Covariance analysis with unequal subclass numbers: component-estimation in quantitative genetics.
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Analysis of Variance↗
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The derivation of a common transformation as the solution to a quantitative genetics problem.
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Animals↗
Quantitative genetics and the evolution of ontogeny. II. Genetic and environmental correlations among age-specific characters in randombred house mice.
In this study, phenotypic, genetic, maternal and residual environmental correlations among all pairs of six ages (17, 24, 31, 38, 45 and 52 days) were calculated for each of five morphometric characters (body weight, head length, trunk length, trunk circumference, and tail length) in ICR randombred house mice. The maternal correlations between ages averaged nearly unity for all characters, and the overall level (and integration, I) of the genetic correlations (mean level = 0.82, mean I = 0.85) was greater than that for the residual environmental correlations (mean level = 0.26, mean I = 0.32). The patterns of the phenotypic and genetic correlations were similar, with the highest correlations being between contiguous ages and a relatively smooth decrease in correlations for increasingly distant ages. Nearly all of the genetic covariation among ages was explained by the first (average = 86%) and second (average = 14%) components derived from principal components analysis of the genetic correlations. Genes with basically equal pleiotropic effects presumably generated the first component, genes with different effects among ages the second component. From the patterns of genetic correlations, it is predicted that direct selection on any given age would result in a moderate, positive response at most other ages, with more change in the height relative to the shape of the growth curve for each characteristic.
Aging↗
Quantitative genetics of human erythrocyte glycolytic intermediate concentrations.
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Adenosine Triphosphate↗
Quantitative genetic analysis of arterial oxygen saturation in Tibetan highlanders.
This study was designed to test the hypothesis that genetic differences inferred from biological kinship relationships among individuals contribute to individual variation in percentage of oxygen saturation of arterial hemoglobin (SaO2) in a high-altitude native population. SaO2 data were obtained by pulse oximetry from 354 nonpregnant, healthy Tibetan residents of Pen-Dri, two rural agropastoral villages at 3800-4065 m altitude in Lhasa Municipal District, Tibet Autonomous Region, China. Statistical analyses of these data from 46 pedigrees tested the hypothesis of a significant genetic contribution to SaO2 variation. The average SaO2 was 89.4 +/- 0.2%, with a range of 76-97%. Additive genetic effects account for 44% of the interindividual phenotypic variation in SaO2 in the sample. Complex segregation analysis and variance decomposition analysis determined that 21% of the total phenotypic variation could be explained by a major gene influencing SaO2. Homozygotes for the low-SaO2 allele have a mean SaO2 of 83.6%, whereas heterozygotes and homozygotes for the high-SaO2 allele have means of 87.6% and 88.3%, respectively. This confirms findings in another Tibetan sample and extends the known geographic distribution of the major gene. These results suggest the hypothesis that individuals with the dominant allele for higher SaO2 have a selective advantage in their high-altitude hypoxic environment.
Adaptation, Physiological↗
A multivariate quantitative-genetic analysis of behavioral development in mice.
The present experiment attempted a behavior-genetic dissection of early behavioral development in laboratory mice. To this end, we used a full, replicated diallel cross to uncover the genetical architecture as well as the multivariate genetic structure underlying early behavioral ontogeny. A number of standard sensorimotor tests were administered on postnatal Days 3, 5, 8, 10, 13, 17, and 22 to a total of 622 pups from 120 litters (4-6 pups per litter) from a four times replicated complete diallel cross between five inbred mouse strains. The first day on which an animal showed adult performance was taken as its score on that test. MANOVA did not show any effects of the pup's sex on the speed of development. Hayman's analysis of variance for diallel tables indicated no or only weak additive-genetic effects. Dominance was absent in almost all cases, except for the auricular startle response, where weak directional dominance for fast development was found. These results are in accordance with an evolutionary past of directional selection for well-canalized development. Factor analyses of the phenotypic and additive-genetic correlation matrices indicate that at least two factors are necessary to describe the behavioral variation.
Adaptation, Psychological↗
Genetics of quantitative and qualitative aspects of lung tumorigenesis in the mouse: multiple interacting Susceptibility to lung cancer (Sluc) genes with large effects.
