Genotype-Based Score Test for Association Testing in Families.
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ABSTRACT:
SUBMITTER: Uh HW
PROVIDER: S-EPMC4596911 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Genome-wide association studies (GWASs) for complex diseases often collect data on multiple correlated endo-phenotypes. Multivariate analysis of these correlated phenotypes can improve the power to detect genetic variants. Multivariate analysis of variance (MANOVA) can perform such association analysis at a GWAS level, but the behavior of MANOVA under different trait models has not been carefully investigated. In this paper, we show that MANOVA is generally very powerful for detecting association but there are situations, such as when a genetic variant is associated with all the traits, where MANOVA may not have any detection power. In these situations, marginal model based methods, however, perform much better than multivariate methods. We investigate the behavior of MANOVA, both theoretically and using simulations, and derive the conditions where MANOVA loses power. Based on our findings, we propose a unified score-based test statistic USAT that can perform better than MANOVA in such situations and nearly as well as MANOVA elsewhere. Our proposed test reports an approximate asymptotic P-value for association and is computationally very efficient to implement at a GWAS level. We have studied through extensive simulations the performance of USAT, MANOVA, and other existing approaches and demonstrated the advantage of using the USAT approach to detect association between a genetic variant and multivariate phenotypes. We applied USAT to data from three correlated traits collected on 5, 816 Caucasian individuals from the Atherosclerosis Risk in Communities (ARIC, The ARIC Investigators []) Study and detected some interesting associations.
| S-EPMC4785800 | biostudies-literature
Project description:Simultaneous testing of multiple genetic variants for association is widely recognized as a valuable complementary approach to single-marker tests. As such, principal component regression (PCR) has been found to have competitive power. We focus on exploring a robust test for an unknown genetic mode of all SNPs, an unknown Hardy-Weinberg equilibrium (HWE) in a population, and a large number of all SNPs. First, we propose a new global test by means of the use of codominant codes for all markers and PCR. The new global test is built on an empirical Bayes-type score statistic for testing marginal associations with each single marker. The new global test gains power by robustly exploiting the Hardy-Weinberg equilibrium in the control population and effectively using linkage disequilibrium among test markers. The new global test reduces to PCR when the genotype for each marker is coded as the number of minor alleles. This connection lends insight into the power of the new global test relative to PCR and some other popular multimarker test methods. Second, we propose a robust test method based on the new global test and the ordinary PCR test built on a prospective score statistic for testing marginal associations with each single marker when the genotype for each marker is coded as the number of minor alleles by taking the minimum p value of these two tests. Finally, through extensive simulation studies and analysis of the association between pancreatic cancer and some genes of interest, we show that the proposed robust test method has desirable power and can often identify association signals that may be missed by existing methods.
| S-EPMC7229558 | biostudies-literature
Project description:Haplotypes provide a more informative format of polymorphisms for genetic association analysis than do individual single-nucleotide polymorphisms. However, the practical efficacy of haplotype-based association analysis is challenged by a trade-off between the benefits of modeling abundant variation and the cost of the extra degrees of freedom. To reduce the degrees of freedom, several strategies have been considered in the literature. They include (1) clustering evolutionarily close haplotypes, (2) modeling the level of haplotype sharing, and (3) smoothing haplotype effects by introducing a correlation structure for haplotype effects and studying the variance components (VC) for association. Although the first two strategies enjoy a fair extent of power gain, empirical evidence showed that VC methods may exhibit only similar or less power than the standard haplotype regression method, even in cases of many haplotypes. In this study, we report possible reasons that cause the underpowered phenomenon and show how the power of the VC strategy can be improved. We construct a score test based on the restricted maximum likelihood or the marginal likelihood function of the VC and identify its nontypical limiting distribution. Through simulation, we demonstrate the validity of the test and investigate the power performance of the VC approach and that of the standard haplotype regression approach. With suitable choices for the correlation structure, the proposed method can be directly applied to unphased genotypic data. Our method is applicable to a wide-ranging class of models and is computationally efficient and easy to implement. The broad coverage and the fast and easy implementation of this method make the VC strategy an effective tool for haplotype analysis, even in modern genomewide association studies.
| S-EPMC2265651 | biostudies-literature
Project description:We consider the problem of genomewide association testing of a binary trait when some sampled individuals are related, with known relationships. This commonly arises when families sampled for a linkage study are included in an association study. Furthermore, power to detect association with complex traits can be increased when affected individuals with affected relatives are sampled, because they are more likely to carry disease alleles than are randomly sampled affected individuals. With related individuals, correlations among relatives must be taken into account, to ensure validity of the test, and consideration of these correlations can also improve power. We provide new insight into the use of pedigree-based weights to improve power, and we propose a novel test, the MQLS test, which, as we demonstrate, represents an overall, and in many cases, substantial, improvement in power over previous tests, while retaining a computational simplicity that makes it useful in genomewide association studies in arbitrary pedigrees. Other features of the MQLS are as follows: (1) it is applicable to completely general combinations of family and case-control designs, (2) it can incorporate both unaffected controls and controls of unknown phenotype into the same analysis, and (3) it can incorporate phenotype data about relatives with missing genotype data. The methods are applied to data from the Genetic Analysis Workshop 14 Collaborative Study of the Genetics of Alcoholism, where the MQLS detects genomewide significant association (after Bonferroni correction) with an alcoholism-related phenotype for four different single-nucleotide polymorphisms: tsc1177811 (P=5.9x10(-7)), tsc1750530 (P=4.0x10(-7)), tsc0046696 (P=4.7x10(-7)), and tsc0057290 (P=5.2x10(-7)) on chromosomes 1, 16, 18, and 18, respectively. Three of these four significant associations were not detected in previous studies analyzing these data.
