A network-based machine-learning framework to identify both functional modules and disease genes.

Genetic susceptibility in the causation of gallbladder diseases was recognized as early as 1937. A major gallstone susceptibility locus (Lith1) was identified in 1995 by quantitative trait locus mapping in mice. Two attractive positional and functional candidate genes in LXRA and ABCB11 are located in this interval. ABCB11 is associated with progressive familial cholestasis. This study was undertaken to investigate LXRA and ABCB11 as candidate genes for gallstone disease in humans. Eight hundred and ten patients who underwent cholecystectomy for symptomatic gallstone disease (median age of onset, 50 years) were compared with 718 sex-matched control individuals. Control individuals were sonographically free of gallstones. Haplotype tagging and all known coding single nucleotide polymorphisms (SNPs) were genotyped for ABCB11 (n=29) and LXRA (n=10). The investigated high-risk patient sample provides a power of greater than 80% for the detection of odds ratios down to 1.55. No evidence of association of the two genes in the single point tagging markers, coding variants or in the sliding window haplotype analysis was detected (all nominal single-point P values>or=.08). In conclusion, in the investigated German sample, no evidence of association of ABCB11 and LXRA to gallstone susceptibility was detected. The gallstone trait is not allelic to progressive familial cholestasis at the ABCB11 locus. Systematic fine mapping of the Lith1 region is required to identify the causative genetic variants for gallstone in mice and humans.

Identification of candidate genes for Parkinson's disease through blood transcriptome analysis in LRRK2-G2019S carriers, idiopathic cases, and controls

A tomato (Solanum lycopersicum L.) breeding program must use the knowledge and technology available for the development of efficient strategies to solve different problems in the crop value chain. In this work, we start with four phenotypes of commercial interest: (i) ToCV (Crinivirus) tolerance, (ii) resistance to the herbicide metribuzin, (iii) increase in the vitamin C content and (iv) erect growth habit (erectoid leaf). Applying a working model that integrates different tools at the phenotypic, molecular, and genomic level, we set out to to advance in the identification of candidate genes. The strategy included the following steps: definition of the trait, study of its genetic basis, primary location of the genetic factor in the physical map, identification of candidate genes and genetic validation. The presence of four alleles with a major effect in the described phenotypes was confirmed: cvt, mtz and Vtc and Erl. For cvt and mtz, sufficient information was generated to achieve its primary location. For Vtc, progress was made in the identification of two candidate genes that must be confirmed and validated. Erl was related to a gene not yet characterized, which we call SlTAC1. The applied strategy allowed to advance successfully in the identification of candidate genes, generating information that facilitates the use in tomato breeding of new interest traits. At the same time, new research hypotheses are proposed to complement and deepen the generated knowledge.

The integration of approaches based on genetic linkage maps and those based on function-driven holistic expression profiling, termed 'genetical genomics', has great potential for elucidating the expression and inheritance of complex traits. This review discusses the status and potential applications of 'genetical genomics' and how genomic/genetic, transcriptomic and phenotypic information can be used to promote the identification of functional positional candidate genes for traits important in farm animal breeding schemes. Linkage and/or genome-wide association analyses for traits that are relevant in animal breeding provide information about the positional candidacy of a particular gene. Analysing the relationship of the expression levels of genes and these traits adds information about the functional role of a gene and gives insight into functional pathways involved in the conversion of the traits. Mapping of expression quantitative trait loci (eQTL) enables the display of regulatory networks and localization of genomic variations affecting the mRNA expression of a gene either within the genes itself ( cis ) or distant from the gene ( trans ). The key advantage of eQTL mapping is that it connects variation at the level of RNA expression to variation at the level of DNA. Only the latter provides versatile tools for breeding, whereas the first reveals information on the biology of a trait and directs research to new candidate genes.

