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PubMed2005

The evolution of genetic architecture. I. Diversification of genetic backgrounds by genetic drift.

The genetic architecture of a phenotype plays a critical role in determining phenotypic evolution through its effects on patterns of genetic variation. Genetic architecture is often considered to be constant in evolutionary quantitative genetic models. However, genetic architecture may be variable and itself evolve when there are dominance and epistatic interactions among alleles at the same and different loci, respectively. The evolution of genetic architecture by genetic drift is examined here by testing the breeding value of four standard inbred mouse strains mated across a set of 26 related recombinant quasi-inbred (RqI) lines generated from the intercross of the Large (LG/J) and Small (SM/J) inbred mouse strains. Phenotypes of interest include age-specific body weights, growth, and adult body composition. If the genetic architecture of these traits has differentiated by genetic drift during the production of the RqI strains, we should observe interactions between tester strain and RqI strain. The breeding values of the tester strains will change relative to one another depending on which RqI strain they are crossed to. The study included an average of 15.1 offspring per cross, over a total of 100 different crosses. Multivariate and univariate analyses of variance indicate that there is strongly significant interaction for all traits. Interaction is more pronounced in males than in females and accounted for an average of about 40% of the explained variation in males and 30% in females. These results indicate that the genetic architecture of these traits has differentiated by genetic drift in the RqI strains since their isolation from a common founder population. Further analysis indicates that this differentiation results in changes in the order of tester strain effects so that common patterns of selection in these differentiated populations could result in the fixation of different alleles.

Animals↗

PubMed2005

A penalized maximum likelihood method for estimating epistatic effects of QTL.

Although epistasis is an important phenomenon in the genetics and evolution of complex traits, epistatic effects are hard to estimate. The main problem is due to the overparameterized epistatic genetic models. An epistatic genetic model should include potential pair-wise interaction effects of all loci. However, the model is saturated quickly as the number of loci increases. Therefore, a variable selection technique is usually considered to exclude those interactions with negligible effects. With such techniques, we may run a high risk of missing some important interaction effects by not fully exploring the extremely large parameter space of models. We develop a penalized maximum likelihood method. The method developed here adopts a penalty that depends on the values of the parameters. The penalized likelihood method allows spurious QTL effects to be shrunk towards zero, while QTL with large effects are estimated with virtually no shrinkage. A simulation study shows that the new method can handle a model with a number of effects 15 times larger than the sample size. Simulation studies also show that results of the penalized likelihood method are comparable to the Bayesian shrinkage analysis, but the computational speed of the penalized method is orders of magnitude faster.

Bayes Theorem↗

PubMed2002

From Zebrafish to human: modular medical models.

Genetic screens in Drosophila melanogaster, Caenorhabditis elegans, and Danio rerio clarified the logic of metazoan development by revealing critical unitary steps and pathways to embryogenesis. Can genetic screens similarly organize medicine? We here examine human diseases that resemble mutations in Danio rerio, the zebrafish, the one vertebrate species for which large-scale genetic screens have been performed and extensively analyzed. Zebrafish mutations faithfully phenocopy many human disorders. Each mutation, once cloned, provides candidate genes and pathways for evaluation in the human. The collection of mutations in their entirety potentially provides a medical taxonomy, one based in developmental biology and genetics.

Animals↗

PubMed2000

Genetically engineered models with alterations in cardiac membrane calcium-handling proteins.

Regulation of intracellular Ca2+ provides a means by which the strength and duration of cardiac muscle contraction is altered on a beat-to-beat basis. Ca2+ homeostasis is maintained by proteins of the outer cell membrane or sarcolemma and the sarcoplasmic reticulum, which is the major intracellular Ca2+ storage organelle. Recently, genetic engineering techniques designed to induce specific mutations, manipulate expression levels, or change a particular isoform of various membrane Ca(2+)-handling proteins have provided novel approaches in elucidating the physiological role of these gene products in the mammalian heart. This review summarizes findings in murine genetic models with alterations in the expression levels of the sarcolemmal Ca(2+)-ATPase and Na+/Ca2+ exchanger, which move Ca2+ across the cell membrane, and the sarcoplasmic reticulum proteins, which are involved in Ca2+ sequestration (Ca(2+)-ATPase and its regulator, phospholamban), Ca2+ storage (calsequestrin), and Ca2+ release (ryanodine receptor, FK506-binding protein and junctin) during excitation-contraction coupling. Advances in genetic technology, coupled with the development of miniaturized technology to assess cardiac function at multiple levels in the mouse, have added a wealth of new information to our understanding of the functional role of each of these membrane Ca(2+)-handling proteins in cardiac physiology and pathophysiology. Furthermore, these genetic models have provided valuable insights into the compensatory cross-talk mechanisms between the major membrane Ca(2+)-handling proteins in the mammalian heart.

