A Test of Neutrality Based on Interlocus Associations

The evolutionary processes governing variability within genomic regions of low recombination have been the focus of many studies. Here, I investigate the statistical properties of a measure of intrlocus genetic associations under the assumption that mutations are selectively neutral and sites are completely linked. This measure, denoted Z(nS), is based on the squared correlation of allelic identity at pairs of polymorphic sites. Upper bounds for Z(nS) are determined by simulations. Various deviations from the neutral model, including several different forms of natural selection, will inflate the value of Z(nS) relative to its neutral theory expectations. Larger than expected values of Z(nS) are observed in genetic samples from the yellow-ac-scute and Adh regions of Drosophila melanogaster.     

The Implicitome: A Resource for Rationalizing Gene-Disease Associations

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [https://www.force11.org/group/fairgroup] using nanopublications. An online tool (http://knowledge.bio) is available to explore established and potential gene-disease associations in the context of other biomedical relations.     

Nonrandom association of genetic markers in a sample of South African blacks

According to classical genetic theory, allelic genes at one locus are expected to segregate and be manifested independently of allelic genes at another locus. At the population level any significant deviation from this general hypothesis resulting from specific biologic and genetic effects can be recognized in the form of nonrandom associations between genetic markers. The present data, consisting of 24 genetic polymorphisms determined from a sample of 998 unselected and unrelated South African blacks, offers an opportunity to test whether or not any such nonrandom associations exist between the genetic markers. After appropriate statistical calculations on the population data, we found that 13 pairs of genetic polymorphisms demonstrate a nonrandom association (statistically significant). Because the results cannot be explained in terms of known biologic mechanisms, we conclude that the associations observed could be due to random statistical effects (repeated application of the chi-square test) and/or to real (as yet unknown) biologic phenomena in the population studied. This tentative conclusion can serve as a guideline for more specific investigations.     

Performance of MAX test and degree of dominance index in predicting the mode of inheritance

We evaluate power performance to detect the correct mode of inheritance in gene-disease associations of two different approaches: the MAX test and the degree of dominance index or h-index. The MAX test is a special case of the conditional independence tests that simultaneously test for association and select the most likely genetic model based on a three-dimensional normal distribution. The h-index is based on the philosophy of using orthogonal contrasts to infer the mode of inheritance quantitatively. A population genetic model is developed where the real mode of inheritance is known a priori and power performance can be accurately determined. The simulations showed that none of the two approaches generally outperforms the other, nor each of them provides a panacea to estimate efficiently the mode of inheritance in all parameter space. However, the simultaneous application of both approaches can provide insights in determining the underlying mode of inheritance.     

Sex differences in disease risk from reported genome-wide association study findings

Men and women differ in susceptibility to many diseases and in responses to treatment. Recent advances in genome-wide association studies (GWAS) provide a wealth of data for associating genetic profiles with disease risk; however, in general, these data have not been systematically probed for sex differences in gene-disease associations. Incorporating sex into the analysis of GWAS results can elucidate new relationships between single nucleotide polymorphisms (SNPs) and human disease. In this study, we performed a sex-differentiated analysis on significant SNPs from GWAS data of the seven common diseases studied by the Wellcome Trust Case Control Consortium. We employed and compared three methods: logistic regression, Woolf’s test of heterogeneity, and a novel statistical metric that we developed called permutation method to assess sex effects (PMASE). After correction for false discovery, PMASE finds SNPs that are significantly associated with disease in only one sex. These sexually dimorphic SNP-disease associations occur in Coronary Artery Disease and Crohn’s Disease. GWAS analyses that fail to consider sex-specific effects may miss discovering sexual dimorphism in SNP-disease associations that give new insights into differences in disease mechanism between men and women.     

