Quantitative Trait Loci Identification by Estimating the Genetic Model based on the Extremal Samples.
Ontology highlight
ABSTRACT: Background
In genetic association studies with quantitative trait loci (QTL), the association between a candidate genetic marker and the trait of interest is commonly examined by the omnibus F test or by the t-test corresponding to a given genetic model or mode of inheritance. It is known that the t-test with a correct model specification is more powerful than the F test. However, since the underlying genetic model is rarely known in practice, the use of a model-specific t-test may incur substantial power loss. Robust-efficient tests, such as the Maximin Efficiency Robust Test (MERT) and MAX3 have been proposed in the literature.Methods
In this paper, we propose a novel two-step robust-efficient approach, namely, the genetic model selection (GMS) method for quantitative trait analysis. GMS selects a genetic model by testing Hardy-Weinberg disequilibrium (HWD) with extremal samples of the population in the first step and then applies the corresponding genetic model-specific t-test in the second step.Results
Simulations show that GMS is not only more efficient than MERT and MAX3, but also has comparable power to the optimal t-test when the genetic model is known.Conclusion
Application to the data from Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort demonstrates that the proposed approach can identify meaningful biological SNPs on chromosome 19.
SUBMITTER: Yang Z
PROVIDER: S-EPMC8844942 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundRecent studies have indicated that the human genome could be divided into regions with low haplotype diversity interspersed with regions of high haplotype diversity. In regions of low haplotype diversity, a small fraction of SNPs (tag SNPs) are sufficient to account for most of the haplotype diversity of the human genome. These tag SNPs can be extremely useful for testing the association of a marker locus with a qualitative or quantitative trait locus in that it may not be necessary to genotype all the SNPs. When tag SNPs are used to reduce the genotyping effort in association studies, it is important to know how much power is lost. It is also important to know how much power is gained when tag SNPs instead of the same number of randomly chosen SNPs are used.ResultsWe design a simulation study to tackle these problems for a variety of quantitative association tests using either case-parent samples or unrelated population samples. First, the samples are generated based on the quantitative trait model with the assumption of either an extremal sampling scheme or a random sampling scheme. Second, a small number of samples are selected to determine the haplotype blocks and the tag SNPs. Third, the statistical power of the tests is evaluated using four kinds of data: (1) all the SNPs and the corresponding haplotypes, (2) the tag SNPs and the corresponding haplotypes, (3) the same number of evenly spaced SNPs with minor allele frequency greater than a threshold and the corresponding haplotypes, (4) the same number of randomly chosen SNPs and their corresponding haplotypes.ConclusionOur results suggest that in most situations genotyping efforts can be significantly reduced by using tag SNPs for mapping the QTL in association studies without much loss of power, which is consistent with previous studies on association mapping of qualitative traits. For all situations considered, two-locus haplotype analysis using tag SNPs are more powerful than those using the same number of randomly selected SNPs, but the degree of such power differences depends upon the sampling scheme and the population history.
| S-EPMC1274312 | biostudies-literature
Project description:Detecting quantitative trait loci (QTL) and estimating QTL variances (represented by the squared QTL effects) are two main goals of QTL mapping and genome-wide association studies (GWAS). However, there are issues associated with estimated QTL variances and such issues have not attracted much attention from the QTL mapping community. Estimated QTL variances are usually biased upwards due to estimation being associated with significance tests. The phenomenon is called the Beavis effect. However, estimated variances of QTL without significance tests can also be biased upwards, which cannot be explained by the Beavis effect; rather, this bias is due to the fact that QTL variances are often estimated as the squares of the estimated QTL effects. The parameters are the QTL effects and the estimated QTL variances are obtained by squaring the estimated QTL effects. This square transformation failed to incorporate the errors of estimated QTL effects into the transformation. The consequence is biases in estimated QTL variances. To correct the biases, we can either reformulate the QTL model by treating the QTL effect as random and directly estimate the QTL variance (as a variance component) or adjust the bias by taking into account the error of the estimated QTL effect. A moment method of estimation has been proposed to correct the bias. The method has been validated via Monte Carlo simulation studies. The method has been applied to QTL mapping for the 10-week-body-weight trait from an F2 mouse population.
| S-EPMC8942241 | biostudies-literature
Project description:Many genes with major effects on quantitative traits have been reported to interact with other genes. However, finding a group of interacting genes from thousands of SNPs is challenging. Hence, an efficient and robust algorithm is needed. The genetic algorithm (GA) is useful in searching for the optimal solution from a very large searchable space. In this study, we show that genome-wide interaction analysis using GA and a statistical interaction model can provide a practical method to detect biologically interacting loci. We focus our search on transcriptional regulators by analyzing gene x gene interactions for cancer-related genes. The expression values of three cancer-related genes were selected from the expression data of the Genetic Analysis Workshop 15 Problem 1 data set. We implemented a GA to identify the expression quantitative trait loci that are significantly associated with expression levels of the cancer-related genes. The time complexity of the GA was compared with that of an exhaustive search algorithm. As a result, our GA, which included heuristic methods, such as archive, elitism, and local search, has greatly reduced computational time in a genome-wide search for gene x gene interactions. In general, the GA took one-fifth the computation time of an exhaustive search for the most significant pair of single-nucleotide polymorphisms.
