Pregnancy loss and autoantibodies against phospholipid-binding proteins

We investigated the utility of two approaches for exploiting pleiotropy to search for genes influencing related traits. To do this we first assessed the genetic correlations among a set of five closely related quantitative traits (Q1, Q2, Q3, Q4, Q5). We then used the genetic correlations among these five traits both to remove the common genetic effects of the four remaining traits, thereby identifying the unique genetic contribution to each trait, and to extract a synthetic phenotype which exploits the shared genetic information (pleiotropy) among these five traits. After obtaining these conditional traits, we then searched for evidence of quantitative trait loci (QTLs) (using variance component linkage) influencing the unique residual genetic component for each trait as well as those influencing the expression of the synthetic traits. From this work, we conclude that the removal of the common genetic effects of other traits in a group may be of greater utility when the majority of the pleiotropy initially detected between traits is attributable to the shared additive effects of polygenes, rather than to those of major loci. By contrast, decomposition of the genetic covariance matrix to its principal components is a greater utility when the majority of pleiotropy is attributable to major loci.     

Advances in statistical methods to map quantitative trait loci in outbred populations

Statistical methods to map quantitative trait loci (QTL) in outbred populations are reviewed, extensions and applications to human and plant genetic data are indicated, and areas for further research are identified. Simple and computationally inexpensive methods include (multiple) linear regression of phenotype on marker genotypes and regression of squared phenotypic differences among relative pairs on estimated proportions of identity-by-descent at a locus. These methods are less suited for genetic parameter estimation in outbred populations but allow the determination of test statistic distributions via simulation or data permutation; however, further inferences including confidence intervals of QTL location require the use of Monte Carlo or bootstrap sampling techniques. A method which is intermediate in computational requirements is residual maximum likelihood (REML) with a covariance matrix of random QTL effects conditional on information from multiple linked markers. Testing for the number of QTLs on a chromosome is difficult in a classical framework. The computationally most demanding methods are maximum likelihood and Bayesian analysis, which take account of the distribution of multilocus marker-QTL genotypes on a pedigree and permit investigators to fit different models of variation at the QTL. The Bayesian analysis includes the number of QTLs on a chromosome as an unknown.     

Genes on mouse chromosomes 2 and 9 determine variation in ethanol consumption

Quantitative trait locus (QTL) mapping efforts in alcohol (ethanol) research are beginning to generate promising data that may ultimately lead to the identification of genes influencing alcohol addiction. Rodents have been extensively utilized to study ethanol's rewarding and aversive effects, and to demonstrate the existence of genetic influences on traits such as free-choice ethanol-consumption, ethanol-conditioned place preference and ethanol-conditioned taste aversion. The purpose of the current investigation was to verify or eliminate from further consideration putative QTLs for free-choice ethanol consumption originally identified in BXD Recombinant Inbred (RI) strains and other informative genetic crosses. B6D2F2 mice were utilized in a verification testing strategy to evaluate the viability of putative ethanol consumption QTLs. When data were combined from BXD RI, B6D2F2 and short-term selected line (STSL) mapping studies, verification was obtained for two QTLs, one on Chromosome (Chr) 9 (proximal-mid) and another on Chr 2 (distal), and suggestive verification was obtained for QTLs on Chrs 2 (proximal), 3, 4, 7, and 15. In addition, the possible genetic association of ethanol consumption with conditioned place preference was evaluated. Genetic correlations were estimated from BXD RI strain means, and QTL maps for these traits were compared to evaluate the possibility of a genetic association. The correlational analysis yielded a trend (r = 0.34, p = 0.09), but no statistically significant results. However, comparisons of QTL mapping results between phenotypes suggested some possible genetic overlap for these traits, both putative measures of ethanol reward. These data suggest that the determinants of these two measures are genetically diverse, but may share some common genetic elements.     

Surgical treatment of primary malignant chest wall tumours

Our objective was to infer the genetic model for the quantitative traits using a variety of methods developed in our group. Only a single data set was analyzed in any one analysis, although some comparison between data sets was made. In addition, the simulated model was not known during the course of the analysis. Basic modeling and segregation analyses for the five quantitative traits was followed by several simple genome scans to indicate areas of interest. A Markov chain Monte Carlo (MCMC) multipoint quantitative trait locus (QTL) mapping approach was then used to estimate the posterior probabilities of linkage of QTL to each chromosome simultaneously with trait model parameters, and to further localize the genes. Comparisons between the nuclear family and pedigree data sets indicated a greater power for QTL detection and mapping with the pedigree data sets. Even with the pedigree data, however, precise localization of the QTL did not appear to be possible using single replicate data sets. Two of the three genes with effects on trait Q1 were detected by the MCMC method.     

