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.     

The role of pleiotropy vs signaller-receiver gene epistasis in life history trade-offs: dissecting the genomic architecture of organismal design in social systems

Traditional life history theory ignores trade-offs due to social interactions, yet social systems expand the set of possible trade-offs affecting a species evolution--by introducing asymmetric interactions between the sexes, age classes and invasion of alternative strategies. We outline principles for understanding gene epistasis due to signaller-receiver dynamics, gene interactions between individuals, and impacts on life history trade-offs. Signaller-receiver epistases create trade-offs among multiple correlated traits that affect fitness, and generate multiple fitness optima conditional on frequency of alternative strategies. In such cases, fitness epistasis generated by selection can maintain linkage disequilibrium, even among physically unlinked loci. In reviewing genetic methods for studying life history trade-offs, we conclude that current artificial selection or gene manipulation experiments focus on pleiotropy. Multi-trait selection experiments, multi-gene engineering methods or multiple endocrine manipulations can test for epistasis and circumvent these limitations. In nature, gene mapping in field pedigrees is required to study social gene epistases and associated trade-offs. Moreover, analyses of correlational selection and frequency-dependent selection are necessary to study epistatic social system trade-offs, which can be achieved with group-structured versions of Price's (1970) equation.     

Methods for assessing gene content diversity of KIR with examples from a global set of populations

A number of statistical methods are widely used to describe allelic variation at specific genetic loci and its implication on the evolutionary history of these loci. Although the methods were developed primarily to study allelic variation at loci that are virtually always present in the genome, they are often applied to data of gene content variation (i.e., presence/absence of multiple homologous genes) at the killer cell immunoglobulin-like receptor (KIR) gene cluster. In this paper, we discuss methodological issues involved in the analysis of gene content variation data in the KIR region and also its covariation with polymorphism at the human leukocyte antigen class I loci, which encode ligands for KIR. A comparison of several statistical methods and measures (gene frequency, haplotype frequency, and linkage disequilibrium estimation) using the Centre d’Etude du Polymorphisme Humain data will be provided using KIR haplotypes that have been determined by segregation analysis, noting the strengths and weaknesses of the methods when only the presence/absence data is considered. Finally, application of these methods to a set of globally distributed populations is described (see Single et al., Nat Genet 39:1114–1119, 2007) in order to illustrate the challenges faced when inferring the joint effects of natural selection and demographic history on these immune-related genes.     

A major susceptibility gene for asthma maps to chromosome 14q24

Asthma is a complex genetic disorder with a heterogeneous phenotype, largely attributed to the interactions among many genes and between these genes and the environment. Numerous loci and candidate genes have been reported to show linkage and association to asthma and atopy. Although some studies reporting these observations are compelling, no gene has been mapped that confers a sufficiently high risk of asthma to meet the stringent criteria for genomewide significance. Using 175 extended Icelandic families that included 596 patients with asthma, we performed a genomewide scan with 976 microsatellite markers. The families were identified by cross-matching a list of patients with asthma from the Department of Allergy/Pulmonary Medicine of the National University Hospital of Iceland with a genealogy database of the entire Icelandic nation. We detected linkage of asthma to chromosome 14q24, with an allele-sharing LOD score of 2.66. After we increased the marker density within the locus to an average of one microsatellite every 0.2 cM, the LOD score rose to 4.00. We designate this locus "asthma locus one" (AS1). Taken together, these results provide evidence of a novel susceptibility gene for asthma on chromosome 14q24.     

Detection of gene x gene interactions in genome-wide association studies of human population data

Empirical evidence supporting the commonality of gene x gene interactions, coupled with frequent failure to replicate results from previous association studies, has prompted statisticians to develop methods to handle this important subject. Nonparametric methods have generated intense interest because of their capacity to handle high-dimensional data. Genome-wide association analysis of large-scale SNP data is challenging mathematically and computationally. In this paper, we describe major issues and questions arising from this challenge, along with methodological implications. Data reduction and pattern recognition methods seem to be the new frontiers in efforts to detect gene x gene interactions comprehensively. Currently, there is no single method that is recognized as the 'best' for detecting, characterizing, and interpreting gene x gene interactions. Instead, a combination of approaches with the aim of balancing their specific strengths may be the optimal approach to investigate gene x gene interactions in human data.     

