Two adaptive weighting methods to test for rare variant associations in family-based designs

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.

A modified association test for rare and common variants based on affected sib-pair design

Current genome-wide association analysis has identified a great number of rare and common variants associated with common complex traits, however, more effective approaches for detecting associations between rare and common variants with common diseases are still demanded. Approaches for detecting rare variant association analysis will compromise the power when detecting the effects of rare and common variants simultaneously. In this paper, we extend an existing method of testing for rare variant association based on affected sib pairs (TOW-sib) and propose a variable weight test for rare and common variants association based on affected sib pairs (abbreviated as VW-TOWsib). The VW-TOWsib can be used to achieve the purpose of detecting the association of rare and common variants with complex diseases. Simulation results in various scenarios show that our proposed method is more powerful than existing methods for detecting effects of rare and common variants. At the same time, the VW-TOWsib also performs well as a method for rare variant association analysis.

Detecting the Common and Individual Effects of Rare Variants on Quantitative Traits by Using Extreme Phenotype Sampling

Next-generation sequencing technology has made it possible to detect rare genetic variants associated with complex human traits. In recent literature, various methods specifically designed for rare variants are proposed. These tests can be broadly classified into burden and nonburden tests. In this paper, we take advantage of the burden and nonburden tests, and consider the common effect and the individual deviations from the common effect. To achieve robustness, we use two methods of combining p-values, Fisher's method and the minimum-p method. In rare variant association studies, to improve the power of the tests, we explore the advantage of the extreme phenotype sampling. At first, we dichotomize the continuous phenotypes before analysis, and the two extremes are treated as two different groups representing a dichotomous phenotype. We next compare the powers of several methods based on extreme phenotype sampling and random sampling. Extensive simulation studies show that our proposed methods by using extreme phenotype sampling are the most powerful or very close to the most powerful one in various settings of true models when the same sample size is used.

A nonparametric method to test for associations between rare variants and multiple traits

More and more rare genetic variants are being detected in the human genome, and it is believed that besides common variants, some rare variants also explain part of the phenotypic variance for human diseases. Due to the importance of rare variants, many statistical methods have been proposed to test for associations between rare variants and human traits. However, in existing studies, most methods only test for associations between multiple loci and one trait; therefore, the joint information of multiple traits has not been considered simultaneously and sufficiently. In this article, we present a study of testing for associations between rare variants and multiple traits, where trait value can be binary, ordinal, quantitative and/or any mixture of them. Based on the method of generalized Kendall’s τ, a nonparametric method called NM-RV is proposed. A new kernel function for U-statistic, which could incorporate the information of each rare variant itself, is also presented and is expected to enhance the power of rare variant analysis. We further consider the asymptotic distribution of the proposed association test statistic. Our simulation work suggests that the proposed method is more powerful and robust than existing methods in testing for associations between rare variants and multiple traits,especially for multivariate ordinal traits.

Detecting association of rare and common variants by adaptive combination of P-values

Genome-wide association studies (GWAS) can detect common variants associated with diseases. Next generation sequencing technology has made it possible to detect rare variants. Most of association tests, including burden tests and nonburden tests, mainly target rare variants by upweighting rare variant effects and downweighting common variant effects. But there is increasing evidence that complex diseases are caused by both common and rare variants. In this paper, we extend the ADA method (adaptive combination of P-values; Lin et al., 2014) for rare variants only and propose a RC-ADA method (common and rare variants by adaptive combination of P-values). Our proposed method combines the per-site P-values with the weights based on minor allele frequencies (MAFs). The RC-ADA is robust to directions of effects of causal variants and inclusion of a high proportion of neutral variants. The performance of the RC-ADA method is compared with several other association methods. Extensive simulation studies show that the RC-ADA method is more powerful than other association methods over a wide range of models.

Robust and Powerful Affected Sibpair Test for Rare Variant Association

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 nonshared 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.

