Population genetic simulation study of power in association testing across genetic architectures and study designs.
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ABSTRACT:
SUBMITTER: Tong DMH
PROVIDER: S-EPMC6980249 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Statistical power calculations constitute an essential first step in the planning of scientific studies. If sufficient summary statistics are available, power calculations are in principle straightforward and computationally light. In designs, which comprise distinct groups (e.g., MZ & DZ twins), sufficient statistics can be calculated within each group, and analyzed in a multi-group model. However, when the number of possible groups is prohibitively large (say, in the hundreds), power calculations on the basis of the summary statistics become impractical. In that case, researchers may resort to Monte Carlo based power studies, which involve the simulation of hundreds or thousands of replicate samples for each specified set of population parameters. Here we present exact data simulation as a third method of power calculation. Exact data simulation involves a transformation of raw data so that the data fit the hypothesized model exactly. As in power calculation with summary statistics, exact data simulation is computationally light, while the number of groups in the analysis has little bearing on the practicality of the method. The method is applied to three genetic designs for illustrative purposes.
| S-EPMC2257998 | biostudies-other
Project description:BackgroundPedGenie software, introduced in 2006, includes genetic association testing of cases and controls that may be independent or related (nuclear families or extended pedigrees) or mixtures thereof using Monte Carlo significance testing. Our aim is to demonstrate that PedGenie, a unique and flexible analysis tool freely available in Genie 2.4 software, is significantly enhanced by incorporating meta statistics for detecting genetic association with disease using data across multiple study groups.MethodsMeta statistics (chi-squared tests, odds ratios, and confidence intervals) were calculated using formal Cochran-Mantel-Haenszel techniques. Simulated data from unrelated individuals and individuals in families were used to illustrate meta tests and their empirically-derived p-values and confidence intervals are accurate, precise, and for independent designs match those provided by standard statistical software.ResultsPedGenie yields accurate Monte Carlo p-values for meta analysis of data across multiple studies, based on validation testing using pedigree, nuclear family, and case-control data simulated under both the null and alternative hypotheses of a genotype-phenotype association.ConclusionPedGenie allows valid combined analysis of data from mixtures of pedigree-based and case-control resources. Added meta capabilities provide new avenues for association analysis, including pedigree resources from large consortia and multi-center studies.
| S-EPMC2200673 | biostudies-literature
Project description:Genome-wide association studies (GWAS) explore the relationship between genome variability and disease susceptibility with either population- or family-based data. Here, we have evaluated the utility of combining population- and family-based statistical association tests and have proposed a method for reducing the burden of multiple testing. Unrelated singleton and parent-offspring trio cases and controls from the Genetics of Kidneys in Diabetes (GoKinD) study were analyzed for genetic association with diabetic nephropathy (DN) in type 1 diabetics (T1D). The Cochran-Armitage test for trend and the family-based association test were employed using either unrelated cases and controls or trios, respectively. In addition to combining single nucleotide polymorphism (SNP) p-values across these tests via Fisher's method, we employed a novel screening approach to rank SNPs based on conditional power for more efficient testing. Using either the population-based or family-based subset alone predictably limited resolution to detect DN SNPs. For 384,197 SNPs passing quality control (QC), none achieved strict genome-wide significance (1.4 × 10(-7)) using 1171 singletons (577/594 cases/controls) or 1738 pooled singletons and offspring probands (841/897). Similarly, none of the 352,004 SNPs passing QC in 567 family trios (264/303 case/control proband trios) reached genome-wide significance. Testing the top 10 SNPs ranked using aggregated conditional power resulted in two SNPs reaching genome-wide significance, rs11645147 on chromosome 16 (p = 1.74 × 10(-4) < 0.05/10 = 0.005) and rs7866522 on chromosome 9 (p = 0.0033). Efficient usage of mixed designs incorporating both unrelated and family-based data may help to uncover associations otherwise difficult to detect in the presence of massive multiple testing corrections. Capitalizing on the strengths of both types while using screening approaches may be useful especially in light of large-scale, next-generation sequencing and rare variant studies.
