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PubMed2005

Genetic linkage map of medaka with polymerase chain reaction length polymorphisms.

With recent improvements in genetic and genomic infrastructures, great interest has been taken in genetic dissection of multi-factorial traits. A genetic map consisting of markers that are highly polymorphic and rapidly genotyped is essential for the genetic mapping of such a complex trait. Medaka, Oryzias latipes, is an excellent model system for genetic studies. To promote genetic mapping of complex traits in medaka we developed the first high-throughput and genome-wide marker set in the organism by using its genomic information and the bioinformatic techniques. We tested 545 primer pairs and obtained 265 co-dominant markers between two inbred strains, HNI and Hd-rR. Our map, consisting of 231 uniquely mapped markers, covers 1257.3 centimorgan (cM) of the medaka genome with an average interval distance of 5.4 cM. Furthermore, the newly designed markers were examined for polymorphisms among six medaka inbred strains: HNI, Hd-rR and four additional strains. Most of our markers are simple sequence length polymorphisms (SSLPs) and can be rapidly genotyped by an automated system under a single polymerase chain reaction (PCR) condition. Together with the genotyping data of six medaka inbred strains, our new marker set provides a powerful tool for genome-wide analysis of complex biological phenomena found widely in medaka populations.

Animals↗
PubMed2002

Not all isolates are equal: linkage disequilibrium analysis on Xq13.3 reveals different patterns in Sardinian sub-populations.

Recent studies indicate that, whereas the Sardinian population as a whole is comparable to outbred populations for linkage disequilibrium (LD) mapping of common variants, LD in Sardinian sub-isolates is more extended, making these populations particularly suitable for this approach. To evaluate the extent of LD between microsatellite markers, we compared different sub-populations within Sardinia selected on the basis of their geographical position and isolation: two small isolated villages (Talana, Urzulei), two larger but remote areas (Ogliastra, Nuoro province) and a cohort of samples representing the wider Sardinian population. LD analysis was carried out by using six microsatellite markers that are located on Xq13.3 and that have been extensively studied in different populations. We found different extents and patterns of LD in the sub-population samples depending on their degree of isolation and demographic history. All LD measurements and haplotype analyses indicate that there is a decreasing trend from Talana (the most inbred population, LD up to 9.5-11.5 Mb) to the more outbred Sardinian population (LD only for intervals <2 Mb). In one village (Talana), five haplotype classes accounting for 80% of the entire sample perfectly matched five Ogliastra clusters, supporting the origin of the village from the Ogliastra genetic pool. In contrast, the other village (Urzulei) showed a different pattern of haplotypes with a closer relationship to the Nuoro region sub-population. LD analyses therefore show that even neighbouring isolate villages may differ in their genetic background. Here, we highlight the importance of selecting appropriate populations and/or sub-populations for the analysis of complex traits. Isolated sub-populations showing different extents of LD can provide a powerful method for mapping complex traits by LD scanning at relatively low marker density.

Chromosome Mapping↗
PubMed1997

Behavioral genetics '97: ASHG statement. Recent developments in human behavioral genetics: past accomplishments and future directions.

The field of behavioral genetics has enormous potential to uncover both genetic and environmental influences on normal and deviant behavior. Behavioral-genetic methods are based on a solid foundation of theories and methods that successfully have delineated components of complex traits in plants and animals. New resources are now available to dissect the genetic component of these complex traits. As specific genes are identified, we can begin to explore how these interact with environmental factors in development. How we interpret such findings, how we ask new questions, how we celebrate the knowledge, and how we use or misuse this knowledge are all important considerations. These issues are pervasive in all areas of human research, and they are especially salient in human behavioral genetics.

Adoption↗
PubMed2001

A train of thoughts on gene mapping.

Complex traits, by definition, are the pheonotypic outcome from multiple interacting genes. The traditional analysis of association studies on complex traits is to test one locus at a time, but a better approach is to analyze all markers simultaneously. We previously proposed a two-stage approach, first selecting the influential markers and then modeling main and interaction effects of these markers. Here we introduce alternative approaches to marker selection and discuss issues regarding analytical tools for disease gene mapping, marker selection, and statistical modeling.

Analysis of Variance↗
PubMed2026

Genetic architectures of brain-related traits are shaped by strong selective constraints.

