Coevolution of pathogens and cultural practices: a new look at behavioral heterogeneity in epidemics

The effect of heterogeneity within populations on the spread of infectious diseases has been a recent focus of research. Such heterogeneity may be, for example, spatial, temporal or behavioral in form. Generally, models that include population subdivision have assumed that individuals are permanently assigned to given behavioral states represented by the subpopulations. We consider a simple epidemic model in which a behavioral trait affects disease transmission, and this trait may be transferred among hosts as a consequence of social interaction. This creates a situation where the frequencies of different behavioral traits and disease states as well as their associations may change over time. We consider the impact of the culturally transmitted trait on the criterion for initial spread of the disease. We also explore the evolution of cultural traits in response to pathogen dynamics and show some conditions under which behavioral traits that reduce transmission evolve. We find that behaviors increasing the risk of infection can also evolve when they are inherently favored or when there is sufficient clustering of contacts between like behaviors.     

Complex inheritance and localizing disease genes.

Most methods for localizing genes underlying complex traits work under the implicit or explicit assumption of a single disease gene with the possible exception of heterogeneity, that is, different disease genes in different families. We discuss current single-locus and multi-locus methods. Novel approaches are proposed that take into account all marker loci over the genome. A simple example is given for an unconventional statistic, i.e. the mean of allele sharing over all markers on a chromosome.     

Systematic analysis and comparison of nucleotide-binding site disease resistance genes in maize

Nucleotide-binding site (NBS) disease resistance genes play an integral role in defending plants from a range of pathogens and insect pests. Consequently, a number of recent studies have focused on NBS-encoding genes in molecular disease resistance breeding programmes for several important plant species. Little information, however, has been reported with an emphasis on systematic analysis and a comparison of NBS-encoding genes in maize. In the present study, 109 NBS-encoding genes were identified based on the complete genome sequence of maize (Zea mays cv. B73), classified as four different subgroups, and then characterized according to chromosomal locations, gene duplications, structural diversity and conserved protein motifs. Subsequent phylogenetic comparisons indicated that several maize NBS-encoding genes possessed high similarity to function-known NBS-encoding genes, and revealed the evolutionary relationships of NBS-encoding genes in maize comparede to those in other model plants. Analyses of the physical locations and duplications of NBS-encoding genes showed that gene duplication events of disease resistance genes were lower in maize than in other model plants, which may have led to an increase in the functional diversity of the maize NBS-encoding genes. Various expression patterns of maize NBS-encoding genes in different tissues were observed using an expressed-sequence tags database and, alternatively, after southern leaf blight infection or the application of exogenous salicylic acid. The results reported in the present study contribute to an improved understanding of the NBS-encoding gene family in maize.     

Genetic dissection of the relationship between carbon metabolism and early growth in maize,with emphasis on key-enzyme loci

The determinism of carbon metabolism traits during early growth in maize has been investigated using a marker-based quantitative genetics approach. In addition to growth traits, concentration of carbohydrates and activity of four key enzymes of their metabolism (sucrose phosphate synthase, ADP-glucose pyrophosphorylase, invertases and sucrose synthase) have been measured in leaves of individuals of a recombinant inbred line population. Using more than 100 RFLP markers, quantitative trait loci (QTLs) were mapped for each biochemical and developmental trait. Causal relationships, suggested by previous physiological studies, were reinforced by common locations of QTLs for different traits. Thus, the strong correlation between growth rate and invertase activity, which may reflect sink organ strength, could be explained to a large extent by a single region of chromosome 8. Moreover, some of the structural genes of the enzymes mapped to regions with QTLs affecting the activity of the encoded enzyme and/or concentration of its product, and sometimes growth traits. These results emphasize the possible role of the polymorphism of key-enzyme genes in physiological processes, and hence in maize growth.     

Autosomal dominant retinitis pigmentosa: linkage to rhodopsin and evidence for genetic heterogeneity

Retinitis pigmentosa (RP) is the most prevalent human retinopathy of genetic origin. Chromosomal locations for X-linked RP and autosomal dominant RP genes have recently been established. Multipoint analyses with ADRP and seven markers on the long arm of chromosome 3 demonstrate that the gene for rhodopsin, the pigment of the rod photoreceptors, cosegregates with the disease locus with a maximum lod score of approximately 19, implicating rhodopsin as a causative gene. Recent studies have indicated the presence of a point mutation at codon 23 in exon 1 of rhodopsin which results in the substitution of histidine for the highly conserved amino acid proline, suggesting that this mutation is a cause of rhodopsin-linked ADRP. This mutation is not present in the Irish pedigree in which ADRP has been mapped close to rhodopsin. Another mutation in the rhodopsin gene or in a gene closely linked to rhodopsin may be involved. Moreover, the gene in a second ADRP pedigree, with Type II late onset ADRP, does not segregate with chromosome 3q markers, indicating that nonallelic as well as perhaps allelic genetic heterogeneity exists in the autosomal dominant form of this disease.     