| S-EPMC3784826 | biostudies-literature
Project description:Recent studies have shown that the human genome has a haplotype block structure, such that it can be divided into discrete blocks of limited haplotype diversity. In each block, a small fraction of single-nucleotide polymorphisms (SNPs), referred to as "tag SNPs," can be used to distinguish a large fraction of the haplotypes. These tag SNPs can potentially be extremely useful for association studies, in that it may not be necessary to genotype all SNPs; however, this depends on how much power is lost. Here we develop a simulation study to quantitatively assess the power loss for a variety of study designs, including case-control designs and case-parental control designs. First, a number of data sets containing case-parental or case-control samples are generated on the basis of a disease model. Second, a small fraction of case and control individuals in each data set are genotyped at all the loci, and a dynamic programming algorithm is used to determine the haplotype blocks and the tag SNPs based on the genotypes of the sampled individuals. Third, the statistical power of tests was evaluated on the basis of three kinds of data: (1) all of the SNPs and the corresponding haplotypes, (2) the tag SNPs and the corresponding haplotypes, and (3) the same number of randomly chosen SNPs as the number of tag SNPs and the corresponding haplotypes. We study the power of different association tests with a variety of disease models and block-partitioning criteria. Our study indicates that the genotyping efforts can be significantly reduced by the tag SNPs, without much loss of power. Depending on the specific haplotype block-partitioning algorithm and the disease model, when the identified tag SNPs are only 25% of all the SNPs, the power is reduced by only 4%, on average, compared with a power loss of approximately 12% when the same number of randomly chosen SNPs is used in a two-locus haplotype analysis. When the identified tag SNPs are approximately 14% of all the SNPs, the power is reduced by approximately 9%, compared with a power loss of approximately 21% when the same number of randomly chosen SNPs is used in a two-locus haplotype analysis. Our study also indicates that haplotype-based analysis can be much more powerful than marker-by-marker analysis.
| S-EPMC378580 | biostudies-other
Project description:In a two stage genome-wide association study (2S-GWAS), a sample of cases and controls is allocated into two groups, and genetic markers are analyzed sequentially with respect to these groups. For such studies, experimental design considerations have primarily focused on minimizing study cost as a function of the allocation of cases and controls to stages, subject to a constraint on the power to detect an associated marker. However, most treatments of this problem implicitly restrict the set of feasible designs to only those that allocate the same proportions of cases and controls to each stage. In this paper, we demonstrate that removing this restriction can improve the cost advantages demonstrated by previous 2S-GWAS designs by up to 40%. Additionally, we consider designs that maximize study power with respect to a cost constraint, and show that recalculated power maximizing designs can recover a substantial amount of the planned study power that might otherwise be lost if study funding is reduced. We provide open source software for calculating cost minimizing or power maximizing 2S-GWAS designs.
| S-EPMC3435377 | biostudies-literature
Project description:Current common wisdom posits that association analyses using family-based designs have inflated type 1 error rates (if relationships are ignored) and independent controls are more powerful than familial controls. We explore these suppositions. We show theoretically that family-based designs can have deflated type-error rates. Through simulation, we examine the validity and power of family designs for several scenarios: cases from randomly or selectively ascertained pedigrees; and familial or independent controls. Family structures considered are as follows: sibships, nuclear families, moderate-sized and extended pedigrees. Three methods were considered with the ?(2) test for trend: variance correction (VC), weighted (weights assigned to account for genetic similarity), and naïve (ignoring relatedness) as well as the Modified Quasi-likelihood Score (MQLS) test. Selectively ascertained pedigrees had similar levels of disease enrichment; random ascertainment had no such restriction. Data for 1,000 cases and 1,000 controls were created under the null and alternate models. The VC and MQLS methods were always valid. The naïve method was anti-conservative if independent controls were used and valid or conservative in designs with familial controls. The weighted association method was generally valid for independent controls, and was conservative for familial controls. With regard to power, independent controls were more powerful for small-to-moderate selectively ascertained pedigrees, but familial and independent controls were equivalent in the extended pedigrees and familial controls were consistently more powerful for all randomly ascertained pedigrees. These results suggest a more complex situation than previously assumed, which has important implications for study design and analysis.
