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PubMed1994

DNA markers associated with high versus low IQ: the IQ Quantitative Trait Loci (QTL) Project.

General cognitive ability (intelligence, often indexed by IQ scores) is one of the most highly heritable behavioral dimensions. In an attempt to identify some of the many genes (quantitative trait loci; QTL) responsible for the substantial heritability of this quantitative trait, the IQ QTL Project uses an allelic association strategy. Allelic frequencies are compared for the high and low extremes of the IQ dimension using DNA markers in or near genes that are likely to be relevant to neural functioning. Permanent cell lines have been established for low-IQ (mean IQ = 82; N = 18), middle-IQ (mean IQ = 105; N = 21), and high-IQ (mean IQ = 130; N = 24) groups and for a replication sample consisting of even more extreme low-IQ (mean IQ = 59; N = 17) and high-IQ (mean IQ = 142; N = 27) groups. Subjects are Caucasian children tested from 6 to 12 years of age. This first report of the IQ QTL Project presents allelic association results for 46 two-allele markers and for 26 comparisons for 14 multiple-allele markers. Two markers yielded significant (p < .01) allelic frequency differences between the high- and the low-IQ groups in the combined sample-a new HLA marker for a gene unique to the human species and a new brain-expressed triplet repeat marker (CTGB33). The prospects for harnessing the power of molecular genetic techniques to identify QTL for quantitative dimensions of human behavior are discussed.

Alleles↗
PubMed2002

Mapping quantitative trait loci and identification of genes that control fatness in poultry.

Chicken genomics has benefited from the rapid technological advances in the genomics of model organisms and man. A number of resources and approaches are now well established, in the chicken, including genetic markers and maps (both genetic and physical), quantitative trait loci mapping, comparative mapping, expressed sequence tag and bacterial artificial chromosome resources, and physical mapping. In addition, the next phase of gene discovery, functional genomics, is underway. Progress in mapping quantitative trait loci for growth and fatness traits will be discussed, as an application of these new technologies and approaches in the study of avian physiology and genetics.

Adipose Tissue↗
PubMed2006

Weight-adjusted genome scan analysis for mapping quantitative trait Loci for menarchal age.

CONTEXT: Twin and family studies indicate that genetic factors contribute to the variability of age at menarche (AAM), a multifactorial trait of major importance to human reproductive success. Individual variability of premenarcheal fatness is known to be an important determinant of AAM. OBJECTIVE: The objective of the study was mapping quantitative trait loci (QTLs) for AAM. DESIGN AND METHODS: AAM was assessed in 98 sister pairs of recent European ancestry whose growth charts were available. There was a negative correlation between menarcheal body weight sd score (SDS) and AAM (r = 0.47, P < 0.0001). We designed a genome scan approach and used the variance components model implemented in Merlin for quantitative traits to evaluate linkage of AAM and AAM adjusted for menarcheal weight SDS to 418 genome-wide microsatellites. RESULTS: Multipoint linkage analysis for AAM revealed nominal QTLs defined by LOD scores between 1.06 and 1.69 on chromosomes 1p, 1q, 7p, 8q, 16p, 19q, and 20q. The genome scan for AAM adjusted for menarcheal weight SDS revealed several QTLs with strongly suggestive LOD scores in 16q21 (LOD = 3.33), 16q12 (LOD = 3.12), and 8p12 (LOD = 2.18) and a number of other nominally significant QTLs yet viewed as hypothetical. CONCLUSIONS: We found several regions that may contain determinants of AAM, but there is still a long series of steps to confirm these QTLs and identify the genomic polymorphisms implicated in AAM variability.

Age Factors↗
PubMed1999

Sibling-based tests of linkage and association for quantitative traits.

The transmission/disequilibrium test (TDT) developed by Spielman et al. can be a powerful family-based test of linkage and, in some cases, a test of association as well as linkage. It has recently been extended in several ways; these include allowance for implementation with quantitative traits, allowance for multiple alleles, and, in the case of dichotomous traits, allowance for testing in the absence of parental data. In this article, these three extensions are combined, and two procedures are developed that offer valid joint tests of linkage and (in the case of certain sibling configurations) association with quantitative traits, with use of data from siblings only, and that can accommodate biallelic or multiallelic loci. The first procedure uses a mixed-effects (i.e., random and fixed effects) analysis of variance in which sibship is the random factor, marker genotype is the fixed factor, and the continuous phenotype is the dependent variable. Covariates can easily be accommodated, and the procedure can be implemented in commonly available statistical software. The second procedure is a permutation-based procedure. Selected power studies are conducted to illustrate the relative power of each test under a variety of circumstances.

