Genetical genomics in livestock: potentials and pitfalls

Genetical genomics combines gene mapping and gene expression approaches to identify loci controlling gene expression (eQTLs) that may underlie functional trait variation. The combination of genomic tools has great potential to facilitate dissection of complex traits, but studies need careful design and interpretation. Here we explore both the potential and the pitfalls of this approach with illustrations from actual studies. There are now an appreciable number of studies in model species and even humans demonstrating the feasibility of genetical genomics. However, most studies are too limited in size and design to unlock the full potential of the approach. Limited statistical power of studies exacerbates the problem of detection of false-positive eQTL and some reported results should be interpreted with caution. As one approach to more successful implementation of genetical genomics, we propose to combine expression studies with fine mapping of functional trait loci. This synergistic approach facilitates the implementation of genetical genomics for species without inbred resources but is equally applicable to model species. These properties make it particularly suitable for livestock populations where many QTL are already in the public domain and potentially very large pedigreed populations can be accessed.     

遗传基因组学(Genetical genomics)的研究进展

遗传基因组学(geneticalgenomics)是将微阵列技术和数量性状座位(QTL)分析结合起来,全基因组水平上定位基因表达的QTL(eQTL).它为研究复杂性状的分子机理和调控网络提供全新的手段.遗传基因组这个概念和研究策略在2001年由Janson和Nap首先提出,到目前为止,遗传基因组学已应用于酵母、老鼠、人以及玉米等植物.研究结果表明:基因表达水平的差异是可遗传的复杂性状;eQTL可以分为顺式作用eQTL和反式作用eQTL,顺式作用eQTL就是某个基因的eQTL定位到该基因所在的基因组区域,表明可能是该基因本身的差别引起mRNA水平的差别,反式作用就是eQTL定位到其他基因组区域,表明其他基因的差别控制该基因mRNA水平的差异.将eQTL结果、基因功能注解以及多种统计分析方法相结合,不仅能更准确地鉴别控制复杂性状及其相关基因表达的候选基因,而且能构建相应的基因调控网络.     

The effect of cultural transmission on continuous variation.

Cultural transmission may depend on the non-genetic transfer of information from parent to offspring. The consequences of such cultural transmission for continuous variation are investigated theoretically for randomly mating populations. Cultural inheritance may act on genetical and environmental differences between individuals. The consequences for cultural inheritance of polygenic variation and variation due to chance environmental factors are considered. An equilibrium may occur in which the population variance and the covariances between relatives can be expressed as functions of estimable parameters of genetical and environmental variation. Whatever the ultimate origin of culturally inherited differences they are expected to lead to environmental differences between families ("E2" variation). In addition, if cultural transmission maintains differences due ultimately to segregation at many gene loci we may find genotype-environmental covariation is generated.     

A genetical model for vitiligo.

A genetical model is found to provide a good fit to family data on vitiligo. The model postulates that recessive alleles at a set of four unlinked diallelic loci are involved in the causation of the disorder. Under this multiple recessive homozygosis model, for normal X affected families ascertained through the affected parent, the expected segregation probability is .063; the estimated value is 0.53, which is not significantly different from the expected value. For normal X normal families ascertained through an affected offspring, the expected segregation probability is .037; the estimated value is .04.     

Learning Gene Networks under SNP Perturbations Using eQTL Datasets

The standard approach for identifying gene networks is based on experimental perturbations of gene regulatory systems such as gene knock-out experiments, followed by a genome-wide profiling of differential gene expressions. However, this approach is significantly limited in that it is not possible to perturb more than one or two genes simultaneously to discover complex gene interactions or to distinguish between direct and indirect downstream regulations of the differentially-expressed genes. As an alternative, genetical genomics study has been proposed to treat naturally-occurring genetic variants as potential perturbants of gene regulatory system and to recover gene networks via analysis of population gene-expression and genotype data. Despite many advantages of genetical genomics data analysis, the computational challenge that the effects of multifactorial genetic perturbations should be decoded simultaneously from data has prevented a widespread application of genetical genomics analysis. In this article, we propose a statistical framework for learning gene networks that overcomes the limitations of experimental perturbation methods and addresses the challenges of genetical genomics analysis. We introduce a new statistical model, called a sparse conditional Gaussian graphical model, and describe an efficient learning algorithm that simultaneously decodes the perturbations of gene regulatory system by a large number of SNPs to identify a gene network along with expression quantitative trait loci (eQTLs) that perturb this network. While our statistical model captures direct genetic perturbations of gene network, by performing inference on the probabilistic graphical model, we obtain detailed characterizations of how the direct SNP perturbation effects propagate through the gene network to perturb other genes indirectly. We demonstrate our statistical method using HapMap-simulated and yeast eQTL datasets. In particular, the yeast gene network identified computationally by our method under SNP perturbations is well supported by the results from experimental perturbation studies related to DNA replication stress response.     

