A model for the effect of homo- or heterozygosity on animal growth intensity

A model has been developed that describes the dependence of a quantitative selective trait on the animal homo- and heterozygosity for the genes that control the biochemical reaction rate (isoenzyme systems). The model includes any cases of gene control of a quantitative trait and is applicable to real, genetically heterogeneous populations. The results of testing the model show that it can be used for identifying the genes involved in additive polygenic determination of quantitative commercially valuable traits.Translated from Genetika, Vol. 41, No. 2, 2005, pp. 237–245.Original Russian Text Copyright © 2005 by Nikitin, Knyazev, Orlova, Bekenev, Danilchenko.     

Nonparametric functional mapping of quantitative trait loci underlying programmed cell death

The development of an organism represents a complex dynamic process, which is controlled by a network of genes and multiple environmental factors. Programmed cell death (PCD), a physiological cell suicide process, occurs during the development of most organisms and is, typically, a complex dynamic trait. Understanding how genes control this complex developmental process has been a long-standing topic in PCD studies. In this article, we propose a nonparametric model, based on orthogonal Legendre polynomials, to map genes or quantitative trait loci (QTLs) that govern the dynamic features of the PCD process. The model is built under the maximum likelihood-based functional mapping framework and is implemented with the EM algorithm. A general information criterion is proposed for selecting the optimal Legendre order that best fits the dynamic pattern of the PCD process. The consistency of the order selection criterion is established. A nonstationary structured antedependence model (SAD) is applied to model the covariance structure among the phenotypes measured at different time points. The developed model generates a number of hypothesis tests regarding the genetic control mechanism of the PCD process. Extensive simulation studies are conducted to investigate the statistical behavior of the model. Finally, we apply the model to a rice tiller number data set in which several QTLs are identified. The developed model provides a quantitative and testable framework for assessing the interplay between genes and the developmental PCD process, and will have great implications for elucidating the genetic architecture of the PCD process.     

Concordance of murine quantitative trait loci for salt-induced hypertension with rat and human loci

To investigate the genetic control of salt-induced hypertension, we performed a quantitative trait locus analysis on male mice from a reciprocal backcross between the salt-sensitive C57BL/6J and the normotensive A/J inbred mouse strains after they were provided with water containing 1% salt for 2 weeks. Genome-wide scans performed on these mice and analyzed with a combination of conventional marker-based regressions and a novel simultaneous search for pairs revealed six significant quantitative trait loci associated with salt-induced blood pressure, two of which were interacting loci. These six loci, named Bpq1-6 for blood pressure quantitative trait loci, mapped to D1Mit334, D1Mit14, D4Mit164, D5Mit31, D6Mit15, and D15Mit13. Furthermore, five of these six loci were concordant with hypertension loci in rats, and four were concordant with hypertension loci in humans, suggesting that quantitative trait loci mapping in model organisms can be used to guide the search for human blood pressure genes.     

Integration of genetic and genomic methods for identification of genes and gene variants encoding QTLs in the nonhuman primate

We have developed an integrated approach, using genetic and genomic methods, in conjunction with resources from the Southwest National Primate Research Center (SNPRC) baboon colony, for the identification of genes and their functional variants that encode quantitative trait loci (QTL). In addition, we use comparative genomic methods to overcome the paucity of baboon specific reagents and to augment translation of our findings in a nonhuman primate (NHP) to the human population. We are using the baboon as a model to study the genetics of cardiovascular disease (CVD). A key step for understanding gene–environment interactions in cardiovascular disease is the identification of genes and gene variants that influence CVD phenotypes. We have developed a sequential methodology that takes advantage of the SNPRC pedigreed baboon colony, the annotated human genome, and current genomic and bioinformatic tools. The process of functional polymorphism identification for genes encoding QTLs involves comparison of expression profiles for genes and predicted genes in the genomic region of the QTL for individuals discordant for the phenotypic trait mapping to the QTL. After comparison, genes of interest are prioritized, and functional polymorphisms are identified in candidate genes by genotyping and quantitative trait nucleotide analysis. This approach reduces the time and labor necessary to prioritize and identify genes and their polymorphisms influencing variation in a quantitative trait compared with traditional positional cloning methods.     