Animals↗

PubMed2002

Dilated cardiomyopathy: concepts derived from gene deficient and transgenic animal models.

Genetic forms of human dilated cardiomyopathy (DCM) are briefly discussed, and a variety of animal models of genetic DCM are presented, some of which are caused by the gene mutations that also cause DCM in humans. The forms of DCM related to mutations or deletion of genes coding for extrasarcomeric or intrasarcomeric proteins, as well as to overexpression or knockout of genes in the beta-adrenergic signaling pathway, are included. Finally, novel approaches to treatment in experimental animal models are discussed, including double transgenesis and newer recombination methods, as well as in vivo somatic gene transfer which, based on initial experiments in animals, seems likely to find eventual application in human cardiac failure.

Animals↗

PubMed2001

Statistical analysis of uniparental disomy data using hidden Markov models.

Genetic studies of uniparental disomy (UPD) employing many markers have helped geneticists to gain a better understanding of the molecular mechanisms underlying nondisjunction. However, most existing methods cannot simultaneously analyze all genetic markers and consistently incorporate crossover interference; they thus fail to make the most use of genetic information in the data. In the present article, we describe a hidden Markov model for multilocus uniparental disomy data. This method is based on the chi-square model for the crossover process and can simultaneously incorporate all marker information including untyped and uninformative markers. We then apply this novel method to analyze a set of UPD15 data.

Biometry↗

PubMed2003

Bone marrow reconstitution as a relevant model of genetically programmed leukemia.

Mouse model systems which allow bone marrow reconstitution can be used to analyse genetically programmed leukemia. The original and most widely used system is that of post 5-fluorouracil mouse hematopoietic stem cells (HSC) into lethally irradiated syngeneic mice. Another more recent system allows analysis of human HSCs in the NOD-SCID mouse. Both systems are discussed as models for analysis of gene induced leukemia.

Animals↗

PubMed1997

Genetic changes associated with the acquisition of androgen-independent growth, tumorigenicity and metastatic potential in a prostate cancer model.

Genetic changes underlying the progression of human prostate cancer are incompletely understood. Recently, an experimental model system that resembles human prostate cancer progression was developed based on the serial passage of an androgen-responsive, non-tumorigenic LNCaP prostate cancer cell line into athymic castrated mice. Six different sublines, derived after one, two or three rounds of in vivo passage, sequentially acquired androgen independence and tumorigenicity as well as metastatic capacity. Here, we used comparative genomic hybridization (CGH) and locus-specific fluorescence in situ hybridization (FISH) analysis to search for genetic changes that may underlie the phenotypic progression events in this model system. Six genetic aberrations were seen by CGH in the parental LNCaP cell line. The derivative sublines shared virtually all these changes, indicating a common clonal origin, but also contained 3-7 additional genetic changes. Gain of the 13q12-q13 chromosomal region as well as losses of 4, 6q24-qter, 20p and 21q were associated with androgen independence and tumorigenicity with additional changes correlating with metastasis. In conclusion, an accumulation of genetic changes correlates with tumour progression in this experimental in vivo model of prostate cancer progression. It is possible that the specific chromosomal aberrations involved in this model system may provide clues to the location of genes involved in human prostate cancer progression and metastasis.

Androgens↗

PubMed2003

Tests for candidate-gene association using case-parents design.

In the case-parents design for testing candidate-gene association, the conditional likelihood method based on genotype relative risks has been developed recently. A specific relation of the genotype relative risks is referred to as a genetic model. The efficient score tests have been used when the genetic model is correctly specified under the alternative hypothesis. In practice, however, it is usually not able to specify the genetic model correctly. In the latter situation, tests such as the likelihood ratio test (LRT) and the MAX3 (the maximum of the three score statistics for dominant, additive, and recessive models) have been used. In this paper, we consider the restricted likelihood ratio test (RLRT). For a specific genetic model, simulation results demonstrate that RLRT is asymptotically equivalent to the score test, and both are more powerful than the LRT. When the genetic model cannot be correctly specified, the simulation results show that RLRT is most robust and powerful in the situations we studied. MAX3 is the next most robust and powerful test. The TDT is the easiest statistic to compute, compared to MAX3 and RLRT. When the recessive model can be eliminated, it is also as robust and powerful as RLRT for other genetic models.

Computer Simulation↗

PubMed2001

Joint modeling of genetic association and population stratification using latent class models.