Fine mapping of autistic disorder to chromosome 15q11-q13 by use of phenotypic subtypes

Autistic disorder (AutD) is a complex genetic disease. Available evidence suggests that several genes contribute to the underlying genetic risk for the development of AutD. However, both etiologic heterogeneity and genetic heterogeneity confound the discovery of AutD-susceptibility genes. Chromosome 15q11-q13 has been identified as a strong candidate region on the basis of both the frequent occurrence of chromosomal abnormalities in that region and numerous suggestive linkage and association findings. Ordered-subset analysis (OSA) is a novel statistical method to identify a homogeneous subset of families that contribute to overall linkage at a given chromosomal location and thus to potentially help in the fine mapping and localization of the susceptibility gene within a chromosomal area. For the present analysis, a factor that represents insistence on sameness (IS)--derived from a principal-component factor analysis using data on 221 patients with AutD from the repetitive behaviors/stereotyped patterns domain in the Autism Diagnostic Interview-Revised--was used as a covariate in OSA. Analysis of families sharing high scores on the IS factor increased linkage evidence for the 15q11-q13 region, at the GABRB3 locus, from a LOD score of 1.45 to a LOD score of 4.71. These results narrow our region of interest on chromosome 15 to an area surrounding the gamma-aminobutyric acid-receptor subunit genes, in AutD, and support the hypothesis that the analysis of phenotypic homogeneous subtypes may be a powerful tool for the mapping of disease-susceptibility genes in complex traits.     

Comparative Analyses of QTLs Influencing Obesity and Metabolic Phenotypes in Pigs and Humans

The pig is a well-known animal model used to investigate genetic and mechanistic aspects of human disease biology. They are particularly useful in the context of obesity and metabolic diseases because other widely used models (e.g. mice) do not completely recapitulate key pathophysiological features associated with these diseases in humans. Therefore, we established a F2 pig resource population (n = 564) designed to elucidate the genetics underlying obesity and metabolic phenotypes. Segregation of obesity traits was ensured by using breeds highly divergent with respect to obesity traits in the parental generation. Several obesity and metabolic phenotypes were recorded (n = 35) from birth to slaughter (242 ± 48 days), including body composition determined at about two months of age (63 ± 10 days) via dual-energy x-ray absorptiometry (DXA) scanning. All pigs were genotyped using Illumina Porcine 60k SNP Beadchip and a combined linkage disequilibrium-linkage analysis was used to identify genome-wide significant associations for collected phenotypes. We identified 229 QTLs which associated with adiposity- and metabolic phenotypes at genome-wide significant levels. Subsequently comparative analyses were performed to identify the extent of overlap between previously identified QTLs in both humans and pigs. The combined analysis of a large number of obesity phenotypes has provided insight in the genetic architecture of the molecular mechanisms underlying these traits indicating that QTLs underlying similar phenotypes are clustered in the genome. Our analyses have further confirmed that genetic heterogeneity is an inherent characteristic of obesity traits most likely caused by segregation or fixation of different variants of the individual components belonging to cellular pathways in different populations. Several important genes previously associated to obesity in human studies, along with novel genes were identified. Altogether, this study provides novel insight that may further the current understanding of the molecular mechanisms underlying human obesity.     

Comparative mapping reveals similar linkage of functional genes to QTL of yield-related traits between <Emphasis Type="Italic">Brassica napus</Emphasis> and <Emphasis Type="Italic">Oryza sativa</Emphasis>

Oryza sativa and Brassica napus-two important crops for food and oil, respectively-share high seed yield as a common breeding goal. As a model plant, O. sativa genomics have been intensively investigated and its agronomic traits have been advanced. In the present study, we used the available information on O. sativa to conduct comparative mapping between O. sativa and B. napus, with the aim of advancing research on seed-yield and yield-related traits in B. napus. Firstly, functional markers (from 55 differentially expressed genes between a hybrid and its parents) were used to detect B. napus genes that co-localized with yield-related traits in an F(2:3) population. Referring to publicly available sequences of 55 B. napus genes, 53 homologous O. sativa genes were subsequently detected by screening, and their chromosomal locations were determined using silico mapping. Comparative location of yield-related QTL between the two species showed that a total of 37 O. sativa and B. napus homologues were located in similar yield-related QTL between species. Our results indicate that homologous genes between O. sativa and B. napus may have consistent function and control similar traits, which may be helpful for agronomic gene characterization in B. napus based on what is known in O. sativa.     