Genetic Linkage↗
PubMed2005

Mapping quantitative trait loci for cross-sectional geometry at the femoral neck.

UNLABELLED: A genome-wide linkage scan was performed in a sample of 79 multiplex pedigrees to identify genomic regions linked to femoral neck cross-sectional geometry. Potential quantitative trait loci were detected at several genomic regions, such as 10q26, 20p12-q12, and chromosome X. INTRODUCTION: Bone geometry is an important determinant of bone strength and osteoporotic fractures. Previous studies have shown that femoral neck cross-sectional geometric variables are under genetic controls. To identify genetic loci underlying variation in femoral neck cross-sectional geometry, we conducted a whole genome linkage scan for four femoral neck cross-sectional geometric variables in 79 multiplex white pedigrees. MATERIALS AND METHODS: A total of 1816 subjects from 79 pedigrees were genotyped with 451 microsatellite markers across the human genome. We performed linkage analyses on the entire data, as well as on men and women separately. RESULTS: Significant linkage evidence was identified at 10q26 for buckling ratio (LOD = 3.27) and Xp11 (LOD = 3.45) for cortical thickness. Chromosome region 20p12-q12 showed suggestive linkage with cross-sectional area (LOD = 2.33), cortical thickness (LOD = 2.09), and buckling ratio (LOD = 1.94). Sex-specific linkage analyses further supported the importance of 20p12-q12 for cortical thickness (LOD = 2.74 in females and LOD = 1.88 in males) and buckling ratio (LOD = 5.00 in females and LOD = 3.18 in males). CONCLUSIONS: This study is the first genome-wide linkage scan searching for quantitative trait loci underlying femoral neck cross-sectional geometry in humans. The identification of the genes responsible for bone geometric variation will improve our knowledge of bone strength and aid in development of diagnostic approaches and interventions for osteoporotic fractures.

Absorptiometry, Photon↗
PubMed1996

Molecular dissection of a major gene effect on a quantitative trait: the level of alcohol dehydrogenase expression in Drosophila melanogaster.

A molecular mapping experiment shows that a major gene effect on a quantitative trait, the level of alcohol dehydrogenase expression in Drosophila melanogaster, is due to multiple polymorphisms within the Adh gene. These polymorphisms are located in an intron, the coding sequence, and the 3' untranslated region. Because of nonrandom associations among polymorphisms at different sites, the individual effects combine (in some cases epistatically) to produce "superalleles" with large effect. These results have implications for the interpretation of major gene effects detected by quantitative trait locus mapping methods. They show that large effects due to a single locus may be due to multiple associated polymorphisms (or sequential fixations in isolated populations) rather than individual mutations of large effect.

Alcohol Dehydrogenase↗
PubMed2006

Quantitative trait linkage analysis using Gaussian copulas.

Mapping and identifying variants that influence quantitative traits is an important problem for genetic studies. Traditional QTL mapping relies on a variance-components (VC) approach with the key assumption that the trait values in a family follow a multivariate normal distribution. Violation of this assumption can lead to inflated type I error, reduced power, and biased parameter estimates. To accommodate nonnormally distributed data, we developed and implemented a modified VC method, which we call the "copula VC method," that directly models the nonnormal distribution using Gaussian copulas. The copula VC method allows the analysis of continuous, discrete, and censored trait data, and the standard VC method is a special case when the data are distributed as multivariate normal. Through the use of link functions, the copula VC method can easily incorporate covariates. We use computer simulations to show that the proposed method yields unbiased parameter estimates, correct type I error rates, and improved power for testing linkage with a variety of nonnormal traits as compared with the standard VC and the regression-based methods.

Analysis of Variance↗
PubMed2004

Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits.