The genetic mapping of a defective LPS response gene in C3H/HeJ mice.

The expression of a defective LPS response gene Lps and the major urinary protein (Mup-1) are concordantly inherited in backcross (C3H/HeJ x C57BL/6J)F1 x C3H/HeJ mice, indicating genetic linkage of these loci. Mup-1 is known to be linked to the brown coat color locus on chromosome 4 in mice; thus Lps can now be assigned to chromosome 4. A value of 0.06 +/- 0.02 has been estimated for the recombination frequency between Mup-1 and Lps. We have used the polysyndactyly (Ps) mutation further to localize Lps on chromosome 4. Lps is located between the Mup-1 and Ps loci.     

Two-locus genome-wide linkage scan for prostate cancer susceptibility genes with an interaction effect

Prostate cancer represents a significant worldwide public health burden. Epidemiological and genetic epidemiological studies have consistently provided data supporting the existence of inherited prostate cancer susceptibility genes. Segregation analyses of prostate cancer suggest that a multigene model may best explain familial clustering of this disease. Therefore, modeling gene–gene interactions in linkage analysis may improve the power to detect chromosomal regions harboring these disease susceptibility genes. In this study, we systematically screened for prostate cancer linkage by modeling two-locus gene–gene interactions for all possible pairs of loci across the genome in 426 prostate cancer families from Johns Hopkins Hospital, University of Michigan, University of Umeå, and University of Tampere. We found suggestive evidence for an epistatic interaction for six sets of loci (target chromosome-wide/reference marker-specific P≤0.0001). Evidence for these interactions was found in two independent subsets from within the 426 families. While the validity of these results requires confirmation from independent studies and the identification of the specific genes underlying this linkage evidence, our approach of systematically assessing gene–gene interactions across the entire genome represents a promising alternative approach for gene identification for prostate cancer.     

Quantitative trait locus mapping using human pedigrees

In the past decade phenomenal progress has been made in molecular and statistical genetic methods for localizing quantitative trait loci. Because of these advances, we can anticipate a long period of active genetic research in which the genes influencing human quantitative variability will be mapped and their effects accurately evaluated. Here, we review the current state of the science in statistical genetic methods for quantitative trait linkage analysis. In particular, we detail a variance component-based framework for localizing quantitative trait loci and for accurately estimating their relative effect sizes. Attention is paid to the optimal design of human family studies for localizing genes of small to moderate effect. In addition, methods and strategies are described for dealing with the most important complications of quantitative variation, including the assessment of genotype x environment interaction and epistasis.     

Meta-analysis of linkage data under worst-case conditions: a demonstration using the human OB region.

To date, few methods have been developed explicitly for meta-analysis of linkage analyses. Moreover, the methods that have been developed or suggested generally depend on certain ideal situations and have not been widely applied. In this article, we apply standard statistical theory and meta-analytic techniques in novel ways to five published papers discussing the evidence of linkage of body mass index (BMI) to the region of the human genome containing the OB gene. These methods are "inference based," meaning that they allow one to make statements about the statistical significance of the entire body of evidence. As currently developed, they do not allow specific statements to be made about the amount of variance explained by any putative locus or allow precise confidence intervals to be placed around the putative location of a linked locus. By applying these techniques to the literature on linkage in the human OB gene region, we are able to show that the evidence for linkage somewhere in the region is extremely strong (P = 1.5 x 10[-5]).     