Footprints of ancient‐balanced polymorphisms in genetic variation data from closely related species

When long‐lasting, balancing selection can lead to “trans‐species” polymorphisms that are shared by two or more species identical by descent. In such cases, the gene genealogy at the selected site clusters by allele instead of by species, and nearby neutral sites also have unusual genealogies because of linkage. While this scenario is expected to leave discernible footprints in genetic variation data, the specific patterns remain poorly characterized. Motivated by recent findings in primates, we focus on the case of a biallelic polymorphism under ancient balancing selection and derive approximations for summaries of the polymorphism data from two species. Specifically, we characterize the length of the segment that carries most of the footprints, the expected number of shared neutral single nucleotide polymorphisms (SNPs), and the patterns of allelic associations among them. We confirm the accuracy of our approximations by coalescent simulations. We further show that for humans and chimpanzees—more generally, for pairs of species with low genetic diversity levels—these patterns are highly unlikely to be generated by neutral recurrent mutations. We discuss the implications for the design and interpretation of genome scans for ancient balanced polymorphisms in primates and other taxa.     

Analysis of allelic association: estimation of the criteria power of the TDT

Analysis of allelic associations is an increasingly more widely used approach to fine mapping of genes of various diseases. To interpret the results correctly, it is necessary to estimate the power of the statistical test used. The principle of the analysis of associations and testing of hypothesis are described, and analytically obtained estimates of the power of the transmission disequilibrium test (TDT), one of the most popular methods of analysis of allelic associations, are presented. These estimates are applicable to arbitrary models of inheritance formulated in terms of relative genotypic risk. The proposed method is illustrated by analysis of the associations of idiopathic scoliosis and aggrecan gene alleles.     

Analysis of Allelic Association: Estimation of the Power of the TDT

Analysis of allelic associations is an increasingly more widely used approach to fine mapping of genes of various diseases. To interpret the results correctly, it is necessary to estimate the power of the statistical test used. The principle of the analysis of associations and testing of hypothesis are described, and analytically obtained estimates of the power of the transmission disequilibrium test (TDT), one of the most popular methods of analysis of allelic associations, are presented. These estimates are applicable to arbitrary models of inheritance formulated in terms of genotypic relative risk. The proposed method is illustrated by analysis of the associations of idiopathic scoliosis and aggrecan gene alleles.     

A critical appraisal of the scientific basis of commercial genomic profiles used to assess health risks and personalize health interventions

Predictive genomic profiling used to produce personalized nutrition and other lifestyle health recommendations is currently offered directly to consumers. By examining previous meta-analyses and HuGE reviews, we assessed the scientific evidence supporting the purported gene-disease associations for genes included in genomic profiles offered online. We identified seven companies that offer predictive genomic profiling. We searched PubMed for meta-analyses and HuGE reviews of studies of gene-disease associations published from 2000 through June 2007 in which the genotypes of people with a disease were compared with those of a healthy or general-population control group. The seven companies tested at least 69 different polymorphisms in 56 genes. Of the 56 genes tested, 24 (43%) were not reviewed in meta-analyses. For the remaining 32 genes, we found 260 meta-analyses that examined 160 unique polymorphism-disease associations, of which only 60 (38%) were found to be statistically significant. Even the 60 significant associations, which involved 29 different polymorphisms and 28 different diseases, were generally modest, with synthetic odds ratios ranging from 0.54 to 0.88 for protective variants and from 1.04 to 3.2 for risk variants. Furthermore, genes in cardiogenomic profiles were more frequently associated with noncardiovascular diseases than with cardiovascular diseases, and though two of the five genes of the osteogenomic profiles did show significant associations with disease, the associations were not with bone diseases. There is insufficient scientific evidence to conclude that genomic profiles are useful in measuring genetic risk for common diseases or in developing personalized diet and lifestyle recommendations for disease prevention.     

Selective neutrality and enzyme kinetics

This article appeals to a recent theory of enzyme evolution to show that the properties, neutral or adaptive, which characterize the observed allelic variation in natural populations can be inferred from the functional parameters, substrate specificity, and reaction rate. This study delineates the following relations between activity variables, and the forces—adaptive or neutral—determining allelic variation: (1) Enzymes with broad substrate specificity: The observed polymorphism is adaptive; mutations in this class of enzymes can result in increased fitness of the organism and hence be relevant for positive selection. (2) Enzymes with absolute substrate specificity and diffusion-controlled rates: Observed allelic variation will be absolutely neutral; mutations in this class of enzymes will be either deleterious or have no effect on fitness. (3) Enzymes with absolute or group specificity and nondiffusion-controlled rates: Observed variation will be partially neutral; mutants which are selectively neutral may become advantageous under an appropriate environmental condition or different genetic background. We illustrate each of the relations between kinetic properties and evolutionary states with examples drawn from enzymes whose evolutionary dynamics have been intensively studied. Received: 12 December 1996 / Accepted: 22 April 1997     

Identification of highly related references about gene-disease association

Background

Curation of gene-disease associations published in literature should be based on careful and frequent survey of the references that are highly related to specific gene-disease associations. Retrieval of the references is thus essential for timely and complete curation.

