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PubMed2003

Mapping quantitative trait loci underlying triploid endosperm traits.

Endosperm, which is derived from two polar nuclei fusing with one sperm, is a triploid tissue in cereals. Endosperm tissue determines the grain quality of cereals. Improving grain quality is one of the important breeding objectives in cereals. However, current statistical methods for mapping quantitative trait loci (QTL) under diploid genetic control have not been effective for dealing with endosperm traits because of the complexity of their triploid inheritance. In this paper, we derive for the first time the conditional probabilities of F(3) endosperm QTL genotypes given different flanking marker genotypes in F(2) plants. Using these probabilities, we develop a multiple linear regression method implemented via the iteratively reweighted least-squares (IRWLS) algorithm and a maximum likelihood method (ML) implemented via the expectation-maximization (EM) algorithm to map QTL underlying endosperm traits. We use the mean value of endosperm traits of F(3) seeds as the dependent variable and the expectations of genotypic indicators for additive and dominance effect of a putative QTL flanked by a pair of markers as independent variables for IRWLS mapping. However, if an endosperm trait is measured quantitatively using a single endosperm sample, the ML mapping method can be used to separate the two dominance effects. Efficiency of the methods is verified through extensive Monte Carlo simulation studies. Results of simulation show that the proposed methods provide accurate estimates of both the QTL effects and locations with very high statistical power. With these methods, we are now ready to map endosperm traits, as we can for regular quantitative trait under diploid control.

Chromosome Mapping↗
PubMed1999

Whole genome scan in chickens for quantitative trait loci affecting carcass traits.

An experiment was conducted to enable quantitative trait loci (QTL) mapping for carcass traits. The population consisted of 10 full-sib families originating from a cross between male and female founders chosen from two different outcross broiler lines. Founder animals, parents, offspring, and grandoffspring are denoted as generation (G) 0, 1, 2, and 3 animals, respectively. Microsatellite marker genotypes were collected on G1 and G2 animals. Phenotypic observations were collected on G3 animals. Recorded traits were BW at 48 d, carcass weight, carcass percentage, breast meat color, and leg score. Average adjusted progeny trait values were calculated for each G2 animal and for each trait after adjusting phenotypic observations on G3 animals for fixed effects, covariables, the additive genetic contribution of the other parent, and differences between sexes. The average adjusted progeny trait values were used as the dependent variable in the QTL analysis. A QTL analysis was undertaken by modeling the segregation from G1 to G2, using a full-sib across family regression interval mapping approach. In total, 27 autosomal linkage groups covered with 420 markers were analyzed. Genomewise significance thresholds were derived using the permutation test and a Bonferroni correction. Two QTL, affecting two of the five analyzed traits, exceeded suggestive linkage. The most significant QTL was located on Chromosome 1 at 466 cM and showed an effect on carcass percentage. The other QTL, which affected meat color, was located on Chromosome 2 and gave a peak at 345 and 369 cM.

Animals↗
PubMed1998

Mapping quantitative trait loci for carcass and meat quality traits in a wild boar x Large White intercross.

An intercross between wild boar and a domestic Large White pig population was used to map quantitative trait loci (QTL) for body proportions, weight of internal organs, carcass composition, and meat quality. The results concerning growth traits and fat deposition traits have been reported elsewhere. In the present study, all 200 F2 animals, their parents, and their grandparents were genotyped for 236 markers. The marker genotypes were used to calculate the additive and dominance coefficients at fixed positions in the genome of each F2 animal, and the trait values were regressed onto these coefficients in intervals of 1 cM. In addition, the effect of proportion of wild boar alleles was tested for each chromosome. Significant QTL effects were found for percentage lean meat and percentage lean meat plus bone in various cuts, proportion of bone in relation to lean meat in ham, muscle area, and carcass length. The significant QTL were located on chromosomes 2, 3, 4, and 8. Each QTL explained 9 to 16% of the residual variance of the traits. Gene action for most QTL was largely additive. For meat quality traits, there were no QTL that reached the significance threshold. However, the average proportion of wild boar alleles across the genome had highly significant effects on reflectance and drip loss. The results show that there are several chromosome regions with a considerable effect on carcass traits in pigs.