Genetic heterogeneity of gene defects responsible for familial Alzheimer disease

Inherited Alzheimer's disease is a genetically heterogeneous disorder that involves gene defects on at least five chromosomal loci. Three of these loci have been found by genetic linkage studies to reside on chromosomes 21, 19, and 14. On chromosomes 21, the gene encoding the precursor protein of Alzheimerassociated amyloid (APP) has been shown to contain several mutations in exons 16 and 17 which account for roughly 2–3% of familial Alzheimer's disease (FAD). The other loci include what appears to be a susceptibility gene on chromosome 19 associated with late-onset (>65 years) FAD, and a major early-onset FAD gene defect on the long arm of chromosome 14. In other early-and late-onset FAD kindreds, the gene defects involved do not appear to be linked to any of these three loci, indicating the existence of additional and as of yet unlocalized FAD genes. This review provides a historical perspective of the search for FAD gene defects and summarizes the progress made in world-wide attempts to isolate and characterize the genes responsible for this disorder.     

A theoretical treatment of interval mapping of a disease gene using transmission disequilibrium tests

The genetic basis of the transmission disequilibrium test (TDT) for two-marker loci is explored from first principles. In this case, parents doubly heterozygous for a given haplotype at the pair of marker loci that are each in linkage disequilibrium with the disease gene with the further possibility of a second-order linkage disequilibrium are considered. The number of times such parents transmit the given haplotype to their affected offspring is counted and compared with the frequencies of haplotypes that are not transmitted. This is done separately for the coupling and repulsion phases of doubly heterozygous genotypes. Expectations of the counts for each of the sixteen cells possible with four-marker gametic types (transmitted vs not transmitted) are derived. Based on a test of symmetry in a square 4 x 4 contingency table, chi-square tests are proposed for the null hypothesis of no linkage between the markers and the disease gene. The power of the tests is discussed in terms of the corresponding non-centrality parameters for the alternative hypothesis that both the markers are linked with the disease locus. The results indicate that the power increases with the decrease in recombination probability and that it is higher for a lower frequency of the disease gene. Taking a pair of markers in an interval for exploring the linkage with the disease gene seems to be more informative than the single-marker case since the values of the non-centrality parameters tend to be consistently higher than their counterparts in the single-marker case. Limitations of the proposed test are also discussed.     

Cloning and gene map assignment of the Xiphophorus DNA ligase 1 gene

Fishes represent the stem vertebrate condition and have maintained several gene arrangements common to mammalian genomes throughout the 450 Myr of divergence from a common ancestor. One such syntenic arrangement includes the GPI-PEPD enzyme association on Xiphophorus linkage group IV and human chromosome 19. Previously we assigned the Xiphophorus homologue of the human ERCC2 gene to linkage group U5 in tight association with the CKM locus. CKM is also tightly linked to the ERCC2 locus on human chromosome 19, leading to speculation that human chromosome 19 may have arisen by fusion of two ancestral linkage groups which have been maintained in fishes. To investigate this hypothesis further, we isolated and sequenced Xiphophorus fish genomic regions exhibiting considerable sequence similarity to the human DNA ligase 1 amino acid sequence. Comparison of the fish DNA ligase sequence with those of other species suggests several modes of amino acid conservation in this gene. A 2.2-kb restriction fragment containing part of an X. maculatus DNA ligase 1 exon was used in backcross hybrid mapping with 12 enzyme or RFLP loci. Significant linkage was observed between the nucleoside phosphorylase (NP2) and the DNA ligase (LIG1) loci on Xiphophorus linkage group VI. This assignment suggests that the association of four DNA repair-related genes on human chromosome 19 may be the result of chance chromosomal rearrangements.      

Gene-environment interaction effects on behavioral variation and risk of complex disorders: the example of alcoholism and other psychiatric disorders.

There have been few replicated examples of genotype x environment interaction effects on behavioral variation or risk of psychiatric disorder. We review some of the factors that have made detection of genotype x environment interaction effects difficult, and show how genotype x shared environment interaction (GxSE) effects are commonly confounded with genetic parameters in data from twin pairs reared together. Historic data on twin pairs reared apart can in principle be used to estimate such GxSE effects, but have rarely been used for this purpose. We illustrate this using previously published data from the Swedish Adoption Twin Study of Aging (SATSA), which suggest that GxSE effects could account for as much as 25% of the total variance in risk of becoming a regular smoker. Since few separated twin pairs will be available for study in the future, we also consider methods for modifying variance components linkage analysis to allow for environmental interactions with linked loci.     