A statistical approach for rare-variant association testing in affected sibships

Sequencing and exome-chip technologies have motivated development of novel statistical tests to identify rare genetic variation that influences complex diseases. Although many rare-variant association tests exist for case-control or cross-sectional studies, far fewer methods exist for testing association in families. This is unfortunate, because cosegregation of rare variation and disease status in families can amplify association signals for rare variants. Many researchers have begun sequencing (or genotyping via exome chips) familial samples that were either recently collected or previously collected for linkage studies. Because many linkage studies of complex diseases sampled affected sibships, we propose a strategy for association testing of rare variants for use in this study design. The logic behind our approach is that rare susceptibility variants should be found more often on regions shared identical by descent by affected sibling pairs than on regions not shared identical by descent. We propose both burden and variance-component tests of rare variation that are applicable to affected sibships of arbitrary size and that do not require genotype information from unaffected siblings or independent controls. Our approaches are robust to population stratification and produce analytic p values, thereby enabling our approach to scale easily to genome-wide studies of rare variation. We illustrate our methods by using simulated data and exome chip data from sibships ascertained for hypertension collected as part of the Genetic Epidemiology Network of Arteriopathy (GENOA) study.

Permutation testing in the presence of polygenic variation

This article discusses problems with and solutions to performing valid permutation tests for quantitative trait loci in the presence of polygenic effects. Although permutation testing is a popular approach for determining statistical significance of a test statistic with an unknown distribution--for instance, the maximum of multiple correlated statistics or some omnibus test statistic for a gene, gene-set, or pathway--naive application of permutations may result in an invalid test. The risk of performing an invalid permutation test is particularly acute in complex trait mapping where polygenicity may combine with a structured population resulting from the presence of families, cryptic relatedness, admixture, or population stratification. I give both analytical derivations and a conceptual understanding of why typical permutation procedures fail and suggest an alternative permutation-based algorithm, MVNpermute, that succeeds. In particular, I examine the case where a linear mixed model is used to analyze a quantitative trait and show that both phenotype and genotype permutations may result in an invalid permutation test. I provide a formula that predicts the amount of inflation of the type 1 error rate depending on the degree of misspecification of the covariance structure of the polygenic effect and the heritability of the trait. I validate this formula by doing simulations, showing that the permutation distribution matches the theoretical expectation, and that my suggested permutation-based test obtains the correct null distribution. Finally, I discuss situations where naive permutations of the phenotype or genotype are valid and the applicability of the results to other test statistics.

Adjusting family relatedness in data-driven burden test of rare variants

Family data represent 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 for the inflation of false positives. We compare WSMM with a nondata-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.

A rare variant association test based on combinations of single-variant tests

Next generation sequencing technologies make direct testing rare variant associations possible. However, the development of powerful statistical methods for rare variant association studies is still underway. Most of existing methods are burden and quadratic tests. Recent studies show that the performance of each of burden and quadratic tests depends strongly upon the underlying assumption and no test demonstrates consistently acceptable power. Thus, combined tests by combining information from the burden and quadratic tests have been proposed recently. However, results from recent studies (including this study) show that there exist tests that can outperform both burden and quadratic tests. In this article, we propose three classes of tests that include tests outperforming both burden and quadratic tests. Then, we propose the optimal combination of single-variant tests (OCST) by combining information from tests of the three classes. We use extensive simulation studies to compare the performance of OCST with that of burden, quadratic and optimal single-variant tests. Our results show that OCST either is the most powerful test or has similar power with the most powerful test. We also compare the performance of OCST with that of the two existing combined tests. Our results show that OCST has better power than the two combined tests.

Flexible and robust methods for rare-variant testing of quantitative traits in trios and nuclear families

