Genome-wide Association Studies Methods Research Articles

Performance of epistasis detection methods in semi-simulated GWAS

BackgroundPart of the missing heritability in Genome Wide Association Studies (GWAS) is expected to be explained by interactions between genetic variants, also called epistasis. Various statistical methods have been developed to detect epistasis in case-control GWAS. These methods face major statistical challenges due to the number of tests required, the complexity of the Linkage Disequilibrium (LD) structure, and the lack of consensus regarding the definition of epistasis. Their limited impact in terms of uncovering new biological knowledge might be explained in part by the limited amount of experimental data available to validate their statistical performances in a realistic GWAS context. In this paper, we introduce a simulation pipeline for generating real scale GWAS data, including epistasis and realistic LD structure. We evaluate five exhaustive bivariate interaction methods, fastepi, GBOOST, SHEsisEpi, DSS, and IndOR. Two hundred thirty four different disease scenarios are considered in extensive simulations. We report the performances of each method in terms of false positive rate control, power, area under the ROC curve (AUC), and computation time using a GPU. Finally we compare the result of each methods on a real GWAS of type 2 diabetes from the Welcome Trust Case Control Consortium.ResultsGBOOST, SHEsisEpi and DSS allow a satisfactory control of the false positive rate. fastepi and IndOR present an increase in false positive rate in presence of LD between causal SNPs, with our definition of epistasis. DSS performs best in terms of power and AUC in most scenarios with no or weak LD between causal SNPs. All methods can exhaustively analyze a GWAS with 6.105 SNPs and 15,000 samples in a couple of hours using a GPU.ConclusionThis study confirms that computation time is no longer a limiting factor for performing an exhaustive search of epistasis in large GWAS. For this task, using DSS on SNP pairs with limited LD seems to be a good strategy to achieve the best statistical performance. A combination approach using both DSS and GBOOST is supported by the simulation results and the analysis of the WTCCC dataset demonstrated that this approach can detect distinct genes in epistasis. Finally, weak epistasis between common variants will be detectable with existing methods when GWAS of a few tens of thousands cases and controls are available.

Eigen decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein

BackgroundPseudo-phenotypes, such as 305-day yields, estimated breeding values or deregressed proofs, are usually used as response variables for genome-wide association studies (GWAS) of milk production traits in dairy cattle. Computational inefficiency challenges the direct use of test-day records for longitudinal GWAS with large datasets.ResultsWe propose a rapid longitudinal GWAS method that is based on a random regression model. Our method uses Eigen decomposition of the phenotypic covariance matrix to rotate the data, thereby transforming the complex mixed linear model into weighted least squares analysis. We performed a simulation study that showed that our method can control type I errors well and has higher power than a longitudinal GWAS method that does not include time-varied additive genetic effects. We also applied our method to the analysis of milk production traits in the first three parities of 6711 Chinese Holstein cows. The analysis for each trait was completed within 1 day with known variances. In total, we located 84 significant single nucleotide polymorphisms (SNPs) of which 65 were within previously reported quantitative trait loci (QTL) regions.ConclusionsOur rapid method can control type I errors in the analysis of longitudinal data and can be applied to other longitudinal traits. We detected QTL that were for the most part similar to those reported in a previous study in Chinese Holstein. Moreover, six additional SNPs for fat percentage and 13 SNPs for protein percentage were identified by our method. These additional 19 SNPs could be new candidate quantitative trait nucleotides for milk production traits in Chinese Holstein.

Use of meta-analyses and joint analyses to select variants in whole genome sequences for genomic evaluation: An application in milk production of French dairy cattle breeds