| S-EPMC2367540 | biostudies-literature
Project description:Meta-analysis is an important tool in linkage analysis. The pooling of results across primary linkage studies allows greater statistical power to detect quantitative-trait loci (QTLs) and more-precise estimation of their genetic effects and, hence, yields conclusions that are stronger relative to those of individual studies. Previous methods for the meta-analysis of linkage studies have been proposed, and, although some methods address the problem of between-study heterogeneity, most methods still require linkage analysis at the same marker or set of markers across studies, whereas others do not result in an estimate of genetic variance. In this study, we present a meta-analytic procedure to evaluate evidence from several studies that report Haseman-Elston statistics for linkage to a QTL at multiple, possibly distinct, markers on a chromosome. This technique accounts for between-study heterogeneity and estimates both the location of the QTL and the magnitude of the genetic effect more precisely than does an individual study. We also provide standard errors for the genetic effect and for the location (in cM) of the QTL, using a resampling method. The approach can be applied under other conditions, provided that the various studies use the same linkage statistic.
| S-EPMC384993 | biostudies-other
Project description:Motivation:The identification of quantitative trait loci (QTL) is critical to the study of causal relationships between genetic variations and disease abnormalities. We focus on identifying the QTLs associated to the brain endophenotypes in imaging genomics study for Alzheimer's Disease (AD). Existing research works mainly depict the association between single nucleotide polymorphisms (SNPs) and the brain endophenotypes via the linear methods, which may introduce high bias due to the simplicity of the models. Since the influence of QTLs on brain endophenotypes is quite complex, it is desired to design the appropriate non-linear models to investigate the associations of genotypes and endophenotypes. Results:In this paper, we propose a new additive model to learn the non-linear associations between SNPs and brain endophenotypes in Alzheimer's disease. Our model can be flexibly employed to explain the non-linear influence of QTLs, thus is more adaptive for the complex distribution of the high-throughput biological data. Meanwhile, as an important computational learning theory contribution, we provide the generalization error analysis for the proposed approach. Unlike most previous theoretical analysis under independent and identically distributed samples assumption, our error bound is based on m-dependent observations, which is more appropriate for the high-throughput and noisy biological data. Experiments on the data from Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort demonstrate the promising performance of our approach for identifying biological meaningful SNPs. Availability and implementation:An executable is available at https://github.com/littleq1991/additive_FNNRW.
| S-EPMC6129276 | biostudies-literature
Project description:Identification of microRNA expression quantitative trait loci (miR-eQTL) can yield insights into regulatory mechanisms of microRNA transcription, and can help elucidate the role of microRNA as mediators of complex traits. Here we present a miR-eQTL mapping study of whole blood from 5,239 individuals, and identify 5,269 cis-miR-eQTLs for 76 mature microRNAs. Forty-nine per cent of cis-miR-eQTLs are located 300-500 kb upstream of their associated intergenic microRNAs, suggesting that distal regulatory elements may affect the interindividual variability in microRNA expression levels. We find that cis-miR-eQTLs are highly enriched for cis-mRNA-eQTLs and regulatory single nucleotide polymorphisms. Among 243 cis-miR-eQTLs that were reported to be associated with complex traits in prior genome-wide association studies, many cis-miR-eQTLs miRNAs display differential expression in relation to the corresponding trait (for example, rs7115089, miR-125b-5p and high-density lipoprotein cholesterol). Our study provides a roadmap for understanding the genetic basis of miRNA expression, and sheds light on miRNA involvement in a variety of complex traits.
| S-EPMC4369777 | biostudies-literature
Project description:We report a meta-Genome Wide Association Study involving 73 published studies in soybean [Glycine max L. (Merr.)] covering 17,556 unique accessions, with improved statistical power for robust detection of loci associated with a broad range of traits. De novo GWAS and meta-analysis were conducted for composition traits including fatty acid and amino acid composition traits, disease resistance traits, and agronomic traits including seed yield, plant height, stem lodging, seed weight, seed mottling, seed quality, flowering timing, and pod shattering. To examine differences in detectability and test statistical power between single- and multi-environment GWAS, comparison of meta-GWAS results to those from the constituent experiments were performed. Using meta-GWAS analysis and the analysis of individual studies, we report 483 peaks at 393 unique loci. Using stringent criteria to detect significant marker-trait associations, 59 candidate genes were identified, including 17 agronomic traits loci, 19 for seed-related traits, and 33 for disease reaction traits. This study identified potentially valuable candidate genes that affect multiple traits. The success in narrowing down the genomic region for some loci through overlapping mapping results of multiple studies is a promising avenue for community-based studies and plant breeding applications.
