Ranking analysis for identifying differentially expressed genes

Microarrays allow researchers to examine the expression of thousands of genes simultaneously. However, identification of genes differentially expressed in microarray experiments is challenging. With an optimal test statistic, we rank genes and estimate a threshold above which genes are considered to be differentially expressed genes (DE). This overcomes the embarrassing shortcoming of many statistical methods to determine the cut-off values in ranking analysis. Experiments demonstrate that our method is a good performance and avoids the problems with graphical examination and multiple hypotheses testing that affect alternative approaches. Comparing to those well known methods, our method is more sensitive to data sets with small differentially expressed values and not biased in favor of data sets based on certain distribution models.     

Discovering susceptibility genes for asthma and allergy

Asthma and asthma-related traits are complex diseases with strong genetic and environmental components. Rapid progress in asthma genetics has led to the identification of several candidate genes that are associated with asthma-related traits. Typically the phenotypic impact of each of these genes, including the ones most often replicated in association studies, is mild, but larger effects may occur when multiple variants synergize within a permissive environmental context. Despite the achievements made in asthma genetics formidable challenges remain. The development of novel, powerful tools for gene discovery, and a closer integration of genetics and biology, should help to overcome these challenges.     

Targeted next-generation sequencing for identifying genes related to horse temperament

It is a fundamental challenge to discover the association of genetic traits with phenotypic traits. In this study, we aimed to identify possible genetic traits related to horse temperament. Based on previous findings, we selected 71 candidate genes related to temperamental trait and examined them in the human and horse reference genomes (hg38 and equCab2, respectively). We found 16 orthologous genes and, by comparing with the human reference genome, 17 homologous genes in the horse reference genome. We designed probes specific for the 33 horse genes. Using the probes, we built sequencing libraries of the genomic DNA samples from eight aggressive and eight docile horses, and sequenced the constructed libraries using the Illumina Hiseq2500 platform. Through the analysis of the targeted exome sequences, we identified single nucleotide polymorphisms (SNPs) in the genes. SNPs could be served as genetic markers to evaluate aggressive or docile levels of horses. To examine whether any genetic variants are associated with horse temperament, we performed genome-wide association study (GWAS) using the SNP data. GWAS analysis identified ten variants (p-value?<0.05) which could be related to horse temperament. We validated the variants using Sanger sequencing. The most significant variants were found in MAOA (c.1164+41T>C) and AR (c.1047+27G>T) genes with 8.09?×?10^?4 p-value. We suggest that the variants might be used to assess horse temperament and to determine superior horses for riding or racing.     

A novel approach for identifying candidate imprinted genes through sequence analysis of imprinted and control genes

Through the sequence analysis of 27 imprinted human genes and a set of 100 control genes we have developed a novel approach for identifying candidate imprinted genes based on the differences in sequence composition observed. The imprinted genes were found to be associated with significantly reduced numbers of short interspersed transposable element (SINE) Alus and mammalian-wide interspersed repeat (MIR) repeat elements, as previously reported. In addition, a significant association between imprinted genes and increased numbers of low-complexity repeats was also evident. Numbers of the Alu classes AluJ and AluS were found to be significantly depleted in some parts of the flanking regions of imprinted genes. A recent study has proposed that there is active selection against SINE elements in imprinted regions. Alternatively, there may be differences in the rates of insertion of Alu elements. Our study indicates that this difference extends both upstream and downstream of the coding region. This and other consistent differences between the sequence characteristics of imprinted and control genes has enabled us to develop discriminant analysis, which can be used to screen the genome for candidate imprinted genes. We have applied this function to a number of genes whose imprinting status is disputed or uncertain.     