Inbred strains of mice exhibit large differences in their susceptibility to various complex quantitative genetic traits, among which is the susceptibility to lung cancer. These differences are caused by the combined effects of multiple quantitative trait loci (QTLs). Due to their multiplicity, it is relatively difficult and laborious to study the effects of individual QTLs. To dissect complex genetic traits the authors make use of recombinant congenic strains (RCS), a system of mouse inbred strains in which the genetic complexity is reduced. The susceptibility to lung cancer is studied by using the series of O20-congenic-B10.O20 (OcB) RC strains. They are derived from the parental background strain O20 and the parental donor strain B10.O20, two mouse inbred strains that differ from each other in both quantitative and qualitative aspects of lung tumorigenesis. This study describes the segregation of lung tumor number, size, and histology among the OcB RC strains, and indicates that these traits are influenced by multiple interacting QTLs with considerable individual effects. The results suggest that some of the susceptibility loci to lung cancer affect the susceptibility to other types of cancer as well, possibly by functioning systematically.
Adenoma↗
Stability of the G-matrix in a population experiencing pleiotropic mutation, stabilizing selection, and genetic drift.
Quantitative genetics theory provides a framework that predicts the effects of selection on a phenotype consisting of a suite of complex traits. However, the ability of existing theory to reconstruct the history of selection or to predict the future trajectory of evolution depends upon the evolutionary dynamics of the genetic variance-covariance matrix (G-matrix). Thus, the central focus of the emerging field of comparative quantitative genetics is the evolution of the G-matrix. Existing analytical theory reveals little about the dynamics of G, because the problem is too complex to be mathematically tractable. As a first step toward a predictive theory of G-matrix evolution, our goal was to use stochastic computer models to investigate factors that might contribute to the stability of G over evolutionary time. We were concerned with the relatively simple case of two quantitative traits in a population experiencing stabilizing selection, pleiotropic mutation, and random genetic drift. Our results show that G-matrix stability is enhanced by strong correlational selection and large effective population size. In addition, the nature of mutations at pleiotropic loci can dramatically influence stability of G. In particular, when a mutation at a single locus simultaneously changes the value of the two traits (due to pleiotropy) and these effects are correlated, mutation can generate extreme stability of G. Thus, the central message of our study is that the empirical question regarding G-matrix stability is not necessarily a general question of whether G is stable across various taxonomic levels. Rather, we should expect the G-matrix to be extremely stable for some suites of characters and unstable for others over similar spans of evolutionary time.
Analysis of Variance↗
Statistical genetics of an annual plant, Impatiens capensis. I. Genetic basis of quantitative variation.
Analysis of quantitative genetics in natural populations has been hindered by computational and methodological problems in statistical analysis. We developed and validated a jackknife procedure to test for existence of broad sense heritabilities and dominance or maternal effects influencing quantitative characters in Impatiens capensis. Early life cycle characters showed evidence of dominance and/or maternal effects, while later characters exhibited predominantly environmental variation. Monte Carlo simulations demonstrate that these jackknife tests of variance components are extremely robust to heterogeneous error variances. Statistical methods from human genetics provide evidence for either a major locus influencing germination date, or genes that affect phenotypic variability per se. We urge explicit consideration of statistical behavior of estimation and testing procedures for proper biological interpretation of statistical results.
Genetic Variation↗
Genetics of the quantitative Lp(a) lipoprotein trait. I. Relation of LP(a) glycoprotein phenotypes to Lp(a) lipoprotein concentrations in plasma.
The Lp(a) lipoprotein is a complex particle composed of a low density lipoprotein (LDL)-like lipoprotein and the disulfide bonded Lp(a) glycoprotein. The complex represents a quantitative genetic trait. SDS gel electrophoresis under reducing conditions of sera followed by immunoblotting with affinity-purified polyclonal anti-Lp(a) demonstrated inter- and intra-individual size heterogeneity of the glycoprotein with apparent Mr in the range 400-700kDa. According to their relative mobilities compared to apo B-100 the Lp(a) patterns were categorized into phenotypes F, B, S1, S2, S3 und S4 and into the respective double-band phenotypes. This size heterogeneity seems to be controlled by multiple alleles designated LpF, LpB, LpS1, LpS2, LpS3, LpS4 and a null allele (LpO) at a single locus. Phenotype frequencies observed in 441 unrelated subjects were in good agreement with those expected from the genetic hypothesis. Comparison of Lp(a) lipoprotein concentrations in the different phenotypes revealed a highly significant association of phenotypes B, S1 and S2 with high, and phenotypes S3 und S4 with intermediate Lp(a) concentrations. A third mode is represented by the null phenotype were no Lp(a) band is detected upon immunoblotting and Lp(a) lipoprotein is low or absent. We conclude that the same gene locus is involved in determining Lp(a) glycoprotein phenotype and Lp(a) lipoprotein concentrations in plasma. This major gene seems to be the Lp(a) glycoprotein structural gene locus.