The influence of gene conversion on linkage disequilibrium around a selective sweep

In a 2007 article, McVean studied the effect of recombination on linkage disequilibrium (LD) between two neutral loci located near a third locus that has undergone a selective sweep. The results demonstrated that two loci on the same side of a selected locus might show substantial LD, whereas the expected LD for two loci on opposite sides of a selected locus is zero. In this article, we extend McVean's model to include gene conversion. We show that one of the conclusions is strongly affected by gene conversion: when gene conversion is present, there may be substantial LD between two loci on opposite sides of a selective sweep.     

Genetic linkage studies of autosomal dominant polycystic kidney disease: search for the second gene in a large Sicilian family

Summary Polycystic kidney disease (PKD) is a common autosomal dominant genetic disorder caused by mutation in at least two different gene loci. The PKD1 gene has been localized on the short arm of chromosome 16. The location of a second genetic locus in the human genome is not yet known. A large PKD kindred, unlinked to chromosome 16, with over 250 members was studied using both DNA and classical markers. In total, 29 informative marker loci on 11 autosomes have been analyzed for linkage with PKD. The data significantly exclude the linkage with disease locus from 17 marker loci and show no evidence of close linkage with the other loci.     

True and false positive peaks in genomewide scans: applications of length-biased sampling to linkage mapping.

Disease-susceptibility loci are now being mapped via genomewide scans in which a linkage statistic is computed at each of a large number of markers. Such disease-susceptibility loci may be identified via a peak in the test statistic when the latter is plotted against the genetic map. In this paper we establish, by appealing to renewal theory, that true positive peaks are expected to be longer than false positive peaks. These results are verified by a realistic simulation of a genomewide linkage study based on the affected-sib-pair design. Since longer peaks are more likely to contain a gene of interest than are shorter peaks, these differences may aid in linkage mapping, justifying assignment of lower priority to shorter peaks. However, since these differences are generally small, statistics based on both peak length and height may not be much more powerful than those based on height alone. The results presented here also provide a theoretical framework for methods that use the length of shared haplotypes in populations to map disease genes.     

Loss of normal allele of the APC gene in an adrenocortical carcinoma from a patient with familial adenomatous polyposis

Summary Salla disease is a lysosomal storage disorder due to impaired transport of free sialic acid across the lysosomal membrane. The clinical presentation of this autosomal recessive trait is severe psychomotor retardation from early infancy on. In order to determine the gene locus for the disease we have initiated a genetic linkage study using polymorphic gene markers in rep-resentative family material comprising about 60% of all families known to be affected with Salla disease. Here we present an exclusion map based on combined linkage data from 64 informative loci on 19 autosomes. Theoretically, at least 55% of the genome has been excluded as a locus for the disease gene, while some chromosome areas, particularly the long arm of chromosome 2, are highlighted as possible sites for the gene locus.     

Detecting susceptibility genes in case-control studies using set association

Complex diseases are generally caused by intricate interactions of multiple genes and environmental factors. Most available linkage and association methods are developed to identify individual susceptibility genes assuming a simple disease model blind to any possible gene - gene and gene - environmental interactions. We used a set association method that uses single-nucleotide polymorphism markers to locate genetic variation responsible for complex diseases in which multiple genes are involved. Here we extended the set association method from bi-allelic to multiallelic markers. In addition, we studied the type I error rates and power for both approaches using simulations based on the coalescent process. Both bi-allelic set association (BSA) and multiallelic set association (MSA) tests have the correct type I error rates. In addition, BSA and MSA can have more power than individual marker analysis when multiple genes are involved in a complex disease. We applied the MSA approach to the simulated data sets from Genetic Analysis Workshop 13. High cholesterol level was used as the definitive phenotype for a disease. MSA failed to detect markers with significant linkage disequilibrium with genes responsible for cholesterol level. This is due to the wide spacing between the markers and the lack of association between the marker loci and the simulated phenotype.