Most rare-variant association tests for complex traits are applicable only to population-based or case-control resequencing studies. There are fewer rare-variant association tests for family-based resequencing studies, which is unfortunate because pedigrees possess many attractive characteristics for such analyses. Family-based studies can be more powerful than their population-based counterparts due to increased genetic load and further enable the implementation of rare-variant association tests that, by design, are robust to confounding due to population stratification. With this in mind, we propose a rare-variant association test for quantitative traits in families; this test integrates the QTDT approach of Abecasis et al. [Abecasis et al., ] into the kernel-based SNP association test KMFAM of Schifano et al. [Schifano et al., ]. The resulting within-family test enjoys the many benefits of the kernel framework for rare-variant association testing, including rapid evaluation of P-values and preservation of power when a region harbors rare causal variation that acts in different directions on phenotype. Additionally, by design, this within-family test is robust to confounding due to population stratification. Although within-family association tests are generally less powerful than their counterparts that use all genetic information, we show that we can recover much of this power (although still ensuring robustness to population stratification) using a straightforward screening procedure. Our method accommodates covariates and allows for missing parental genotype data, and we have written software implementing the approach in R for public use.

Evaluation of the power and type I error of recently proposed family-based tests of association for rare variants

Until very recently, few methods existed to analyze rare-variant association with binary phenotypes in complex pedigrees. We consider a set of recently proposed methods applied to the simulated and real hypertension phenotype as part of the Genetic Analysis Workshop 18. Minimal power of the methods is observed for genes containing variants with weak effects on the phenotype. Application of the methods to the real hypertension phenotype yielded no genes meeting a strict Bonferroni cutoff of significance. Some prior literature connects 3 of the 5 most associated genes (p <1 × 10⁻⁴) to hypertension or related phenotypes. Further methodological development is needed to extend these methods to handle covariates, and to explore more powerful test alternatives.

Test of rare variant association based on affected sib-pairs

With the development of sequencing techniques, there is increasing interest to detect associations between rare variants and complex traits. Quite a few statistical methods to detect associations between rare variants and complex traits have been developed for unrelated individuals. Statistical methods for detecting rare variant associations under family-based designs have not received as much attention as methods for unrelated individuals. Recent studies show that rare disease variants will be enriched in family data and thus family-based designs may improve power to detect rare variant associations. In this article, we propose a novel test to test association between the optimally weighted combination of variants and trait of interests for affected sib-pairs. The optimal weights are analytically derived and can be calculated from sampled genotypes and phenotypes. Based on the optimal weights, the proposed method is robust to the directions of the effects of causal variants and is less affected by neutral variants than existing methods are. Our simulation results show that, in all the cases, the proposed method is substantially more powerful than existing methods based on unrelated individuals and existing methods based on affected sib-pairs.

Utilising family-based designs for detecting rare variant disease associations

Rare genetic variants are thought to be important components in the causality of many diseases but discovering these associations is challenging. We demonstrate how best to use family-based designs to improve the power to detect rare variant disease associations. We show that using genetic data from enriched families (those pedigrees with greater than one affected member) increases the power and sensitivity of existing case-control rare variant tests. However, we show that transmission- (or within-family-) based tests do not benefit from this enrichment. This means that, in studies where a limited amount of genotyping is available, choosing a single case from each of many pedigrees has greater power than selecting multiple cases from fewer pedigrees. Finally, we show how a pseudo-case-control design allows a greater range of statistical tests to be applied to family data.

A novel test for testing the optimally weighted combination of rare and common variants based on data of parents and affected children

With the development of sequencing technologies, the direct testing of rare variant associations has become possible. Many statistical methods for detecting associations between rare variants and complex diseases have recently been developed, most of which are population-based methods for unrelated individuals. A limitation of population-based methods is that spurious associations can occur when there is a population structure. For rare variants, this problem can be more serious, because the spectrum of rare variation can be very different in diverse populations, as well as the current nonexistence of methods to control for population stratification in population-based rare variant associations. A solution to the problem of population stratification is to use family-based association tests, which use family members to control for population stratification. In this article, we propose a novel test for Testing the Optimally Weighted combination of variants based on data of Parents and Affected Children (TOW-PAC). TOW-PAC is a family-based association test that tests the combined effect of rare and common variants in a genomic region, and is robust to the directions of the effects of causal variants. Simulation studies confirm that, for rare variant associations, family-based association tests are robust to population stratification although population-based association tests can be seriously confounded by population stratification. The results of power comparisons show that the power of TOW-PAC increases with an increase of the number of affected children in each family and TOW-PAC based on multiple affected children per family is more powerful than TOW based on unrelated individuals.