Genome-wide association studies (GWAS) have identified hundreds of significant loci for psychiatric disorders, yet the strength of these associations remains modest compared to other human complex traits with similar numbers of hits. Whether this pattern reflects statistical artifacts or real biological differences - and, if the latter, what underlies it - remains unclear. In addition to psychiatric disorders, we find that other traits with functional enrichment in the central nervous system (CNS), whether binary or quantitative, also share similar genetic architectures, characterized by GWAS hits of limited statistical significance and generally higher allele frequencies. To robustly compare traits that differ in GWAS statistical power, we demonstrate how binarizing a quantitative trait reduces power. This loss of power can be replicated by a matched "effective sample size" on the liability scale. After matching "effective sample sizes", we show that CNS-enriched traits have large mutational target sizes, with contributing variants and genes experiencing stronger selection than those for other traits. Our findings reveal heterogeneity among diseases and provide insights into traits that more effectively capture fitness-relevant processes. More broadly, our results suggest that the genetic architectures of complex traits are shaped by the tissues through which these traits are mediated.

Journal Article↗
PubMed2004

Genetic approaches to identify disease genes for birth defects with cleft lip/palate as a model.

BACKGROUND: Understanding the etiology of birth defects is an important step toward developing improved treatment and preventive strategies. Most birth defects have an underlying genetic basis, ranging from single genes playing dominant or recessive roles in Mendelian disorders to a mixture of contributions from multiple genes and environmental triggers in complex traits. The purpose of this article is to provide an overview of genetic approaches to identifying disease genes for genetically complex birth defects. METHODS: A review of the literature describing successes and limitations for identifying disease genes for complex traits was conducted. RESULTS: Cleft lip and cleft palate are common congenital anomalies with significant medical, psychological, social, and economic ramifications. The Online Mendelian Inheritance in Man catalog (OMIM; http://www3.ncbi.nlm.nih.gov/Omim) lists more than 400 single-gene causes of clefts of the lip and/or palate. Genetic causes of clefting also include chromosomal rearrangements, genetic susceptibility to teratogenic exposures, and complex genetic contributions of multiple genes. CONCLUSIONS: Genetic causes of birth defects can be identified using an increasingly powerful combination of careful sample collection, molecular analytic methods, and statistical evaluations. We will describe a range of approaches to search for genetic factors of birth defects and use our own work with cleft lip and palate as a model.

Chromosome Aberrations↗
PubMed2005

Genetic analysis of complex cardiovascular traits in the spontaneously hypertensive rat.

Identification of the genetic determinants of common diseases is a major challenge for current biomedical research. Combining linkage analyses of essentially monogenic cis-regulated expression phenotypes with oligogenic intermediate physiological phenotypes represents a promising approach for identification of quantitative trait loci at the molecular level. In the present review, a genetic analysis of cardiovascular phenotypes studied at several levels of complexity in rat recombinant inbred strains is described.

Animals↗
PubMed2003

Segregation analysis comparing liability and quantitative trait models for hypertension using the Genetic Analysis Workshop 13 simulated data.

Discrete (qualitative) data segregation analysis may be performed assuming the liability model, which involves an underlying normally distributed quantitative phenotype. The appropriateness of the liability model for complex traits is unclear. The Genetic Analysis Workshop 13 simulated data provides measures on systolic blood pressure, a highly complex trait, which may be dichotomized into a discrete trait (hypertension). We perform segregation analysis under the liability model of hypertensive status as a qualitative trait and compare this with results using systolic blood pressure as a quantitative trait (without prior knowledge at that stage of the true underlying simulation model) using 1050 pedigrees ascertained from four replicates on the basis of at least one affected member. Both analyses identify models with major genes and polygenic components to explain the family aggregation of systolic blood pressure. Neither of the methods estimates the true parameters well (as the true model is considerably more complicated than those considered for the analysis), but both identified the most complicated model evaluated as the preferred model. Segregation analysis of complex diseases using relatively simple models is unlikely to provide accurate parameter estimates but is able to indicate major gene and/or polygenic components in familial aggregation of complex diseases.

Adult↗
PubMed2003

Locomotor activity: a complex behavioural trait to unravel.

Locomotor activity in Drosophila, as in other organisms, is an important trait since it is at the basis of almost all behaviours. Indeed, the locomotor centre is implicated in all complex behaviours consisting of a change in the position of the animal with respect to its environment. Despite its importance, locomotor activity itself has received sparse attention for the following two reasons: first, until recently, the study of locomotor activity has lacked a well automated and standardised paradigm which is necessary for a detailed description. Second, locomotor activity is complicated by many factors (genetic, feeding, temperature), and as such is rather difficult to study. With recent technological developments, locomotor activity is now more accessible to automated paradigms. These have permitted us to reveal that locomotor activity is a very complex and rich behaviour that follows strict rules, harbours an organised (fractal-like) structure, and consequently might adhere to highly organised neurophysiological processes. Undoubtedly, locomotor activity has now reached a scientific maturity that allows it to be studied with the panoply of neuroethological approaches, in particular genetic, to unravel its mechanisms and neural circuitry. Consequently, we propose that locomotor activity can now represent a relevant biomarker to study various model diseases such as addiction, Parkinson, Alzheimer, Huntington, and diabetes.