We show how latent class log-linear models can be used to test for an association between a candidate gene and a disease phenotype in a stratified population when the stratification is unobserved. The stratification may arise because of several ethnic groups or immigration and may lead to spurious associations between several loci and the disease. The information about the stratification is drawn from additional markers that are chosen to be independent of the disease and unlinked to the candidate gene and to each other within each population stratum. We use the EM algorithm to simultaneously estimate all the model parameters, including proportions of individuals in the latent population strata. The latent class model is used to test the phenotype association of single nucleotide polymorphism markers in four candidate regions in population-based case-control data selected from simulated Genetic Analysis Workshop (GAW) 12 population isolate 30. The analysis clearly demonstrates how the number of false positive associations can be reduced when the model accounts for population stratification.

Case-Control Studies↗

PubMed2005

Genetic analyses of mastitis data using animal threshold models and genetic correlation with production traits.

In the present study, 6 different mastitis data sets of 3 dairy herds with an overall herd size of 3200 German Holstein cows were analyzed. Data collection periods included the first 50, 100, or 300 d of lactation. The 3 data collection periods were analyzed with a lactation model and a test-day model. All models were animal threshold models. Mastitis frequencies in the lactation model data sets varied between 29 and 45%, and varied between 3 and 6% in the test-day model data sets. Depending on the period of data collection, heritabilities of liability to mastitis in the lactation models were 0.05 (50 d), 0.06 (100 d), and 0.07 (300 d). In the test-day models, heritabilities were slightly higher with values of 0.09 (50 and 100 d), and 0.06 (300 d). Between lactation models, the rank correlations between the relative breeding values were high and varied between 0.86 and 0.94. Rank correlations between the relative breeding values of the test-day models ranged from 0.68 to 0.87. The rank correlations between the relative breeding values of lactation models and test-day models varied from 0.51 and 0.80. Genetic correlations between mastitis and milk production traits were estimated with a linear animal test-day model. The correlations with mastitis were 0.29 (milk yield), 0.30 (fat yield), 0.20 (fat content), 0.34 (protein yield), and 0.20 (protein content). The estimated genetic correlation between mastitis and somatic cell score was 0.84.

Animals↗

PubMed2006

Ocean ecology: don't fence me in.

New research that combines ocean circulation and genetic models to predict population structure of corals will help conservation efforts in tropical reef ecosystems.

Animals↗

PubMed2005

Abnormal patterns of maternal behavior in a genetic animal model of depression.

The Flinders Sensitive Line (FSL) model is considered a genetic animal model of depression. Among other characteristics, FSL rats express stress-induced anhedonia and an abnormal dopaminergic system. Our hypothesis was that FSL rats would show abnormal maternal behaviors, especially reduced motivation to reach and care for pups and reduced licking and non-nutritive contact, based on their anhedonic characteristics. Mother-infant interactions were assessed by time limited observations in FSL and Sprague-Dawley (SD) controls. In study 1, differences were found in consummatory behaviors: FSL dams compared to SD dams showed less licking and significant decrease in non-nutritive contact from the first to the third postpartum weeks. In addition, shorter duration of nursing postures was seen in FSL compared to SD dams in the first week postpartum, and this difference was significantly increased by the third week postpartum. In study 2, after exposure to acute swim stress, differences emerged in appetitive behaviors: latencies to reach and care for pups were longer in FSL dams compared to controls, suggesting a stress-induced motivational deficit in FSL dams. Possible explanations, especially regarding the FSL dams' reward system are discussed.

Analysis of Variance↗

PubMed1997

Sensitivity of segregation analysis to data structure and transformation: a case study of trypanotolerance in mice.

Sensitivity of segregation analysis for data structure and data transformation was studied using data from two trials in which mice were challenged at three months of age with a cloned isolate of Trypanosoma congolense and survival time was recorded. Data included records from three inbred strains (C57BL/6 (tolerant), A/J, and BALB/c (both susceptible)) and their crosses. Data were standardized and normalized using a modified power transformation. Segregation analysis was applied to both untransformed and transformed data to determine the genetic inheritance of trypanotolerance in these mice. Data from the two trials were analysed separately and combined. Four genetic models were compared; a one locus model, a polygenic model, a mixed model with common variance, and a mixed model with different variances for each major genotype. Even though the separate data sets and the combined data set all supported the hypothesis of a major gene (or a tightly linked cluster of genes) with different variances within each genotype, parameter estimates were highly sensitive to data transformation and several sets of parameter estimates gave similar likelihood values because of high dependency between parameters. Based on the results segregation analysis can be very sensitive to data structure in a crossbreeding design and to data transformation. Interpretation of the results can be misleading if the entire parameter space is not studied carefully.