Results

We present a technique CRFref (Conclusive, Rich, and Focused References) that, given a gene-disease pair < g, d>, ranks high those biomedical references that are likely to provide conclusive, rich, and focused results about g and d. Such references are expected to be highly related to the association between g and d. CRFref ranks candidate references based on their scores. To estimate the score of a reference r, CRFref estimates and integrates three measures: degree of conclusiveness, degree of richness, and degree of focus of r with respect to < g, d>. To evaluate CRFref, experiments are conducted on over one hundred thousand references for over one thousand gene-disease pairs. Experimental results show that CRFref performs significantly better than several typical types of baselines in ranking high those references that expert curators select to develop the summaries for specific gene-disease associations.

Conclusion

CRFref is a good technique to rank high those references that are highly related to specific gene-disease associations. It can be incorporated into existing search engines to prioritize biomedical references for curators and researchers, as well as those text mining systems that aim at the study of gene-disease associations.     

Rapid vascular responses to anthrax lethal toxin in mice containing a segment of chromosome 11 from the CAST/Ei strain on a C57BL/6 genetic background

Host allelic variation controls the response to B. anthracis and the disease course of anthrax. Mouse strains with macrophages that are responsive to anthrax lethal toxin (LT) show resistance to infection while mouse strains with LT non-responsive macrophages succumb more readily. B6.CAST.11M mice have a region of chromosome 11 from the CAST/Ei strain (a LT responsive strain) introgressed onto a LT non-responsive C57BL/6J genetic background. Previously, B6.CAST.11M mice were found to exhibit a rapid inflammatory reaction to LT termed the early response phenotype (ERP), and displayed greater resistance to B. anthracis infection compared to C57BL/6J mice. Several ERP features (e.g., bloat, hypothermia, labored breathing, dilated pinnae vessels) suggested vascular involvement. To test this, Evan's blue was used to assess vessel leakage and intravital microscopy was used to monitor microvascular blood flow. Increased vascular leakage was observed in lungs of B6.CAST.11M mice compared to C57BL/6J mice 1 hour after systemic administration of LT. Capillary blood flow was reduced in the small intestine mesentery without concomitant leukocyte emigration following systemic or topical application of LT, the latter suggesting a localized tissue mechanism in this response. Since LT activates the Nlrp1b inflammasome in B6.CAST.11M mice, the roles of inflammasome products, IL-1β and IL-18, were examined. Topical application to the mesentery of IL-1β but not IL-18 revealed pronounced slowing of blood flow in B6.CAST.11M mice that was not present in C57BL/6J mice. A neutralizing anti-IL-1β antibody suppressed the slowing of blood flow induced by LT, indicating a role for IL-1β in the response. Besides allelic differences controlling Nlrp1b inflammasome activation by LT observed previously, evidence presented here suggests that an additional genetic determinant(s) could regulate the vascular response to IL-1β. These results demonstrate that vessel leakage and alterations to blood flow are part of the rapid response in mice resistant to B. anthracis infection.     

Rate of adaptive peak shifts with partial genetic robustness

How adaptive evolution occurs with individually deleterious but jointly beneficial mutations has been one of the major problems in population genetics theory. Adaptation in this case is commonly described as a population's escape from a local peak to a higher peak on Sewall Wright's fitness landscape. Recent molecular genetic and computational studies have suggested that genetic robustness can facilitate such peak shifts. If phenotypic expressions of new mutations are suppressed under genetic robustness, mutations that are otherwise deleterious can accumulate in the population as neutral variants. When the robustness is perturbed by an environmental change or a major mutation, these variants become exposed to natural selection. It is argued that this process promotes adaptation because allelic combinations enriched under genetic robustness can then be positively selected. Here, I propose simple two- and three-locus models of adaptation with partial genetic robustness as suggested by recent studies. The waiting time until the fixation of an adaptive haplotype was observed in stochastic simulations and compared to the expectation without robustness. It is shown that peak shifts can be delayed or accelerated depending on the conditions of genetic robustness. The evolutionary significance of these processes is discussed.     

