Integrating multiple traits for improving polygenic risk prediction in disease and pharmacogenomics GWAS.

Polygenic risk score (PRS) has been recently developed for predicting complex traits and drug responses. It remains unknown whether multi-trait PRS (mtPRS) methods, by integrating information from multiple genetically correlated traits, can improve prediction accuracy and power for PRS analysis compared with single-trait PRS (stPRS) methods. In this paper, we first review commonly used mtPRS methods and find that they do not directly model the underlying genetic correlations among traits, which has been shown to be useful in guiding multi-trait association analysis in the literature. To overcome this limitation, we propose a mtPRS-PCA method to combine PRSs from multiple traits with weights obtained from performing principal component analysis (PCA) on the genetic correlation matrix. To accommodate various genetic architectures covering different effect directions, signal sparseness and across-trait correlation structures, we further propose an omnibus mtPRS method (mtPRS-O) by combining P values from mtPRS-PCA, mtPRS-ML (mtPRS based on machine learning) and stPRSs using Cauchy Combination Test. Our extensive simulation studies show that mtPRS-PCA outperforms other mtPRS methods in both disease and pharmacogenomics (PGx) genome-wide association studies (GWAS) contexts when traits are similarly correlated, with dense signal effects and in similar effect directions, and mtPRS-O is consistently superior to most other methods due to its robustness under various genetic architectures. We further apply mtPRS-PCA, mtPRS-O and other methods to PGx GWAS data from a randomized clinical trial in the cardiovascular domain and demonstrate performance improvement of mtPRS-PCA in both prediction accuracy and patient stratification as well as the robustness of mtPRS-O in PRS association test.

Applying polygenic risk score methods to pharmacogenomics GWAS: challenges and opportunities.

Polygenic risk scores (PRSs) have emerged as promising tools for the prediction of human diseases and complex traits in disease genome-wide association studies (GWAS). Applying PRSs to pharmacogenomics (PGx) studies has begun to show great potential for improving patient stratification and drug response prediction. However, there are unique challenges that arise when applying PRSs to PGx GWAS beyond those typically encountered in disease GWAS (e.g. Eurocentric or trans-ethnic bias). These challenges include: (i) the lack of knowledge about whether PGx or disease GWAS/variants should be used in the base cohort (BC); (ii) the small sample sizes in PGx GWAS with corresponding low power and (iii) the more complex PRS statistical modeling required for handling both prognostic and predictive effects simultaneously. To gain insights in this landscape about the general trends, challenges and possible solutions, we first conduct a systematic review of both PRS applications and PRS method development in PGx GWAS. To further address the challenges, we propose (i) a novel PRS application strategy by leveraging both PGx and disease GWAS summary statistics in the BC for PRS construction and (ii) a new Bayesian method (PRS-PGx-Bayesx) to reduce Eurocentric or cross-population PRS prediction bias. Extensive simulations are conducted to demonstrate their advantages over existing PRS methods applied in PGx GWAS. Our systematic review and methodology research work not only highlights current gaps and key considerations while applying PRS methods to PGx GWAS, but also provides possible solutions for better PGx PRS applications and future research.

Beyond the standard GWAS - a guide for plant biologists.

Classic genome-wide association studies (GWAS) look for associations between individual SNPs and phenotypes of interest. With the rapid progress of high-throughput genotyping and phenotyping technologies, GWAS have become increasingly powerful for detecting genetic determinants and their molecular mechanisms underpinning natural phenotypic variation. However, GWAS frequently yield results with neither expected nor promising loci, nor any significant associations. This is often because associations between SNPs and a single phenotype are confounded, for example with the environment, other traits, or complex genetic structures. Such confounding can mask true genotype-phenotype associations, or inflate spurious associations. To address these problems, numerous methods have been developed that go beyond the standard model. Such advanced GWAS models are flexible and can offer improved statistical power for understanding the genetics underlying complex traits. Despite this advantage, these models have not been widely adopted and implemented compared to the standard GWAS approach, partly because this literature is diverse and often technical. In this review, our aim is to provide an overview of the application and the benefits of various advanced GWAS models for handling complex traits and genetic structures, targeting plant biologists who wish to carry out GWAS more effectively.

Multi-trait transcriptome-wide association studies with probabilistic Mendelian randomization.