Alleles↗
Are genetically informed designs genetically informative? Comment on McGue, Elkins, Walden, and Iacono (2005) and quantitative behavioral genetics.
M. McGue, I. Elkins, B. Walden, and W. G. Iacono presented the findings from a twin study examining the relative contributions of genetic and environmental factors to the developmental trajectories of parent-adolescent relationships. From a behavioral genetics perspective, this study is well conceptualized, is well implemented, and raises some interesting developmental questions. Yet, the classic twin methodology and heritability estimates obfuscate the dynamic gene-ecology transactions that underlie these social developmental trajectories. There is a growing divide between the findings of quantitative behavioral genetics, with its foundational estimate of a statistical genetic influence, and developmental molecular genetics. This comment provides a brief overview of this divide and its implications for the findings of McGue et al. as well as quantitative behavioral genetics more broadly.
Adolescent↗
QU-GENE: a simulation platform for quantitative analysis of genetic models.
MOTIVATION: Classical quantitative genetics theory makes a number of simplifying assumptions in order to develop mathematical expressions that describe the mean and variation (genetic and phenotypic) within and among populations, and to predict how these are expected to change under the influence of external forces. These assumptions are often necessary to render the development of many aspects of the theory mathematically tractable. The availability of high-speed computers today provides opportunity for the use of computer simulation methodology to investigate the implications of relaxing many of the assumptions that are commonly made. RESULTS: QU-GENE (QUantitative-GENEtics) was developed as a flexible computer simulation platform for the quantitative analysis of genetic models. Three features of the QU-GENE software that contribute to its flexibility are (i) the core E(N:K) genetic model, where E is the number of types of environment, N is the number of genes, K indicates the level of epistasis and the parentheses indicate that different N:K genetic models can be nested within types of environments, (ii) the use of a two-stage architecture that separates the definition of the genetic model and genotype-environment system from the detail of the individual simulation experiments and (iii) the use of a series of interactive graphical windows that monitor the progress of the simulation experiments. The E(N:K) framework enables the generation of families of genetic models that incorporate the effects of genotype-by-environment (G x E) interactions and epistasis. By the design of appropriate application modules, many different simulation experiments can be conducted for any genotype-environment system. The structure of the QU-GENE simulation software is explained and demonstrated by way of two examples. The first concentrates on some aspects of the influence of G x E interactions on response to selection in plant breeding, and the second considers the influence of multiple-peak epistasis on the evolution of a four-gene epistatic network. AVAILABILITY: QU-GENE is available over the Internet at (http://pig.ag.uq.edu.au/qu-gene/) CONTACT: m.cooper@mailbox.uq.edu. au
Computer Simulation↗
Genetics of quantitative traits in Arabidopsis thaliana.
The genetic control of 22 quantitative traits, including developmental rates and sizes, was examined in generations of Arabidopsis thaliana derived from the cross between the ecotypes, Columbia (Col) and Landsberg erecta (Ler). The data were obtained from three sets of families raised in the same trial: the 16 basic generations, that is, parents, F(1)'s, F(2)'s, backcrosses, recombinant inbred lines (RILs) and a triple test cross (TTC), the latter produced by crossing the RILs to Col, Ler and their F(1). The data were analysed by two approaches. The first (approach A) involved traditional generation mean and variance component analysis and the second (B), based around the RILs and TTC families, involved marker-based QTL analysis. From (A), genetic differences between Col and Ler were detected for all traits with moderate heritabilities. Height at flowering was the only trait to show heterosis. Dominance was partial to complete for all height traits, and there was no overdominance but there was strong evidence for directional dominance. For most other traits, dominance was ambidirectional and incomplete, with average dominance ratios of around 80%. Epistasis, particularly of the duplicate type that opposes dominance, was a common feature of all traits. The presence of epistasis must imply multiple QTL for all traits. The QTL analysis located 38 significant effects in four regions of chromosomes I, II, IV and V, but not III. QTL affecting rosette size and leaf number were identified in all four regions, with days to maturity on chromosomes IV and V. The only QTL for height was located at the expected position of the erecta gene (chromosome II; 50 cM), but the additive and dominance effects of this single QTL did not adequately explain the generation means. The possible involvement of other interacting height QTL is discussed.
Analysis of Variance↗
Possible consequences of genes of major effect: transient changes in the G-matrix.