Rare familial forms of Alzheimer's disease (AD) are thought to be caused by elevated proteolytic production of the Abeta42 peptide from the beta-amyloid-precursor protein (APP). Although the pathogenesis of the more common late-onset AD (LOAD) is not understood, BACE1, the protease that cleaves APP to generate the N terminus of Abeta42, is more active in patients with LOAD, suggesting that increased amyloid production processing might also contribute to the sporadic disease. Using high-throughput siRNA screening technology, we assessed 15,200 genes for their role in Abeta42 secretion and identified leucine-rich repeat transmembrane 3 (LRRTM3) as a neuronal gene that promotes APP processing by BACE1. siRNAs targeting LRRTM3 inhibit the secretion of Abeta40, Abeta42, and sAPPbeta, the N-terminal APP fragment produced by BACE1 cleavage, from cultured cells and primary neurons by up to 60%, whereas overexpression increases Abeta secretion. LRRTM3 is expressed nearly exclusively in the nervous system, including regions affected during AD, such as the dentate gyrus. Furthermore, LRRTM3 maps to a region of chromosome 10 linked to both LOAD and elevated plasma Abeta42, and is structurally similar to a family of neuronal receptors that includes the NOGO receptor, an inhibitor of neuronal regeneration and APP processing. Thus, LRRTM3 is a functional and positional candidate gene for AD, and, given its receptor-like structure and restricted expression, a potential therapeutic target.

BackgroundParkinson's disease is the second most common neurodegenerative disorder. The pathological hallmark of the disease is degeneration of midbrain dopaminergic neurons. Genetic association studies have linked 13 human chromosomal loci to Parkinson's disease. Identification of gene(s), as part of the etiology of Parkinson's disease, within the large number of genes residing in these loci can be achieved through several approaches, including screening methods, and considering appropriate criteria. Since several of the indentified Parkinson's disease genes are expressed in substantia nigra pars compact of the midbrain, expression within the neurons of this area could be a suitable criterion to limit the number of candidates and identify PD genes.MethodsIn this work we have used the combination of findings from six rodent transcriptome analysis studies on the gene expression profile of midbrain dopaminergic neurons and the PARK loci in OMIM (Online Mendelian Inheritance in Man) database, to identify new candidate genes for Parkinson's disease.ResultsMerging the two datasets, we identified 20 genes within PARK loci, 7 of which are located in an orphan Parkinson's disease locus and one, which had been identified as a disease gene. In addition to identifying a set of candidates for further genetic association studies, these results show that the criteria of expression in midbrain dopaminergic neurons may be used to narrow down the number of genes in PARK loci for such studies.

Complexity of multifactorial diseases as Parkinson's disease (PD) often complicate identifying causal genetic factors by traditional approaches such as positional cloning and candidate gene analyses. PD is etiologically and genetically complex disease and second most common neurodegenerative disorder after Alzheimer's disease. The most cases of PD are idiopathic and small growing subset of individuals have single gene defect as the cause. The main goal of this research was to identify the potential candidate genes for idiopathic PD by using biomedical discovery support system (BITOLA). For detecting the potential candidate genes for PD was used opened system of bioinformatics tool BITOLA. Data of chromosome location, tissue specific expression of potential candidate genes and their potential association with PD were obtained from Medline, Locus Link, Gene Cards and OMIM. By using BITOLA system is identified 17 genes as potential candidate genes for PD. The role of three genes (MAPT, PARK2, UCHL1) in PD were confirmed earlier. Discovering the novel candidate genes for multifactiorial diseases by using specially mentioned bioinformatics tool BITOLA could offer the new opportunity for researching genetics base of PD without using tissue samples of patients.