| S-EPMC3055958 | biostudies-literature
Project description:The discovery of quantitative trait loci (QTL) in model organisms has relied heavily on the ability to perform controlled breeding to generate genotypic and phenotypic diversity. Recently, we and others have demonstrated the use of a set of diverse inbred mice as a QTL mapping population. The use of this population has many advantages, including increased phenotypic diversity, a higher recombination frequency and the ability to collect genotype data in community databases. However, these methods are complicated by inherent population structure and the inability to accurately assess statistical power. To address these issues, we measured gene expression levels in hypothalamus across the diverse inbred mapping population. We then mapped these phenotypes as quantitative traits with our association algorithm, resulting in a large set of expression QTLs. We utilized these eQTLs (and specifically the cis-eQTLs) to devise a relative measure of statistical power which does not rely on parametrically simulated data. Finally, we utilized this approach to develop and optimize a novel method of accounting for population structure in the Mouse Diversity Panel. Keywords: strain and gender
2007-04-18 | GSE5961 | GEO
Project description:BACKGROUND:Phenome-wide association studies (PheWAS) are a high-throughput approach to evaluate comprehensive associations between genetic variants and a wide range of phenotypic measures. PheWAS has varying sample sizes for quantitative traits, and variable numbers of cases and controls for binary traits across the many phenotypes of interest, which can affect the statistical power to detect associations. The motivation of this study is to investigate the various parameters which affect the estimation of statistical power in PheWAS, including sample size, case-control ratio, minor allele frequency, and disease penetrance. RESULTS:We performed a PheWAS simulation study, where we investigated variations in statistical power based on different parameters, such as overall sample size, number of cases, case-control ratio, minor allele frequency, and disease penetrance. The simulation was performed on both binary and quantitative phenotypic measures. Our simulation on binary traits suggests that the number of cases has more impact on statistical power than the case to control ratio; also, we found that a sample size of 200 cases or more maintains the statistical power to identify associations for common variants. For quantitative traits, a sample size of 1000 or more individuals performed best in the power calculations. We focused on common genetic variants (MAF?>?0.01) in this study; however, in future studies, we will be extending this effort to perform similar simulations on rare variants. CONCLUSIONS:This study provides a series of PheWAS simulation analyses that can be used to estimate statistical power for some potential scenarios. These results can be used to provide guidelines for appropriate study design for future PheWAS analyses.
| S-EPMC5885318 | biostudies-literature
Project description:BackgroundPopulation structure is an important cause leading to inconsistent results in population-based association studies (PBAS) of human diseases. Various statistical methods have been proposed to reduce the negative impact of population structure on PBAS. Due to lack of structural information in real populations, it is difficult to evaluate the impact of population structure on PBAS in real populations.ResultsWe developed a genetic simulation platform, HAPSIMU, based on real haplotype data from the HapMap ENCODE project. This platform can simulate heterogeneous populations with various known and controllable structures under the continuous migration model or the discrete model. Moreover, both qualitative and quantitative traits can be simulated using additive genetic model with various genetic parameters designated by users.ConclusionHAPSIMU provides a common genetic simulation platform to evaluate the impact of population structure on PBAS, and compare the relative performance of various population structure identification and PBAS methods.
| S-EPMC2518933 | biostudies-literature
Project description:The number of effectively independent tests performed in genome-wide association studies (GWAS) varies by population, making a universal P-value threshold inappropriate. We estimated the number of independent SNPs in Phase 3 HapMap samples by: (1) the LD-pruning function in PLINK, and (2) an autocorrelation-based approach. Autocorrelation was also used to estimate the number of independent SNPs in whole genome sequences from 1000 Genomes. Both approaches yielded consistent estimates of numbers of independent SNPs, which were used to calculate new population-specific thresholds for genome-wide significance. African populations had the most stringent thresholds (1.49 × 10(-7) for YRI at r(2) = 0.3), East Asian populations the least (3.75 × 10(-7) for JPT at r(2) = 0.3). We also assessed how using population-specific significance thresholds compared to using a single multiple testing threshold at the conventional 5 × 10(-8) cutoff. Applied to a previously published GWAS of melanoma in Caucasians, our approach identified two additional genes, both previously associated with the phenotype. In a Chinese breast cancer GWAS, our approach identified 48 additional genes, 19 of which were in or near genes previously associated with the phenotype. We conclude that the conventional genome-wide significance threshold generates an excess of Type 2 errors, particularly in GWAS performed on more recently founded populations.
| S-EPMC4334751 | biostudies-literature