From simulation studies it is known that the allocation of experimental resources has a crucial effect on power of QTL detection as well as on accuracy and precision of QTL estimates. In this study, we used a very large experimental data set composed of 976 F(5) maize testcross progenies evaluated in 19 environments and cross-validation to assess the effect of sample size (N), number of test environments (E), and significance threshold on the number of detected QTL, the proportion of the genotypic variance explained by them, and the corresponding bias of estimates for grain yield, grain moisture, and plant height. In addition, we used computer simulations to compare the usefulness of two cross-validation schemes for obtaining unbiased estimates of QTL effects. The maximum, validated genotypic variance explained by QTL in this study was 52.3% for grain moisture despite the large number of detected QTL, thus confirming the infinitesimal model of quantitative genetics. In both simulated and experimental data, the effect of sample size on power of QTL detection as well as on accuracy and precision of QTL estimates was large. The number of detected QTL and the proportion of genotypic variance explained by QTL generally increased more with increasing N than with increasing E. The average bias of QTL estimates and its range were reduced by increasing N and E. Cross-validation performed well with respect to yielding asymptotically unbiased estimates of the genotypic variance explained by QTL. On the basis of our findings, recommendations for planning of QTL mapping experiments and allocation of experimental resources are given.

Chromosome Mapping↗
PubMed2002

Quantitative trait locus mapping for meat quality traits in an Iberian x Landrace F2 pig population.

An experimental F2 cross between Iberian and Landrace pig strains was performed to map quantitative trait loci (QTL) for diverse productive traits. Here we report results for meat quality traits from 369 F2 animals with records for pH 24 h postmortem (pH 24 h), muscle color Minolta measurements L* (lightness), a* (redness), and b* (yellowness), H* (hue angle), C* (chroma), intramuscular fat (IMF) and haematin pigment content measured in the longissimus thoracis. Pigs were genotyped for 92 markers covering the 18 porcine autosomes (SSC). Results of the genome scan show evidence for QTL for IMF (SSC6; F = 27.16), pH 24 h (SSC3; F = 7.73), haematin pigments (SSC4 and SSC7; F = 8.68 and 9.47 respectively) and Minolta color measurements L* (SSC4 and SSC7; F =16.42 and 7.17 respectively), and a* (SSC4 and SSC8; F = 8.05 and 7.36 respectively). No QTL were observed for the color measurements b*, H*, and C*. Alternative models fitting epistasis between QTL were also tested, but detected epistatic interactions were not significant at a genome-wise level. In this work we identify genomic regions related with meat quality traits. Improvement by traditional selection methods is complicated, and finer mapping would be required for their application in introgression programs.

Animals↗
PubMed2006

Quantitative trait loci analysis of phenotypic traits and principal components of maize tassel inflorescence architecture.

Maize tassel inflorescence architecture is relevant to efficient production of F(1) seed and yield performance of F(1) hybrids. The objectives of this study were to identify genetic relationships among seven measured tassel inflorescence architecture traits and six calculated traits in a maize backcross population derived from two lines with differing tassel architectures, and identify Quantitative Trait Loci (QTL) involved in the inheritance of those tassel inflorescence architecture traits. A Principal Component (PC) analysis was performed to examine relationships among correlated traits. Traits with high loadings for PC1 were branch number and branch number density, for PC2 were spikelet density on central spike and primary branch, and for PC3 were lengths of tassel and central spike. We detected 45 QTL for individual architecture traits and eight QTL for the three PCs. For control of inflorescence architecture, important QTL were found in bins 7.02 and 9.02. The interval phi034-ramosa1 (ral) in bin 7.02 was associated with six individual architecture trait QTL and explained the largest amount of phenotypic variation (17.3%) for PC1. Interval bnlg344-phi027 in bin 9.02 explained the largest amount of phenotypic variation (14.6%) for PC2. Inflorescence architecture QTL were detected in regions with candidate genes fasciated ear2, thick tassel dwarf1, and ral. However, the vast majority of QTL mapped to regions without known candidate genes, indicating positional cloning efforts will be necessary to identify these genes.

Chromosome Mapping↗
PubMed2006

Identification of distinct quantitative trait Loci affecting length or weight variability at birth in humans.

