Dataset Information

Clustering and principal-components approach based on heritability for mapping multiple gene expressions.

ABSTRACT: When the number of phenotypes in a genetic study is on the scale of thousands, such as in studies concerning thousands of gene expression levels, the single-trait analysis is computationally intensive, and heavy adjustment of multiple comparisons is required. Traditional multivariate genetic linkage analysis for quantitative traits focuses on mapping only a few phenotypes and is not feasible for a large number of traits. To cope with high-dimensional phenotype data, clustering analysis and principal-component analysis (PCA) are proposed to reduce the data dimensionality and to map shared genetic contributions for multiple traits. However, standard clustering analysis and PCA are applicable for independent observations. In most genetic studies, where family data are collected, these standard analyses can only be applied to founders and can lead to the loss of information. Here, we proposed a clustering method that can exploit family structure information and applied the method to 29 gene expression levels mapped to a reported hot spot on chromosome 14. We then used a PCA approach based on heritability applicable to small number of traits to combine phenotypes in the clusters. Lastly, we used a penalized PCA approach based on heritability applicable to arbitrary number of traits to combine 150 gene expression levels with the highest heritability. Genome-wide multipoint linkage analysis was carried out on the individual traits and on the combined traits. Two previously reported peaks on chromosomes 14 and 20 were identified. Linkage evidence was stronger for traits derived from methods that incorporate family structure information.

SUBMITTER: Wang Y

PROVIDER: S-EPMC2367519 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Clustering and principal-components approach based on heritability for mapping multiple gene expressions.

Wang Yuanjia Y Fang Yixin Y Wang Shuang S

BMC proceedings 20071218

When the number of phenotypes in a genetic study is on the scale of thousands, such as in studies concerning thousands of gene expression levels, the single-trait analysis is computationally intensive, and heavy adjustment of multiple comparisons is required. Traditional multivariate genetic linkage analysis for quantitative traits focuses on mapping only a few phenotypes and is not feasible for a large number of traits. To cope with high-dimensional phenotype data, clustering analysis and princ ...[more]

PMID: 18466463

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Project description:BackgroundConcentrated breeding effort to produce various body structures and behaviors of dogs to suit human demand has inadvertently produced unwanted traits and diseases that accompany the morphological and behavioral phenotypes. We explored the relationship between pelvic conformation and canine hip dysplasia (HD) because purebred dogs which are predisposed, or not, to HD share common morphologic features, respectively. Thirteen unique bilateral anatomical features of the pelvis were measured on 392 dogs of 51 breeds and 95 mixed breed dogs. Principal components (PCs) were derived to describe pelvic morphology. Dogs were genotyped at ~183,000 single nucleotide polymorphisms and their hip conformation was measured by the Norberg angle and angle of inclination between the femoral neck and diaphysis.ResultsNo associations reached genome wide significance for the Norberg angle when averaged over both hips. PC1 was negatively correlated with the Norberg angle (r = -0.31; P < 0.05) but not the angle of inclination (r = -0.08; P > 0.05). PC1, 2, 4, and 5 differed significantly between male and female dogs confirming pelvic sexual dimorphism. With sex as a covariate, the eigenvector contribution to PC1 reflected the overall size of the pelvis and was significantly associated with the IGF-1 locus, a known contributor to canine body size. PC3, which represented a tradeoff between ilial length and ischial length in which a longer ischium is associated with a shorter ilium, was significantly associated with a marker on canine chromosome 16:5181388 bp. The closest candidate gene is TPK1, a thiamine-dependent enzyme and part of the PKA complex. Associations with the remaining PCs did not reach genome wide significance.ConclusionIGF-1 was associated with the overall size of the pelvis and sex is related to pelvic size. Ilial/ischial proportion is genetically controlled and the closest candidate gene is thiamine-dependent and affects birth weight and development of the nervous system. Dogs with larger pelves tend to have smaller NAs consistent with increased tendency toward HD in large breed dogs. Based on the current study, pelvic shape alone was not strongly associated with canine hip dysplasia.

Project description:BackgroundThere are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many instances the biologically important goal is to identify relatively small sets of genes that share coherent expression across only some conditions, rather than all or most conditions as required in traditional clustering; e.g. genes that are highly up-regulated and/or down-regulated similarly across only a subset of conditions. Equally important is the need to learn which conditions are the decisive ones in forming such gene sets of interest, and how they relate to diverse conditional covariates, such as disease diagnosis or prognosis.ResultsWe present a method for automatically identifying such candidate sets of biologically relevant genes using a combination of principal components analysis and information theoretic metrics. To enable easy use of our methods, we have developed a data analysis package that facilitates visualization and subsequent data mining of the independent sources of significant variation present in gene microarray expression datasets (or in any other similarly structured high-dimensional dataset). We applied these tools to two public datasets, and highlight sets of genes most affected by specific subsets of conditions (e.g. tissues, treatments, samples, etc.). Statistically significant associations for highlighted gene sets were shown via global analysis for Gene Ontology term enrichment. Together with covariate associations, the tool provides a basis for building testable hypotheses about the biological or experimental causes of observed variation.ConclusionWe provide an unsupervised data mining technique for diverse microarray expression datasets that is distinct from major methods now in routine use. In test uses, this method, based on publicly available gene annotations, appears to identify numerous sets of biologically relevant genes. It has proven especially valuable in instances where there are many diverse conditions (10's to hundreds of different tissues or cell types), a situation in which many clustering and ordering algorithms become problematic. This approach also shows promise in other topic domains such as multi-spectral imaging datasets.

Dataset Information

Clustering and principal-components approach based on heritability for mapping multiple gene expressions.

Publications

Clustering and principal-components approach based on heritability for mapping multiple gene expressions.

Similar Datasets

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