Molecular dissection of developmental behavior of plant height in rice (Oryza sativa L.)

A doubled haploid population of 123 lines from IR64/Azucena was used to dissect the developmental behavior and genotype by environment interaction for plant height by conditional and unconditional quantitative trait loci (QTL) mapping methods in rice. It was shown that the number of QTL detected was different at various measuring stages. Some QTL could be detected at all stages and some only at one or several stages. More QTL could be found on the basis of time-dependent measures of different stages. By conditional QTL mapping of time-dependent measures, it is possible to reveal dynamic gene expression for quantitative traits. Mapping QTL for genetic main effects and GE interaction effects could help us in understanding the nature of QTL x environment interaction for the development of quantitative traits.     

The molecular basis of quantitative genetic variation in central and secondary metabolism in Arabidopsis

To find the genes controlling quantitative variation, we need model systems where functional information on physiology, development, and gene regulation can guide evolutionary inferences. We mapped quantitative trait loci (QTLs) influencing quantitative levels of enzyme activity in primary and secondary metabolism in Arabidopsis. All 10 enzymes showed highly significant quantitative genetic variation. Strong positive genetic correlations were found among activity levels of 5 glycolytic enzymes, PGI, PGM, GPD, FBP, and G6P, suggesting that enzymes with closely related metabolic functions are coregulated. Significant QTLs were found influencing activity of most enzymes. Some enzyme activity QTLs mapped very close to known enzyme-encoding loci (e.g., hexokinase, PGI, and PGM). A hexokinase QTL is attributable to cis-acting regulatory variation at the AtHXK1 locus or a closely linked regulatory locus, rather than polypeptide sequence differences. We also found a QTL on chromosome IV that may be a joint regulator of GPD, PGI, and G6P activity. In addition, a QTL affecting PGM activity maps within 700 kb of the PGM-encoding locus. This QTL is predicted to alter starch biosynthesis by 3.4%, corresponding with theoretical models, suggesting that QTLs reflect pleiotropic effects of mutant alleles.     

Multiple-trait mapping of quantitative trait loci after selective genotyping using logistic regression

Experiments to map QTL usually measure several traits, and not uncommonly genotype only those animals that are extreme for some trait(s). Analysis of selectively genotyped, multiple-trait data presents special problems, and most simple methods lead to biased estimates of the QTL effects. The use of logistic regression to estimate QTL effects is described, where the genotype is treated as the dependent variable and the phenotype as the independent variable. In this way selection on phenotype does not bias the results. If normally distributed errors are assumed, the logistic-regression analysis is almost equivalent to a maximum-likelihood analysis, but can be carried out with standard statistical packages. Analysis of a simulated half-sib experiment shows that logistic regression can estimate the effect and position of a QTL without bias and confirms the increased power achieved by multiple-trait analysis.     

Selection with recurrent backcrossing to develop congenic lines for quantitative trait loci analysis

SEWALL WRIGHT suggested that genes of large effect on a quantitative trait could be isolated by recurrent backcrossing with selection on the trait. Loci [quantitative trait loci (QTL)] at which the recurrent and nonrecurrent lines have genes of different large effect on the trait would remain segregating, while other loci would become fixed for the gene carried by the recurrent parent. If the recurrent line is inbred and the backcrossing and selection is conducted in a series of replicate lines, in each of which only one backcross parent is selected for each generation, the lines will become congenic to the recurrent parent except for the QTL of large effect and closely linked regions of the genome, and these regions can be identified using a dense set of markers that differ between the parental lines. Such lines would be particularly valuable for subsequent fine-scale mapping and gene cloning; but by chance, even QTL of large effect will be lost from some lines. The probability that QTL of specified effect remain segregating is computed as a function of its effect on the trait, the intensity of selection, and the number of generations of backcrossing. Analytical formulas are given for one or two loci, and simulation is used for more. It is shown that the method could have substantial discriminating ability and thus potential practical value.     