An allelism test for quantitative trait genes

Analytical modeling has been used to test assumptions on the mode of inheritance of a quantitative trait in the course of diallel crossing between pure lines that are sufficient for adequacy of a simple regression model. This model frequently proved to be adequate in analysis of numerous data on diallel crossings of wheat and maize. An allelism test for quantitative trait genes has been suggested. Computer simulation has been used to estimate the effect of random experimental errors and deviations from the model assumptions.     

GENETICS OF FLUCTUATING ASYMMETRY: A DEVELOPMENTAL MODEL OF DEVELOPMENTAL INSTABILITY

Although numerous studies have found that fluctuating asymmetry (FA) can have a heritable component, the genetic and developmental basis of FA is poorly understood. We used a developmental model of a trait, according to a diffusion-threshold process, whose parameters are under genetic control. We added a small amount of random variation to the parameter values of this model to simulate developmental noise. As a result of the nonlinearity of the model, different genotypes differed in their sensitivity to developmental noise, even though the noise is completely random and independent of the genotype. The heritable component of FA can thus be understood as genetically modulated expression of variation that is itself entirely nongenetic. The loci responsible for this genetic variation of FA are the same that affect the left/right mean of the trait, showing that genetic variation for FA does not require genes that specifically control FA. Furthermore, the model offers alternative explanations for phenomena widely discussed in the literature on FA, for instance, the correlations between FA and heterozygosity and between FA and trait size. The model underscores the importance of dominance and epistasis, and therefore unites the study of FA with the classical theory of quantitative genetics.     

Genetic assimilation can occur in the absence of selection for the assimilating phenotype, suggesting a role for the canalization heuristic

Genetic assimilation occurs when an acquired trait loses dependency on its environmental trigger and becomes an inherited trait. According to the standard quantitative genetic model for genetic assimilation, the trait is determined by the contributions of multiple genes. Trait expression occurs at a lower threshold with the trigger. Selection for the trait in the presence of the trigger increases the frequency of the trait-determining alleles. Eventually these alleles become frequent enough to breach the higher threshold for expression in the absence of the trigger. This loss of dependence on the trigger signifies genetic assimilation. Here I show that genetic assimilation can occur in the absence of selection for the trait in an evolutionary simulation of a gene network model. This contradicts the prediction of the standard quantitative genetic model, but is consistent with an explanation in terms of the canalization heuristic.     

Characterization of the quantitative trait locus for haloperidol-induced catalepsy on distal mouse chromosome 1

We report here the confirmation of the quantitative trait locus for haloperidol-induced catalepsy on distal chromosome (Chr) 1. We determined that this quantitative trait locus was captured in the B6.D2- Mtv7a /Ty congenic mouse strain, whose introgressed genomic interval extends from approximately 169.1 to 191.3 Mb. We then constructed a group of overlapping interval-specific congenic strains to further break up the interval and remapped the locus between 177.5 and 183.4 Mb. We next queried single nucleotide polymorphism (SNP) data sets and identified three genes with nonsynonymous coding SNPs in the quantitative trait locus. We also queried two brain gene expression data sets and found five known genes in this 5.9-Mb interval that are differentially expressed in both whole brain and striatum. Three of the candidate quantitative trait genes were differentially expressed using quantitative real-time polymerase chain reaction analyses. Overall, the current study illustrates how multiple approaches, including congenic fine mapping, SNP analysis and microarray gene expression screens, can be integrated both to reduce the quantitative trait locus interval significantly and to detect promising candidate quantitative trait genes.     