Osteopenia in 37 members of seven families: analysis based on a model of dominant inheritance.

BACKGROUND: The genetic factors involved in determining bone mineral density (BMD) have not been fully elucidated. We have begun genetic linkage analysis of seven families in which many members are osteopenic, in order to identify chromosomal loci that are potentially involved in determining BMD. MATERIALS AND METHODS: Spine BMD was measured in 143 members of seven kindred with familial osteopenia. The absolute BMD values for the spine (L2-L4) were converted to the age-, gender-, and weight-adjusted Z scores, and this corrected value was used as the quantitative trait on which to base subsequent genetic analyses. Simulations of linkage were performed in order to determine the information content of the pedigree set, and actual linkage analysis was conducted using polymorphic markers either within or near three candidate loci: COL1A1, COL1A2, and vitamin D receptor (VDR). RESULTS: The distribution of the corrected Z scores was bimodal (p = 0.001) suggesting a monogenic mode of inheritance of the low BMD trait. Simulation of linkage analysis suggested that the family data set was sufficient to detect linkage under a single major gene model. Actual linkage analysis did not support linkage to the three candidate loci. In addition, the VDR genotype was not statistically associated with low bone density at the spine. CONCLUSIONS: Loci other than COL1A1, COL1A2 and VDR are very likely responsible for the low BMD trait observed in these families. These families are suitable for a genome-wide screen using microsatellite repeats in order to identify the loci that are involved in osteopenia.     

Mapping determinants of gene expression plasticity by genetical genomics in C. elegans

Recent genetical genomics studies have provided intimate views on gene regulatory networks. Gene expression variations between genetically different individuals have been mapped to the causal regulatory regions, termed expression quantitative trait loci. Whether the environment-induced plastic response of gene expression also shows heritable difference has not yet been studied. Here we show that differential expression induced by temperatures of 16 °C and 24 °C has a strong genetic component in Caenorhabditis elegans recombinant inbred strains derived from a cross between strains CB4856 (Hawaii) and N2 (Bristol). No less than 59% of 308 trans-acting genes showed a significant eQTL-by-environment interaction, here termed plasticity quantitative trait loci. In contrast, only 8% of an estimated 188 cis-acting genes showed such interaction. This indicates that heritable differences in plastic responses of gene expression are largely regulated in trans. This regulation is spread over many different regulators. However, for one group of trans-genes we found prominent evidence for a common master regulator: a transband of 66 coregulated genes appeared at 24 °C. Our results suggest widespread genetic variation of differential expression responses to environmental impacts and demonstrate the potential of genetical genomics for mapping the molecular determinants of phenotypic plasticity.     

Qxpak: a versatile mixed model application for genetical genomics and QTL analyses

MOTIVATION: Current methodology and software for quantitative trait loci (QTL) analyses do not use all available information and are inadequate to deal with the huge amount of QTL analyses to be needed in forecoming genetical genomics' studies. RESULTS: We show that a mixed model statistical framework provides a very flexible tool for QTL modeling in a variety of populations, be it a cross between inbred lines, a within population study, or experiments involving a mixture of populations or crosses. The software allows multitrait and multiQTL analyses, inclusion of infinitesimal genetic value and a batch multitrait option suitable for genetical genomics studies. It also allows massive association studies between single nucleotide polymorphisms and the trait(s) of interest. AVAILABILITY: A software (Qxpak), together with a manual and example files, is freely available for research purposes. So far, the compiled program is available for linux systems, the windows version will follow soon. See http://www.icrea.es/pag.asp?id=Miguel.Perez     

Interpretation of various ratios of genetic parameters in Hayman's diallelic analysis

The genetical algebra of diallele analysis was used to interpret some relations of genetic parameters which were not earlier applied in research of genetic control (GC). It was discovered that in the case of gene correlation, the relation H2 greater than H1 is possible, meaning the regression among frequencies of alleles in loci. We demonstrated, in what way to distinguish two reasons for changes in H1, H2 and h2, when an external treatment is applied: Due to frequency changes of dominant and recessive alleles in the majority of loci, in case new loci are involved in GC; Due to changes in dominance contributions. The latter could provide new information about a situation in loci by analysing the changes in h2 and the correlation coefficient of parental trait levels with their dominant properties as well as changes in D. A few experimental examples illustrate the theory implication.     