Quantitative trait loci and metabolic pathways

The interpretation of quantitative trait locus (QTL) studies is limited by the lack of information on metabolic pathways leading to most economic traits. Inferences about the roles of the underlying genes with a pathway or the nature of their interaction with other loci are generally not possible. An exception is resistance to the corn earworm Helicoverpa zea (Boddie) in maize (Zea mays L.) because of maysin, a C-glycosyl flavone synthesized in silks via a branch of the well characterized flavonoid pathway. Our results using flavone synthesis as a model QTL system indicate: (i) the importance of regulatory loci as QTLs, (ii) the importance of interconnecting biochemical pathways on product levels, (iii) evidence for "channeling" of intermediates, allowing independent synthesis of related compounds, (iv) the utility of QTL analysis in clarifying the role of specific genes in a biochemical pathway, and (v) identification of a previously unknown locus on chromosome 9S affecting flavone level. A greater understanding of the genetic basis of maysin synthesis and associated corn earworm resistance should lead to improved breeding strategies. More broadly, the insights gained in relating a defined genetic and biochemical pathway affecting a quantitative trait should enhance interpretation of the biological basis of variation for other quantitative traits.     

Estimation of effects of quantitative trait loci in large complex pedigrees

A method was derived to estimate effects of quantitative trait loci (QTL) using incomplete genotype information in large outbreeding populations with complex pedigrees. The method accounts for background genes by estimating polygenic effects. The basic equations used are very similar to the usual linear mixed model equations for polygenic models, and segregation analysis was used to estimate the probabilities of the QTL genotypes for each animal. Method R was used to estimate the polygenic heritability simultaneously with the QTL effects. Also, initial allele frequencies were estimated. The method was tested in a simulated data set of 10,000 animals evenly distributed over 10 generations, where 0, 400 or 10,000 animals were genotyped for a candidate gene. In the absence of selection, the bias of the QTL estimates was < 2%. Selection biased the estimate of the Aa genotype slightly, when zero animals were genotyped. Estimates of the polygenic heritability were 0.251 and 0.257, in absence and presence of selection, respectively, while the simulated value was 0.25. Although not tested in this study, marker information could be accommodated by adjusting the transmission probabilities of the genotypes from parent to offspring according to the marker information. This renders a QTL mapping study in large multi-generation pedigrees possible.     

Quantitative trait locus (QTL) mapping using different testers and independent population samples in maize reveals low power of QTL detection and large bias in estimates of QTL effects

The efficiency of marker-assisted selection (MAS) depends on the power of quantitative trait locus (QTL) detection and unbiased estimation of QTL effects. Two independent samples N = 344 and 107 of F2 plants were genotyped for 89 RFLP markers. For each sample, testcross (TC) progenies of the corresponding F3 lines with two testers were evaluated in four environments. QTL for grain yield and other agronomically important traits were mapped in both samples. QTL effects were estimated from the same data as used for detection and mapping of QTL (calibration) and, based on QTL positions from calibration, from the second, independent sample (validation). For all traits and both testers we detected a total of 107 QTL with N = 344, and 39 QTL with N = 107, of which only 20 were in common. Consistency of QTL effects across testers was in agreement with corresponding genotypic correlations between the two TC series. Most QTL displayed no significant QTL x environment nor epistatic interactions. Estimates of the proportion of the phenotypic and genetic variance explained by QTL were considerably reduced when derived from the independent validation sample as opposed to estimates from the calibration sample. We conclude that, unless QTL effects are estimated from an independent sample, they can be inflated, resulting in an overly optimistic assessment of the efficiency of MAS.     

Mapping quantitative trait loci for immune capacity in the pig

Immune capacity traits show considerable genetic variation in outbred populations. To identify quantitative trait loci (QTLs) for immune capacity in the pig, various measures of immune function (total and differential leukocyte counts, neutrophil phagocytosis, mitogen-induced proliferation, IL-2 production, and virus induced IFN-alpha production in whole blood cultures, and Ab responses to two Escherichia coli antigens) were determined in 200 F2 animals from a wild pig-Swedish Yorkshire intercross. The pedigree has been typed for 236 genetic markers covering all autosomes, the X chromosome and the X/Y pseudoautosomal region. Through interval mapping using a least-squares method, four QTLs with significant effects were identified; one for total leukocyte counts, one for mitogen-induced proliferation, one for prevaccination levels of Abs to E. coli Ag K88, and one for Ab response to the O149 Ag. In addition, several putative QTLs were indicated. The results from the present study conclusively show that it is possible to identify QTLs for immune capacity traits in outbred pig populations by genome analysis.     