As a result of the 1000 Bull Genome Project, it has become possible to impute millions of variants, with many of these potentially causative for traits of interest, for thousands of animals that have been genotyped with medium-density chips. This enormous source of data opens up very interesting possibilities for the inclusion of these variants in genomic evaluations. However, for computational reasons, it is not possible to include all variants in genomic evaluation procedures. One potential approach could be to select the most relevant variants based on the results of genome-wide association studies (GWAS); however, the identification of causative mutations is still difficult with this method, partly because of weak imputation accuracy for rare variants. To address this problem, this study assesses the ability of different approaches based on multi-breed GWAS (joint and meta-analyses) to identify single-nucleotide polymorphisms (SNP) for use in genomic evaluation in the 3 main French dairy cattle breeds. A total of 6,262 Holstein bulls, 2,434 Montbéliarde bulls, and 2,175 Normande bulls with daughter yield deviations for 5 milk production traits were imputed for 27 million variants. Within-breed and joint (including all 3 breeds) GWAS were performed and 3 models of meta-analysis were tested: fixed effect, random effect, and Z-score. Comparison of the results of within- and multi-breed GWAS showed that most of the quantitative trait loci identified using within-breed approaches were also found with multi-breed methods. However, the most significant variants identified in each region differed depending on the method used. To determine which approach highlighted the most predictive SNP for each trait, we used a marker-assisted best unbiased linear prediction model to evaluate lists of SNP generated by the different GWAS methods; each list contained between 25 and 2,000 candidate variants per trait, which were identified using a single within- or multi-breed GWAS approach. Among all the multi-breed methods tested in this study, variant selection based on meta-analysis (fixed effect) resulted in the most-accurate genomic evaluation (+1 to +3 points compared with other multi-breed approaches). However, the accuracies of genomic evaluation were always better when variants were selected using the results of within-breed GWAS. As has generally been found in studies of quantitative trait loci, these results suggest that part of the genetic variance of milk production traits is breed specific in Holstein, Montbéliarde, and Normande cattle.

A phylogenetic method to perform genome-wide association studies in microbes that accounts for population structure and recombination.

Genome-Wide Association Studies (GWAS) in microbial organisms have the potential to vastly improve the way we understand, manage, and treat infectious diseases. Yet, microbial GWAS methods established thus far remain insufficiently able to capitalise on the growing wealth of bacterial and viral genetic sequence data. Facing clonal population structure and homologous recombination, existing GWAS methods struggle to achieve both the precision necessary to reject spurious findings and the power required to detect associations in microbes. In this paper, we introduce a novel phylogenetic approach that has been tailor-made for microbial GWAS, which is applicable to organisms ranging from purely clonal to frequently recombining, and to both binary and continuous phenotypes. Our approach is robust to the confounding effects of both population structure and recombination, while maintaining high statistical power to detect associations. Thorough testing via application to simulated data provides strong support for the power and specificity of our approach and demonstrates the advantages offered over alternative cluster-based and dimension-reduction methods. Two applications to Neisseria meningitidis illustrate the versatility and potential of our method, confirming previously-identified penicillin resistance loci and resulting in the identification of both well-characterised and novel drivers of invasive disease. Our method is implemented as an open-source R package called treeWAS which is freely available at https://github.com/caitiecollins/treeWAS.

IPat: intelligent prediction and association tool for genomic research.

The ultimate goal of genomic research is to effectively predict phenotypes from genotypes so that medical management can improve human health and molecular breeding can increase agricultural production. Genomic prediction or selection (GS) plays a complementary role to genome-wide association studies (GWAS), which is the primary method to identify genes underlying phenotypes. Unfortunately, most computing tools cannot perform data analyses for both GWAS and GS. Furthermore, the majority of these tools are executed through a command-line interface (CLI), which requires programming skills. Non-programmers struggle to use them efficiently because of the steep learning curves and zero tolerance for data formats and mistakes when inputting keywords and parameters. To address these problems, this study developed a software package, named the Intelligent Prediction and Association Tool (iPat), with a user-friendly graphical user interface. With iPat, GWAS or GS can be performed using a pointing device to simply drag and/or click on graphical elements to specify input data files, choose input parameters and select analytical models. Models available to users include those implemented in third party CLI packages such as GAPIT, PLINK, FarmCPU, BLINK, rrBLUP and BGLR. Users can choose any data format and conduct analyses with any of these packages. File conversions are automatically conducted for specified input data and selected packages. A GWAS-assisted genomic prediction method was implemented to perform genomic prediction using any GWAS method such as FarmCPU. iPat was written in Java for adaptation to multiple operating systems including Windows, Mac and Linux. The iPat executable file, user manual, tutorials and example datasets are freely available at http://zzlab.net/iPat. zhiwu.zhang@wsu.edu.