Exploiting interactions among polymorphisms contributing to complex disease traits with boosted generative modeling.

Although there has been great success in identifying disease genes for simple, monogenic Mendelian traits, deciphering the genetic mechanisms involved in complex diseases remains challenging. One major approach is to identify configurations of interacting factors such as single nucleotide polymorphisms (SNPs) that confer susceptibility to disease. Traditional methods, such as the multiple dimensional reduction method and the combinatorial partitioning method, provide good tools to decipher such interactions amid a disease population with a single genetic cause. However, these traditional methods have not managed to resolve the issue of genetic heterogeneity, which is believed to be a very common phenomenon in complex diseases. There is rarely prior knowledge of the genetic heterogeneity of a disease, and traditional methods based on estimation over the entire population are unlikely to succeed in the presence of heterogeneity. We present a novel Boosted Generative Modeling (BGM) approach for structure-model the interactions leading to diseases in the context of genetic heterogeneity. Our BGM method bridges the ensemble and generative modeling approaches to genetic association studies under a case-control design. Generative modeling is employed to model the interaction network configuration and the causal relationships, while boosting is used to address the genetic heterogeneity problem. We perform our method on simulation data of complex diseases. The results indicate that our method is capable of modeling the structure of interaction networks among disease-susceptible loci and of addressing genetic heterogeneity issues where the traditional methods, such as multiple dimensional reduction method, fail to apply. Our BGM method provides an exploratory tool that identifies the variables (e.g., disease-susceptible loci) that are likely to correlate and contribute to the disease.     

Use of multivariate linkage analysis for dissection of a complex cognitive trait

Replication of linkage results for complex traits has been exceedingly difficult, owing in part to the inability to measure the precise underlying phenotype, small sample sizes, genetic heterogeneity, and statistical methods employed in analysis. Often, in any particular study, multiple correlated traits have been collected, yet these have been analyzed independently or, at most, in bivariate analyses. Theoretical arguments suggest that full multivariate analysis of all available traits should offer more power to detect linkage; however, this has not yet been evaluated on a genomewide scale. Here, we conduct multivariate genomewide analyses of quantitative-trait loci that influence reading- and language-related measures in families affected with developmental dyslexia. The results of these analyses are substantially clearer than those of previous univariate analyses of the same data set, helping to resolve a number of key issues. These outcomes highlight the relevance of multivariate analysis for complex disorders for dissection of linkage results in correlated traits. The approach employed here may aid positional cloning of susceptibility genes in a wide spectrum of complex traits.     

Temperature-specific outcomes of cytoplasmic-nuclear interactions on egg-to-adult development time in seed beetles

The integration of the mitochondrial and nuclear genomes coordinates cellular energy production and is fundamental to life among eukaryotes. Therefore, there is potential for strong selection to shape the interactions between the two genomes. Several studies have now demonstrated that epistatic interactions between cytoplasmic and nuclear genes for fitness can occur both at a "within" and "across" population level. Genotype-by-environment interactions are common for traits that are encoded by nuclear genes, but the effects of environmental heterogeneity on traits that are partly encoded by cytoplasmic genes have received little attention despite the fact that there are reasons to believe that phenotypic effects of cytoplasmic genetic variation may often be environment specific. Consequently, the importance of environmental heterogeneity to the outcomes of cyto-nuclear fitness interactions and to the maintenance of mitochondrial polymorphism is unclear. Here, we assess the influence of temperature on cyto-nuclear effects on egg-to-adult development time in seed beetles (Callosobruchus maculatus). We employed an "across-population" design, sourcing beetles from five distinct populations and using backcrossing to create orthogonal combinations of distinct introgression lines, fixed for their cytoplasmic and nuclear lineages. We then assayed development times at two different temperatures and found sizeable cyto-nuclear effects in general, as well as temperature- and block-specific cyto-nuclear effects. These results demonstrate that environmental factors such as temperature do exert selection on cytoplasmic genes by favoring specific cyto-nuclear genetic combinations, and are consistent with the suggestion that complex genotype-by-environment interactions may promote the maintenance of polymorphism in mitochondrial genes.     

Assessing familial aggregation of age at onset, by using estimating equations, with application to breast cancer.