Genetical Genomics of Behavior: A Novel Chicken Genomic Model for Anxiety Behavior

The identification of genetic variants responsible for behavioral variation is an enduring goal in biology, with wide-scale ramifications, ranging from medical research to evolutionary theory on personality syndromes. Here, we use for the first time a large-scale genetical genomics analysis in the brains of chickens to identify genes affecting anxiety as measured by an open field test. We combine quantitative trait locus (QTL) analysis in 572 individuals and expression QTL (eQTL) analysis in 129 individuals from an advanced intercross between domestic chickens and Red Junglefowl. We identify 10 putative quantitative trait genes affecting anxiety behavior. These genes were tested for an association in the mouse Heterogeneous Stock anxiety (open field) data set and human GWAS data sets for bipolar disorder, major depressive disorder, and schizophrenia. Although comparisons between species are complex, associations were observed for four of the candidate genes in mice and three of the candidate genes in humans. Using a multimodel approach we have therefore identified a number of putative quantitative trait genes affecting anxiety behavior, principally in chickens but also with some potentially translational effects as well. This study demonstrates that chickens are an excellent model organism for the genetic dissection of behavior.     

Functional mapping - how to map and study the genetic architecture of dynamic complex traits

The development of any organism is a complex dynamic process that is controlled by a network of genes as well as by environmental factors. Traditional mapping approaches for analysing phenotypic data measured at a single time point are too simple to reveal the genetic control of developmental processes. A general statistical mapping framework, called functional mapping, has been proposed to characterize, in a single step, the quantitative trait loci (QTLs) or nucleotides (QTNs) that underlie a complex dynamic trait. Functional mapping estimates mathematical parameters that describe the developmental mechanisms of trait formation and expression for each QTL or QTN. The approach provides a useful quantitative and testable framework for assessing the interplay between gene actions or interactions and developmental changes.     

Genome-wide search for new genes controlling plasma lipid concentrations in mice and humans

PURPOSE OF REVIEW: Quantitative trait locus analysis has been used in both humans and mice for the purpose of finding new genes regulating plasma lipid levels. We review these methods and discuss new approaches that can help find quantitative trait locus genes. RECENT FINDINGS: Many quantitative trait loci have been found that regulate plasma levels for HDL cholesterol (37 in mice and 30 in humans), LDL cholesterol (25 in mice and 20 in humans) and triglycerides (19 in mice and 30 in humans). Most of the human quantitative trait loci have concordant mouse quantitative trait loci mapping to homologous regions (93% for HDL cholesterol, 100% for LDL cholesterol and 80% for triglycerides), suggesting that many genes identified in mice may also regulate the same traits in humans. New approaches based on recently developed genomic and bioinformatic technologies and resources should greatly facilitate finding these genes. SUMMARY: New genes regulating plasma lipid levels can be found in mice and then tested in humans. Some of these genes could be potential therapeutic targets for human atherosclerosis.     

A statistical model for the identification of genes governing the incidence of cancer with age

The cancer incidence increases with age. This epidemiological pattern of cancer incidence can be attributed to molecular and cellular processes of individual subjects. Also, the incidence of cancer with ages can be controlled by genes. Here we present a dynamic statistical model for explaining the epidemiological pattern of cancer incidence based on individual genes that regulate cancer formation and progression. We incorporate the mathematical equations of age-specific cancer incidence into a framework for functional mapping aimed at identifying quantitative trait loci (QTLs) for dynamic changes of a complex trait. The mathematical parameters that specify differences in the curve of cancer incidence among QTL genotypes are estimated within the context of maximum likelihood. The model provides testable quantitative hypotheses about the initiation and duration of genetic expression for QTLs involved in cancer progression. Computer simulation was used to examine the statistical behavior of the model. The model can be used as a tool for explaining the epidemiological pattern of cancer incidence.     