Recent developments in statistical methods for GWAS and high-throughput sequencing association studies of complex traits

ABSTRACTThe advent of large-scale genetic studies has helped bring a new era of biomedical research on dissecting the genetic architecture of complex human disease. Genome-wide association studies (GWASs) and next-generation sequencing studies are two popular tools for identifying genetic variants that are associated with complex traits. This article overviews some of the most important statistical tools for analyzing data from these two types of studies, with an emphasis on single-SNP tests for common variants and region-based tests for rare variants. We compare various statistical methods for common and rare variants in humans, and describe some critical considerations to guide the choice of an analysis method. Also discussed are the related topics of sample ascertainment, missing heritability, and multiple testing correction, as well as some remaining analytical challenges presented by complex trait association mapping using genomic data obtained via high-throughput technologies.

How to Reveal Magnitude of Gene Signals: Hierarchical Hypergeometric Complementary Cumulative Distribution Function

This article introduces a new method for genome-wide association study (GWAS), hierarchical hypergeometric complementary cumulative distribution function (HH-CCDF). Existing GWAS methods, e.g. the linear model and hierarchical association coefficient algorithm, calculate the association between a marker variable and a phenotypic variable. The ideal GWAS practice is to calculate the association between a marker variable and a gene-signal variable. If the gene-signal variable and phenotypic variable are imperfectly proportional, the existing methods do not properly reveal the magnitude of the association between the gene-signal variable and marker variable. The HH-CCDF mitigates the impact of the imperfect proportionality between the phenotypic variable and gene-signal variable and thus better reveals the magnitude of gene signals. The HH-CCDF will provide new insights into GWAS approaches from the viewpoint of revealing the magnitude of gene signals.

PKWmEB: integration of Kruskal\u2013Wallis test with empirical Bayes under polygenic background control for multi-locus genome-wide association study

Although nonparametric methods in genome-wide association studies (GWAS) are robust in quantitative trait nucleotide (QTN) detection, the absence of polygenic background control in single-marker association in genome-wide scans results in a high false positive rate. To overcome this issue, we proposed an integrated nonparametric method for multi-locus GWAS. First, a new model transformation was used to whiten the covariance matrix of polygenic matrix K and environmental noise. Using the transferred model, Kruskal–Wallis test along with least angle regression was then used to select all the markers that were potentially associated with the trait. Finally, all the selected markers were placed into multi-locus model, these effects were estimated by empirical Bayes, and all the nonzero effects were further identified by a likelihood ratio test for true QTN detection. This method, named pKWmEB, was validated by a series of Monte Carlo simulation studies. As a result, pKWmEB effectively controlled false positive rate, although a less stringent significance criterion was adopted. More importantly, pKWmEB retained the high power of Kruskal–Wallis test, and provided QTN effect estimates. To further validate pKWmEB, we re-analyzed four flowering time related traits in Arabidopsis thaliana, and detected some previously reported genes that were not identified by the other methods.

Dissolving the Missing Heritability Problem

Heritability estimates obtained from genome-wide association studies (GWAS) are much lower than those of traditional quantitative methods. This phenomenon has been called the “missing heritability problem.” By analyzing and comparing GWAS and traditional quantitative methods, we first show that the estimates obtained from the latter involve some terms other than additive genetic variance, while the estimates from the former do not. Second, GWAS, when used to estimate heritability, do not take into account additive epigenetic factors transmitted across generations, while traditional quantitative methods do. Given these two points we show that the missing heritability problem can largely be dissolved.

Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs

Abstract. Quantitative or complex traits are controlled by many genes and environmental factors. Most traits in livestock breeding are quantitative traits. Mapping genes and causative mutations generating the genetic variance of these traits is still a very active area of research in livestock genetics. Since genome-wide and dense SNP panels are available for most livestock species, genome-wide association studies (GWASs) have become the method of choice in mapping experiments. Different statistical models are used for GWASs. We will review the frequently used single-marker models and additionally describe Bayesian multi-marker models. The importance of nonadditive genetic and genotype-by-environment effects along with GWAS methods to detect them will be briefly discussed. Different mapping populations are used and will also be reviewed. Whenever possible, our own real-data examples are included to illustrate the reviewed methods and designs. Future research directions including post-GWAS strategies are outlined.

