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PubMed2005

Meat quality traits were unaffected by a quantitative trait locus affecting leg composition traits in Texel sheep.

A QTL affecting leg muscle and fat traits has been identified within the New Zealand Texel population. The QTL maps to a region on OAR 2 with a two-marker haplotype test established at markers BULGE20 and BM81124. These markers encompass the likely position of Growth Differentiation Factor 8 (GDF8). The pleiotropic effects of this QTL on meat quality traits are tested. Objective measures of meat quality including pH, color (L*, a*, and b*), and tenderness (as assessed by Warner-Bratzler shear force measurements) were assessed on longissimus and semi-membranosus muscles of 540 progeny from six Texel sires. Four of these sires were subsequently identified as segregating for leg muscle and fat traits. For these segregating sires, comparison of progeny that had inherited the favorable haplotype from their sire with those that had received the alternate haplotype revealed no significant differences in the meat quality traits assessed. This finding suggests that the muscling QTL does not have pleiotropic effects on meat quality. A general scan for meat quality QTL was carried out using genotype data for eight markers from FCB128 to RM356 flanking 122cM of OAR 2 using Haley-Knott regression. This analysis revealed two QTL for a single sire. A QTL detected in the region of Marker INRA40 for color L* mapped to a site close to the muscling QTL, but there was evidence to suggest it is at a distinct locus. The QTL in the region of Marker RM356 might map distal to Marker RM356, as no peak was observed. This QTL, which seems to affect pH, color a*, color b*, and Warner-Bratzler shear measurements, requires further characterization.

Animals↗
PubMed1994

Identification of quantitative trait loci influencing wood specific gravity in an outbred pedigree of loblolly pine.

We report the identification of quantitative trait loci (QTL) influencing wood specific gravity (WSG) in an outbred pedigree of loblolly pine (Pinus taeda L.). QTL mapping in an outcrossing species is complicated by the presence of multiple alleles (> 2) at QTL and marker loci. Multiple alleles at QTL allow the examination of interaction among alleles at QTL (deviation from additive gene action). Restriction fragment length polymorphism (RFLP) marker genotypes and wood specific gravity phenotypes were determined for 177 progeny. Two RFLP linkage maps were constructed, representing maternal and paternal parent gamete segregations as inferred from diploid progeny RFLP genotypes. RFLP loci segregating for multiple alleles were vital for aligning the two maps. Each RFLP locus was assayed for cosegregation with WSG QTL using analysis of variance (ANOVA). Five regions of the genome contained one or more RFLP loci showing differences in mean WSG at or below the P = 0.05 level for progeny as grouped by RFLP genotype. One region contained a marker locus (S6a) whose QTL-associated effects were highly significant (P > 0.0002). Marker S6a segregated for multiple alleles, a prerequisite for determining the number of alleles segregating at the linked QTL and analyzing the interactions among QTL alleles. The QTL associated with marker S6a appeared to be segregating for multiple alleles which interacted with each other and with environments. No evidence for digenic epistasis was found among the five QTL.

Alleles↗
PubMed2000

Power of linkage versus association analysis of quantitative traits, by use of variance-components models, for sibship data.

Optimal design of quantitative-trait loci (QTL) mapping studies requires a precise understanding of the power of QTL linkage versus QTL association analysis, under a range of different conditions. In this article, we investigate the power of QTL linkage and association analyses for simple random sibship samples, under the variance-components model proposed by Fulker et al. After a brief description of an extension of this variance-components model, we show that the powers of both linkage and association analyses are crucially dependent on the proportion of phenotypic variance attributable to the QTL. The main difference between the two tests is that, whereas the power of association is directly related to the QTL heritability, the power of linkage is related more closely to the square of the QTL heritability. We also describe both how the power of linkage is attenuated by incomplete linkage and incomplete marker information and how the power of association is attenuated by incomplete linkage disequilibrium.

Chi-Square Distribution↗
PubMed2003

Development of 50 gene-associated microsatellite markers using BAC clones and the construction of a linkage map of swine chromosome 4.