| S-EPMC1950805 | biostudies-literature
Project description: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.
| S-EPMC4748859 | biostudies-literature
Project description:High-throughput sequencing technology has enabled population-based studies of the role of the human microbiome in disease etiology and exposure response. Distance-based analysis is a popular strategy for evaluating the overall association between microbiome diversity and outcome, wherein the phylogenetic distance between individuals' microbiome profiles is computed and tested for association via permutation. Despite their practical popularity, distance-based approaches suffer from important challenges, especially in selecting the best distance and extending the methods to alternative outcomes, such as survival outcomes. We propose the microbiome regression-based kernel association test (MiRKAT), which directly regresses the outcome on the microbiome profiles via the semi-parametric kernel machine regression framework. MiRKAT allows for easy covariate adjustment and extension to alternative outcomes while non-parametrically modeling the microbiome through a kernel that incorporates phylogenetic distance. It uses a variance-component score statistic to test for the association with analytical p value calculation. The model also allows simultaneous examination of multiple distances, alleviating the problem of choosing the best distance. Our simulations demonstrated that MiRKAT provides correctly controlled type I error and adequate power in detecting overall association. "Optimal" MiRKAT, which considers multiple candidate distances, is robust in that it suffers from little power loss in comparison to when the best distance is used and can achieve tremendous power gain in comparison to when a poor distance is chosen. Finally, we applied MiRKAT to real microbiome datasets to show that microbial communities are associated with smoking and with fecal protease levels after confounders are controlled for.
| S-EPMC4570290 | biostudies-literature
Project description:BackgroundWe have conducted a genome-wide association study on the Genetic Analysis Workshop (GAW) 16 rheumatoid arthritis data using a multilocus score test based on wavelet transform proposed recently by the authors. The wavelet-based test automatically adjusts for the amount of noise suppressed from the data. The power of the test is also increased by using the genetic information contained in the spatial ordering of single-nucleotide polymorphisms on a chromosome.ResultsAfter adjusting for the effect of population stratification, the test identified some previously discovered rheumatoid arthritis susceptibility loci (HLA-DRB1 and rs3761847) as well as some loci (rs2076530 and rs3130340) known to have association with sarcoidosis and bone mineral density. It was previously reported that patients with rheumatoid arthritis have elevated prevalence of sarcoidosis and have reduced bone mass.ConclusionThis new test provides a useful tool in genome-wide association studies.
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Project description:Although epidemiological studies have suggested an association between atopy and glioma risk, these observations have been based on self-reporting of allergic conditions raising the possibility that associations may be noncausal and arise as a consequence of bias, reverse causation or other artifacts. Genetic information provides an alternative approach to investigate the relationship avoiding such biases. We analyzed 1,878 glioma cases and 3,670 controls for variants at 2q12, 5q12.1, 11q13 and 17q21 that are associated with asthma or eczema risk at p < 5.0 × 10(-7) . The SNP rs7216389, which tags the 3' flanking region of ORMDL3 at 17q21 and has been associated with childhood asthma, was correlated with increased glioma risk (OR = 1.10; 95% CI: 1.01-1.19). These data provide evidence for a correlation between asthma susceptibility and glioma risk and illustrate the value of using genetics as an investigative tool for developing etiological hypotheses.
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Project description:Recent advances in genotyping and sequencing technologies have enabled genetic association studies to leverage high-quality genotyped data to identify variants accounting for a substantial portion of disease risk. The usage of external controls, whose genomes have already been genotyped and are publicly available, could be a cost-effective approach to increase the power of association testing. There has been recent effort to integrate external controls while adjusting for possible batch effects, such as the integrating External Controls into Association Test (iECAT). The original iECAT test, however, cannot adjust for covariates such as age, gender, and so forth. Hence, based on the insight of iECAT, we propose a novel score-based test that allows for covariate adjustment and constructs a shrinkage score statistic that is a weighted sum of the score statistics using exclusively internal samples and uses both internal and external control samples. We assess the existence of batch effect at a variant by comparing control samples of internal and external sources. We show by simulation studies that our method has increased power over the original iECAT while controlling for type I error rates. We present the application of our method to the association studies of age-related macular degeneration (AMD) utilizing data from the International AMD Genomics Consortium and Michigan Genomics Initiative. Through the incorporation of the score test approach, we extend the use of iECAT to adjust for covariates and improve power, further honing the statistical methods needed to identify disease-causing variants within the human genome.
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Project description:We aimed to develop a brief, preclinical test to screen the reduced hippocampal volume that is a marker of early dementia [Cognitive Quotient (CQ) test]. We performed an observational study of adult subjects who underwent brain MRI in seven institutions from February 2018 to May 2019. The CQ test consists of five components: (1) digits forward, (2) digits backward, (3) Stroop test, (4) simple calculation, and (5) mental rotation. The primary outcome measure was hippocampal volume. We separated the data into derivation (n = 322) and validation cohorts (n = 96). In the derivation cohort, we built two scoring systems using the results of CQ test (model 1 and 2). In the validation cohort, we validated the correlation of the scoring systems with hippocampal volume. In the derivation cohort, there was a moderate correlation between the scoring systems and hippocampal volume [e.g., correlation coefficient = 0.62 in model 1 (95% CI 0.44-0.75)]. Likewise, in the validation cohort, there was a moderate correlation between the scoring systems and hippocampal volume [e.g., correlation coefficient = 0.54 in model 2 (95% CI 0.38-0.67)]. In this analysis of 418 participants, the score of newly developed CQ test was correlated with hippocampal volume.
| S-EPMC7542449 | biostudies-literature