| S-EPMC8495947 | biostudies-literature
Project description:Trauma- and stress-related disorders are clinically heterogeneous and associated with substantial genetic risk. Understanding the biological origins of heterogeneity of key intermediate phenotypes such as cognition and emotion can provide novel mechanistic insights into disorder pathogenesis. Performing quantitative genetics in animal models is a tractable strategy for examining both the genetic basis of intermediate phenotypes and functional testing of candidate quantitative traits genes (QTGs). Here, existing and newly collected data were used for collaborative genome-wide mapping of cued fear acquisition and expression in 65 mouse strains from the BXD genetic reference panel. For fear acquisition, we identified a significant locus on chromosome (Chr) 10 and eight suggestive loci on Chr 2, 4, 5, 11, 13, and 15. For fear expression, we identified one significant and another highly suggestive locus on Chr 13, as well as four suggestive loci on Chr 10, 11, and X. Across these loci, 60 putative QTGs were identified. The quantitative trait locus on distal Chr 13 contained a single, highly promising gene at the location of the peak likelihood ratio statistic score. The gene, hyperpolarization-activated cyclic nucleotide-gated channel 1 (Hcn1), regulates neuronal excitability. Validation experiments using behavioral pharmacology revealed that functional Hcn channels in the basolateral amygdala are necessary for conditioned fear acquisition and expression. Hcn1, together with the other candidate QTGs, thus provide new targets for neurobiological and treatment studies of fear learning and trauma- and stress-related disorders.There is a knowledge gap in understanding the genetic contributions to behavioral heterogeneity in typical and atypical populations. Mouse genetic reference panels (GRPs) provide one approach for identifying genetic sources of variation. Here, we identified three loci for conditioned fear acquisition and expression in a mouse GRP. Each locus contained candidate quantitative trait genes (QTGs). One locus had a single QTG, Hcn1 (hyperpolarization-activated cyclic nucleotide-gated channel 1), which has been implicated in neuronal excitability and learning. This discovery was validated using behavioral pharmacology, revealing that Hcn channels in the basolateral amygdala are required for fear acquisition and expression. The study thus identifies novel candidate QTGs that may contribute to variation in emotional learning and highlight the utility of mouse GRPs for the identification of genes underlying complex traits.
| S-EPMC4899527 | biostudies-other
Project description:It is extremely expensive to conduct large sample size array- or sequencing based genome scale association studies. For a quantitative trait, an extreme case-control study design may improve the power and reduce the cost of variant calling. We investigated the performance of extreme study design when various proportions of samples are selected from the tails of phenotype distribution. Using simulations, we show that when risk genotypes become rare in the population and effect size is relatively small, it is beneficial to carry out an extreme sampling study. In particular, the number of selected cases and controls can even be unbalanced such that power is further increased, compared with a balanced selection. Our application to two data sets: methadone dose data and yearling weight data, demonstrated that similar results for full data analysis can be obtained using extreme sampling with only a fraction of the data. Using power analysis with simulated data and an experimental data application, we conclude that when full data is unavailable due to restricted budget, it is rewarding to employ an extreme sampling design in the sense that there can be immense cost reductions and qualitatively similar power as in the full data analysis.
| S-EPMC6820758 | biostudies-literature
Project description:Sesame is prized for its oil. Genetic improvement of sesame can be enhanced through marker-assisted breeding. However, few simple sequence repeat (SSR) markers and SSR-based genetic maps were available in sesame. In this study, 7,357 SSR markers were developed from the sesame genome and transcriptomes, and a genetic map was constructed by generating 424 novel polymorphic markers and using a cross population with 548 recombinant inbred lines (RIL). The genetic map had 13 linkage groups, equalling the number of sesame chromosomes. The linkage groups ranged in size from 113.6 to 179.9 centimorgans (cM), with a mean value of 143.8?cM over a total length of 1869.8?cM. Fourteen quantitative trait loci (QTL) for sesame charcoal rot disease resistance were detected, with contribution rates of 3-14.16% in four field environments; ~60% of the QTL were located within 5?cM at 95% confidence interval. The QTL with the highest phenotype contribution rate (qCRR12.2) and those detected in different environments (qCRR8.2 and qCRR8.3) were used to predict candidate disease response genes. The new SSR-based genetic map and 14 novel QTLs for charcoal rot disease resistance will facilitate the mapping of agronomic traits and marker-assisted selection breeding in sesame.
| S-EPMC5566338 | biostudies-literature