The development of informative polymorphic markers is essential for QTL mapping. We developed 50 microsatellite markers from BAC clones containing genes that were predicted to map swine chromosome 4 (SSC4) according to comparative analysis between human and swine chromosomes, and constructed a linkage map that consisted of 37 markers including 24 markers closely linked to genes in BAC clones. Microsatellite markers were developed by direct-sequencing of BAC clones and our results demonstrated that this method was effective for developing microsatellite markers in specific regions on chromosomes. Effective development of microsatellite markers closely linked to genes can further accelerate the comparative studies of chromosomes between different species.

Animals↗
PubMed2000

Quantitative trait loci mapping for cholesterol gallstones in AKR/J and C57L/J strains of mice.

Quantitative trait locus (QTL) mapping was used to locate genes that determine the difference in cholesterol gallstone disease between the gallstone-susceptible strain C57L/J and the gallstone-resistant strain AKR/J. Gallstone weight was determined in 231 male (AKR x C57L) F(1) x AKR backcross mice fed a lithogenic diet containing 1% cholesterol, 0.5% cholic acid, and 15% butterfat for 8 wk. Mice having no stones and mice having the largest stones were genotyped at approximately 20-cM intervals to find the loci determining cholesterol gallstone formation. The major locus, Lith1, mapped near D2Mit56 and was confirmed by constructing a congenic strain, AK. L-Lith1(s). Another locus, Lith2, mapped near D19Mit58 and was also confirmed by constructing a congenic strain AK.L-Lith2(s). Other suggestive, but not statistically significant, loci mapped to chromosomes 6, 7, 8, 10, and X. The identification of these Lith genes will elucidate the pathophysiology of cholesterol gallstone formation.

Animals↗
PubMed1993

Recent developments in alcoholism:genetic transmission.

This chapter is an overview of developments in the study of genetic factors in vulnerability in alcoholism. The focus is on recent developments, including heritability studies in twins and transmission studies in families, both of which have continued to reveal evidence for a substantial role for genetic factors but also for etiologic complexity and variation in vulnerability across generations and across cultures. Studies are discussed which utilized psychophysiological and neurochemical markers for alcoholism for analysis of genetic association, transmission, and linkage. These markers include the low P300 event-related potential, sensitivity to ethanol's intoxicating and euphoric effects, platelet adenylate cyclase, and neurotransmitter metabolite concentrations. Although it is highly likely that many alcoholism-associated physiologic phenotypes are secondary traits, these approaches have increased the specificity of genetic analyses and genetic analyses are clarifying their relationship to alcoholism. For example, early efforts to study, in families, the co-occurrence of the P300 marker and alcoholism have yielded results indicating that the P300 abnormality precedes significant exposure to alcohol and that relatives of alcoholics are more likely to have this trait. In the area of animal models, two nonhuman primate species, the vervet monkey and the rhesus macaque, were shown to willingly consume alcohol to intoxicating blood levels. Also, linkage studies using the quantitative trait locus (QTL) mapping strategy were attempted for phenotypes relevant for alcoholism. The QTL strategy is theoretically capable of identifying determinant genes which contribute only a small portion of the variance. In human linkage studies, a genetic association was found to the DRD2 dopamine receptor. The DRD2 finding generated controversy, as a number of other groups failed to replicate it, and also focused attention on the advantages and pitfalls of the population association approach for detecting genes influencing behavior. The relationship of the alcohol metabolic gene variants to alcoholism was clarified by the finding that functional variants of alcohol and aldehyde dehydrogenases can act additively to determine vulnerability to alcoholism.

Alcoholism↗
PubMed2006

Causal inference of regulator-target pairs by gene mapping of expression phenotypes.