Genetic determinants of autoimmune disease and coronary vasculitis in the MRL-lpr/lpr mouse model of systemic lupus erythematosus

MRL-lpr/lpr (MRL/lpr) mice are a model of human autoimmune disease. They exhibit a number of characteristics of systemic lupus erythematosus, including anti-DNA Abs, anti-cardiolipin Abs, immune complex-mediated vasculitis, lymphadenopathy, and severe glomerulonephritis. Although the autoimmune disorder is mediated primarily by mutation of the Fas gene (lpr), which interferes with lymphocyte apoptosis, MRL/lpr mice also have other predisposing genetic factors. In an effort to identify these additional factors, we have applied quantitative trait locus (QTL) mapping using an intercross between MRL/lpr mice and the nonautoimmune inbred strain BALB/cJ. A complete linkage map spanning the entire genome was constructed for 189 intercross progeny, and genetic loci contributing to features of the autoimmunity were identified using statistical analytic procedures. As expected, the primary genetic determinant of autoimmune disease in this cross was the Fas gene on mouse chromosome 19, exhibiting a lod score of 60. In addition, two novel loci, one on chromosome 2 (lod score, 4.3) and one on chromosome 11 (lod score, 3.1), were found to contribute to levels of anti-DNA Abs. Interestingly, the chromosome 19 and chromosome 11 QTLs, but not the chromosome 2 QTL, also exhibited associations with anti-cardiolipin Abs (lod scores, 38.4 and 2.6). We further examined the effects of these QTLs on the development of coronary vasculitis in the F2 mice. Our results indicate that the QTLs on chromosomes 11 and 19 also control the development of vasculitis, demonstrating common genetic determinants of autoantibody levels and vasculitis.     

Genetic mechanisms underlying apimaysin and maysin synthesis and corn earworm antibiosis in maize (Zea mays L.)

C-glycosyl flavones in maize silks confer resistance (i.e., antibiosis) to corn earworm (Helicoverpa zea [Boddie]) larvae and are distinguished by their B-ring substitutions, with maysin and apimaysin being the di- and monohydroxy B-ring forms, respectively. Herein, we examine the genetic mechanisms underlying the synthesis of maysin and apimaysin and the corresponding effects on corn earworm larval growth. Using an F2 population, we found a quantitative trait locus (QTL), rem1, which accounted for 55.3% of the phenotypic variance for maysin, and a QTL, pr1, which explained 64.7% of the phenotypic variance for apimaysin. The maysin QTL did not affect apimaysin synthesis, and the apimaysin QTL did not affect maysin synthesis, suggesting that the synthesis of these closely related compounds occurs independently. The two QTLs, rem1 and pr1, were involved in a significant epistatic interaction for total flavones, suggesting that a ceiling exists governing the total possible amount of C-glycosyl flavone. The maysin and apimaysin QTLs were significant QTLs for corn earworm antibiosis, accounting for 14. 1% (rem1) and 14.7% (pr1) of the phenotypic variation. An additional QTL, represented by umc85 on the short arm of chromosome 6, affected antibiosis (R2 = 15.2%), but did not affect the synthesis of the C-glycosyl flavones.     

Age-specific inbreeding depression and components of genetic variance in relation to the evolution of senescence

Two major theories of the evolution of senescence (mutation accumulation and antagonistic pleiotropy) make different predictions about the relationships between age, inbreeding effects, and the magnitude of genetic variance components of life-history components. We show that, under mutation accumulation, inbreeding decline and three major components of genetic variance are expected to increase with age in randomly mating populations. Under the simplest version of the antagonistic pleiotropy model, no changes in the severity of inbreeding decline, dominance variance, or the genetic variance of chromosomal homozygotes are expected, but additive genetic variance may increase with age. Age-specific survival rates and mating success were measured on virgin males, using lines extracted from a population of Drosophila melanogaster. For both traits, inbreeding decline and several components of genetic variance increase with age. The results are consistent with the mutation accumulation model, but can only be explained by antagonistic pleiotropy if there is a general tendency for an increase with age in the size of allelic effects on these life-history traits.     

Quantitative trait loci for murine growth

Body size is an archetypal quantitative trait with variation due to the segregation of many gene loci, each of relatively minor effect, and the environment. We examine the effects of quantitative trait loci (QTLs) on age-specific body weights and growth in the F2 intercross of the LG/J and SM/J strains of inbred mice. Weekly weights (1-10 wk) and 75 microsatellite genotypes were obtained for 535 mice. Interval mapping was used to locate and measure the genotypic effects of QTLs on body weight and growth. QTL effects were detected on 16 of the 19 autosomes with several chromosomes carrying more than one QTL. The number of QTLs for age-specific weights varied from seven at 1 week to 17 at 10 wk. The QTLs were each of relatively minor, subequal effect. QTLs affecting early and late growth were generally distinct, mapping to different chromosomal locations indicating separate genetic and physiological systems for early and later murine growth.     