Genome-wide association study using single marker analysis and Bayesian methods for the gonadosomatic index in the large yellow croaker

The gonadosomatic index (GSI) is an economically important trait in the large yellow croaker (Larimichthys crocea), a mariculture fish species with high commercial value in Southeast China. However, progress in the genetic research on this species is relatively lacking. To identify the relevant genes and chromosome regions associated with the GSI, we performed a genome-wide association study (GWAS) on a population of 500 individuals (237 males and 263 females), using single marker analysis and Bayesian methods. The genotyping-by-sequencing method was used to construct the libraries, and 29,748 SNPs were subsequently found in the genome of Larimichthys crocea. A total of six different SNPs, located on LG3, LG6, LG8, and LG16, were determined to be significantly associated with the GSI. Among them, only two SNPs were found by both GWAS methods. By integrating the results of two GWAS methods with the biological functions of the genes, six candidate genes (viz., FZD3, DDX4, EDC3, CITED3, SOX9-b, and LOC106602108) were revealed to be associated with the GSI in the large yellow croaker. These functional genes are involved in gonadal development or gonadogenesis, providing the basis for further study on genetic mechanisms and marker-assisted selection in this fish species. In this study, we found that the combination of single marker analysis and Bayesian methods could identify more SNP loci associated with the phenotypes. Thus, we suggest performing these two methods simultaneously in GWAS.

Trans-ethnic predicted expression genome-wide association analysis identifies a gene for estrogen receptor-negative breast cancer.

Genome-wide association studies (GWAS) have identified more than 90 susceptibility loci for breast cancer, but the underlying biology of those associations needs to be further elucidated. More genetic factors for breast cancer are yet to be identified but sample size constraints preclude the identification of individual genetic variants with weak effects using traditional GWAS methods. To address this challenge, we utilized a gene-level expression-based method, implemented in the MetaXcan software, to predict gene expression levels for 11,536 genes using expression quantitative trait loci and examine the genetically-predicted expression of specific genes for association with overall breast cancer risk and estrogen receptor (ER)-negative breast cancer risk. Using GWAS datasets from a Challenge launched by National Cancer Institute, we identified TP53INP2 (tumor protein p53-inducible nuclear protein 2) at 20q11.22 to be significantly associated with ER-negative breast cancer (Z = -5.013, p = 5.35×10−7, Bonferroni threshold = 4.33×10−6). The association was consistent across four GWAS datasets, representing European, African and Asian ancestry populations. There are 6 single nucleotide polymorphisms (SNPs) included in the prediction of TP53INP2 expression and five of them were associated with estrogen-receptor negative breast cancer, although none of the SNP-level associations reached genome-wide significance. We conducted a replication study using a dataset outside of the Challenge, and found the association between TP53INP2 and ER-negative breast cancer was significant (p = 5.07x10-3). Expression of HP (16q22.2) showed a suggestive association with ER-negative breast cancer in the discovery phase (Z = 4.30, p = 1.70x10-5) although the association was not significant after Bonferroni adjustment. Of the 249 genes that are 250 kb within known breast cancer susceptibility loci identified from previous GWAS, 20 genes (8.0%) were statistically significant associated with ER-negative breast cancer (p<0.05), compared to 582 (5.2%) of 11,287 genes that are not close to previous GWAS loci. This study demonstrated that expression-based gene mapping is a promising approach for identifying cancer susceptibility genes.

An efficient unified model for genome-wide association studies and genomic selection

