Mapping the genetic architecture of gene expression in human liver

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process. © 2008 Schadt et al

Detecting high-order interactions of single nucleotide polymorphisms using genetic programming

Motivation: Not individual single nucleotide polymorphisms (SNPs), but high-order interactions of SNPs are assumed to be responsible for complex diseases such as cancer. Therefore, one of the major goals of genetic association studies concerned with such genotype data is the identification of these high-order interactions. This search is additionally impeded by the fact that these interactions often are only explanatory for a relatively small subgroup of patients. Most of the feature selection methods proposed in the literature, unfortunately, fail at this task, since they can either only identify individual variables or interactions of a low order, or try to find rules that are explanatory for a high percentage of the observations. In this paper, we present a procedure based on genetic programming and multi-valued logic that enables the identification of high-order interactions of categorical variables such as SNPs. This method called GPAS (Genetic Programming for Association Studies) cannot only be used for feature selection, but can also be employed for discrimination. Results: In an application to the genotype data from the GENICA study, an association study concerned with sporadic breast cancer, GPAS is able to identify high-order interactions of SNPs leading to a considerably increased breast cancer risk for different subsets of patients that are not found by other feature selection methods. As an application to a subset of the HapMap data shows, GPAS is not restricted to association studies comprising several ten SNPs, but can also be employed to analyze whole-genome data. --

High-Density Genotypes of Inbred Mouse Strains: Improved Power and Precision of Association Mapping.

Human genome-wide association studies have identified thousands of loci associated with disease phenotypes. Genome-wide association studies also have become feasible using rodent models and these have some important advantages over human studies, including controlled environment, access to tissues for molecular profiling, reproducible genotypes, and a wide array of techniques for experimental validation. Association mapping with common mouse inbred strains generally requires 100 or more strains to achieve sufficient power and mapping resolution; in contrast, sample sizes for human studies typically are one or more orders of magnitude greater than this. To enable well-powered studies in mice, we have generated high-density genotypes for ∼175 inbred strains of mice using the Mouse Diversity Array. These new data increase marker density by 1.9-fold, have reduced missing data rates, and provide more accurate identification of heterozygous regions compared with previous genotype data. We report the discovery of new loci from previously reported association mapping studies using the new genotype data. The data are freely available for download, and Web-based tools provide easy access for association mapping and viewing of the underlying intensity data for individual loci

No association of CTLA-4 polymorphisms with susceptibility to Behcet disease

Background: Cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) is a key negative regulator of T lymphocytes and has been shown to be associated with a number of autoimmune diseases. The present study was performed to assess the association between CTLA-4 polymorphisms and Behcet disease (BD) in Chinese patients. Methods: Two hundred and twenty-eight BD patients and 207 controls were analysed for four single nucleotide polymorphisms (SNPs) (21661A/G, 2318C/T, + 49G/A and CT60G/A) in the CTLA-4 gene by PCR-restriction fragment length polymorphism (RFLP) analysis. The association between SNP +49A/G and BD in Chinese population as well as other ethnic groups was analysed by meta-analysis. Results: No association could be detected between CTLA-4 SNPs or haplotypes and BD. Also, no association was observed between CTLA-4 polymorphisms and BD subgroups, stratified by clinical features. A meta-analysis showed that there was no heterogeneity between studies (p = 0.60, I-2 = 0%) and that CTLA-4 SNP + 49 was not associated with BD (overall effect: Z = 0.26, p = 0.79). Conclusion: This study and a meta-analysis failed to demonstrate any association between the tested CTLA-4 polymorphisms and B

Association of the IL-10 gene family locus on chromosome 1 with juvenile idiopathic arthritis (JIA)

The cytokine IL-10 and its family members have been implicated in autoimmune diseases and we have previously reported that genetic variants in IL-10 were associated with a rare group of diseases called juvenile idiopathic arthritis (JIA). The aim of this study was to fine map genetic variants within the IL-10 cytokine family cluster on chromosome 1 using linkage disequilibrium (LD)-tagging single nucleotide polymorphisms (tSNPs) approach with imputation and conditional analysis to test for disease associations

Genetic mapping of legume orthologs reveals high conservation of synteny between lentil species and the sequenced genomes of Medicago and chickpea.

Lentil (Lens culinaris Medik.) is a global food crop with increasing importance for food security in south Asia and other regions. Lens ervoides, a wild relative of cultivated lentil, is an important source of agronomic trait variation. Lens is a member of the galegoid clade of the Papilionoideae family, which includes other important dietary legumes such as chickpea (Cicer arietinum) and pea (Pisum sativum), and the sequenced model legume Medicago truncatula. Understanding the genetic structure of Lens spp. in relation to more fully sequenced legumes would allow leveraging of genomic resources. A set of 1107 TOG-based amplicons were identified in L. ervoides and a subset thereof used to design SNP markers for mapping. A map of L. ervoides consisting of 377 SNP markers spread across seven linkage groups was developed using a GoldenGate genotyping array and single SNP marker assays. Comparison with maps of M. truncatula and L. culinaris documented considerable shared synteny and led to the identification of a few major translocations and a major inversion that distinguish Lens from M. truncatula, as well as a translocation that distinguishes L. culinaris from L. ervoides. The identification of chromosome-level differences among Lens spp. will aid in the understanding of introgression of genes from L. ervoides into cultivated L. culinaris, furthering genetic research and breeding applications in lentil

Discover hidden splicing variations by mapping personal transcriptomes to personal genomes.

RNA-seq has become a popular technology for studying genetic variation of pre-mRNA alternative splicing. Commonly used RNA-seq aligners rely on the consensus splice site dinucleotide motifs to map reads across splice junctions. Consequently, genomic variants that create novel splice site dinucleotides may produce splice junction RNA-seq reads that cannot be mapped to the reference genome. We developed and evaluated an approach to identify 'hidden' splicing variations in personal transcriptomes, by mapping personal RNA-seq data to personal genomes. Computational analysis and experimental validation indicate that this approach identifies personal specific splice junctions at a low false positive rate. Applying this approach to an RNA-seq data set of 75 individuals, we identified 506 personal specific splice junctions, among which 437 were novel splice junctions not documented in current human transcript annotations. 94 splice junctions had splice site SNPs associated with GWAS signals of human traits and diseases. These involve genes whose splicing variations have been implicated in diseases (such as OAS1), as well as novel associations between alternative splicing and diseases (such as ICA1). Collectively, our work demonstrates that the personal genome approach to RNA-seq read alignment enables the discovery of a large but previously unknown catalog of splicing variations in human populations

Profiling allele-specific gene expression in brains from individuals with autism spectrum disorder reveals preferential minor allele usage.

One fundamental but understudied mechanism of gene regulation in disease is allele-specific expression (ASE), the preferential expression of one allele. We leveraged RNA-sequencing data from human brain to assess ASE in autism spectrum disorder (ASD). When ASE is observed in ASD, the allele with lower population frequency (minor allele) is preferentially more highly expressed than the major allele, opposite to the canonical pattern. Importantly, genes showing ASE in ASD are enriched in those downregulated in ASD postmortem brains and in genes harboring de novo mutations in ASD. Two regions, 14q32 and 15q11, containing all known orphan C/D box small nucleolar RNAs (snoRNAs), are particularly enriched in shifts to higher minor allele expression. We demonstrate that this allele shifting enhances snoRNA-targeted splicing changes in ASD-related target genes in idiopathic ASD and 15q11-q13 duplication syndrome. Together, these results implicate allelic imbalance and dysregulation of orphan C/D box snoRNAs in ASD pathogenesis