Animals↗
PubMed2004

Detection of quantitative trait loci affecting milk production, health, and reproductive traits in Holstein cattle.

We report putative quantitative trait loci affecting female fertility and milk production traits using the merged data from two research groups that conducted independent genome scans in Dairy Bull DNA Repository grandsire families to identify quantitative trait loci (QTL) affecting economically important traits. Six families used by both groups had been genotyped for 367 microsatellite markers covering 2713.5 cM of the cattle genome (90%), with an average spacing of 7.4 cM. Phenotypic traits included PTA for pregnancy rate and daughter deviations for milk, protein and fat yields, protein and fat percentages, somatic cell score, and productive life. Analysis of the merged dataset identified putative quantitative trait loci that were not detected in the separate studies, and the pregnancy rate PTA estimates that recently became available allowed detection of pregnancy rate QTL for the first time. Sixty-one putative significant marker effects were identified within families, and 13 were identified across families. Highly significant effects were found on chromosome 3 affecting fat percentage and protein yield, on chromosome 6 affecting protein and fat percentages, on chromosome 14 affecting fat percentage, on chromosome 18 affecting pregnancy rate, and on chromosome 20 affecting protein percentage. Within-family analysis detected putative QTL associated with pregnancy rate on six chromosomes, with the effect on chromosome 18 being the most significant statistically. These findings may help identify the most useful markers available for QTL detection and, eventually, for marker-assisted selection for improvement of these economically important traits.

Animals↗
PubMed2005

Detection of quantitative trait loci influencing conformation traits and calving ease in Holstein-Friesian cattle.

An extension of our previous genome scan of a North American Holstein-Friesian population was conducted to identify quantitative trait loci (QTL) affecting conformation traits. Resource families consisted of 1404 sons of 10 elite sires. Genome coverage was estimated to be 2713.5 cM (90%) for 406 markers using a granddaughter design. Regression interval mapping was used to detect QTL affecting 22 conformation traits, including body, udder, feet and legs, and dairy conformation as well as calving ease. Analysis of the families jointly identified 41 chromosome-wise significant QTL influencing conformation traits and 3 significant QTL influencing calving ease on 20 chromosomes. The false discovery rate method was used to account for multiple testing and 3/4 of the suggestive and 5/6 of significant QTL should be real effects. Fourteen of the 44 QTL were significant at the genome-wise level. Comparison of these results with other published reports identifies common QTL affecting conformation traits. Regions on 10 chromosomes appear to affect multiple traits, including conformation, milk production, and somatic cell score, within these particular US Holstein families. Additional work is needed to determine the precise locations of the QTL and select positional candidate genes influencing these traits.

Animals↗
PubMed2007

Detection of quantitative trait loci affecting lameness and leg conformation traits in Danish Holstein cattle.

Lameness is an important factor for culling animals. Strong legs and feet improve herd life of dairy cows. Therefore, many countries include leg and feet conformation traits in their breeding programs, often as early predictors of longevity. However, few countries directly measure lameness related traits to include these in a breeding program. Lameness indices in 3 different lactations and 5 leg conformation traits (rear legs side view, rear legs rear view, hock quality, bone quality, and foot angle) were measured on granddaughters of 19 Danish Holstein grandsires with 33 to 105 sons. A genome scan was performed to detect quantitative trait loci (QTL) based on the 29 autosomes using microsatellite markers. Data were analyzed across and within families for QTL affecting lameness and leg conformation traits. A regression method and a variance component method were used for QTL detection. Two QTL each for lameness in the first [Bos taurus autosome (BTA); BTA5, BTA26] and second (BTA19, BTA22) lactations were detected. For the 5 different leg conformation traits, 7 chromosome-wise significant QTL were detected across families for rear legs side view, 5 for rear legs rear view, 4 for hock quality, 4 for bone quality, and 1 for foot angle. For those chromosomes where a QTL associated with 2 different traits was detected (BTA1, BTA11, BTA15, BTA26, and BTA27), a multitrait-1-QTL model and a multitrait-2-QTL model were performed to characterize these QTL as single QTL with pleiotropic effects or distinct QTL.

Animals↗
PubMed2026

Multi-trait GWAS identifies pleiotropic loci shared between early pregnancy bleeding and psychiatric traits.