A transcriptome-wide association study (TWAS) integrates data from genome-wide association studies and gene expression mapping studies for investigating the gene regulatory mechanisms underlying diseases. Existing TWAS methods are primarily univariate in nature, focusing on analyzing one outcome trait at a time. However, many complex traits are correlated with each other and share a common genetic basis. Consequently, analyzing multiple traits jointly through multivariate analysis can potentially improve the power of TWASs. Here, we develop a method, moPMR-Egger (multiple outcome probabilistic Mendelian randomization with Egger assumption), for analyzing multiple outcome traits in TWAS applications. moPMR-Egger examines one gene at a time, relies on its cis-SNPs that are in potential linkage disequilibrium with each other to serve as instrumental variables, and tests its causal effects on multiple traits jointly. A key feature of moPMR-Egger is its ability to test and control for potential horizontal pleiotropic effects from instruments, thus maximizing power while minimizing false associations for TWASs. In simulations, moPMR-Egger provides calibrated type I error control for both causal effects testing and horizontal pleiotropic effects testing and is more powerful than existing univariate TWAS approaches in detecting causal associations. We apply moPMR-Egger to analyze 11 traits from 5 trait categories in the UK Biobank. In the analysis, moPMR-Egger identified 13.15% more gene associations than univariate approaches across trait categories and revealed distinct regulatory mechanisms underlying systolic and diastolic blood pressures.

The eigen higher criticism and eigen Berk-Jones tests for multiple trait association studies based on GWAS summary statistics.

In this article, we propose the eigen higher criticism and the eigen Berk-Jones testing procedures to test the association between a single genetic variant and multiple correlated traits based on summary statistics from single-trait genome-wide association studies. Since the association pattern between each genetic variant and multiple traits varies across the whole genome, we further develop an omnibus (OMNI) test using the aggregated Cauchy association test to achieve more robust performance. The p values of our proposed tests can be computed analytically, thus, our methods are appealing in large-scale multiple phenotype association studies. Through extensive simulation studies, we found that all of our proposed tests can maintain the correct type I error rates and our proposed tests have greater power in certain settings. In addition, the OMNI test can always provide robust power performance across a wide range of scenarios. We apply the proposed tests to the Global Lipids Genetics Consortium summary statistics data set and identify additional genetic variants that were missed by the original single-trait analyses. We also develop an R package EBMMT publicly available at https://github.com/Vivian-Liu-Wei64/EBMMT.

A Bayesian hierarchically structured prior for gene-based association testing with multiple traits in genome-wide association studies.

Although genome-wide association studies (GWAS) often collect data on multiple correlated traits for complex diseases, conventional gene-based analysis is usually univariate, and therefore, treating traits as uncorrelated. Multivariate analysis of multiple correlated traits can potentially increase the power to detect genes that affect some or all of these traits. In this study, we propose the multivariate hierarchically structured variable selection (HSVS-M) model, a flexible Bayesian model that tests the association of a gene with multiple correlated traits. With only summary statistics, HSVS-M can account for the correlations among genetic variants and among traits simultaneously and can also estimate the various directions and magnitudes of associations between a gene and multiple traits. Simulation studies show that HSVS-M substantially outperforms competing methods in various scenarios, particularly when variants in a gene are associated with a trait in similar directions and magnitudes. We applied HSVS-M to the summary statistics of a meta-analysis GWAS on four lipid traits from the Global Lipids Genetics Consortium and identified 15 genes that have also been confirmed as risk factors in previous studies.

Integrative functional linear model for genome-wide association studies with multiple traits.

In recent biomedical research, genome-wide association studies (GWAS) have demonstrated great success in investigating the genetic architecture of human diseases. For many complex diseases, multiple correlated traits have been collected. However, most of the existing GWAS are still limited because they analyze each trait separately without considering their correlations and suffer from a lack of sufficient information. Moreover, the high dimensionality of single nucleotide polymorphism (SNP) data still poses tremendous challenges to statistical methods, in both theoretical and practical aspects. In this article, we innovatively propose an integrative functional linear model for GWAS with multiple traits. This study is the first to approximate SNPs as functional objects in a joint model of multiple traits with penalization techniques. It effectively accommodates the high dimensionality of SNPs and correlations among multiple traits to facilitate information borrowing. Our extensive simulation studies demonstrate the satisfactory performance of the proposed method in the identification and estimation of disease-associated genetic variants, compared to four alternatives. The analysis of type 2 diabetes data leads to biologically meaningful findings with good prediction accuracy and selection stability.

Estimation of genetic parameters and trends for production traits of dairy cattle in Thailand using a multiple-trait multiple-lactation test day model.

