Localization and identification of human quantitative trait loci: king harvest has surely come

The scientific process of localization and subsequent identification of genes influencing risk of common diseases is still in its infancy. Initial localization of disease-related loci has traditionally been performed using family-based linkage methods to scan the genome. Early pronouncements of the failure of this approach for common diseases were premature and based on comparing suboptimal linkage designs with overly optimistic and empirically unproven association-based designs. On the contrary, substantial recent progress in the positional cloning of genes influencing such complex phenotypes suggests that modern approaches based around a family-based linkage paradigm will be successful. In particular, the rapidly growing emphasis on the analysis of the genetic basis of quantitative correlates of disease risk represents a novel and promising approach in which initial localization is performed using linkage and subsequent identification utilizes association approaches in positional candidate genes.     

Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes

Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray co-expressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown.     

Systems genetics approach reveals candidate genes for parasite resistance from quantitative trait loci studies in agricultural species

A systems genetics approach combining pathway analysis of quantitative trait loci (QTL) and gene expression information has provided strong evidence for common pathways associated with genetic resistance to internal parasites. Gene data, collected from published QTL regions in sheep, cattle, mice, rats and humans, and microarray data from sheep, were converted to human Entrez Gene IDs and compared to the KEGG pathway database. Selection of pathways from QTL data was based on a selection index that ensured that the selected pathways were in all species and the majority of the projects overall and within species. Pathways with either up- and down-regulated genes, primarily up-regulated genes or primarily down-regulated genes, were selected from gene expression data. After comparing the data sets independently, the pathways from each data set were compared and the common set of pathways and genes was identified. Comparisons within data sets identified 21 pathways from QTL data and 66 pathways from gene expression data. Both selected sets were enriched with pathways involved in immune functions, disease and cell responses to signals. The analysis identified 14 pathways that were common between QTL and gene expression data, and four directly associated with IFNγ or MHCII, with 31 common genes, including three MHCII genes. In conclusion, a systems genetics approach combining data from multiple QTL and gene expression projects led to the discovery of common pathways associated with genetic resistance to internal parasites. This systems genetics approach may prove significant for the discovery of candidate genes for many other multifactorial, economically important traits.     

Differential gene expression in ovaries of pregnant pigs with high and low prolificacy levels and identification of candidate genes for litter size

Previous results from a genome scan in an F(2) Iberian × Meishan pig intercross showed several chromosome regions associated with litter size traits in this species. In order to identify candidate genes underlying these quantitative trait loci (QTL), we performed an ovary gene expression analysis during the sow's pregnancy. F(2) sows were ranked by their estimated breeding values for prolificacy: six sows with the highest estimated breeding value (EBV) (i.e., high prolificacy) and six sows with the lowest EBV (low prolificacy) were selected. Samples were hybridized using an Affymetrix GeneChip porcine genome array. Statistical analysis with a mixed model approach identified 221 differentially expressed probes, representing 189 genes. These genes were functionally annotated in order to identify genetic pathways overrepresented in this list. Among the functional groups most represented was, in first position, immune system response activation against external stimulus. The second group consisted of integrated genes that regulate maternal homeostasis by complement and coagulation cascades. A third group was involved in lipid and fatty acid enzymes of metabolic processes, which participate in the steroidogenesis pathway. In order to identify powerful candidate genes for prolificacy, the second approach of this study was to merge microarray data with the QTL positional information affecting litter size, previously detected in the same experimental cross. As a result, we have identified 27 differentially expressed genes colocalizing with QTL for litter size traits, which fulfill the biological, positional, and functional criteria.     

Genetic inheritance of gene expression in human cell lines

Combining genetic inheritance information, for both molecular profiles and complex traits, is a promising strategy not only for detecting quantitative trait loci (QTLs) for complex traits but for understanding which genes, pathways, and biological processes are also under the influence of a given QTL. As a primary step in determining the feasibility of such an approach in humans, we present the largest survey to date, to our knowledge, of the heritability of gene-expression traits in segregating human populations. In particular, we measured expression for 23,499 genes in lymphoblastoid cell lines for members of 15 Centre d'Etude du Polymorphisme Humain (CEPH) families. Of the total set of genes, 2,340 were found to be expressed, of which 31% had significant heritability when a false-discovery rate of 0.05 was used. QTLs were detected for 33 genes on the basis of at least one P value <.000005. Of these, 13 genes possessed a QTL within 5 Mb of their physical location. Hierarchical clustering was performed on the basis of both Pearson correlation of gene expression and genetic correlation. Both reflected biologically relevant activity taking place in the lymphoblastoid cell lines, with greater coherency represented in Kyoto Encyclopedia of Genes and Genomes database (KEGG) pathways than in Gene Ontology database pathways. However, more pathway coherence was observed in KEGG pathways when clustering was based on genetic correlation than when clustering was based on Pearson correlation. As more expression data in segregating populations are generated, viewing clusters or networks based on genetic correlation measures and shared QTLs will offer potentially novel insights into the relationship among genes that may underlie complex traits.     