BackgroundA quantitative trait is controlled both by major variants with large genetic effects and by minor variants with small effects. Genome-wide association studies (GWAS) are an efficient approach to identify quantitative trait loci (QTL), and genomic selection (GS) with high-density single nucleotide polymorphisms (SNPs) can achieve higher accuracy of estimated breeding values than conventional best linear unbiased prediction (BLUP). GWAS and GS address different aspects of quantitative traits, but, as statistical models, they are quite similar in their description of the genetic mechanisms that underlie quantitative traits.MethodsHere, we propose a stepwise linear regression mixed model (StepLMM) to unify GWAS and GS in a single statistical model. First, the variance components of the genomic-BLUP (GBLUP) model are estimated. Then, in the SNP selection step, the linear mixed model (LMM) for GWAS is equivalently transformed into a simple linear regression to improve computation speed, and the most significant SNP is selected and included into the evaluation model. In the SNP dropping step, the SNPs in the evaluation model are tested according to the standard errors of their estimated effects. If non-significant SNPs are present, the least significant one is dropped from the model and variance components are re-estimated. We used extended Bayesian information criteria (eBIC) to evaluate the model optimization, i.e. the model with the smallest eBIC is the final one and includes only significant SNPs.ResultsWe simulated scenarios with different heritabilities with 100 QTL. StepLMM estimated heritability accurately and mapped QTL precisely. Genomic prediction accuracy was much higher with StepLMM than with GBLUP. The comparison of StepLMM with other GWAS and GS methods based on a dataset from the 16th QTLMAS Workshop showed that StepLMM had medium mapping power, the lowest rate of false positives for QTL mapping, and the highest accuracy for genomic prediction.ConclusionsStepLMM is a combination of GWAS and GBLUP. GWAS and GBLUP are beneficial to each other in a single statistical model, GWAS improves genomic prediction accuracy, while GBLUP increases mapping precision and decreases the rate of false positives of GWAS. StepLMM has a high performance in both GWAS and GS and is feasible for agricultural breeding programs and human genetic studies.

FGWAS: Functional genome wide association analysis

Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs.

Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules.

Network-based genome-wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings. Although gene functions are relevant to tissue context, most existing methods analyze tissue-free networks without reflecting phenotypic specificity. We propose a novel module identification framework for imaging genetic studies using the tissue-specific functional interaction network. Our method includes three steps: (i) re-prioritize imaging GWAS findings by applying machine learning methods to incorporate network topological information and enhance the connectivity among top genes; (ii) detect densely connected modules based on interactions among top re-prioritized genes; and (iii) identify phenotype-relevant modules enriched by top GWAS findings. We demonstrate our method on the GWAS of [18F]FDG-PET measures in the amygdala region using the imaging genetic data from the Alzheimer's Disease Neuroimaging Initiative, and map the GWAS results onto the amygdala-specific functional interaction network. The proposed network-based GWAS method can effectively detect densely connected modules enriched by top GWAS findings. Tissue-specific functional network can provide precise context to help explore the collective effects of genes with biologically meaningful interactions specific to the studied phenotype. The R code and sample data are freely available at http://www.iu.edu/shenlab/tools/gwasmodule/. shenli@iu.edu. Supplementary data are available at Bioinformatics online.

Multivariate simulation framework reveals performance of multi-trait GWAS methods

Burgeoning availability of genome-wide association study (GWAS) results and national biobank data has led to growing interest in performing multi-trait genetic analyses. Numerous multi-trait GWAS methods that exploit either summary statistics or individual-level data have been developed, but their relative performance is unclear. Here we develop a simulation framework to model the complex networks underlying multivariate genetic epidemiology, enabling the vast model space of genetic effects on multiple correlated traits to be explored systematically. We perform a comprehensive comparison of the leading multi-trait GWAS methods, finding: (1) method performance is highly sensitive to the specific combination of genetic effects and phenotypic correlations, (2) most of the current multivariate methods have remarkably similar statistical power, and (3) multivariate methods may offer a substantial increase in the discovery of genetic variants over the standard univariate approach. We believe our findings offer the clearest picture to date of the relative performance of multi-trait GWAS methods and act as a guide for method selection. We provide a web application and open-source software program implementing our simulation framework, for: (i) further benchmarking of multivariate GWAS methods, (ii) power calculations for multivariate genetic studies, and (iii) generating data for testing any multivariate method in genetic epidemiology.

Bosco: Boosting Corrections for Genome-Wide Association Studies With Imbalanced Samples.

In genome-wide association studies (GWAS), the acquired sequential data may exhibit imbalance structure: abundant control vs. limited case samples. Such sample imbalance issue is particularly serious when investigating rare diseases or common diseases on rare populations. Conventional GWAS methods may suffer from severe statistic biases to the major group, leading to power losses in uncovering true suspicious loci. We introduce a boosting correction method termed as Bosco to deal with such imbalanced problem. Bosco is motivated by the boost learning theory in machine learning and is implemented in a coarse-to-fine learning framework: the coarse step assigns importance scores for all samples in the major group and the fine step calculates P -values by a weighted logistic regression. On simulated data sets, we demonstrate the proposed methods can dramatically improve the discovery power even on extremely imbalanced datasets, with well controlling the false positives. The Bosco is also applied to a genome-scale gastric cancer data set to conduct genome-wide analysis. Our method replicates existing reported findings (from the likelihood ratio test) with high statistical significance and shows the ability to identify new suspicious SNPs.