In genetic research of chronic diseases, age-at-onset outcomes within families are often correlated. The nature of correlation of age-at-onset outcomes is indicative of common genetic and/or shared environmental risk factors among family members. Understanding patterns of such correlation may shed light on the disease etiology and, hence, is an important step to take prior to further searching for the responsible genes via segregation and linkage studies. Age-at-onset outcomes are different from those familiar quantitative or qualitative traits for which many statistical methods have been developed. In comparison with the quantitative traits, age-at-onset outcomes are often censored, i.e., instead of actual age-at-onset outcomes, only the current ages or ages at death are observed. They are also different from qualitative traits because of their continuity. Because of the complexity of correlated censored outcomes, few methods have yet been developed. A traditional approach is to impose a parametric joint distribution for the correlated age-at-onset outcomes, which has been criticized for requiring a stringent assumption about the entire distribution of age at onset. The purpose of this paper is to describe a method for assessing familial aggregation of correlated age-at-onset outcomes semiparametrically, by use of estimating equations. This method does not require any parametric assumption for modeling the age at onset. The estimates of parameters, including those quantifying the correlation within families, are consistent and have an asymptotic normal distribution that can be used to make inferences. To illustrate this new method, we analyzed two age-at-onset data sets that were obtained from studies conducted in the States of Washington and Hawaii, with the objective of quantifying the familial aggregations of age at onset of breast cancer.     

Global analysis of genes involved in freshwater adaptation in threespine sticklebacks (Gasterosteus aculeatus)

Examples of parallel evolution of phenotypic traits have been repeatedly demonstrated in threespine sticklebacks (Gasterosteus aculeatus) across their global distribution. Using these as a model, we performed a targeted genome scan--focusing on physiologically important genes potentially related to freshwater adaptation--to identify genetic signatures of parallel physiological evolution on a global scale. To this end, 50 microsatellite loci, including 26 loci within or close to (<6 kb) physiologically important genes, were screened in paired marine and freshwater populations from six locations across the Northern Hemisphere. Signatures of directional selection were detected in 24 loci, including 17 physiologically important genes, in at least one location. Although no loci showed consistent signatures of selection in all divergent population pairs, several outliers were common in multiple locations. In particular, seven physiologically important genes, as well as reference ectodysplasin gene (EDA), showed signatures of selection in three or more locations. Hence, although these results give some evidence for consistent parallel molecular evolution in response to freshwater colonization, they suggest that different evolutionary pathways may underlie physiological adaptation to freshwater habitats within the global distribution of the threespine stickleback.     

Impact of the diagnosis definition on linkage detection

Previous genome scan linkage analyses of the disease Kofendrerd Personality Disorder (KPD) with microsatellites led to detect some regions on chromosomes 1, 3, 5, and 9 that were identical for the three populations AI, KA, and DA but with large differences in significance levels. These differences in results may be explained by the different diagnosis definitions depending on the presence/absence of 12 traits that were used in the 3 populations AI, KA, and DA. Heterogeneity of linkage was thus investigated here according to the absence/presence of each of the 12 traits in the 3 populations. For this purpose, two methods, the triangle test statistic and the predivided sample test were applied to search for genetic heterogeneity. Three regions with a strong heterogeneity of linkage were detected: the region on chromosome 1 according to the presence/absence of the traits a and b, the region on chromosome 3 for the trait b, and the region on chromosome 9 for the traits k and l. These 3 regions were the same as those detected by linkage analyses. No novel region was detected by the heterogeneity tests. Concerning chromosome 1, linkage analyses showed a much stronger evidence of linkage for traits a and b and for a combination of these traits than for KPD. Moreover, there was no indication of linkage to any of the other traits used to define the diagnosis of KPD. A genetic factor located on the chromosome 1 may have been detected here which would be involved specifically in traits a and b or in a combination of these traits.     

Dissecting trait heterogeneity: a comparison of three clustering methods applied to genotypic data

Background  

Trait heterogeneity, which exists when a trait has been defined with insufficient specificity such that it is actually two or more distinct traits, has been implicated as a confounding factor in traditional statistical genetics of complex human disease. In the absence of detailed phenotypic data collected consistently in combination with genetic data, unsupervised computational methodologies offer the potential for discovering underlying trait heterogeneity. The performance of three such methods – Bayesian Classification, Hypergraph-Based Clustering, and Fuzzy k-Modes Clustering – appropriate for categorical data were compared. Also tested was the ability of these methods to detect trait heterogeneity in the presence of locus heterogeneity and/or gene-gene interaction, which are two other complicating factors in discovering genetic models of complex human disease. To determine the efficacy of applying the Bayesian Classification method to real data, the reliability of its internal clustering metrics at finding good clusterings was evaluated using permutation testing.     