CONTEXT: The variability of human fetal growth is multifactorial. Twin and family studies demonstrate that genetic determinants influence normal fetal growth, but the responsible genetic polymorphisms are unknown. OBJECTIVE: The objective of the study was the mapping of quantitative trait loci (QTLs) for birth length and weight. DESIGN AND METHODS: To approach the genetic factors implicated in the normal variation of birth length and weight, we conducted a genome-wide approach of these two quantitative traits in 220 French Caucasian pedigrees (412 sibling pairs) using a variance components method. RESULTS: We observed evidence for several QTLs influencing birth length or birth weight independently. Whereas birth length and weight showed a close correlation (r = 0.76, P < 0.0001), their genetic variability appeared largely determined by distinct genomic loci. Birth length was influenced by two major QTLs located in 2p21 and 2q11 (LOD scores 2.69 and 3.57). The variability of birth weight was linked to another QTL on 7q35 (LOD score 3.1). Several other regions showed more modest evidence for linkage with LOD score values of 1-2 on chromosomes 7, 8, 10, 13, and 17 for birth length and chromosomes 1, 2, 6, 8, 10, 13, 14, 15, 17, and 20 for birth weight. CONCLUSION: These preliminary QTLs provide a first step toward the identification of the genomic variants involved in the variability of human fetal growth. Our results should, however, be considered preliminary until they are replicated in other studies.

Body Height↗
PubMed2004

Mapping multiple Quantitative Trait Loci by Bayesian classification.

We developed a classification approach to multiple quantitative trait loci (QTL) mapping built upon a Bayesian framework that incorporates the important prior information that most genotypic markers are not cotransmitted with a QTL or their QTL effects are negligible. The genetic effect of each marker is modeled using a three-component mixture prior with a class for markers having negligible effects and separate classes for markers having positive or negative effects on the trait. The posterior probability of a marker's classification provides a natural statistic for evaluating credibility of identified QTL. This approach performs well, especially with a large number of markers but a relatively small sample size. A heat map to visualize the results is proposed so as to allow investigators to be more or less conservative when identifying QTL. We validated the method using a well-characterized data set for barley heading values from the North American Barley Genome Mapping Project. Application of the method to a new data set revealed sex-specific QTL underlying differences in glucose-6-phosphate dehydrogenase enzyme activity between two Drosophila species. A simulation study demonstrated the power of this approach across levels of trait heritability and when marker data were sparse.

Animals↗
PubMed1987

Testing the association between polymorphic markers and quantitative traits in pedigrees.

A statistical model that uses an iterative maximum likelihood estimation procedure is proposed for measuring and testing the association between polymorphic genetic markers and quantitative traits in human pedigrees, after adjusting for covariates such as age and sex. The model allows the quantitative trait to have a familial correlation structure among the individuals in the sample and to follow one of a broad class of skewed or kurtotic underlying distributions. The use of the model is illustrated, and the results are compared to those using models that assume normality without any transformation and do not incorporate familial correlations.

Analysis of Variance↗
PubMed1994

Empirical threshold values for quantitative trait mapping.

The detection of genes that control quantitative characters is a problem of great interest to the genetic mapping community. Methods for locating these quantitative trait loci (QTL) relative to maps of genetic markers are now widely used. This paper addresses an issue common to all QTL mapping methods, that of determining an appropriate threshold value for declaring significant QTL effects. An empirical method is described, based on the concept of a permutation test, for estimating threshold values that are tailored to the experimental data at hand. The method is demonstrated using two real data sets derived from F(2) and recombinant inbred plant populations. An example using simulated data from a backcross design illustrates the effect of marker density on threshold values.

Chromosome Mapping↗
PubMed2001

Transmission/disequilibrium tests for quantitative traits.

Spielman et al. [1993] proposed a transmission-disequilibrium test (TDT), based on marker data collected on affected offspring and their parents, to test for linkage between a genetic marker and a binary trait provided there is allelic association. It has been shown that this TDT is powerful and is not affected by allelic association due to population stratification in the absence of linkage. For quantitative traits, George and Elston [1987] proposed a likelihood method to detect the effect of a candidate gene in pedigree data when familial correlations are present. This test will detect allelic association but will do so in the absence of linkage. In this paper, we investigate two new likelihood-ratio test statistics for multi-generational quantitative traits to test either for linkage in the presence of allelic association or for allelic association in the presence of linkage, such as may be due to linkage disequilibrium. We compare these two tests analytically and by simulation with respect to 1) the sample size required for the asymptotic null distributions to be valid and 2) their power to detect association in those cases in which they are not sensitive to population stratification unless linkage is present. In general, 80 nuclear families with two children each and at least one heterozygous parent, or the equivalent number of children in large pedigrees, are enough for the asymptotic null distribution of the proposed conditional and TDT methods to be valid. The theoretical power is close to the simulated power except for the case of a recessive allele with low frequency. A sampling strategy is proposed that dramatically improves power.