OBJECTIVE: The objective of this study was to estimate the genetic parameters and trends for milk, fat, and protein yields in the first three lactations of Thai dairy cattle using a 3-trait,- 3-lactation random regression test-day model. METHODS: Data included 168,996, 63,388, and 27,145 test-day records from the first, second, and third lactations, respectively. Records were from 19,068 cows calving from 1993 to 2013 in 124 herds. (Co) variance components were estimated by Bayesian methods. Gibbs sampling was used to obtain posterior distributions. The model included herd-year-month of testing, breed group-season of calving-month in tested milk group, linear and quadratic age at calving as fixed effects, and random regression coefficients for additive genetic and permanent environmental effects, which were defined as modified constant, linear, quadratic, cubic and quartic Legendre coefficients. RESULTS: Average daily heritabilities ranged from 0.36 to 0.48 for milk, 0.33 to 0.44 for fat and 0.37 to 0.48 for protein yields; they were higher in the third lactation for all traits. Heritabilities of test-day milk and protein yields for selected days in milk were higher in the middle than at the beginning or end of lactation, whereas those for test-day fat yields were high at the beginning and end of lactation. Genetics correlations (305-d yield) among production yields within lactations (0.44 to 0.69) were higher than those across lactations (0.36 to 0.68). The largest genetic correlation was observed between the first and second lactation. The genetic trends of 305-d milk, fat and protein yields were 230 to 250, 25 to 29, and 30 to 35 kg per year, respectively. CONCLUSION: A random regression model seems to be a flexible and reliable procedure for the genetic evaluation of production yields. It can be used to perform breeding value estimation for national genetic evaluation in the Thai dairy cattle population.

Methods for meta-analysis of multiple traits using GWAS summary statistics.

Genome-wide association studies (GWAS) for complex diseases have focused primarily on single-trait analyses for disease status and disease-related quantitative traits. For example, GWAS on risk factors for coronary artery disease analyze genetic associations of plasma lipids such as total cholesterol, LDL-cholesterol, HDL-cholesterol, and triglycerides (TGs) separately. However, traits are often correlated and a joint analysis may yield increased statistical power for association over multiple univariate analyses. Recently several multivariate methods have been proposed that require individual-level data. Here, we develop metaUSAT (where USAT is unified score-based association test), a novel unified association test of a single genetic variant with multiple traits that uses only summary statistics from existing GWAS. Although the existing methods either perform well when most correlated traits are affected by the genetic variant in the same direction or are powerful when only a few of the correlated traits are associated, metaUSAT is designed to be robust to the association structure of correlated traits. metaUSAT does not require individual-level data and can test genetic associations of categorical and/or continuous traits. One can also use metaUSAT to analyze a single trait over multiple studies, appropriately accounting for overlapping samples, if any. metaUSAT provides an approximate asymptotic P-value for association and is computationally efficient for implementation at a genome-wide level. Simulation experiments show that metaUSAT maintains proper type-I error at low error levels. It has similar and sometimes greater power to detect association across a wide array of scenarios compared to existing methods, which are usually powerful for some specific association scenarios only. When applied to plasma lipids summary data from the METSIM and the T2D-GENES studies, metaUSAT detected genome-wide significant loci beyond the ones identified by univariate analyses. Evidence from larger studies suggest that the variants additionally detected by our test are, indeed, associated with lipid levels in humans. In summary, metaUSAT can provide novel insights into the genetic architecture of a common disease or traits.

Multiple genotype-phenotype association study reveals intronic variant pair on SIDT2 associated with metabolic syndrome in a Korean population.

BACKGROUND: Metabolic syndrome is a risk factor for type 2 diabetes and cardiovascular disease. We identified common genetic variants that alter the risk for metabolic syndrome in the Korean population. To isolate these variants, we conducted a multiple-genotype and multiple-phenotype genome-wide association analysis using the family-based quasi-likelihood score (MFQLS) test. For this analysis, we used 7211 and 2838 genotyped study subjects for discovery and replication, respectively. We also performed a multiple-genotype and multiple-phenotype analysis of a gene-based single-nucleotide polymorphism (SNP) set. RESULTS: We found an association between metabolic syndrome and an intronic SNP pair, rs7107152 and rs1242229, in SIDT2 gene at 11q23.3. Both SNPs correlate with the expression of SIDT2 and TAGLN, whose products promote insulin secretion and lipid metabolism, respectively. This SNP pair showed statistical significance at the replication stage. CONCLUSIONS: Our findings provide insight into an underlying mechanism that contributes to metabolic syndrome.

