Region-Based Association Test for Familial Data under Functional Linear Models

Abstract

Region-based association analysis is a more powerful tool for gene mapping than testing of individual genetic variants, particularly for rare genetic variants. The most powerful methods for regional mapping are based on the functional data analysis approach, which assumes that the regional genome of an individual may be considered as a continuous stochastic function that contains information about both linkage and linkage disequilibrium. Here, we extend this powerful approach, earlier applied only to independent samples, to the samples of related individuals. To this end, we additionally include a random polygene effects in functional linear model used for testing association between quantitative traits and multiple genetic variants in the region. We compare the statistical power of different methods using Genetic Analysis Workshop 17 mini-exome family data and a wide range of simulation scenarios. Our method increases the power of regional association analysis of quantitative traits compared with burden-based and kernel-based methods for the majority of the scenarios. In addition, we estimate the statistical power of our method using regions with small number of genetic variants, and show that our method retains its advantage over burden-based and kernel-based methods in this case as well. The new method is implemented as the R-function ‘famFLM’ using two types of basis functions: the B-spline and Fourier bases. We compare the properties of the new method using models that differ from each other in the type of their function basis. The models based on the Fourier basis functions have an advantage in terms of speed and power over the models that use the B-spline basis functions and those that combine B-spline and Fourier basis functions. The ‘famFLM’ function is distributed under GPLv3 license and is freely available at http://mga.bionet.nsc.ru/soft/famFLM/.

A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next‐generation sequencing

“…In prior studies, fixed effect functional regression models were found to outperform SKAT, its optimal unified test (SKAT‐O), and a combined sum test of rare and common variant effect (SKAT‐C) in most cases (Fan et al., , , , ,b,c; Luo et al., , , ; Svishcheva et al., ; Vsevolozhskaya et al., , ). In Fan et al.…”

Uncovering Local Trends in Genetic Effects of Multiple Phenotypes via Functional Linear Models

“…A genotypic function is obtained by either (i) a cubic B‐spline basis expansion over a dense set of knots, , over the range of the variant's genomic positions 's (in the one‐base coordinate system) or (ii) penalized spline smoothing that avoids the knot selection problem completely [e.g., Luo et al., , Vsevolozhskaya et al., ]. Earlier investigations of FLMs designed for genetic association testing include comprehensive coverage of the estimation procedure for the genotypic functions 's [Fan et al., , ; Lee et al., ; Luo et al., , , b; Svishcheva et al., ; Vsevolozhskaya et al., ; Wang et al., ; Zhu and Xiong, ].…”

“…As discussed elsewhere [e.g., Svishcheva et al., ; Vsevolozhskaya et al., ], statistical power of functional methods may depend on the quality of genotype data smoothing. To obtain smooth genotypic functions, we first coded allelic dosage based on the minor allele counts (i.e., either 0, 1, or 2) and applied the “flipping algorithm” [Vsevolozhskaya et al., ] to minimize the number of 0‐2 (or 2‐0) patterns in every two subsequent variant positions.…”

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models

“…10,[16][17][18][19][20][21][22][23][24][25][26][27][28][29] In most cases, it was shown that the functional regression test statistics perform better than sequence kernel association test (SKAT), its optimal unified test (SKAT-O), and a combined sum test of rare and common variant effect (SKAT-C) of mixed models. 4,[16][17][18][19][20][21][22][23][24][25][26][27][30][31][32][33] Specifically, mixed model-based SKAT/SKATO/ SKAT-C performs well when (a) the number of causal variants is large and (b) each causal variant contributes a small amount to the traits, as the assumption of mixed models is satisfied under these circumstances. 7,21,34 In most cases, however, fixed models perform better since the causal variants of complex disorders can be common or rare or a combination of the two and some causal variants may have relatively large effects.…”

“…7,21,34 In most cases, however, fixed models perform better since the causal variants of complex disorders can be common or rare or a combination of the two and some causal variants may have relatively large effects. 10,[16][17][18][19][20][21][22][23][24][25][26][27] If the number of causal variants is large and each causal variant contributes a small amount to the traits, it would be hard to show association as the power of a test can be low. 35 One may want to note that SKAT and SKAT-O were shown to have higher power than burden tests, which is another main method to analyze rare variants.…”