Understanding the process of evolutionary divergence requires knowledge of the strength, form, and targets of selection, as well as the genetic architecture of the divergent traits. Quantitative genetic approaches to understanding multivariate selection and genetic response to selection have proven to be powerful tools in this endeavor, particularly with respect to short-term evolution. However, the application of quantitative genetic theory over periods of substantial phenotypic change is controversial because it requires that the requisite genetic parameters remain constant over the period of time in question. We show herein how attempts to determine the stability of key genetic parameters may be misled by the 'many genes of small effect' type of genetic architecture generally assumed in quantitative genetics. The presence of genes of major effect (GOMEs) can alter the genetic variance-covariance matrix dramatically for brief periods of time, significantly alter the rate and trajectory of multivariate evolution, and thereby mislead attempts to reconstruct or predict long term evolution.
Biological Evolution↗
The genetic basis of complex human behaviors.
Quantitative genetic research has built a strong case for the importance of genetic factors in many complex behavioral disorders and dimensions in the domains of psychopathology, personality, and cognitive abilities. Quantitative genetics can also provide an empirical guide and a conceptual framework for the application of molecular genetics. The success of molecular genetics in elucidating the genetic basis of behavioral disorders has largely relied on a reductionistic one gene, one disorder (OGOD) approach in which a single gene is necessary and sufficient to develop a disorder. In contrast, a quantitative trait loci (QTL) approach involves the search for multiple genes, each of which is neither necessary nor sufficient for the development of a trait. The OGOD and QTL approaches have both advantages and disadvantages for identifying genes that affect complex human behaviors.
Genetics, Behavioral↗
Novel reference molecules for quantitation of genetically modified maize and soybean.
New quantitation methods based on a real-time polymerase chain reaction (PCR) technique were developed for 5 lines of genetically modified (GM) maize, including MON810, Event176, Bt11, T25, and GA21, and a GM soy, Roundup Ready. Oligonucleotide DNA, including specific primers and fluorescent dye-labeled probes, were designed for PCRs. Two plasmids were constructed as reference molecules (RMs) for the detection of GM maize and GM soy. The molecules contain the DNA sequences of a specific region found in each GM line, universal sequences used in various GM lines, such as cauliflower mosaic virus 35S promoter and nopaline synthase terminator, and the endogenous DNA sequences of maize or soy. By using these plasmids, no GM maize and GM soy were required as reference materials for the qualitative and quantitative PCR technique. Test samples containing 0, 0.10, 0.50, 1.0, 5.0, and 10% GM maize or GM soy were quantitated. At the 5.0% level, the bias (mean-true value) ranged from 2.8 to 19.4% and the relative standard deviation was <5.2%. These results show that our method involving the use of these plasmids as RMs is reliable and practical for quantitation of GM maize and GM soy.
Base Sequence↗
Inferring linkage disequilibrium between a polymorphic marker locus and a trait locus in natural populations.
Three approaches are proposed in this study for detecting or estimating linkage disequilibrium between a polymorphic marker locus and a locus affecting quantitative genetic variation using the sample from random mating populations. It is shown that the disequilibrium over a wide range of circumstances may be detected with a power of 80% by using phenotypic records and marker genotypes of a few hundred individuals. Comparison of ANOVA and regression methods in this article to the transmission disequilibrium test (TDT) shows that, given the genetic variance explained by the trait locus, the power of TDT depends on the trait allele frequency, whereas the power of ANOVA and regression analyses is relatively independent from the allelic frequency. The TDT method is more powerful when the trait allele frequency is low, but much less powerful when it is high. The likelihood analysis provides reliable estimation of the model parameters when the QTL variance is at least 10% of the phenotypic variance and the sample size of a few hundred is used. Potential use of these estimates in mapping the trait locus is also discussed.
Alleles↗
On the detection of imprinted quantitative trait loci in experimental crosses of outbred species.
In this article, the quantitative genetic aspects of imprinted genes and statistical properties of methods to detect imprinted QTL are studied. Different models to detect imprinted QTL and to distinguish between imprinted and Mendelian QTL were compared in a simulation study. Mendelian and imprinted QTL were simulated in an F2 design and analyzed under Mendelian and imprinting models. Mode of expression was evaluated against the H(0) of a Mendelian QTL as well as the H(0) of an imprinted QTL. It was shown that imprinted QTL might remain undetected when analyzing the genome with Mendelian models only. Compared to testing against a Mendelian QTL, using the H(0) of an imprinted QTL gave a higher proportion of correctly identified imprinted QTL, but also gave a higher proportion of false inference of imprinting for Mendelian QTL. When QTL were segregating in the founder lines, spurious detection of imprinting became more prominent under both tests, especially for designs with a small number of F1 sires.
Computer Simulation↗