BBS4 is a minor contributor to Bardet-Biedl syndrome and may also participate in triallelic inheritance

Bardet-Biedl syndrome (BBS) is an uncommon multisystemic disorder characterized primarily by retinal dystrophy, obesity, polydactyly, and renal dysfunction. BBS has been modeled historically as an autosomal recessive trait, under which premise six independent BBS loci (BBS1-BBS6) have been mapped in the human genome. However, extended mutational analyses of BBS2 and BBS6, the first two BBS genes cloned, suggest that BBS exhibits a more complex pattern of inheritance, in which three mutations at two loci simultaneously are necessary and sufficient in some families to manifest the phenotype. We evaluated the spectrum of mutations in the recently identified BBS4 gene with a combination of haplotype analysis and mutation screening on a multiethnic cohort of 177 families. Consistent with predictions from previous genetic analyses, our data suggest that mutations in BBS4 contribute to BBS in <3% of affected families. Furthermore, integrated mutational data from all three currently cloned BBS genes raise the possibility that BBS4 may participate in triallelic inheritance with BBS2 and BBS1, but not the other known loci. Establishment of the loci pairing in triallelism is likely to be important for the elucidation of the functional relationships among the different BBS proteins.     

Genetic interaction of BBS1 mutations with alleles at other BBS loci can result in non-Mendelian Bardet-Biedl syndrome

Bardet-Biedl syndrome is a genetically and clinically heterogeneous disorder caused by mutations in at least seven loci (BBS1-7), five of which are cloned (BBS1, BBS2, BBS4, BBS6, and BBS7). Genetic and mutational analyses have indicated that, in some families, a combination of three mutant alleles at two loci (triallelic inheritance) is necessary for pathogenesis. To date, four of the five known BBS loci have been implicated in this mode of oligogenic disease transmission. We present a comprehensive analysis of the spectrum, distribution, and involvement in non-Mendelian trait transmission of mutant alleles in BBS1, the most common BBS locus. Analyses of 259 independent families segregating a BBS phenotype indicate that BBS1 participates in complex inheritance and that, in different families, mutations in BBS1 can interact genetically with mutations at each of the other known BBS genes, as well as at unknown loci, to cause the phenotype. Consistent with this model, we identified homozygous M390R alleles, the most frequent BBS1 mutation, in asymptomatic individuals in two families. Moreover, our statistical analyses indicate that the prevalence of the M390R allele in the general population is consistent with an oligogenic rather than a recessive model of disease transmission. The distribution of BBS oligogenic alleles also indicates that all BBS loci might interact genetically with each other, but some genes, especially BBS2 and BBS6, are more likely to participate in triallelic inheritance, suggesting a variable ability of the BBS proteins to interact genetically with each other.     

Genetical genomics in humans and model organisms

Genetical genomics has been proposed to map loci controlling gene-expression differences (eQTLs) that might underlie functional trait variation. We briefly review the studies in model species and conclude that, although they successfully demonstrate the utility of genetical genomics, they are too limited to unlock the full potential of this approach and some results should be interpreted with caution. We subsequently elaborate on two recent studies that use this approach in humans. The many differences between these studies complicate meaningful comparisons between them. A joint analysis of the two experiments offers some scope for more powerful genetical genomics.     

Variance in quantitative traits due to linked dominant genes and variance in heterozygosity in small populations

The influence of small population size (N) on the genetic variance within and between randomly bred unselected lines, with selfing permitted, is investigated for a model of a quantitative trait determined by linked genes that show dominance within loci but are additive over loci. Formulae for within-line variance include terms in linkage disequilibrum, which occurs by chance in the lines and these are evaluated in terms of N, map length and gene number.—The expected variance within lines is increased by this disequilibrium, quite substantially if there are many loci, with most of the increase being between or within full-sib families and almost no change expected between half-sib families or in the covariance of offspring and parent. If all loci are unlinked, there is no increase in variance within full-sib families. The variance between lines is little affected by disequilibrum generated by chance.—Expressions for the variance between individuals in heterozygosity over the whole genome are special cases of those for the variance due to linked dominated genes, and formulae are given and evaluated. The coefficient of variation of heterozygosity is at least (see PDF) and can be much higher for species with few chromosomes.     

Linkage analysis of a cluster-based quantitative phenotype constructed from pulmonary function test data in 27 multigenerational families with multiple asthmatic members

OBJECTIVE: To identify genes involved in phenotypes that increase one's risk for developing asthma, a complex disease that is likely genetically heterogeneous. Unlike other approaches to locus discovery in the presence of heterogeneity, this method seeks loci that segregate in all or most ascertained families while recognizing that other genes and environmental factors that modify the action of the common gene may vary across families. METHODS: The method is based on seeking groups of families that differ, between groups, in the way affected individuals express the genotype. Then we use the distance of each individual to the cluster center for his family to define a quantitative trait. This quantitative trait is then subjected to a genome scan using variance components methods. RESULTS: The method is applied to a data set of 27 multigenerational families with asthma, and a novel locus at 2q33 (at 210 cM) is identified. CONCLUSIONS: The proposed method has the potential to identify loci near genes that increase risk for asthma related phenotypes. The method could be used for other complex disorders that exhibit locus heterogeneity.