Dataset Information

Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci.

ABSTRACT: The analysis of gene sets is usually carried out based on gene ontology terms and known biological pathways. These approaches may not establish any formal relation between genotype and trait specific phenotype. In plant biology and breeding, analysis of gene sets with trait specific Quantitative Trait Loci (QTL) data are considered as great source for biological knowledge discovery. Therefore, we proposed an innovative statistical approach called Gene Set Analysis with QTLs (GSAQ) for interpreting gene expression data in context of gene sets with traits. The utility of GSAQ was studied on five different complex abiotic and biotic stress scenarios in rice, which yields specific trait/stress enriched gene sets. Further, the GSAQ approach was more innovative and effective in performing gene set analysis with underlying QTLs and identifying QTL candidate genes than the existing approach. The GSAQ approach also provided two potential biological relevant criteria for performance analysis of gene selection methods. Based on this proposed approach, an R package, i.e., GSAQ ( https://cran.r-project.org/web/packages/GSAQ ) has been developed. The GSAQ approach provides a valuable platform for integrating the gene expression data with genetically rich QTL data.

SUBMITTER: Das S

PROVIDER: S-EPMC5799309 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci.

Das Samarendra S Rai Anil A Mishra D C DC Rai Shesh N SN

Scientific reports 20180205 1

The analysis of gene sets is usually carried out based on gene ontology terms and known biological pathways. These approaches may not establish any formal relation between genotype and trait specific phenotype. In plant biology and breeding, analysis of gene sets with trait specific Quantitative Trait Loci (QTL) data are considered as great source for biological knowledge discovery. Therefore, we proposed an innovative statistical approach called Gene Set Analysis with QTLs (GSAQ) for interpreti ...[more]

PMID: 29402907

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Project description:BackgroundWe introduce a QTL-mapping algorithm based on Statistical Machine Learning (SML) that is conceptually quite different to existing methods as there is a strong focus on generalisation ability. Our approach combines ridge regression, recursive feature elimination, and estimation of generalisation performance and marker effects using bootstrap resampling. Model performance and marker effects are determined using independent testing samples (individuals), thus providing better estimates. We compare the performance of SML against Composite Interval Mapping (CIM), Bayesian Interval Mapping (BIM) and single Marker Regression (MR) on synthetic datasets and a multi-trait and multi-environment dataset of the progeny for a cross between two barley cultivars.ResultsIn an analysis of the synthetic datasets, SML accurately predicted the number of QTL underlying a trait while BIM tended to underestimate the number of QTL. The QTL identified by SML for the barley dataset broadly coincided with known QTL locations. SML reported approximately half of the QTL reported by either CIM or MR, not unexpected given that neither CIM nor MR incorporates independent testing. The latter makes these two methods susceptible to producing overly optimistic estimates of QTL effects, as we demonstrate for MR. The QTL resolution (peak definition) afforded by SML was consistently superior to MR, CIM and BIM, with QTL detection power similar to BIM. The precision of SML was underscored by repeatedly identifying, at < or = 1-cM precision, three QTL for four partially related traits (heading date, plant height, lodging and yield). The set of QTL obtained using a 'raw' and a 'curated' version of the same genotypic dataset were more similar to each other for SML than for CIM or MR.ConclusionThe SML algorithm produces better estimates of QTL effects because it eliminates the optimistic bias in the predictive performance of other QTL methods. It produces narrower peaks than other methods (except BIM) and hence identifies QTL with greater precision. It is more robust to genotyping and linkage mapping errors, and identifies markers linked to QTL in the absence of a genetic map.

Project description:Genetic dissection of the S rat genome has provided strong evidence for the presence of two interacting blood pressure (BP) quantitative trait loci (QTLs), termed QTL1 and QTL2, on rat chromosome 5. However, the identities of the underlying interacting genetic factors remain unknown. Further experiments targeted to identify the interacting genetic factors by the substitution mapping approach alone are difficult because of the interdependency of natural recombinations to occur at the two QTLs. We hypothesized that the interacting genetic factors underlying these two QTLs may interact at the level of gene transcription and thereby represent expression QTLs (eQTLs). To detect these interacting eQTLs, a custom QTL chip containing the annotated genes within QTL1 and QTL2 was developed and used to conduct a transcriptional profiling study of S and two congenic strains that retain either one or both the QTLs. The results uncovered an interaction between two transcription factors, DMRTA2 and NFIA. Further, the âbiological signatureâ elicited by these two transcription factors was differential between the congenic strain that retained LEW alleles at both QTL1 and 2 compared to the congenic strain that retained LEW alleles at QTL1 alone. A network of transcription factors potentially affecting BP could be traced, lending support to our hypothesis. Pairs of Cy5 and Cy3 labeled targets were co-hybridized onto either a custom long oligonucleotide microarray for the interrogation of 231 genes encompassed by QTL1 and QTL2, or a TIGR rat cDNA array consisting of 26,401 probe elements representing 20,465 unique non-QTL genes. A âflip-dyeâ or âbalanced blockâ design was used as the experimental method of choice to account for potential dye-bias labeling effects. Six âbalanced blockâ normalized files are submitted for the long oligonucleotide array interrogating the hearts from S versus S.LEW(5)x6x9 animals, fourteen âflip-dyeâ hybridizations are submitted for the cDNA array interrogating the hearts from S versus S.LEW(5)x6x9 animals, and twelve âflip-dyeâ hybridizations are submitted for the cDNA array interrogating the hearts from S versus S.LEW(5)x6x11 animals. GPR and MEV files cannot be located.

Project description:Parent-of-origin (PofO) effects, such as imprinting are a phenomenon in which homologous chromosomes exhibit differential gene expression and epigenetic modifications according to their parental origin. Such non-Mendelian inheritance patterns are generally ignored by conventional association studies, as these tests consider the maternal and paternal alleles as equivalent. To identify regulatory regions that show PofO effects on gene expression (imprinted expression Quantitative Trait Loci, ieQTLs), here we have developed a novel method in which we associate SNP genotypes of defined parental origin with gene expression levels. We applied this method to study 59 HapMap phase II parent-offspring trios. By analyzing mother/father/child trios, rules of Mendelian inheritance allowed the parental origin to be defined for ~95% of SNPs in each child. We used 680,475 informative SNPs and corresponding expression data for 92,167 probe sets from Affymetrix GeneChip Human Exon 1.0 ST arrays and performed four independent cis-association analyses with the expression level of RefSeq genes within 1 Mb using PLINK. Independent analyses of maternal and paternal genotypes identified two significant cis-ieQTLs (p<10(-7)) at which expression of genes SFT2D2 and SRRT associated exclusively with maternally inherited SNPs rs3753292 and rs6945374, respectively. 28 additional suggestive cis-associations with only maternal or paternal SNPs were found at a lower stringency threshold of p<10(-6), including associations with two known imprinted genes PEG10 and TRAPPC9, demonstrating the efficacy of our method. Furthermore, comparison of our method that utilizes independent analyses of maternal and paternal genotypes with the Likelihood Ratio Test (LRT) showed it to be more effective for detecting imprinting effects than the LRT. Our method represents a novel approach that can identify imprinted regulatory elements that control gene expression, suggesting novel PofO effects in the human genome.

Dataset Information

Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci.

Publications

Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci.

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