Journal Article↗
PubMed2004

The genetic architecture of quantitative traits: lessons from Drosophila.

Understanding the genetic architecture of quantitative traits begins with identifying the genes regulating these traits, mapping the subset of genetically varying quantitative trait loci (QTLs) in natural populations, and pinpointing the molecular polymorphisms defining QTL alleles. Studies in Drosophila have revealed large numbers of pleiotropic genes that interact epistatically to regulate quantitative traits, and large numbers of QTLs with sex-, environment- and genotype-specific effects. Multiple molecular polymorphisms in regulatory regions of candidate genes are often associated with variation for complex traits. These observations offer valuable lessons for understanding the genetic basis of variation for complex traits in other organisms, including humans.

Animals↗
PubMed2003

A whole genome screen for linkage in Turkish multiple sclerosis.

Factors exerting recessive effects on susceptibility to complex traits are expected to be over-represented in communities having a higher frequency of consanguineous marriage. Multiple sclerosis, a typical complex trait, is relatively common in Turkey where cultural factors also determine a high rate of consanguineous marriage. Previous genetic studies of multiple sclerosis in Turkey have been confined to the search for associations with candidate genes. In order to exploit the special genetic features of the Turkish population, we performed a whole genome screen for linkage in 43 Turkish multiplex families employing 392 microsatellite markers. Two genomic regions where maximum lod score (MLS) values were suggestive of linkage were identified (chromosomes 13q and 18q23) along with a further 14 regions of potential linkage. Parametric analysis of these data using a recessive model, appropriate for populations with a high frequency of consanguinity, increased the LOD scores in four regions.

Chromosome Mapping↗
PubMed1996

Discovery of genes involved with learning and memory: an experimental synthesis of Hirschian and Benzerian perspectives.

The biological bases of learning and memory are being revealed today with a wide array of molecular approaches, most of which entail the analysis of dysfunction produced by gene disruptions. This perspective derives both from early "genetic dissections" of learning in mutant Drosophila by Seymour Benzer and colleagues and from earlier behavior-genetic analyses of learning and in Diptera by Jerry Hirsh and coworkers. Three quantitative-genetic insights derived from these latter studies serve as guiding principles for the former. First, interacting polygenes underlie complex traits. Consequently, learning/memory defects associated with single-gene mutants can be quantified accurately only in equilibrated, heterogeneous genetic backgrounds. Second, complex behavioral responses will be composed of genetically distinct functional components. Thus, genetic dissection of complex traits into specific biobehavioral properties is likely. Finally, disruptions of genes involved with learning/memory are likely to have pleiotropic effects. As a result, task-relevant sensorimotor responses required for normal learning must be assessed carefully to interpret performance in learning/memory experiments. In addition, more specific conclusions will be obtained from reverse-genetic experiments, in which gene disruptions are restricted in time and/or space.

Animals↗
PubMed2005

Complex quality traits: now time to model.

A recently published dynamic peach model by Françoise Lescourret and Michel Génard simulates changes in quality during final stages of fruit growth. It predicts quality, emerging from complex processes and influenced by environment and management, remarkably well and describes complex emerging behaviour. The model could prove an excellent tool to integrate novel insight from metabolic profiling and pathways of taste-related compounds, ultimately enabling the analysis of gene networks responsible for fruit maturation processes.

Crops, Agricultural↗
PubMed2003

Genetic correlates of gene expression in recombinant inbred strains: a relational model system to explore neurobehavioral phenotypes.

Full genome sequencing, high-density genotyping, expanding sets of microarray assays, and systematic phenotyping of neuroanatomical and behavioral traits are producing a wealth of data on the mouse central nervous system (CNS). These disparate resources are still poorly integrated. One solution is to acquire these data using a common reference population of isogenic lines of mice, providing a point of integration between the data types. Recombinant inbred (RI) mice, derived through inbreeding of progeny from an inbred cross, are a powerful tool for complex trait mapping and analysis of the challenging phenotypes of neuroscientific interest. These isogenic RI lines are a retrievable genetic resource that can be repeatedly studied using a wide variety of assays. Diverse data sets can be related through fixed and known genomes, using tools such as the interactive web-based system for complex trait analysis, www.WebQTL.org. In this report, we demonstrate the use of WebQTL to explore complex interactions among a wide variety of traits--from mRNA transcripts to the impressive behavioral and pharmacological variation among RI strains. The relational approach exploiting a common set of strains facilitates study of multiple effects of single genes (pleiotropy) without a priori hypotheses required. Here we demonstrate the power of this technique through genetic correlation of gene expression with a database of neurobehavioral phenotypes collected in these strains of mice through more than 20 years of experimentation. By repeatedly studying the same panel of mice, early data can be re-examined in light of technological advances unforeseen at the time of their initial collection.

