetic information-including rare variants-is becoming available on individuals treated with drugs and other therapies. We can use this information to evaluate whether rare variants impact treatment response. The sparseness of rare variants, however, raises issues of how the resulting data should be best analyzed. As shown here, simply evaluating the association between each rare variant and treatment response one-at-a-time will require enormous sample sizes. Combining the rare variants together can substantially reduce the required sample sizes, but require a number of assumptions about the similarity among the rare variants' effects on treatment response. We have developed an empirical approach for aggregating and analyzing rare variants that limit such assumptions and work well under a range of scenarios. Such analyses provide a valuable opportunity to more fully decipher the genomic basis of response to treatment.

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Association of Early-Onset Alzheimer Disease With Elevated Low-Density Lipoprotein Cholesterol Levels and Rare Genetic Coding Variants of APOB

et al. 2019

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IMPORTANCE Early-onset Alzheimer disease (EOAD) is a rare form of Alzheimer disease (AD) with a large genetic basis that is only partially understood. In late-onset AD, elevated circulating cholesterol levels increase AD risk even after adjusting for the apolipoprotein E ε4 (APOE E4) allele, a major genetic factor for AD and elevated cholesterol levels; however, the role of circulating cholesterol levels in EOAD is unclear. OBJECTIVES To investigate the association between circulating cholesterol levels and EOAD and to identify genetic variants underlying this possible association.

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Pooled Association Tests for Rare Variants in Exon-Resequencing Studies

Cited by 201 publications

References 0 publications

Modeling and testing for joint association using a genetic random field model

Modeling and testing for joint association using a genetic random field model

Rare genetic variants and treatment response: sample size and analysis issues

Association of Early-Onset Alzheimer Disease With Elevated Low-Density Lipoprotein Cholesterol Levels and Rare Genetic Coding Variants of APOB

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