A strategy to identify positional candidate genes conferring Marek''s disease resistance by integrating DNA microarrays and genetic mapping

Marker-assisted selection (MAS) to enhance genetic resistance to Marek's disease (MD), a herpesvirus-induced T cell cancer in chicken, is an attractive alternative to augment control with vaccines. Our earlier studies indicate that there are many quantitative trait loci (QTL) containing one or more genes that confer genetic resistance to MD. Unfortunately, it is difficult to sufficiently resolve these QTL to identify the causative gene and generate tightly linked markers. One possible solution is to identify positional candidate genes by virtue of gene expression differences between MD resistant and susceptible chicken using deoxyribonucleic acid (DNA) microarrays followed by genetic mapping of the differentially-expressed genes. In this preliminary study, we show that DNA microarrays containing approximately 1200 genes or expressed sequence tags (ESTs) are able to reproducibly detect differences in gene expression between the inbred ADOL lines 63 (MD resistant) and 72 (MD susceptible) of uninfected and Marek's disease virus (MDV)-infected peripheral blood lymphocytes. Microarray data were validated by quantitative polymerase chain reaction (PCR) and found to be consistent with previous literature on gene induction or immune response. Integration of the microarrays with genetic mapping data was achieved with a sample of 15 genes. Twelve of these genes had mapped human orthologues. Seven genes were located on the chicken linkage map as predicted by the human-chicken comparative map, while two other genes defined a new conserved syntenic group. More importantly, one of the genes with differential expression is known to confer genetic resistance to MD while another gene is a prime positional candidate for a QTL.     

A Bayesian ensemble approach with a disease gene network predicts damaging effects of missense variants of human cancers

Large-scale sequencing of cancer genomes has revealed many novel mutations and inter-tumoral heterogeneity. Therefore, prioritizing variants according to their potential deleterious effects has become essential. We constructed a disease gene network and proposed a Bayesian ensemble approach that integrates diverse sources to predict the functional effects of missense variants. We analyzed 23,336 missense disease mutations and 36,232 neutral polymorphisms of 12,039 human proteins. The results showed successful improvement of prediction accuracy in both sensitivity and specificity, and we demonstrated the utility of the method by applying it to somatic mutations obtained from colorectal and breast cancer cell lines. The candidate genes with predicted deleterious mutations as well as known cancer genes were significantly enriched in many KEGG pathways related to carcinogenesis, supporting genetic homogeneity of cancer at the pathway level. The breast cancer-specific network increased the prediction accuracy for breast cancer mutations. This study provides a ranked list of deleterious mutations and candidate cancer genes and suggests that mutations affecting cancer may occur in important pathways and should be interpreted on the phenotype-related network or pathway. A disease gene network may be of value in predicting functional effects of novel disease-specific mutations.     

Genomewide-linkage and haplotype-association studies map intracranial aneurysm to chromosome 7q11

Rupture of intracranial aneurysms (IAs) causes subarachnoid hemorrhage, a devastating condition with high morbidity and mortality. Angiographic and autopsy studies show that IA is a common disorder, with a prevalence of 3%-6%. Although IA has a substantial genetic component, little attention has been given to the genetic determinants. We report here a genomewide linkage study of IA in 104 Japanese affected sib pairs in which positive evidence of linkage on chromosomes 5q22-31 (maximum LOD score [MLS] 2.24), 7q11 (MLS 3.22), and 14q22 (MLS 2.31) were found. The best evidence of linkage is detected at D7S2472, in the vicinity of the elastin gene (ELN), a candidate gene for IA. Fourteen distinct single-nucleotide polymorphisms (SNPs) were identified in ELN, and no obvious allelic association between IA and each SNP was observed. The haplotype between the intron-20/intron-23 polymorphism of ELN is strongly associated with IA (P=3.81x10-6), and homozygous patients are at high risk (P=.002), with an odds ratio of 4.39. These findings suggest that a genetic locus for IA lies within or close to the ELN locus on chromosome 7.     