Animals↗
PubMed2005

New quantitative trait loci that regulate wound healing in an intercross progeny from DBA/1J and 129 x 1/SvJ inbred strains of mice.

Wound healing/regeneration mouse models are few, and studies performed have mainly utilized crosses between MRL/MPJ (a good healer) and SJL/J (a poor healer) or MRL/lpr (a good healer) and C57BL/6J (a poor healer). Wound healing is a complex trait with many genes involved in the expression of the phenotype. Based on data from previous studies that common and additional quantitative trait loci (QTL) were identified using different crosses of inbred strains of mice for various complex traits, we hypothesized that a new cross would identify common and additional QTL, unique modes of inheritance, and interacting loci, which are responsible for variation in susceptibility to fast wound healing. In this study, we crossed DBA/1J (DBA, a good healer) and 129/SvJ (129, a poor healer) and performed a genome-wide scan using 492 (DBA x 129) F2 mice and 98 markers to identify QTL that regulate wound healing/regeneration. Four QTL on chromosomes 1, 4, 12, and 18 were identified which contributed toward wound healing in F2 mice and accounted for 17.1% of the phenotypic variation in ear punch healing. Surprisingly, locus interactions contributed to 55.7% of the phenotype variation in ear punch healing. In conclusion, we have identified novel QTL and shown that minor interacting loci contribute significantly to wound healing in DBA x 129 mice cross.

Animals↗
PubMed2001

Genetic and molecular dissection of naturally occurring variation.

Recent progress in plant genome analysis has made it possible to examine the naturally occurring allelic variation underlying complex traits. Many studies have described the genetic mapping of quantitative trait loci for several kinds of complex phenotypic traits. Some researchers have taken up the challenge of performing the molecular cloning of genes at these loci, and examples of cloning have recently been reported. Naturally occurring allelic variation could be a new resource for the functional analysis of plant genes.

Alleles↗
PubMed2006

Elucidating the murine brain transcriptional network in a segregating mouse population to identify core functional modules for obesity and diabetes.

Complex biological systems are best modeled as highly modular, fluid systems exhibiting a plasticity that allows them to adapt to a vast array of changing conditions. Here we highlight several novel network-based approaches to elucidate genetic networks underlying complex traits. These integrative genomic approaches combine large-scale genotypic and gene expression results in segregating mouse populations to reconstruct reliable genetic networks underlying complex traits such as disease or drug response. We apply these novel approaches to one of the most extensive surveys of gene expression studies ever undertaken in whole brain in a segregating mouse population. More than 23,000 genes were monitored in whole brain samples from more than 300 mice derived from an F2 intercross population and genotyped at over 1200 SNP markers uniformly spread over the entire genome. We explore the topological properties of the brain transcriptional network and highlight different approaches to inferring causal associations among genes by integrating genotypic and expression data. We demonstrate the utility of these approaches by identifying and experimentally validating brain gene expression traits predicted to respond to a strong expression quantitative trait locus (eQTL) for the pituitary tumor-transforming 1 gene (Pttg1) that coincides with the physical location of this gene (a cis eQTL). We identify core functional modules making up the brain transcriptional network in mice that are coherent for core biological processes associated with metabolic disease traits including obesity and diabetes.

Animals↗
PubMedDate not supplied

Nanotechnologies and microchips in genetic diseases.

Microarrays or microchips represent a new area of high technology, which will completely change the methodological approach to basic research and clinical diagnostics. This technology can be used for genotyping, expression profiling and proteome analysis. Genetics and molecular medicine have an expanding need for rapid genotyping, mutational analysis and DNA re-sequencing technologies, i.e. microarrays that have a clear potential for miniaturization, parallelization, and automation and enable high-throughput screening. Expression profiling technology is a new tool for investigating expression patterns, identifying new disease genes either for monogenic disorders or for complex traits, identifying new functional and cellular relationships and identifying new pathways and possible related drugs. This technology has been successfully applied to the study of complex traits, i.e. cardiovascular diseases, cancer and type II diabetes, providing new insights into possible pathogenetic mechanisms and new therapeutical approaches. Finally, microarray can further improve proteome analysis. This review discusses these points.

Gene Expression Profiling↗