INTRODUCTION: Early pregnancy bleeding is a common pregnancy complication, yet its genetic basis and potential links with psychiatric traits remain poorly understood. This study aimed to characterize the shared genetic architecture between early pregnancy bleeding and reproductive, psychiatric, and cardiometabolic traits. METHODS: We integrated linkage disequilibrium score regression (LDSC), local genetic correlation analysis (LAVA), and multi-trait genome-wide association analysis (MTAG). LDSC was used to estimate genome-wide genetic correlations, including sex-stratified analyses. LAVA was applied to identify genomic regions contributing to local genetic sharing. Guided by these correlation patterns, MTAG was performed to improve locus discovery, followed by cis-eQTL analysis using GTEx v8 to explore potential regulatory mechanisms. RESULTS: LDSC revealed significant positive genetic correlations between early pregnancy bleeding and reproductive traits, including endometriosis, miscarriage, and uterine fibroids. Strong positive correlations were also observed with several psychiatric disorders, including major depressive disorder, post-traumatic stress disorder, and attention deficit hyperactivity disorder. Sex-stratified analyses suggested stronger genetic correlations with emotional reactivity-related traits in females, whereas social and behavioral traits were more prominent in males. LAVA localized these shared signals to specific genomic regions and identified pleiotropic hotspots at 8q21 near RUNX1T1 and 9p21 near CDKN2A/B. MTAG identified two novel loci, 15q15.1 marked by rs45457497 and 11q13.1 marked by rs2452681. Cis-eQTL analysis showed that the lead variant at 15q15.1 regulates RMDN3 expression across multiple brain regions, while the 11q13.1 locus regulates PACS1, GAL3ST3, and SF3B2 expression in brain tissues and the pituitary. DISCUSSION: These findings position early pregnancy bleeding as a multifactorial trait shaped by shared reproductive, psychiatric, neuroendocrine, and stress-related biology. The implication of RMDN3, which encodes a mitochondrial outer membrane protein involved in ER-mitochondria tethering and calcium homeostasis, suggests a potential molecular link between neuroendocrine stress pathways, psychiatric susceptibility, and reproductive vulnerability.

RMDN3↗
PubMed1981

Quantitative genetics of skeletal nonmetric traits in the rhesus macaques on Cayo Santiago. II. Phenotypic, genetic, and environmental correlations between traits.

The general lack of phenotypic correlation among skeletal nonmetric traits has been interpreted as indicating a lack of genetic correlation among these traits. Nonmetric traits scored on animals in the skeletal collection of rhesus macaques from Cayo Santiago are used to calculate phenotypic, genetic, and environmental correlations between traits. The results show that even when phenotypic correlations are low, there may be large, significant genetic correlations among these traits. The genetic correlation pattern suggests that genes which affect nonmetric trait variation act primarily at a local level in the cranium, even though there are genes with pleiotropic effects on skeletal nonmetric traits throughout the cranium. Environmental and phenotypic correlations do not show this neighborhood pattern of correlation.

Animals↗
PubMed1999

A genomic scan of porcine reproductive traits reveals possible quantitative trait loci (QTLs) for number of corpora lutea.

Reproductive traits have low heritabilities, are expressed in only one sex, and are not measurable until sexual maturity (Avalos and Smith, Anim Prod 44:153, 1987). Using traditional methods, selection for reproductive traits is relatively less effective than selecting for growth or carcass traits. Traits most affected by a small number of genes with major effects rather than many genes with small effects are most amenable to MAS. As part of our porcine genome scan to identify quantitative trait loci (QTLs) of economic importance in marker-assisted selective (MAS) breeding programs, we examined 8 reproductive and farrowing traits in the University of Illinois (UI) Meishan x Yorkshire Resource Family. Gilts were genotyped with 119 microsatellite markers (MS) with intervals averaging 24 cM over all 18 porcine autosomes. F-ratios supporting QTL location were calculated by the least squares regression method. Results suggestive of linkage at the 5% genome-wide level were observed for the number of stillborn piglets on Chromosome (Chr) 4 (SSC4) (p-value = 0.0001), corpora lutea on SSC8 (p-value = 0.00027), and gestation length on SSC9 (p-value = 0.00019). Results for additional loci relevant to litter size, number of corpora lutea on SSC15 and 7 (p-value = 0.0029 and 0.0028 at 107 and 150 cM, respectively), gestation length on SSC15 and 1 (p-value = 0.0017 and 0.0069 at 96 and 166 cM, respectively), uterine length on SSC7 and 5 (p-value = 0.0044 and 0.0075 at 148 and 1 cM, respectively) and piglets born per litter on SSC6 (p-value = 0.0075 at 102 cM), were not statistically significant at the 5% genome-wide level. Thus, the use of a linked marker to facilitate selection for reproductive traits has considerable potential. By using linked markers, selection can be applied to both sexes before sexual maturity, making genetic selection considerably more efficient and less costly.