Bovine genome mapping: evolutionary inference and the power of comparative genomics

All bovine chromosomes are now represented by a syntenic group, a linkage map and at least one in situ hybridization. Almost 1000 loci are mapped, about 300 of which are coding sequences useful for comparative mapping and evolutionary inference. Economically important loci are beginning to appear on bovine linkage maps and enhanced comparative maps are likely to be necessary to identify many of these genes by a comparative positional candidate gene approach.     

猪的基因图谱及数量性状位点定位

在人类基因组计划的带动下,猪的遗传连锁图谱和细胞遗传学图谱有了较大的进步,利用目前猪基因组图谱的研究成果,通过基因组扫描法和候选基因法,可以对猪重要经济性状的主效基因位点进行区域定位,进而图位克隆,找到主效基因,为现代遗传育种奠定理论基础。     

Identification and characterization of regions of the rice genome associated with broad-spectrum, quantitative disease resistance

Much research has been devoted to understanding the biology of plant-pathogen interactions. The extensive genetic analysis of disease resistance in rice, coupled with the sequenced genome and genomic resources, provides the opportunity to seek convergent evidence implicating specific chromosomal segments and genes in the control of resistance. Published data on quantitative and qualitative disease resistance in rice were synthesized to evaluate the distributions of and associations among resistance loci. Quantitative trait loci (QTL) for resistance to multiple diseases and qualitative resistance loci (R genes) were clustered in the rice genome. R genes and their analogs of the nucleotide binding site-leucine-rich repeat class and genes identified on the basis of differential representation in disease-related EST libraries were significantly associated with QTL. Chromosomal segments associated with broad-spectrum quantitative disease resistance (BS-QDR) were identified. These segments contained numerous positional candidate genes identified on the basis of a range of criteria, and groups of genes belonging to two defense-associated biochemical pathways were found to underlie one BS-QDR region. Genetic dissection of disease QTL confidence intervals is needed to reduce the number of positional candidate genes for further functional analysis. This study provides a framework for future investigations of disease resistance in rice and related crop species.     

Prediction of human disease genes by human-mouse conserved coexpression analysis

Background

Even in the post-genomic era, the identification of candidate genes within loci associated with human genetic diseases is a very demanding task, because the critical region may typically contain hundreds of positional candidates. Since genes implicated in similar phenotypes tend to share very similar expression profiles, high throughput gene expression data may represent a very important resource to identify the best candidates for sequencing. However, so far, gene coexpression has not been used very successfully to prioritize positional candidates.

Methodology/Principal Findings

We show that it is possible to reliably identify disease-relevant relationships among genes from massive microarray datasets by concentrating only on genes sharing similar expression profiles in both human and mouse. Moreover, we show systematically that the integration of human-mouse conserved coexpression with a phenotype similarity map allows the efficient identification of disease genes in large genomic regions. Finally, using this approach on 850 OMIM loci characterized by an unknown molecular basis, we propose high-probability candidates for 81 genetic diseases.

Conclusion

Our results demonstrate that conserved coexpression, even at the human-mouse phylogenetic distance, represents a very strong criterion to predict disease-relevant relationships among human genes.     

Database of cattle candidate genes and genetic markers for milk production and mastitis

A cattle database of candidate genes and genetic markers for milk production and mastitis has been developed to provide an integrated research tool incorporating different types of information supporting a genomic approach to study lactation, udder development and health. The database contains 943 genes and genetic markers involved in mammary gland development and function, representing candidates for further functional studies. The candidate loci were drawn on a genetic map to reveal positional overlaps. For identification of candidate loci, data from seven different research approaches were exploited: (i) gene knockouts or transgenes in mice that result in specific phenotypes associated with mammary gland (143 loci); (ii) cattle QTL for milk production (344) and mastitis related traits (71); (iii) loci with sequence variations that show specific allele-phenotype interactions associated with milk production (24) or mastitis (10) in cattle; (iv) genes with expression profiles associated with milk production (207) or mastitis (107) in cattle or mouse; (v) cattle milk protein genes that exist in different genetic variants (9); (vi) miRNAs expressed in bovine mammary gland (32) and (vii) epigenetically regulated cattle genes associated with mammary gland function (1). Fourty-four genes found by multiple independent analyses were suggested as the most promising candidates and were further in silico analysed for expression levels in lactating mammary gland, genetic variability and top biological functions in functional networks. A miRNA target search for mammary gland expressed miRNAs identified 359 putative binding sites in 3'UTRs of candidate genes.     