Unravelling the genetic loci for growth and carcass traits in Chinese Bamaxiang pigs based on a 1.4 million SNP array.

Bamaxiang pig is from Guangxi province in China, characterized by its small body size and two-end black coat colour. It is an important indigenous breed for local pork market and excellent animal model for biomedical research. In this study, we performed genomewide association studies (GWAS) on 43 growth and carcass traits in 315 purebred Bamaxiang pigs based on a 1.4 million SNP array. We observed considerable phenotypic variability in the growth and carcass traits in the Bamaxiang pigs. The corresponding SNP based heritability varied greatly across the 43 traits and ranged from 9.0% to 88%. Through a conditional GWAS, we identified 53 significant associations for 35 traits at p value threshold of 10-6 . Among which, 26 associations on chromosome 3, 7, 14 and X passed a genomewide significance threshold of 5 × 10-8 . The most remarkable loci were at around 30.6 Mb on chromosome 7, which had growth stage-dependent effects on body lengths and cannon circumferences and showed large effects on multiple carcass traits. We discussed HMGA1 NUDT3, EIF2AK1, TMEM132C and AFF2 that near the lead SNP of significant loci as plausible candidate genes for corresponding traits. We also showed that including phenotypic covariate in GWAS can help to reveal additional significant loci for the target traits. The results provide insight into the genetic architecture of growth and carcass traits in Bamaxiang pigs.

A novel association test for multiple secondary phenotypes from a case-control GWAS.

In the past decade, many genome-wide association studies (GWASs) have been conducted to explore association of single nucleotide polymorphisms (SNPs) with complex diseases using a case-control design. These GWASs not only collect information on the disease status (primary phenotype, D) and the SNPs (genotypes, X), but also collect extensive data on several risk factors and traits. Recent literature and grant proposals point toward a trend in reusing existing large case-control data for exploring genetic associations of some additional traits (secondary phenotypes, Y) collected during the study. These secondary phenotypes may be correlated, and a proper analysis warrants a multivariate approach. Commonly used multivariate methods are not equipped to properly account for the non-random sampling scheme. Current ad hoc practices include analyses without any adjustment, and analyses with D adjusted as a covariate. Our theoretical and empirical studies suggest that the type I error for testing genetic association of secondary traits can be substantial when X as well as Y are associated with D, even when there is no association between X and Y in the underlying (target) population. Whether using D as a covariate helps maintain type I error depends heavily on the disease mechanism and the underlying causal structure (which is often unknown). To avoid grossly incorrect inference, we have proposed proportional odds model adjusted for propensity score (POM-PS). It uses a proportional odds logistic regression of X on Y and adjusts estimated conditional probability of being diseased as a covariate. We demonstrate the validity and advantage of POM-PS, and compare to some existing methods in extensive simulation experiments mimicking plausible scenarios of dependency among Y, X, and D. Finally, we use POM-PS to jointly analyze four adiposity traits using a type 2 diabetes (T2D) case-control sample from the population-based Metabolic Syndrome in Men (METSIM) study. Only POM-PS analysis of the T2D case-control sample seems to provide valid association signals.

Approximate score-based testing with application to multivariate trait association analysis.

For genome-wide association studies and DNA sequencing studies, several powerful score-based tests, such as kernel machine regression and sum of powered score tests, have been proposed in the last few years. However, extensions of these score-based tests to more complex models, such as mixed-effects models for analysis of multiple and correlated traits, have been hindered by the unavailability of the score vector, due to either no output from statistical software or no closed-form solution at all. We propose a simple and general method to asymptotically approximate the score vector based on an asymptotically normal and consistent estimate of a parameter vector to be tested and its (consistent) covariance matrix. The proposed method is applicable to both maximum-likelihood estimation and estimating function-based approaches. We use the derived approximate score vector to extend several score-based tests to mixed-effects models. We demonstrate the feasibility and possible power gains of these tests in association analysis of multiple and correlated quantitative or binary traits with both real and simulated data. The proposed method is easy to implement with a wide applicability.

Joint Analysis of Multiple Traits in Rare Variant Association Studies.