BACKGROUND: Correlations between polymorphic markers and observed phenotypes provide the basis for mapping traits in quantitative genetics. When the phenotype is gene expression, then loci involved in regulatory control can theoretically be implicated. Recent efforts to construct gene regulatory networks from genotype and gene expression data have shown that biologically relevant networks can be achieved from an integrative approach. In this paper, we consider the problem of identifying individual pairs of genes in a direct or indirect, causal, trans-acting relationship. RESULTS: Inspired by epistatic models of multi-locus quantitative trait (QTL) mapping, we propose a unified model of expression and genotype to identify quantitative trait genes (QTG) by extending the conventional linear model to include both genotype and expression of regulator genes and their interactions. The model provides mapping of specific genes in contrast to standard linkage approaches that implicate large QTL intervals typically containing tens of genes. In simulations, we found that the method can often detect weak trans-acting regulators amid the background noise of thousands of traits and is robust to transcription models containing multiple regulator genes. We reanalyze several pleiotropic loci derived from a large set of yeast matings and identify a likely alternative regulator not previously published. However, we also found that many regulators can not be so easily mapped due to the presence of cis-acting QTLs on the regulators, which induce close linkage among small neighborhoods of genes. QTG mapped regulator-target pairs linked to ARN1 were combined to form a regulatory module, which we observed to be highly enriched in iron homeostasis related genes and contained several causally directed links that had not been identified in other automatic reconstructions of that regulatory module. Finally, we also confirm the surprising, previously published results that regulators controlling gene expression are not enriched for transcription factors, but we do show that our more precise mapping model reveals functional enrichment for several other biological processes related to the regulation of the cell. CONCLUSION: By incorporating interacting expression and genotype, our QTG mapping method can identify specific regulator genes in contrast to standard QTL interval mapping. We have shown that the method can recover biologically significant regulator-target pairs and the approach leads to a general framework for inducing a regulatory module network topology of directed and undirected edges that can be used to identify leads in pathway analysis.

Algorithms↗
PubMed2005

Identification and mapping of SNPs from ESTs in sunflower.

More than 67,000 expressed sequence tags (ESTs) have recently been generated for sunflower (Helianthus), including 44,000 from cultivated confectionery (RHA280) and oilseed (RHA801) lines of Helianthus annuus and 23,000 from drought- and salt-tolerant wild sunflowers, H. argophyllus and H. paradoxus, respectively. To create a transcript map for sunflower, we identified 605 ESTs that displayed small insertion-deletion polymorphism (SNP) variation in silico, had apparent tissue-specific expression patterns, and/or were ESTs with candidate functions in traits such as development, cell transport, metabolism, plant defense, and tolerance to abiotic stress. Primer pairs for 535 of the loci were designed from the ESTs and screened for polymorphism in recombinant inbred lines derived from a cross between the same cultivars (RHA280 x RHA801) employed for sequencing. In total, 273 of the loci amplified polymorphic products, of which 243 mapped to the 17 linkage groups previously identified for sunflower. Comparisons with previously mapped QTL revealed some cases where ESTs with putatively related functions mapped near QTLs identified in other crosses for salt tolerance and for domestication traits such as stem diameter, shattering, flowering time, and achene size.

Chromosome Mapping↗
PubMed2005

Quantitative trait loci mapping for intracellular calcium in spontaneously hypertensive rats.

BACKGROUND: Increased intracellular calcium ([Ca2+]i) in platelets is also proposed as an intermediate phenotype for hypertension in spontaneously hypertensive rats (SHR). Increased [Ca2+]i in platelets is hypothesized to contribute to atherothrombotic events. Platelet hyperactivity is frequently associated with cardiovascular disease. METHODS: In a genome scan, we performed the quantitative trait loci (QTL) mapping for [Ca2+]i in back-crossed rats derived from SHR and normotensive Fischer 344 rats, which demonstrated a single major QTL for hypertension on chromosome 1. Thrombin-stimulated [Ca2+]i in Ca2+-free and in Ca2+-containing buffers was measured in platelets using the Fura-2 method. RESULTS: Among the parental strains, systolic blood pressure and thrombin-stimulated [Ca2+]i were significantly greater in SHR than in Fischer 344 and F1 rats. The sarco(endo)plasmic reticulum Ca2+-dependent ATPase II gene locus (Serca2) between D12Mgh5 and D12Mgh6 showed the significant linkage for thrombin-stimulated [Ca2+]i in Ca2+-free and Ca2+-containing buffers. The peak logarithm of the odds scores were 3.6 and 3.3, respectively. These QTL explained 19.8% and 17.4% of the total variances, respectively. D3Mit13 and DXMgh1 showed suggestive linkage for thrombin-stimulated [Ca2+]i in Ca2+-free and in Ca2+-containing buffers, respectively. The peak logarithm of the odds scores were 2.6 and 2.1, respectively. CONCLUSIONS: A significant QTL for [Ca2+]i was mapped near Serca2 on chromosome 12, and suggestive QTL were identified near D3Mit13 and DXMgh1 in a genome scan. Genetic abnormalites in platelet [Ca2+]i may contribute to cardiovascular disease via platelet hyperactivity, independent of blood pressure elevation.

