Quantitative trait locus analysis for next-generation sequencing with the functional linear models

Abstract

Background Although in the past few years we have witnessed the rapid development of novel statistical methods for association studies of qualitative traits using next Generation Sequencing (NGS) data, only a few statistics are proposed for testing the association of rare variants with quantitative traits. The QTL analysis of rare variants remains challenging. Analysis from low dimensional data to high dimensional genomic data demands changes in statistical methods from multivariate data analysis to functional data analysis. Methods In this paper, we propose a functional linear model (FLM) as a general principle for developing novel and powerful QTL analysis methods designed for resequencing data. By simulations we calculate the type I error rates and evaluate the power of the FLM and other eight existing statistical methods even in the presence of both positive and negative signs of effects. Results Since the FLM retains all of the genetic information in the data and explores the merits of both variant-by-variant and collective analysis and overcomes their limitation, the FLM has a much higher power than other existing statistics in all the scenarios considered. To further evaluate its performance, the FLM is applied to association analysis of six quantitative traits in the Dallas Heart Study, and RNA-seq eQTL analysis with genetic variation in the low coverage resequencing data of the 1000 Genomes Project. Real data analysis shows that the FLM has much smaller P-values to identify significantly associated variants than other existing methods. Conclusions The FLM is expected to open a new route for QTL analysis.

Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models

“…By extending SKAT and SKAT-O to perform meta-analysis, Lee et al (2013) developed meta-analysis SKAT and SKAT-O (MetaSKAT and MetaSKAT-O) to carry out meta-analysis for rare variants in multiple studies. Both SKAT and MetaSKAT are score tests based on mixed-effect models.The third type is tests based on fixed-effect models that include (1) traditional additive effect models that are well studied (Cordell and Clayton 2002;Fan and Xiong 2002;Fan et al 2006) and (2) functional regression models as shown in our previous research (Luo et al 2012;Fan et al 2013Fan et al , 2014Wang et al 2015). Note that functional regression models are fixed-effect models, which extend traditional population genetics models to analyze multiple genetic variants and can analyze rare variants, common variants, or combinations of the two.…”

“…For individual studies with small and moderate sample sizes, functional linear models (FLMs) were proposed to analyze quantitative traits. The FLMs lead to x 2 -score tests and F-distributed statistics, which are more powerful than SKAT and SKAT-O while controlling type I error correctly (Luo et al 2012;Fan et al 2013;Wang et al 2015). For dichotomous traits, generalized FLMs were developed to perform gene-based association analysis (Fan et al 2014).…”

Meta-analysis of Complex Diseases at Gene Level with Generalized Functional Linear Models

“…The high dimensionality of modern genetic data does not necessarily imply that traditional population genetics theory is not correct because the number of causal variants may not be large. A fixed model should be fine to analyze the major gene locus data in most cases if the dimension of the genetic data can be properly reduced.By viewing genetic variant data as realizations of an underlying stochastic process, functional regression models were proposed to reduce the dimensionality and to perform a gene-based association analysis of quantitative, qualitative, and survival traits (Luo et al 2011(Luo et al , 2012(Luo et al , 2013Fan et al 2013Fan et al , 2014Fan et al , 2015Fan et al , 2016Vsevolozhskaya et al 2014;Zhang et al 2014;Wang et al 2015). For quantitative traits, functional linear models lead to both F-and chi-squared-distributed test statistics that are almost always more powerful than SKAT and SKAT-O (Luo et al 2012;Fan et al 2013Fan et al , 2015Wang et al 2015).…”

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.…”