The joint analysis of multiple traits has recently become popular since it can increase statistical power to detect genetic variants and there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Currently, the majority of existing methods for the joint analysis of multiple traits test association between one common variant and multiple traits. However, the variant-by-variant methods for common variant association studies may not be optimal for rare variant association studies due to the allelic heterogeneity as well as the extreme rarity of individual variants. Current statistical methods for rare variant association studies are for one single trait only. In this paper, we propose an adaptive weighting reverse regression (AWRR) method to test association between multiple traits and rare variants in a genomic region. AWRR is robust to the directions of effects of causal variants and is also robust to the directions of association of traits. Using extensive simulation studies, we compare the performance of AWRR with canonical correlation analysis (CCA), Single-TOW, and the weighted sum reverse regression (WSRR). Our results show that, in all of the simulation scenarios, AWRR is consistently more powerful than CCA. In most scenarios, AWRR is more powerful than Single-TOW and WSRR.

Which cues are sexy? The evolution of mate preference in sympatric species reveals the contrasted effect of adaptation and reproductive interference.

Mate preferences may target traits (a) enhancing offspring adaptation and (b) reducing heterospecific matings. Because similar selective pressures are acting on traits shared by different sympatric species, preference-enhancing offspring adaptation may increase heterospecific mating, in sharp contrast with the classical case of so-called "magic traits." Using a mathematical model, we study which and how many traits will be used during mate choice, when preferences for locally adapted traits increase heterospecific mating. In particular, we study the evolution of preference toward an adaptive versus a neutral trait in sympatric species. We take into account sensory trade-offs, which may limit the emergence of preference for several traits. Our model highlights that the evolution of preference toward adaptive versus neutral traits depends on the selective regimes acting on traits but also on heterospecific interactions. When the costs of heterospecific interactions are high, mate preference is likely to target neutral traits that become a reliable cue limiting heterospecific matings. We show that the evolution of preference toward a neutral trait benefits from a positive feedback loop: The more preference targets the neutral trait, the more it becomes a reliable cue for species recognition. We then reveal the key role of sensory trade-offs and the cost of choosiness favoring the evolution of preferences targeting adaptive traits, rather than traits reducing heterospecific mating. When sensory trade-offs and the cost of choosiness are low, we also show that preferences targeting multiple traits evolve, improving offspring fitness by both transmitting adapted alleles and reducing heterospecific mating. Altogether, our model aims at reconciling "good gene" and reinforcement models to provide general predictions on the evolution of mate preferences within natural communities.

Accommodating and Extending Various Models for Special Effects Within the Generalized Partially Confirmatory Factor Analysis Framework.

Special measurement effects including the method and testlet effects are common issues in educational and psychological measurement. They are typically covered by various bifactor models or models for the multiple traits multiple methods (MTMM) structure for continuous data and by various testlet effect models for categorical data. However, existing models have some limitations in accommodating different type of effects. With slight modification, the generalized partially confirmatory factor analysis (GPCFA) framework can flexibly accommodate special effects for continuous and categorical cases with added benefits. Various bifactor, MTMM and testlet effect models can be linked to different variants of the revised GPCFA model. Compared to existing approaches, GPCFA offers multidimensionality for both the general and effect factors (or traits) and can address local dependence, mixed-type formats, and missingness jointly. Moreover, the partially confirmatory approach allows for regularization of the loading patterns, resulting in a simpler structure in both the general and special parts. We also provide a subroutine to compute the equivalent effect size. Simulation studies and real-data examples are used to demonstrate the performance and usefulness of the proposed approach under different situations.

Phenotyping new rapeseed lines based on multiple traits: Application of GT and GYT biplot analyses.

The selection based on multiple traits enhances the crop cultivars merit to farmers. In this regard, 19 breeding lines as well as two commercial cultivars were studied using a randomized complete block design (RCBD) with three replications in three locations during the 2020-2021 growing season. In this study, to identify the association among different traits and to select the best rapeseed lines based on multiple traits, genotype × trait (GT) and genotype × yield × trait (GYT) biplot analyses were used. The results showed that using GYT biplot is more efficient than GT biplot. Based on the GYT biplot and superiority index (SI), the breeding lines G16 and G18 were considered as superior genotypes in combination with the agronomical traits, that is, 1000-seed weight, number of seeds per pod, number of pods per plant, number of lateral branches, plant height, and pod length with seed yield, which represents a genetic gain in rapeseed breeding program. Based on seed yield combination with phenological traits (early maturity), the breeding line G15 was selected as the best one. Moreover, the line G2 was defined as the superior one in combination of seed yield with pod length. The results indicated that there is a potential for simultaneous genetic improvement of the characteristics (i.e., plant height, number of seeds per pod, early maturity) in rapeseed. Generally, the graphical method of the GYT biplot represented an efficient and practical new way to recognize superior genotypes based on multiple traits in rapeseed breeding programs.