Analysis of quantitative trait loci using hybrid pedigrees: quantitative traits of animals

A method is proposed for analysis of quantitative traits in animal hybrid pedigrees formed by crosses between outbred lines differing in allele frequencies of the genes controlling the trait studied. The method is based on the decomposition of trait variances into components and uses maximization of the likelihood function for estimating model parameters, which allows the estimation of additive and dominance effects of the gene involved in trait determination and its allele frequencies, as well as determination of the chromosomal position of this gene relative to genotyped markers. To test the linkage of this gene with markers, a statistic with the noncentral chi(2) distribution has been chosen. Analytical expressions for the power of this method have been derived. The method has been tested on small model hybrid pedigrees. Phenotypic values of the trait and information on marker genotypes for each individual in hybrid pedigrees are original data for the analysis of a quantitative trait.     

Resistance of tomato line Hawaii7996 to Ralstonia solanacearum Pss4 in Taiwan is controlled mainly by a major strain-specific locus

Bacterial wilt caused by the soilborne bacterium Ralstonia solanacearum attacks hundreds of plant species, including many agriculturally important crops. Natural resistance to this disease has been found in some species and is usually inherited as a polygenic trait. In tomato, a model crop plant, genetic analysis previously revealed the involvement of several QTL (quantitative trait loci) controlling resistance and, in all of these studies with different strains of the pathogen, loci on chromosome 6 played the predominant role in controlling this trait. Using quantitative data collected from a greenhouse test F3 population, we identified a new locus on chromosome 12 that appears to be active specifically against a race 1 biovar 3 Pss4 bacterial strain endemic to Taiwan. Chromosome 6 still contributes significantly to the control of the resistance, and weaker associations of the trait to other regions of the genome are observed. These results are discussed in the context of current molecular knowledge about the strain specificity of disease resistance genes.     

Generalized linear mixed models for mapping multiple quantitative trait loci

Many biological traits are discretely distributed in phenotype but continuously distributed in genetics because they are controlled by multiple genes and environmental variants. Due to the quantitative nature of the genetic background, these multiple genes are called quantitative trait loci (QTL). When the QTL effects are treated as random, they can be estimated in a single generalized linear mixed model (GLMM), even if the number of QTL may be larger than the sample size. The GLMM in its original form cannot be applied to QTL mapping for discrete traits if there are missing genotypes. We examined two alternative missing genotype-handling methods: the expectation method and the overdispersion method. Simulation studies show that the two methods are efficient for multiple QTL mapping (MQM) under the GLMM framework. The overdispersion method showed slight advantages over the expectation method in terms of smaller mean-squared errors of the estimated QTL effects. The two methods of GLMM were applied to MQM for the female fertility trait of wheat. Multiple QTL were detected to control the variation of the number of seeded spikelets.     

Quantitative trait locus mapping for atherosclerosis susceptibility

PURPOSE OF REVIEW: Atherosclerosis is a complex trait with both environmental and genetic aspects. Although some progress has been made in defining genes associated with atherosclerosis in humans, animal models have been useful in learning about pathways and genes involved in atherogenesis. This review describes an unbiased genetic mapping method called quantitative trait locus mapping and progress in using this method to identify genes that alter atherosclerosis susceptibility in mice. RECENT FINDINGS: Approximately 10 well defined genetic loci have been described that are associated with lesion severity in diet-induced or gene knockout mouse models of atherosclerosis. Recently, two of these genetic loci were narrowed considerably by analysis of genetic recombinants within these loci. In addition, a computational method to discover quantitative trait loci has been applied to atherosclerosis. However, none of the genes responsible for these atherosclerosis quantitative trait loci has been definitively identified. The recent completion of the mouse draft genome should facilitate the task of identifying these genes. SUMMARY: Quantitative trait locus mapping studies in mouse models of atherosclerosis have defined genetic regions that alter lesion severity. The identification of the responsible genes may lead to insights into the pathogenesis of atherosclerosis as well as to candidates for human genetic association studies.     