The melanocortin 1 receptor (MC1R): more than just red hair

The melanocortin 1 receptor, a seven pass transmembrane G protein coupled receptor, is a key control point in melanogenesis. Loss-of-function mutations at the MC1R are associated with a switch from eumelanin to phaeomelanin production, resulting in a red or yellow coat colour. Activating mutations, in animals at least, lead to enhanced eumelanin synthesis. In man, a number of loss-of-function mutations in the MC1R have been described. The majority of red-heads (red-haired persons) are compound heterozygotes or homozygotes for up to five frequent loss-of-function mutations. A minority of redheads are, however, only heterozygote. The MC1R is, therefore, a major determinant of sun sensitivity and a genetic risk factor for melanoma and non-melanoma skin cancer. Recent work suggests that the MC1R also shows a clear heterozygote effect on skin type, with up to 30% of the population harbouring loss-of-function mutations. Activating mutations of the MC1R in man have not been described. The MC1R is particularly informative and a tractable gene for studies of human evolution and migration. In particular, study of the MC1R may provide insights into the lightening of skin colour observed in most European populations. The world wide pattern of MC1R diversity is compatible with functional constraint operating in Africa, whereas the greater allelic diversity seen in non-African populations is consistent with neutral predictions rather than selection. Whether this conclusion is as a result of weakness in the statistical testing procedures applied, or whether it will be seen in other pigment genes will be of great interest for studies of human skin colour evolution.     

An expectation-maximization algorithm for the analysis of allelic expression imbalance

A significant proportion of the variation between individuals in gene expression levels is genetic, and it is likely that these differences correlate with phenotypic differences or with risk of disease. Cis-acting polymorphisms are important in determining interindividual differences in gene expression that lead to allelic expression imbalance, which is the unequal expression of homologous alleles in individuals heterozygous for such a polymorphism. This expression imbalance can be detected using a transcribed polymorphism, and, once it is established, the next step is to identify the polymorphisms that are responsible for or predictive of allelic expression levels. We present an expectation-maximization algorithm for such analyses, providing a formal statistical framework to test whether a candidate polymorphism is associated with allelic expression differences.     

The Ischemic Stroke Genetics Study (ISGS) Protocol

Background  

The molecular basis for the genetic risk of ischemic stroke is likely to be multigenic and influenced by environmental factors. Several small case-control studies have suggested associations between ischemic stroke and polymorphisms of genes that code for coagulation cascade proteins and platelet receptors. Our aim is to investigate potential associations between hemostatic gene polymorphisms and ischemic stroke, with particular emphasis on detailed characterization of the phenotype.     

Calpastatin Gene (CAST) Is Not Associated with Late Onset Sporadic Parkinson’s Disease in the Han Chinese Population

Recent studies point to an association between the late-onset sporadic Parkinson’s disease (PD) and single nucleotide polymorphisms (SNPs) rs1559085 and rs27852 in Ca²⁺-dependent protease calpain inhibitor calpastatin (CAST) gene. This finding is of interest since loss of CAST activity could result in over activated calpain, potentially leading to Ca²⁺ dysregulation and loss of substantia nigra neurons in PD. We explored the association between CAST SNPs and late-onset sporadic PD in the Han Chinese population. The study included 615 evaluable patients (363 male, 252 female) with PD and 636 neurologically healthy controls (380 male, 256 female) matched for age, gender, ethnicity, and area of residence. PD cases were identified from the PD cohort of the Chinese National Consortium on Neurodegenerative Diseases (www.chinapd.cn). A total of 24 tag-SNPs were genotyped capturing 95% of the genetic variation across the CAST gene. There was no association found between any of the polymorphisms and PD in all models tested (co-dominant, dominant-effect and recessive-effect). Similarly, none of the common haplotypes was associated with a risk for PD. Our data do not support a significant association between the CAST gene polymorphisms and late onset sporadic PD in the Han Chinese population.     

Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases

Background

Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult.

Principal Findings

We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell.

Conclusions

For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases.

Availability

The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download.