Identifying rare variants associated with complex traits via sequencing

Although genome-wide association studies have been successful in detecting associations with common variants, there is currently an increasing interest in identifying low-frequency and rare variants associated with complex traits. Next-generation sequencing technologies make it feasible to survey the full spectrum of genetic variation in coding regions or the entire genome. The association analysis for rare variants is challenging, and traditional methods are ineffective, however, due to the low frequency of rare variants, coupled with allelic heterogeneity. Recently a battery of new statistical methods has been proposed for identifying rare variants associated with complex traits. These methods test for associations by aggregating multiple rare variants across a gene or a genomic region or among a group of variants in the genome. In this unit, we describe key concepts for rare variant association for complex traits, survey some of the recent methods, discuss their statistical power under various scenarios, and provide practical guidance on analyzing next-generation sequencing data for identifying rare variants associated with complex traits.

Detecting association of rare variants by testing an optimally weighted combination of variants for quantitative traits in general families

Although next-generation sequencing technology allows sequencing the whole genome of large groups of individuals, the development of powerful statistical methods for rare variant association studies is still underway. Even though many statistical methods have been developed for mapping rare variants, most of these methods are for unrelated individuals only, whereas family data have been shown to improve power to detect rare variants. The majority of the existing methods for unrelated individuals is essentially testing the effect of a weighted combination of variants with different weighting schemes. The performance of these methods depends on the weights being used. Recently, researchers proposed a test for Testing the effect of an Optimally Weighted combination of variants (TOW) for unrelated individuals. In this article, we extend our previously developed TOW for unrelated individuals to family-based data and propose a novel test for Testing the effect of an Optimally Weighted combination of variants for Family-based designs (TOW-F). The optimal weights are analytically derived. The results of extensive simulation studies show that TOW-F is robust to population stratification in a wide range of population structures, is robust to the direction and magnitude of the effects of causal variants, and is relatively robust to the percentage of neutral variants.

Imputation of rare variants in next-generation association studies

The role of rare variants has become a focus in the search for association with complex traits. Imputation is a powerful and cost-efficient tool to access variants that have not been directly typed, but there are several challenges when imputing rare variants, most notably reference panel selection. Extensions to rare variant association tests to incorporate genotype uncertainty from imputation are discussed, as well as the use of imputed low-frequency and rare variants in the study of population isolates.

Patient-controlled encrypted genomic data: an approach to advance clinical genomics

Background

The revolution in DNA sequencing technologies over the past decade has made it feasible to sequence an individual’s whole genome at a relatively low cost. The potential value of the information generated by genomic technologies for medicine and society is enormous. However, in order for exome sequencing, and eventually whole genome sequencing, to be implemented clinically, a number of major challenges need to be overcome. For instance, obtaining meaningful informed-consent, managing incidental findings and the great volume of data generated (including multiple findings with uncertain clinical significance), re-interpreting the genomic data and providing additional counselling to patients as genetic knowledge evolves are issues that need to be addressed. It appears that medical genetics is shifting from the present “phenotype-first” medical model to a “data-first” model which leads to multiple complexities.

Discussion

This manuscript discusses the different challenges associated with integrating genomic technologies into clinical practice and describes a “phenotype-first” approach, namely, “Individualized Mutation-weighed Phenotype Search”, and its benefits. The proposed approach allows for a more efficient prioritization of the genes to be tested in a clinical lab based on both the patient’s phenotype and his/her entire genomic data. It simplifies “informed-consent” for clinical use of genomic technologies and helps to protect the patient’s autonomy and privacy. Overall, this approach could potentially render widespread use of genomic technologies, in the immediate future, practical, ethical and clinically useful.

Summary

The “Individualized Mutation-weighed Phenotype Search” approach allows for an incremental integration of genomic technologies into clinical practice. It ensures that we do not over-medicalize genomic data but, rather, continue our current medical model which is based on serving the patient’s concerns. Service should not be solely driven by technology but rather by the medical needs and the extent to which a technology can be safely and effectively utilized.