Convergent functional genomics: a Bayesian candidate gene identification approach for complex disorders

Identifying genes involved in complex neuropsychiatric disorders through classic human genetic approaches has proven difficult. To overcome that barrier, we have developed a translational approach called Convergent Functional Genomics (CFG), which cross-matches animal model microarray gene expression data with human genetic linkage data as well as human postmortem brain data and biological role data, as a Bayesian way of cross-validating findings and reducing uncertainty. Our approach produces a short list of high probability candidate genes out of the hundreds of genes changed in microarray datasets and the hundreds of genes present in a linkage peak chromosomal area. These genes can then be prioritized, pursued, and validated in an individual fashion using: (1) human candidate gene association studies and (2) cell culture and mouse transgenic models. Further bioinformatics analysis of groups of genes identified through CFG leads to insights into pathways and mechanisms that may be involved in the pathophysiology of the illness studied. This simple but powerful approach is likely generalizable to other complex, non-neuropsychiatric disorders, for which good animal models, as well as good human genetic linkage datasets and human target tissue gene expression datasets exist.     

Molecular genetics of myocardial infarction

Myocardial infarction (MI) is an important clinical problem because of its large contribution to mortality. The main causal and treatable risk factors for MI include hypertension, hypercholesterolemia or dyslipidemia, diabetes mellitus, and smoking. In addition to these risk factors, recent studies have shown the importance of genetic factors and interactions between multiple genes and environmental factors. Disease prevention is an important strategy for reducing the overall burden of MI, with the identification of markers for disease risk being key both for risk prediction and for potential intervention to lower the chance of future events. Although genetic linkage analyses of families and sib-pairs as well as candidate gene and genome-wide association studies have implicated several loci and candidate genes in predisposition to coronary heart disease (CHD) or MI, the genes that contribute to genetic susceptibility to these conditions remain to be identified definitively. In this review, we summarize both candidate loci for CHD or MI identified by linkage analyses and candidate genes examined by association studies. We also review in more detail studies that have revealed the association with MI or CHD of polymorphisms in MTHFR, LPL, and APOE by the candidate gene approach and those in LTA and at chromosomal region 9p21.3 by genome-wide scans. Such studies may provide insight into the function of implicated genes as well as into the role of genetic factors in the development of CHD and MI.     

The emerging genetic architecture of type 2 diabetes

Type 2 diabetes is a genetically heterogeneous disease, with several relatively rare monogenic forms and a number of more common forms resulting from a complex interaction of genetic and environmental factors. Previous studies using a candidate gene approach, family linkage studies, and gene expression profiling uncovered a number of type 2 genes, but the genetic basis of common type 2 diabetes remained unknown. Recently, a new window has opened on defining potential type 2 diabetes genes through genome-wide SNP association studies of very large populations of individuals with diabetes. This review explores the pathway leading to discovery of these genetic effects, the impact of these genetic loci on diabetes risk, the potential mechanisms of action of the genes to alter glucose homeostasis, and the limitations of these studies in defining the role of genetics in this important disease.     

Mapping of Defense Response Gene Homologs and Their Association with Resistance Loci in Maize

Defense response genes in higher plant species are involved in a variety of signal transduction pathways and biochemical reactions to counterattack invading pathogens. In this study, a total of 366 non-redundant defense response gene homologs （DRHs）, including 124 unigenes/expressed sequence tags, 226 tentative consensuses, and 16 DRH contigs have been identified by mining the Maize Genetics and Genomics and The Institute for Genomic Research maize databases using 35 essential defense response genes. Of 366 DRHs, 202 are mapped to 152 loci across ten maize chromosomes via both the genetic and in silico mapping approaches. The mapped DRHs seem to cluster together rather than be evenly distributed along the maize genome. Approximately half of these DHRs are located in regions harboring either major resistance genes or quantitative trait loci （QTL）. Therefore, this comprehensive DRH linkage map will provide reference sequences to identify either positional candidate genes for resistance genes and/or QTLs or to develop makers for fine-mapping and marker-assisted selection of resistance genes and/or QTLs.