Animals↗
PubMed2004

Confirmation and dissection of QTL controlling resistance to malaria in mice.

We developed an F(11) AIL population from an F(1) cross of A/J (susceptible) and C57BL/6J (resistant) mouse strains. One thousand F(11) mice were challenged with P.c. chabaudi 54X, and 340 mice selected from the phenotypic extremes for susceptibility and resistance were genotyped for microsatellite markers on Chromosomes (Chrs) 5, 8, and 17. QTL originally detected in backcross and F(2) populations were confirmed on the three chromosomes within narrower genomic regions, by maximum likelihood and regression analyses. Each of the previously mapped QTL on Chrs 5 and 17 resolved into two linked QTLs. The distal and proximal QTLs on Chrs 5 and 17, respectively, map to the previously reported QTL.

Animals↗
PubMed2001

Dissecting the genetic pathway to extreme fruit size in tomato using a cross between the small-fruited wild species Lycopersicon pimpinellifolium and L. esculentum var. Giant Heirloom.

In an effort to determine the genetic basis of exceptionally large tomato fruits, QTL analysis was performed on a population derived from a cross between the wild species Lycopersicon pimpinellifolium (average fruit weight, 1 g) and the L. esculentum cultivar var. Giant Heirloom, which bears fruit in excess of 1000 g. QTL analysis revealed that the majority (67%) of phenotypic variation in fruit size could be attributed to six major loci localized on chromosomes 1-3 and 11. None of the QTL map to novel regions of the genome-all have been reported in previous studies involving moderately sized tomatoes. This result suggests that no major QTL beyond those already reported were involved in the evolution of extremely large fruit. However, this is the first time that all six QTL have emerged in a single population, suggesting that exceptionally large-fruited varieties, such as Giant Heirloom, are the result of a novel combination of preexisting QTL alleles. One of the detected QTL, fw2.2, has been cloned and exerts its effect on fruit size through global control of cell division early in carpel/fruit development. However, the most significant QTL detected in this study (fw11.3, lcn11.1) maps to the bottom of chromosome 11 and seems to exert its effect on fruit size through control of carpel/locule number. A second major locus, also affecting carpel number (and hence fruit size), was mapped to chromosome 2 (fw2.1, lcn2.1). We propose that these two carpel number QTL correspond to the loci described by early classical geneticists as fasciated (f) and locule number (lc), respectively.

Chromosome Mapping↗
PubMed2004

Combining congenic coverage with gene profiling in search of candidates for blood pressure quantitative trait loci in Dahl rats.

Chromosomes (Chr) 10 and 16 of the Dahl salt-sensitive (S) rat harbor quantitative trait loci (QTLs) for blood pressure (BP). To facilitate gene discovery of these QTLs, gene profiling based on microarrays was combined with fine QTL mapping to identify potential candidate genes that are differentially expressed. First, the region harboring the BP QTL on Chr 16 was narrowed by comparative congenic mapping. In this endeavor, a number of new chromosome markers were generated and used to physically define the chromosome interval in question. Second, in an effort to minimize the costs of gene profiling without sacrificing the chance of gene discovery, a combination congenic strain was produced by replacing one segment of Chr 10 along with one segment of Chr 16 of the hypertensive S rat by those of the normotensive Lewis (LEW) rat. Both of these regions are known to contain BP QTLs. Third, kidneys of this combination congenic strain and the S strain were employed for expression profiling studies. Finally, a comparison between the two strains yielded a number of potentially differentially expressed candidates. Six Established Sequence Tags (ESTs)/genes among them were located in Chr 10 regions and 1 was found in a Chr 16 region, and the genetic make-ups of all these regions were shown to be different between S and LEW. However, none of these ESTs/genes identified by gene profiling were located in an interval containing a QTL. Thus, the present study highlights the importance of correlating the results of gene expression profiling with fine congenic mapping.