Despite the identification of candidate genes influencing milk protein, the connections between genes and regulatory pathways remains elusive. This study aimed integrate findings from genome-wide association studies (GWAS) and RNA sequencing (RNA-Seq) through meta-analysis to pinpoint single nucleotide polymorphisms (SNPs) and genes responsible for high and low protein yield in cows. Previous GWAS and RNA-Seq analyses had identified 663 SNPs and 1106 genes ( P < 0.05). Twenty SNPs from GWAS, 10 genes from RNA-Seq, and 49 SNP/gene associations from both datasets, were identified using meta-analysis. Meta-analysis validated several SNPs previously identified through GWAS, such as rs135549651 ( P = 2.6 × 10−256), rs109146371 ( P = 3.1 × 10−208), rs109350371 ( P = 4.0 × 10−207), and rs109774038 ( P = 8.6 × 10−587). Genes identified in RNA-Seq experiments, including NR4A1 ( P = 3.2 × 10−7), ATF3 ( P = 9.6 × 10−7), CDH16 ( P = 9.9 × 10−7), VEGFA ( P = 1.0 × 10−6), and SAA3 ( P = 7.3 × 10−11), were confirmed. The combined GWAS and RNA-Seq datasets highlighted CCND2 ( P = 8.9 × 10−111), MAPK15 ( P = 1.3 × 10−151), and CPSF1 ( P = 1.2 × 10−306) as the most significant genes. Additionally, significant gene ontology (GO) terms, including ionizing radiation ( P = 1.5 × 10−4), nuclear pore cytoplasmic filaments ( P = 9.4 × 10−5), and phenylalanine 4-monooxygenase activity ( P = 1.4 × 10−5), were identified. In conclusion, the integration of GWAS and RNA-Seq, coupled with GO enrichment, allowed identification of candidate SNPs and genes with higher accuracy. These findings improve our knowledge about genomic architecture of milk protein and enhance evaluation of Holstein cows.

Disease gene identification is still a challenge despite modern high-throughput methods. Many diseases are very rare or lethal and thus cannot be investigated with traditional methods. Several in silico methods have been developed but they have some limitations. We introduce a new method that combines information about protein-interaction network properties and Gene Ontology terms. Genes with high-calculated network scores and statistically significant gene ontology terms based on known diseases are prioritized as candidate genes. The method was applied to identify novel primary immunodeficiency-related genes, 26 of which were found. The investigation uses the protein-interaction network for all essential immunome human genes available in the Immunome Knowledge Base and an analysis of their enriched gene ontology annotations. The identified disease gene candidates are mainly involved in cellular signaling including receptors, protein kinases and adaptor and binding proteins as well as enzymes. The method can be generalized for any disease group with sufficient information.

Four genes, VTN, KERA, LYZ, and a non-annotated EST (Affymetrix probe set ID: Ssc.25503.1.S1_at), whose candidacy for traits related to water-holding capacity of meat arises from their trait-dependent differential expression, were selected for candidate gene analysis. Based on in silico analysis SNPs were detected, confirmed by sequencing and used to genotype animals of 4 pig populations including 3 commercial herds of Pietrain (PI), Pietrain x (German Large White x German Landrace) (PIF1), German Landrace (DL) and 1 experimental F2 population Duroc x Pietrain (DUPI). Comparative and genetic mapping established the location of VTN on SSC12, of LYZ and KERA on SSC5 and of UN on SSC7, coinciding with QTL regions for meat quality traits. VTN showed association with pH1, pH24 and drip loss. LYZ revealed association with conductivity 24, pH1 and drip loss. KERA was associated with pH. UN showed association with pH24 and drip loss, respectively. However, none of the candidate genes showed significant associations for a particular trait across all populations. This may be due to breed specific effects that are related to the differences in meat quality of theses pig breeds. The studies revealed statistic evidence for a link of genetic variation at these loci or close to them and promoted those four candidate genes as functional and/or positional candidate genes for meat quality traits.

BackgroundThe molecular etiology is still to be identified for about half of the currently described Mendelian diseases in humans, thereby hindering efforts to find treatments or preventive measures. Advances, such as new sequencing technologies, have led to increasing amounts of data becoming available with which to address the problem of identifying disease genes. Therefore, automated methods are needed that reliably predict disease gene candidates based on available data. We have recently developed Exomiser as a tool for identifying causative variants from exome analysis results by filtering and prioritising using a number of criteria including the phenotype similarity between the disease and mouse mutants involving the gene candidates. Initial investigations revealed a variation in performance for different medical categories of disease, due in part to a varying contribution of the phenotype scoring component.ResultsIn this study, we further analyse the performance of our cross-species phenotype matching algorithm, and examine in more detail the reasons why disease gene filtering based on phenotype data works better for certain disease categories than others. We found that in addition to misleading phenotype alignments between species, some disease categories are still more amenable to automated predictions than others, and that this often ties in with community perceptions on how well the organism works as model.ConclusionsIn conclusion, our automated disease gene candidate predictions are highly dependent on the organism used for the predictions and the disease category being studied. Future work on computational disease gene prediction using phenotype data would benefit from methods that take into account the disease category and the source of model organism data.