What is the probability of replicating a statistically significant association in genome-wide association studies?

The goal of genome-wide association studies (GWASs) is to discover genetic variants associated with diseases/traits. Replication is a common validation method in GWASs. We regard an association as true finding when it shows significance in both primary and replication studies. A question worth pondering is what is the probability of a primary association (i.e. a statistically significant association in the primary study) being validated in the replication study? This article systematically reviews the answers to this question from different points of view. As Bayesian methods can help us integrate out the uncertainty about the underlying effect of the primary association, we will mainly focus on the Bayesian view in this article. We refer the Bayesian replication probability as the replication rate (RR). We further describe an estimation method for RR, which makes use of the summary statistics from the primary study. We can use the estimated RR to determine the sample size of the replication study and to check the consistency between the results of the primary study and those of the replication study. We describe an R-package to estimate and apply RR in GWASs. Simulation and real data experiments show that the estimated RR has good prediction and calibration performance. We also use these data to demonstrate the usefulness of RR. The R-package is available at http://bioinformatics.ust.hk/RRate.html.

ACID: Association Correction for Imbalanced Data in GWAS.

Genome-wide association study (GWAS) has been widely witnessed as a powerful tool for revealing suspicious loci from various diseases. However, real world GWAS tasks always suffer from the data imbalance problem of sufficient control samples and limited case samples. This imbalance issue can cause serious biases to the result and thus leads to losses of significance for true causal markers. To tackle this problem, we proposed a computational framework to perform association correction for imbalanced data (ACID) that could potentially improve the performance of GWAS under the imbalance condition. ACID is inspired by the imbalance learning theory but is particularly modified to address the task of association discovery from sequential genomic data. Simulation studies demonstrate ACID can dramatically improve the power of traditional GWAS method on the dataset with severe imbalances. We further applied ACID to two imbalanced datasets (gastric cancer and bladder cancer) to conduct genome wide association analysis. Experimental results indicate that our method has better abilities in identifying suspicious loci than the regression approach and shows consistencies with existing discoveries.

Comparing Analytic Methods for Longitudinal GWAS and a Case-Study Evaluating Chemotherapy Course Length in Pediatric AML. A Report from the Children's Oncology Group.

Regression analysis is commonly used in genome-wide association studies (GWAS) to test genotype-phenotype associations but restricts the phenotype to a single observation for each individual. There is an increasing need for analytic methods for longitudinally collected phenotype data. Several methods have been proposed to perform longitudinal GWAS for family-based studies but few methods are described for unrelated populations. We compared the performance of three statistical approaches for longitudinal GWAS in unrelated subjectes: (1) principal component-based generalized estimating equations (PC-GEE); (2) principal component-based linear mixed effects model (PC-LMEM); (3) kinship coefficient matrix-based linear mixed effects model (KIN-LMEM), in a study of single-nucleotide polymorphisms (SNPs) on the duration of 4 courses of chemotherapy in 624 unrelated children with de novo acute myeloid leukemia (AML) genotyped on the Illumina 2.5 M OmniQuad from the COG studies AAML0531 and AAML1031. In this study we observed an exaggerated type I error with PC-GEE in SNPs with minor allele frequencies < 0.05, wheras KIN-LMEM produces more than expected type II errors. PC-MEM showed balanced type I and type II errors for the observed vs. expected P-values in comparison to competing approaches. In general, a strong concordance was observed between the P-values with the different approaches, in particular among P < 0.01 where the between-method AUCs exceed 99%. PC-LMEM accounts for genetic relatedness and correlations among repeated phenotype measures, shows minimal genome-wide inflation of type I errors, and yields high power. We therefore recommend PC-LMEM as a robust analytic approach for GWAS of longitudinal data in unrelated populations.