Quantitative trait loci affecting differences in floral morphology between two species of monkeyflower (Mimulus)

Conspicuous differences in floral morphology are partly responsible for reproductive isolation between two sympatric species of monkeyflower because of their effect on visitation of the flowers by different pollinators. Mimulus lewisii flowers are visited primarily by bumblebees, whereas M. cardinalis flowers are visited mostly by hummingbirds. The genetic control of 12 morphological differences between the flowers of M. lewisii and M. cardinalis was explored in a large linkage mapping population of F2 plants n = 465 to provide an accurate estimate of the number and magnitude of effect of quantitative trait loci (QTLs) governing each character. Between one and six QTLs were identified for each trait. Most (9/12) traits appear to be controlled in part by at least one major QTL explaining >/=25% of the total phenotypic variance. This implies that either single genes of individually large effect or linked clusters of genes with a large cumulative effect can play a role in the evolution of reproductive isolation and speciation.     

Dominance is the major genetic basis of heterosis in rice as revealed by QTL analysis using molecular markers

A set of 194 F7 lines derived from a subspecific rice cross showing strong F1 heterosis was backcrossed to the two parents. The materials (388 BC1F7 lines, 194 F8 lines, two parents, F1) were phenotyped for 12 quantitative traits. A total of 37 significant QTLs (LOD > or = 2.0) was detected through 141 RFLP markers in the BC1F7 populations. Twenty-seven (73%) quantitative trait loci (QTLs) were detected in only one of the BC1F7 populations. In 82% of these cases, the heterozygotes were superior to the respective homozygotes. The remaining 10 (27%) QTLs were detected in both BC1F7 populations, and the heterozygote had a phenotype falling between those of the two homozygotes and in no instances were the heterozygotes found to be superior to both homozygotes. These results suggest that dominance complementation is the major genetic basis of heterosis in rice. This conclusion was strengthened by the finding that there was no correlation between most traits and overall genome heterozygosity and that there were some recombinant inbred lines in the F8 population having phenotypic values superior to the F1 for all of the traits evaluated--a result not expected if overdominance was a major contributor to heterosis. Digenic epistasis was not evident.     

Analytical and clinical performance of an automated immunoassay system (Immulite) for estradiol in serum

Mouse strains congenic for individual quantitative trait loci (QTLs) conferring hypnotic sensitivity to ethanol were constructed by backcrossing genotypically selected ILS x ISS N2 individuals to either inbred Long Sleep (ILS) or inbred Short Sleep (ISS) mice. We used a novel "speed congenic" approach in which N2 mice were genotyped for markers flanking each of the five originally identified QTLs. Genotypic selection for ISS regions at four of the five QTLs, and for ILS/ISS at the fifth QTL, allowed rapid fixation of the genetic background. We call this strategy "QTL-Marker-Assisted Counter Selection" or QMACS. By the N4 generation, phenotypic assessments showed that in some sublines the QTL had not been captured; these sublines were discarded and positive lines split to create new replicate sublines. One QTL, on Chromosome (Chr) 8, was not confirmed. At the N8, virtually all sublines on the remaining QTLs retained the phenotypic difference between heterozygotes and ISS homozygotes. Small numbers of interim congenics were produced at the N6 and later generations in which the ILS QTL was made homozygous on the ISS background; as expected, these congenic mice showed an increased sleep time. For later backcrosses (after the N4), the parents were selected on the basis of phenotype as well as genotype. The parent-offspring correlation over all QTLs was significant, supporting the use of phenotypic selection in congenic construction.     

Quantitative trait loci for honey bee stinging behavior and body size

A study was conducted to identify quantitative trait loci (QTLs) that affect colony-level stinging behavior and individual body size of honey bees. An F1 queen was produced from a cross between a queen of European origin and a drone descended from an African subspecies. Haploid drones from the hybrid queen were individually backcrossed to sister European queens to produce 172 colonies with backcross workers that were evaluated for tendency to sting. Random amplified polymorphic DNA markers were scored from the haploid drone fathers of these colonies. Wings of workers and drones were used as a measure of body size because Africanized bees in the Americas are smaller than European bees. Standard interval mapping and multiple QTL models were used to analyze data. One possible QTL was identified with a significant effect on tendency to sting (LOD 3.57). Four other suggestive QTLs were also observed (about LOD 1.5). Possible QTLs also were identified that affect body size and were unlinked to defensive-behavior QTLs. Two of these were significant (LOD 3.54 and 5.15).