Nonparametric methods for identifying differentially expressed genes in microarray data

MOTIVATION: Gene expression experiments provide a fast and systematic way to identify disease markers relevant to clinical care. In this study, we address the problem of robust identification of differentially expressed genes from microarray data. Differentially expressed genes, or discriminator genes, are genes with significantly different expression in two user-defined groups of microarray experiments. We compare three model-free approaches: (1). nonparametric t-test, (2). Wilcoxon (or Mann-Whitney) rank sum test, and (3). a heuristic method based on high Pearson correlation to a perfectly differentiating gene ('ideal discriminator method'). We systematically assess the performance of each method based on simulated and biological data under varying noise levels and p-value cutoffs. RESULTS: All methods exhibit very low false positive rates and identify a large fraction of the differentially expressed genes in simulated data sets with noise level similar to that of actual data. Overall, the rank sum test appears most conservative, which may be advantageous when the computationally identified genes need to be tested biologically. However, if a more inclusive list of markers is desired, a higher p-value cutoff or the nonparametric t-test may be appropriate. When applied to data from lung tumor and lymphoma data sets, the methods identify biologically relevant differentially expressed genes that allow clear separation of groups in question. Thus the methods described and evaluated here provide a convenient and robust way to identify differentially expressed genes for further biological and clinical analysis.     

Non-linear tests for identifying differentially expressed genes or genetic networks

MOTIVATION: One of the recently developed statistics for identifying differentially expressed genetic networks is Hotelling T2 statistic, which is a quadratic form of difference in linear functions of means of gene expressions between two types of tissue samples, and so their power is limited. RESULTS: To improve the power of test statistics, a general statistical framework for construction of non-linear tests is presented, and two specific non-linear test statistics that use non-linear transformations of means are developed. Asymptotical distributions of the non-linear test statistics under the null and alternative hypothesis are derived. It has been proved that under some conditions the power of the non-linear test statistics is higher than that of the T2 statistic. Besides theory, to evaluate in practice the performance of the non-linear test statistics, they are applied to two real datasets. The preliminary results demonstrate that the P-values of the non-linear statistics for testing differential expressions of the genetic networks are much smaller than those of the T2 statistic. And furthermore simulations show the Type I errors of the non-linear statistics agree with the threshold used and the statistics fit the chi2 distribution. SUPPLEMENTARY INFORMATION: Supplementary data are available on Bioinformatics online.     

Mutagenesis strategies in zebrafish for identifying genes involved in development and disease

It has been nearly a decade since the completion of two large-scale chemical mutagenesis screens in zebrafish, and two years since the completion of a large-scale insertional mutagenesis. In this article, we use the accumulated data from these screens to compare the efficiency of each mutagen to isolate mutants and to identify mutated genes, and argue that the two mutagens target the same set of genes. We then review how both forward genetic screens and reverse genetic techniques, such as morpholinos and TILLING, and transgenics are being used to develop models of human disease.     

A genome-wide approach to identifying novel-imprinted genes

Genomic imprinting is an epigenetic process in which the copy of a gene inherited from one parent (maternal or paternal) is consistently silenced or expressed at a significantly lower level than the copy from the other parent. In an effort to begin a systematic genome-wide screen for imprinted genes, we assayed differential allelic expression (DAE) at 3,877 bi-allelic protein-coding sites located in 2,625 human genes in 67 unrelated individuals using genotyping microarrays. We used the presence of both over- and under-expression of the reference allele compared to the alternate allele to identify candidate-imprinted genes. We found 61 genes with at least twofold DAE plus “flipping” of the more highly expressed allele between reference and alternate across heterozygous samples. Sixteen flipping genes were genotyped and assayed for DAE in an independent data set of lymphoblastoid cell lines from two CEPH pedigrees. We confirmed that PEG10 is paternally expressed, identified one gene (ZNF331) with multiple lines of data indicating it is imprinted, and predicted several additional imprinting candidate genes. Our findings suggest that there are at most several hundred genes in the human genome that are universally imprinted. With samples of mRNA from appropriate tissues and a collection of informative cSNPs, a genome-wide search using this methodology could expand the list of genes that undergo genomic imprinting in a tissue- or temporal-specific manner. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

QTLminer: identifying genes regulating quantitative traits

Background  

Quantitative trait locus (QTL) mapping identifies genomic regions that likely contain genes regulating a quantitative trait. However, QTL regions may encompass tens to hundreds of genes. To find the most promising candidate genes that regulate the trait, the biologist typically collects information from multiple resources about the genes in the QTL interval. This process is very laborious and time consuming.