Dissecting genetic networks underlying complex phenotypes: the theoretical framework

Great progress has been made in genetic dissection of quantitative trait variation during the past two decades, but many studies still reveal only a small fraction of quantitative trait loci (QTLs), and epistasis remains elusive. We integrate contemporary knowledge of signal transduction pathways with principles of quantitative and population genetics to characterize genetic networks underlying complex traits, using a model founded upon one-way functional dependency of downstream genes on upstream regulators (the principle of hierarchy) and mutual functional dependency among related genes (functional genetic units, FGU). Both simulated and real data suggest that complementary epistasis contributes greatly to quantitative trait variation, and obscures the phenotypic effects of many 'downstream' loci in pathways. The mathematical relationships between the main effects and epistatic effects of genes acting at different levels of signaling pathways were established using the quantitative and population genetic parameters. Both loss of function and "co-adapted" gene complexes formed by multiple alleles with differentiated functions (effects) are predicted to be frequent types of allelic diversity at loci that contribute to the genetic variation of complex traits in populations. Downstream FGUs appear to be more vulnerable to loss of function than their upstream regulators, but this vulnerability is apparently compensated by different FGUs of similar functions. Other predictions from the model may account for puzzling results regarding responses to selection, genotype by environment interaction, and the genetic basis of heterosis.     

The molecular basis of quantitative genetic variation in natural populations

DNA markers allow us to study quantitative trait loci (QTL) - the genes that control adaptation and quantitative variation. Experiments can map the genes responsible for quantitative variation and address the evolutionary and ecological significance of this variation. Recent studies suggest that major genes segregate within and among natural populations. It is now feasible to study the genes that cause morphological variation, life history trade-offs, heterosis and speciation. These methods can determine the role of epistasis and genotype-by-environment interaction in maintaining genetic variation. QTL mapping is an important tool used to address evolutionary and ecological questions of long-standing interest.     

Genetic analyses of six quantitative traits of a doubled haploid population of Porphyra haitanensis Chang et Zheng (Bangiales, Rhodophyta)

The first doubled haploid (DH) population of Porphyra haitanensis was created by crossing a wild-type line with a red type, artificial pigmentation, mutant line, by means of single somatic cell clone cultivation. Six quantitative traits (frond length (FL), frond width (FW), frond thickness (FT), fresh weight (W), growth rate of frond length (LGR), and growth rate of fresh weight (WGR)) from the DH population were analyzed. The frequency of each quantitative trait is in accordance with a normal distribution. Variable coefficients were between 20.43% and 57.35%, and while the mean of each quantitative trait was between the parents, it was closer to the paternal for six traits. Correlation analysis among the six traits showed significant positive correlations between FL and W, FW, and W, LGR and W, and WGR and W. There appears to be no correlation between FT and W. Heritability, number of genes controlling each quantitative trait and gene interactions, were also analyzed. Heritability of the six traits was greater than 70%, and heritability was not correlated with the number of genes controlling the corresponding quantitative trait. Frond thickness was the most heritable trait (95.30%), but this had the fewest control genes (7.52). According to the estimated coefficients of skewness and kurtosis, gene interaction was absent for LGR, but complementary gene interaction was observed in FW and W. In FL, FT, and WGR it is possible that complementary or duplicate gene interaction is involved. Our results enrich our understanding of Porphyra genetics and will help determine selection and breeding procedures for P. haitanensis.     

From plant genomics to breeding practice

New alleles are constantly accumulated during intentional crop selection. The molecular understanding of these alleles has stimulated new genomic approaches to mapping quantitative trait loci (QTL) and haplotype multiplicity of the genes concerned. A limited number of quantitative trait nucleotides responsible for QTL variation have been described, but an acceleration in their rate of discovery is expected with the adoption of linkage disequilibrium and candidate gene strategies for QTL fine mapping and cloning. Additional layers of regulatory variation have been studied that could also contribute to the molecular basis of quantitative genetics of crop traits. Despite this progress, the role of marker-assisted selection in plant breeding will ultimately depend on the genetic model underlying quantitative variation.