Feed conversion efficiency is a measure of how well an animal converts feed into live weight and it is typically expressed as feed conversion ratio (FCR). FCR and related traits like growth rate (e.g. days to 110 kg—D110) are of high interest for animal breeders, farmers and society due to implications on animal performance, feeding costs and environmental sustainability. The objective of this study was to identify genomic regions associated with FCR and D110 in pigs. A total of 952 terminal line boars, showing an individual variation in FCR, were genotyped using 60K SNP-Chips. Markers were tested for associations with estimated breeding values (EBV) for FCR and D110. For FCR, the largest number of associated SNPs was located on chromosomes 4 (30 SNPs), 1 (25 SNPs), X (15 SNPs) and 6 (12 SNPs). The most prominent genomic regions for D110 were identified on chromosomes 15 (10 SNPs), 1 and 4 (both 9 SNPs). The most significantly associated SNPs for FCR and D110 mapped 129.8 Kb from METTL11B (chromosome 4) and 32Kb from MBD5 (chromosome 15), respectively. A list of positional genes, closest to significantly associated SNPs, was used to identify enriched pathways and biological functions related to the QTL for both traits. A number of candidate genes were significantly overrepresented in pathways of immune cell trafficking, lymphoid tissue structure, organ morphology, endocrine system function, lipid metabolism, and energy production. After resequencing the coding region of selected positional and functional candidate genes, six SNPs were genotyped in a subset of boars. SNPs in PRKDC, SELL, NR2E1 and AKRIC3 showed significant associations with EBVs for FCR/D110. The study revealed a number of chromosomal regions and candidate genes affecting FCR/D110 and pointed to corresponding biological pathways related to lipid metabolism, olfactory reception, and also immunological status.

Asthma is a chronic inflammatory disorder of the airways, and a number of genetic loci are associated with the disease. Candidate gene association studies have been regarded as effective tools to study complex traits. Knowledge of the sequence variation and structure of the candidate genes is required for association studies. Thus, we investigated the genetic variants of 32 asthma candidate genes selected by colocalization of positional and functional candidate genes. We screened all exons and promoter regions of those genes using 12 healthy individuals and 12 asthma patients and identified a total of 418 single nucleotide polymorphisms (SNPs), including 270 known SNPs and 148 novel SNPs. Levels of nucleotide diversity varied from gene to gene (0.72 x 10(-4)-14.53 x 10(-4)), but the average nucleotide diversity between coding SNPs (cSNPs) and noncoding SNPs was roughly equivalent (4.63 x 10(-4) vs 4.69 x 10(-4)). However, nucleotide diversity of cSNPs was strongly correlated to codon degeneracy. Nucleotide diversity was much higher at fourfold degenerate sites than at nondegenerate sites (9.42 x 10(-4) vs 3.14 x 10(-4)). Gene-based haplotype analysis of asthma-associated genes in this study revealed that common haplotypes (frequency >5%) represented 90.5% of chromosomes, and they could be uniquely identified with five or fewer haplotype-tagging SNPs per gene. Therefore, our results may have important implications for the selection of asthma candidate genes and SNP markers for comprehensive association studies using large sample populations.

SFM: A novel sequence-based fusion method for disease genes identification and prioritization

The function of normal and defective candidate genes for human genetic diseases, which are rapidly being identified in large numbers by human geneticists and the biomedical community at large, will be best studied in relevant and predictive model organisms that allow high-speed verification, analysis of underlying developmental, cellular and molecular mechanisms, and establishment of disease models to test therapeutic options. We describe and discuss the pros and cons of the frog Xenopus, which has been extensively used to uncover developmental mechanisms in the past, but which is being underutilized as a biomedical model. We argue that Xenopus complements the more commonly used mouse and zebrafish as a time- and cost-efficient animal model to study human disease alleles and mechanisms.