Recent advances in the genetic investigation of osteoarthritis

Osteoarthritis (OA) demonstrates considerable clinical heterogeneity, generating heated debate over whether OA is a single disease or a complex mix of disparate diseases and concerning which tissues are principally involved in disease initiation and progression. Epidemiological studies have demonstrated a major genetic component to OA risk. However, these studies have also revealed differences in risk between males and females and for disease at different skeletal sites. This observation has resulted in the concept of genes for specific sites rather than a generalised OA phenotype. Recent breakthroughs have shed considerable light on the nature of OA genetic susceptibility. Many candidate genes have been confirmed, such as the interleukin-1 gene cluster and the oestrogen alpha-receptor gene ESR1. Genome-wide linkage scans have revealed several regions harbouring novel loci, some of which are beginning to yield their genes.     

Intragenomic Heterogeneity of 16S rRNA Genes Causes Overestimation of Prokaryotic Diversity

Ever since Carl Woese introduced the use of 16S rRNA genes for determining the phylogenetic relationships of prokaryotes, this method has been regarded as the “gold standard” in both microbial phylogeny and ecology studies. However, intragenomic heterogeneity within 16S rRNA genes has been reported in many investigations and is believed to bias the estimation of prokaryotic diversity. In the current study, 2,013 completely sequenced genomes of bacteria and archaea were analyzed and intragenomic heterogeneity was found in 952 genomes (585 species), with 87.5% of the divergence detected being below the 1% level. In particular, some extremophiles (thermophiles and halophiles) were found to harbor highly divergent 16S rRNA genes. Overestimation caused by 16S rRNA gene intragenomic heterogeneity was evaluated at different levels using the full-length and partial 16S rRNA genes usually chosen as targets for pyrosequencing. The result indicates that, at the unique level, full-length 16S rRNA genes can produce an overestimation of as much as 123.7%, while at the 3% level, an overestimation of 12.9% for the V6 region may be introduced. Further analysis showed that intragenomic heterogeneity tends to concentrate in specific positions, with the V1 and V6 regions suffering the most intragenomic heterogeneity and the V4 and V5 regions suffering the least intragenomic heterogeneity in bacteria. This is the most up-to-date overview of the diversity of 16S rRNA genes within prokaryotic genomes. It not only provides general guidance on how much overestimation can be introduced when applying 16S rRNA gene-based methods, due to its intragenomic heterogeneity, but also recommends that, for bacteria, this overestimation be minimized using primers targeting the V4 and V5 regions.     

Dermatoglyphics in patients with hypothyreosis

About 15% of all females and 3% of all males suffers from hypothyreosis. The thyroid disease is the most frequent cause of hypothyreosis, and among people in Croatia who are suffering from that disease 90% have been affected by its autoimmune form. The thyroid diseases are supposed to be caused by the influence of various genetic and external factors and some forms of genetic influences have not yet been studied. Analysis of digito-palmar dermatoglyphics has been used in the research of the role of genetic predisposition in many various diseases. We have analyzed correlation of qualitative and quantitative traits between the group of 50 females suffering from hypothyreosis and a control group of 100 phenotypically healthy females. Quantitative statistical analysis using t-test has indicated only few significantly different variables, while the discriminant analysis has shown 76.9% correctly classified samples. The factor analysis has shown a high percentage of total variance within patients suffering from hypothyreosis, as well as the different structure of individual factors. Qualitative analysis has shown the heterogeneity between the two examined groups. The results of the research have proved that the qualitative characteristics are more unstable than the quantitative ones and they have also shown the instability of genes taking part in hypothyreosis development implying genetic predisposition of the disease.     

Linkage analysis of quantitative trait loci in the presence of heterogeneity

Variance component modeling for linkage analysis of quantitative traits is a powerful tool for detecting and locating genes affecting a trait of interest, but the presence of genetic heterogeneity will decrease the power of a linkage study and may even give biased estimates of the location of the quantitative trait loci. Many complex diseases are believed to be influenced by multiple genes and therefore genetic heterogeneity is likely to be present for many real applications of linkage analysis. We consider a mixture of multivariate normals to model locus heterogeneity by allowing only a proportion of the sampled pedigrees to segregate trait-influencing allele(s) at a specific locus. However, for mixtures of normals the classical asymptotic distribution theory of the maximum likelihood estimates does not hold, so tests of linkage and/or heterogeneity are evaluated using resampling methods. It is shown that allowing for genetic heterogeneity leads to an increase in power to detect linkage. This increase is more prominent when the genetic effect of the locus is small or when the percentage of pedigrees not segregating trait-influencing allele(s) at the locus is high.