Animals↗
PubMed2005

Linking versus thinking: evidence for the different associative and attributional bases of spontaneous trait transference and spontaneous trait inference.

Informants who describe others' behaviors are perceived as having more of the trait implied by the behavior they describe (the trait transference effect). Associative and attributional explanations for this phenomenon are reviewed and examined in 3 experiments. Findings were inconsistent with attributional interpretations: (a) transference effects persisted with extended coding times, online judgments, and warnings to participants to avoid the effect; (b) negativity effects were absent in transference but occurred with trait inference; and (c) transference effects failed to generalize beyond the particular trait implied by informants' descriptions. Moreover, forcing participants to recall the target of informants' descriptions just prior to trait judgments eliminated the transference effect while enhancing inference effects. These results contradict nonassociative explanations and indicate that different processes underlie spontaneous trait transference and spontaneous trait inference.

Association↗
PubMed1995

Interactions between quantitative trait loci in soybean in which trait variation at one locus is conditional upon a specific allele at another.

A large recombinant inbred population of soybean has been characterized for 220 restriction fragment-length polymorphism (RFLP) markers. Values for agronomic traits also have been measured. Quantitative trait loci (QTL) for height, yield, and maturity were located by their linkage to RFLP markers. QTL controlling large amounts of trait variation were analyzed for the dependence of trait variation on particular alleles at a second locus by comparing cumulative distributions of the trait for each genotype (four genotypes per pair of loci). Interesting pairs of loci were analyzed statistically with maximum likelihood and Monte Carlo comparison of additive and epistatic models. For each locus affecting height, variation was conditional upon the presence of a particular allele at a second unlinked locus that itself explained little or no trait variation. The results show that interactions between QTL are frequent and control large effects. Interactions distinguished between different QTL in a single linkage group and between QTL that affect different traits closely linked to one RFLP marker--i.e., distinguished between pleiotropy and closely linked genes. The implications for the evolution of inbreeding plants and for the construction of agronomic breeding strategies are discussed.

Alleles↗
PubMed2007

Why do people perceive ingroup homogeneity on ingroup traits and outgroup homogeneity on outgroup traits?

People tend to perceive ingroup homogeneity on ingroup stereotypical traits and outgroup homogeneity on outgroup stereotypical traits (e.g., Kelly, 1989; Simon, 1992a; Simon & Pettigrew, 1990). If it is assumed that people use homogeneity ratings to indicate the extent to which groups possess traits, then this stereotype effect may be interpreted as an expression of perceived trait possession (i.e., ingroups possess ingroup stereotypical traits and outgroups possess outgroup stereotypical traits). If it is further assumed that research participants abide by the conversational norm of appropriate quantity (e.g., Bless, Strack, & Schwarz, 1993), then this stereotype effect should be significantly reduced following prior expressions of perceived trait possession. A literature review and two minimal group experiments (Ns = 75, 104) supported this prediction. This evidence is discussed in relation to the outgroup homogeneity effect and self-categorization theory.

Adult↗
PubMedDate not supplied

A bivariate quantitative genetic model for a linear Gaussian trait and a survival trait.