Genotype by yield* trait (GYT) biplot analysis: A novel approach for phenotyping sunflower single cross hybrids based on multiple traits.

Sunflower is one of the most important oilseed plants in the world and its oil has nutritional and high economic value. Selection of high-yielding hybrids is important in sunflower breeding. In this regard, 11 new hybrids along with four cultivars were evaluated in a randomized complete block design with four replications during the 2018-2020 growing seasons. The phenological and agronomic traits including days to flowering, days to ripening, plant height, stem diameter, head diameter, seed number per head, thousand-seed weight, oil content, and seed yield were measured. In this study, the methods of genotype × trait (GT) and genotype × yield × trait biplot (GYT) were used to identify interrelationships between different traits and to select the best sunflower hybrids based on multiple traits. According to the results, GYT biplot method was more efficient compared to the GT biplot method. Considering both superiority index (SI) and GYT biplot, the genotypes G8, G11, G5, and G3 were superior in terms of agronomical attributes such as flowering and maturity times, stem and head diameter, plant height, thousand-seed weight, and seed number per head in close relationship with grain yield. Oil content of the hybrids G8, G11, G5, and G3 was 47.9%, 46.4%, 45.8%, and 46.3%, respectively. The results indicated that there is a potential for simultaneous genetic improvement of the characteristics (i.e., plant height, thousand-seed weight, seed number per head, early maturity) in sunflower. Overall, the GYT graphical biplot method provides a practical and efficient new approach for the identification of suitable hybrids according to the set of intended characteristics in sunflower improvement under multi-years or multi-locations.

Using Drosophila to identify naturally occurring genetic modifiers of amyloid beta 42- and tau-induced toxicity.

Alzheimer's disease is characterized by 2 pathological proteins, amyloid beta 42 and tau. The majority of Alzheimer's disease cases in the population are sporadic and late-onset Alzheimer's disease, which exhibits high levels of heritability. While several genetic risk factors for late-onset Alzheimer's disease have been identified and replicated in independent studies, including the ApoE ε4 allele, the great majority of the heritability of late-onset Alzheimer's disease remains unexplained, likely due to the aggregate effects of a very large number of genes with small effect size, as well as to biases in sample collection and statistical approaches. Here, we present an unbiased forward genetic screen in Drosophila looking for naturally occurring modifiers of amyloid beta 42- and tau-induced ommatidial degeneration. Our results identify 14 significant SNPs, which map to 12 potential genes in 8 unique genomic regions. Our hits that are significant after genome-wide correction identify genes involved in neuronal development, signal transduction, and organismal development. Looking more broadly at suggestive hits (P < 10-5), we see significant enrichment in genes associated with neurogenesis, development, and growth as well as significant enrichment in genes whose orthologs have been identified as significantly or suggestively associated with Alzheimer's disease in human GWAS studies. These latter genes include ones whose orthologs are in close proximity to regions in the human genome that are associated with Alzheimer's disease, but where a causal gene has not been identified. Together, our results illustrate the potential for complementary and convergent evidence provided through multitrait GWAS in Drosophila to supplement and inform human studies, helping to identify the remaining heritability and novel modifiers of complex diseases.

Association detection between multiple traits and rare variants based on family data via a nonparametric method.

Background: The rapid development of next-generation sequencing technologies allow people to analyze human complex diseases at the molecular level. It has been shown that rare variants play important roles for human diseases besides common variants. Thus, effective statistical methods need to be proposed to test for the associations between traits (e.g., diseases) and rare variants. Currently, more and more rare genetic variants are being detected throughout the human genome, which demonstrates the possibility to study rare variants. Yet complex diseases are usually measured as a variety of forms, such as binary, ordinal, quantitative, or some mixture of them. Therefore, the genetic mapping problem can be attributable to the association detection between multiple traits and multiple loci, with sufficiently considering the correlated structure among multiple traits. Methods: In this article, we construct a new non-parametric statistic by the generalized Kendall's τ theory based on family data. The new test statistic has an asymptotic distribution, it can be used to study the associations between multiple traits and rare variants, which broadens the way to identify genetic factors of human complex diseases. Results: We apply our method (called Nonp-FAM) to analyze simulated data and GAW17 data, and conduct comprehensive comparison with some existing methods. Experimental results show that the proposed family-based method is powerful and robust for testing associations between multiple traits and rare variants, even if the data has some population stratification effect.