Animals↗

PubMed2000

Coevolutionary clines across selection mosaics.

Much of the dynamics of coevolution may be driven by the interplay between geographic variation in reciprocal selection (selection mosaics) and the homogenizing action of gene flow. We develop a genetic model of geographically structured coevolution in which gene flow links coevolving communities that may differ in both the direction and magnitude of reciprocal selection. The results show that geographically structured coevolution may lead to allele-frequency clines within both interacting species when fitnesses are spatially uniform or spatially heterogeneous. Furthermore, the results show that the behavior and shape of clines differ dramatically among different types of coevolutionary interaction. Antagonistic interactions produce dynamic clines that change shape rapidly through time, producing shifting patterns of local adaptation and maladaptation. Unlike antagonistic interactions, mutualisms generate stable equilibrium patterns that lead to fixed spatial patterns of adaptation. Interactions that vary between mutualism and antagonism produce both equilibrium and dynamic clines. Furthermore, the results demonstrate that these interactions may allow mutualisms to persist throughout the geographic range of an interaction, despite pockets of locally antagonistic selection. In all cases, the coevolved spatial patterns of allele frequencies are sensitive to the relative contributions of gene flow, selection, and overall habitat size, indicating that the appropriate scale for studies of geographically structured coevolution depends on the relative contributions of each of these factors.

Alleles↗

PubMed2006

Animal models of Huntington's disease: implications in uncovering pathogenic mechanisms and developing therapies.

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder, which is caused by an abnormal expansion of Cytosine Adenine Guanine (CAG) trinucleotide repeat in the gene making huntingtin (Htt). Despite intensive research efforts devoted to investigate molecular mechanisms of pathogenesis, effective therapy for this devastating disease is still not available at present. The development of various animal models of HD has offered alternative approaches in the study of HD molecular pathology. Many HD models, including chemical-induced models and genetic models, mimic some aspects of HD symptoms and pathology. To date, however, there is no ideal model which replicates all of the essential features of neuropathology and progressive motor and cognitive impairments of human HD. As a result, our understanding of molecular mechanisms of pathogenesis in HD is still limited. A new model is needed in order to uncover the pathogenesis and to develop novel therapies for HD. In this review we discussed usefulness and limitations of various animal and cellular models of HD in uncovering molecular mechanisms of pathogenesis and developing novel therapies for HD.

Animals↗

PubMed1980

[Models of genetic hypertension in the rat: recent trends].

Six principal strains of rats have been selected to develop spontaneous or induced hypertension, the SHR (Okamoto) strain alone being widely used by experimenters. This is the case in France and it is to be desired that investigations are extended at two levels: introduction of new substrains; improvement and comparative genetic study of available strains. The Centre de Sélection et d'Elevage d'Animaux de Laboratoire is already engaged in that way, making every effort to promote the utilization of the newly obtained SHR SP subline as well as that of the SHR-fa subline which is in the process of being established.

Animals↗

PubMed2026

Using deep learning models as a genetic architecture for the simulation of breeding schemes.

In several simulation studies, long-term selection led to the rapid depletion of genetic variance. These outcomes differ from real-life observations that we aim to replicate, thereby highlighting a fundamental limitation of current classical quantitative genetic simulation models. Deep learning (DL) models have demonstrated promising results in capturing complex interactions essential for maintaining genetic variance; thus, we hypothesize that DL-based genetic simulation models may preserve more genetic variance than classical models, because the biological pathways underlying complex traits exhibit interactions that classical models ignore. The primary objective of this study was to introduce alternative DL-based genetic simulation models and compare them with classical genetic simulation models in terms of their retention of additive genetic variance under truncation selection in a simulated full-sib pig breeding scheme using real haplotypes as founders. After 20 generations of directional truncation selection, the classical models (A, ADAA, and ADAAADDD) retained between 55% and 64% of their initial additive genetic variance. In contrast, while the DL_simple model lost all its additive variance, the DL medium retained 92% to 98% of its additive variance, and the DL_complex model's initial additive variance increased by 296% to 314%. This paper introduces DL-based genetic simulation models and concludes that their ability to retain additive genetic variance depends on the models' architectural complexity. When sufficiently complex, DL-based models exhibit greater retention of additive genetic variance because they intrinsically capture epistatic interactions that are converted into additive variance, as selection progresses, thus, affirming the role of non-additive genetic effects in maintaining long-term genetic variation.

Deep Learning↗