With the increasing use of survival models in animal breeding to address the genetic aspects of mainly longevity of livestock but also disease traits, the need for methods to infer genetic correlations and to do multivariate evaluations of survival traits and other types of traits has become increasingly important. In this study we derived and implemented a bivariate quantitative genetic model for a linear Gaussian and a survival trait that are genetically and environmentally correlated. For the survival trait, we considered the Weibull log-normal animal frailty model. A Bayesian approach using Gibbs sampling was adopted. Model parameters were inferred from their marginal posterior distributions. The required fully conditional posterior distributions were derived and issues on implementation are discussed. The two Weibull baseline parameters were updated jointly using a Metropolis-Hasting step. The remaining model parameters with non-normalized fully conditional distributions were updated univariately using adaptive rejection sampling. Simulation results showed that the estimated marginal posterior distributions covered well and placed high density to the true parameter values used in the simulation of data. In conclusion, the proposed method allows inferring additive genetic and environmental correlations, and doing multivariate genetic evaluation of a linear Gaussian trait and a survival trait.

Animals↗
PubMed2005

Quantitative trait loci for yield and related traits in the wheat population Ning7840 x Clark.

Grain yield and associated agronomic traits are important factors in wheat (Triticum aestivum L.) improvement. Knowledge regarding the number, genomic location, and effect of quantitative trait loci (QTL) would facilitate marker-assisted selection and the development of cultivars with desirable characteristics. Our objectives were to identify QTLs directly and indirectly affecting grain yield expression. A population of 132 F12 recombinant inbred lines (RILs) was derived by single-seed descent from a cross between the Chinese facultative wheat Ning7840 and the US soft red winter wheat Clark. Phenotypic data were collected for 15 yield and other agronomic traits in the RILs and parental lines from three locations in Oklahoma from 2001 to 2003. Twenty-nine linkage groups, consisting of 363 AFLP and 47 SSR markers, were identified. Using composite interval mapping (CIM) analysis, 10, 16, 30, and 14 QTLs were detected for yield, yield components, plant adaptation (shattering and lodging resistance, heading date, and plant height), and spike morphology traits, respectively. The QTL effects ranged from 7 to 23%. Marker alleles from Clark were associated with a positive effect for the majority of QTLs for yield and yield components, but gene dispersion was the rule rather than the exception for this RIL population. Often, QTLs were detected in proximal positions for different traits. Consistent, co-localized QTLs were identified in linkage groups 1AL, 1B, 4B, 5A, 6A, and 7A, and less consistent but unique QTLs were identified on 2BL, 2BS, 2DL, and 6B. Results of this study provide a benchmark for future efforts on QTL identification for yield traits.

Chromosome Mapping↗
PubMed2006

Identifying genes associated with a quantitative trait or quantitative trait locus via selective transcriptional profiling.

Genetical genomics is an approach that blends the mapping of quantitative trait loci (QTL) with microarray analysis. The approach can be used to identify associations between the allelic state of a genomic region and a gene's transcript abundance. However, the large number of microarrays required for adequate power results in high material and labor costs that prevent wide adoption of the genetical genomics strategy outside of some well-funded laboratories. We present a method called selective transcriptional profiling that involves selecting an optimal subset of individuals to microarray from a larger set of individuals for which relatively inexpensive quantitative trait and molecular marker data are available. We show how to use microarray data from the selected individuals, along with the trait and marker data from all individuals, to identify genes whose transcript abundance is associated with a quantitative trait of interest through linkage to a trait QTL or correlation with the trait. Our methods for selection and analysis are derived within a missing data framework.

Alleles↗
PubMed2006

Fine mapping reveals sex bias in quantitative trait loci affecting growth, skeletal size and obesity-related traits on mouse chromosomes 2 and 11.

Previous speed congenic analysis has suggested that the expression of growth and obesity quantitative trait loci (QTL) on distal mouse chromosomes (MMU) 2 and 11, segregating between the CAST/EiJ (CAST) and C57BL/6J-hg/hg (HG) strains, is dependent on sex. To confirm, fine map, and further evaluate QTL x sex interactions, we constructed congenic by recipient F2 crosses for the HG.CAST-(D2Mit329-D2Mit457)N(6) (HG2D) and HG.CAST-(D11Mit260-D11Mit255)N(6) (HG11) congenic strains. Over 700 F2 mice were densely genotyped and phenotyped for a panel of 40 body and organ weight, skeletal length, and obesity-related traits at 9 weeks of age. Linkage analysis revealed 20 QTL affecting a representative subset of phenotypes in HG2DF2 and HG11F2 mice. The effect of sex was quantified by comparing two linear models: the first model included sex as an additive covariate and the second incorporated sex as an additive and an interactive covariate. Of the 20 QTL, 8 were sex biased, sex specific, or sex antagonistic. Most traits were regulated by single QTL; however, two closely linked loci were identified for five traits in HG2DF2 mice. Additionally, the confidence intervals for most QTL were significantly reduced relative to the original mapping results, setting the stage for quantitative trait gene (QTG) discovery. These results highlight the importance of assessing the contribution of sex in complex trait analyses.