Alleles↗
PubMed2006

Mapping of quantitative trait loci for lactation persistency traits in German Holstein dairy cattle.

A whole genome scan to map quantitative trait loci (QTL) for persistency of milk yield (PMY), persistency of fat yield (PFY), persistency of protein yield (PPY) and persistency of milk energy yield (PEY) was performed in a granddaughter design in the German Holstein dairy cattle population. The analysis included 16 paternal half-sib families with a total of 872 bulls. The analysis was carried out for the first lactation and for the first three lactations combined using univariate weighted multimarker regression. Controlling the false discovery rate across traits and data sets at a level of 0.15 and treating the four persistency traits as different traits revealed 27 significant QTL. A total of 12 chromosomes showed significant QTL effects on a chromosomewise basis. The DGAT1 effect was highly significant for PPY and protein yield. A haplotype analysis using results of previous studies of the same design revealed a co-segregation of various persistency QTL and QTL affecting health traits like dystocia and stillbirth and functional traits like non-return rate 90 and somatic cell score.

Animals↗
PubMed2003

Identification of quantitative trait loci that modify the severity of hereditary spherocytosis in wan, a new mouse model of band-3 deficiency.

Defects in red blood cell (RBC) membrane skeleton components cause hereditary spherocytosis (HS). Clinically, HS varies significantly even among individuals with identical gene defects, illustrating the profound effects of genetic background on disease severity. We exploited a new spontaneous mouse model, wan, which arose on the inbred C3H/HeJ strain, to identify quantitative trait loci (QTL) that modify the HS phenotype. Homozygous wan mice have severe HS due to a complete deficiency of erythroid band 3. A QTL analysis of RBC count, hemoglobin, hematocrit, mean corpuscular volume (MCV), and mean corpuscular hemoglobin content (MCHC) was performed in wan/wan mice from an F2 intercross between C3H/HeJ(+/wan) and CAST/Ei(+/+) F1 hybrids. Hematologic and survival data from C3H, CAST/Ei F2 wan homozygotes support the hypothesis that genetic modifiers significantly influence the band-3 null HS phenotype. Significant QTL were identified for the MCV trait only, suggesting that RBC membrane characteristics are a target for modifier gene action. The most significant quantitative trait locus, Hsm1 (hereditary spherocytosis modifier 1), localizes to mouse Chromosome 12 and is dominant. The peak LOD score was obtained with a marker for Spnb1 encoding erythroid beta-spectrin, an obvious candidate gene.

Animals↗
PubMed2004

The genetic basis of adaptive population differentiation: a quantitative trait locus analysis of fitness traits in two wild barley populations from contrasting habitats.

We used a quantitative trait locus (QTL) approach to study the genetic basis of population differentiation in wild barley, Hordeum spontaneum. Several ecotypes are recognized in this model species, and population genetic studies and reciprocal transplant experiments have indicated the role of local adaptation in shaping population differences. We derived a mapping population from a cross between a coastal Mediterranean population and a steppe inland population from Israel and assessed F3 progeny fitness in the natural growing environments of the two parental populations. Dilution of the local gene pool, estimated as the proportion of native alleles at 96 marker loci in the recombinant lines, negatively affected fitness traits at both sites. QTLs for fitness traits tended to differ in the magnitude but not in the direction of their effects across sites, with beneficial alleles generally conferring a greater fitness advantage at their native site. Several QTLs showed fitness effects at one site only, but no opposite selection on individual QTLs was observed across the sites. In a common-garden experiment, we explored the hypothesis that the two populations have adapted to divergent nutrient availabilities. In the different nutrient environments of this experiment, but not under field conditions, fitness of the F3 progeny lines increased with the number of heterozygous marker loci. Comparison of QTL-effects that underlie genotype x nutrient interaction in the common-garden experiment and genotype x site interaction in the field suggested that population differentiation at the field sites may have been driven by divergent nutrient availabilities to a limited extent. Also in this experiment no QTLs were observed with opposite fitness effects in contrasting environments. Our data are consistent with the view that adaptive differentiation can be based on selection on multiple traits changing gradually along ecological gradients. This can occur without QTLs showing opposite fitness effects in the different environments, that is, in the absence of genetic trade-offs in performance between environments.

Adaptation, Biological↗