Animals↗
PubMed2003

Mapping quantitative trait loci for yield, yield components and morphological traits in an advanced backcross population between Oryza rufipogon and the Oryza sativa cultivar Jefferson.

An advanced backcross population between an accession of Oryza rufipogon (IRGC 105491) and the U.S. cultivar Jefferson (Oryza sativa ssp. japonica) was developed to identify quantitative trait loci (QTLs) for yield, yield components and morphological traits. The genetic linkage map generated for this population consisted of 153 SSR and RFLP markers with an average interval size of 10.3 cM. Thirteen traits were examined, nine of which were measured in multiple environments. Seventy-six QTLs above an experiment-wise significance threshold of P<0.01 (corresponding to an interval mapping LOD>3.6 or a composite interval mapping LOD>3.9) were identified. For the traits measured in multiple environments, 47% of the QTLs were detected in at least two environments. The O. rufipogon allele was favorable for 53% of the yield and yield component QTLs, including loci for yield, grains per panicle, panicle length, and grain weight. Morphological traits related to the domestication process and/or weedy characteristics, including plant height, shattering, tiller type and awns, were found clustered on chromosomes 1 and 4. Comparisons to previous studies involving wild x cultivated crosses revealed O. rufipogon alleles with stable effects in multiple genetic backgrounds and environments, several of which have not been detected in studies between Oryza sativa cultivars, indicating potentially novel alleles from O. rufipogon. Some O. rufipogon-derived QTLs, however, were in similar regions as previously reported QTLs from Oryza sativa cultivars, providing evidence for conservation of these QTLs across the Oryza genus. In addition, several QTLs for grain weight, plant height, and flowering time were localized to putative homeologous regions in maize where QTLs for these traits have been previously reported, supporting the hypothesis of functional conservation of QTLs across the grasses.

Chromosome Mapping↗
PubMed2003

Identification of quantitative trait loci involved in fruit quality traits in melon (Cucumis melo L.).

Two populations [an F(2) and a set of 77 double haploid lines (DHLs)] developed from a cross between a 'Piel de Sapo' cultivar (PS) and the exotic Korean accession PI 161375 were used to detect QTLs involved in melon fruit quality traits: earliness (EA), fruit shape (FS), fruit weight (FW) and sugar content (SSC); and loci involved in the colour traits: external colour (ECOL) and flesh colour (FC). High variation was found, showing transgressive segregations for all traits. The highest correlation among experiments was observed for FS and the lowest for FW and SSC. Correlations among traits within experiments were, in general, not significant. QTL analysis, performed by Composite Interval Mapping, allowed the detection of nine QTLs for EA, eight for FS, six for FW and five for SSC. Major QTLs ( R(2)>25%) were detected for all traits. QTLs for different traits were no clearly co-localised, suggesting low pleiotropic effects at QTLs. Sixty-one per cent of them were detected in two or more experiments. QTLs for FS were detected in more trials than QTLs for FW and SSC, confirming that FS is under highly hereditable polygenic control. ECOL segregated as yellow:green in both experimental populations. The genetic control of ECOL was found to be complex, probably involving more than two loci with epistatic interactions. One of these loci was mapped on linkage group 9, but the other loci could not be clearly resolved. FC segregated as white:green:orange. The locus responsible for the green FC was mapped on linkage group 1, and it was proposed to correspond to the previously described locus gf. The genetic control of orange FC was complex: two loci in linkage groups 2 and 12 were associated with orange flesh, but larger population sizes would be necessary to elucidate completely the genetic control of orange flesh in this cross. Exotic alleles from PI161375 showed beneficial effects on EA, FW and SSC, indicating the usefulness of PI 161375 as a new source of genetic variability to improve European and American cultivars.

Chromosome Mapping↗