Animals↗
PubMedDate not supplied

Bayes factors for detection of quantitative trait loci.

A fundamental issue in quantitative trait locus (QTL) mapping is to determine the plausibility of the presence of a QTL at a given genome location. Bayesian analysis offers an attractive way of testing alternative models (here, QTL vs. no-QTL) via the Bayes factor. There have been several numerical approaches to computing the Bayes factor, mostly based on Markov Chain Monte Carlo (MCMC), but these strategies are subject to numerical or stability problems. We propose a simple and stable approach to calculating the Bayes factor between nested models. The procedure is based on a reparameterization of a variance component model in terms of intra-class correlation. The Bayes factor can then be easily calculated from the output of a MCMC scheme by averaging conditional densities at the null intra-class correlation. We studied the performance of the method using simulation. We applied this approach to QTL analysis in an outbred population. We also compared it with the Likelihood Ratio Test and we analyzed its stability. Simulation results were very similar to the simulated parameters. The posterior probability of the QTL model increases as the QTL effect does. The location of the QTL was also correctly obtained. The use of meta-analysis is suggested from the properties of the Bayes factor.

Algorithms↗
PubMed2001

Quantitative genetic study of maximal electroshock seizure threshold in mice: evidence for a major seizure susceptibility locus on distal chromosome 1.

We conducted a quantitative trait locus (QTL) mapping study to dissect the multifactorial nature of maximal electroshock seizure threshold (MEST) in C57BL/6 (B6) and DBA/2 (D2) mice. MEST determination involved a standard paradigm in which 8- to 12-week-old mice received one shock per day with a daily incremental increase in electrical current until a maximal seizure (tonic hindlimb extension) was induced. Mean MEST values in parental strains were separated by over five standard deviation units, with D2 mice showing lower values than B6 mice. The distribution of MEST values in B6xD2 F2 intercrossed mice spanned the entire phenotypic range defined by parental strains. Statistical mapping yielded significant evidence for QTLs on chromosomes 1, 2, 5, and 15, which together explained over 60% of the phenotypic variance in the model. The chromosome 1 QTL represents a locus of major effect, accounting for about one-third of the genetic variance. Experiments involving a congenic strain (B6.D2-Mtv7(a)/Ty) enabled more precise mapping of the chromosome 1 QTL and indicate that it lies in the genetic interval between markers D1Mit145 and D1Mit17. These results support the hypothesis that the distal portion of chromosome 1 harbors a gene(s) that has a fundamental role in regulating seizure susceptibility.

Animals↗
PubMed1999

Bayesian mapping of multiple quantitative trait loci from incomplete outbred offspring data.

A general fine-scale Bayesian quantitative trait locus (QTL) mapping method for outcrossing species is presented. It is suitable for an analysis of complete and incomplete data from experimental designs of F2 families or backcrosses. The amount of genotyping of parents and grandparents is optional, as well as the assumption that the QTL alleles in the crossed lines are fixed. Grandparental origin indicators are used, but without forgetting the original genotype or allelic origin information. The method treats the number of QTL in the analyzed chromosome as a random variable and allows some QTL effects from other chromosomes to be taken into account in a composite interval mapping manner. A block-update of ordered genotypes (haplotypes) of the whole family is sampled once in each marker locus during every round of the Markov Chain Monte Carlo algorithm used in the numerical estimation. As a byproduct, the method gives the posterior distributions for linkage phases in the family and therefore it can also be used as a haplotyping algorithm. The Bayesian method is tested and compared with two frequentist methods using simulated data sets, considering two different parental crosses and three different levels of available parental information. The method is implemented as a software package and is freely available under the name Multimapper/outbred at URL http://www.rni.helsinki.fi/mjs/.

Algorithms↗
PubMed2003

Estimating polygenic effects using markers of the entire genome.

Molecular markers have been used to map quantitative trait loci. However, they are rarely used to evaluate effects of chromosome segments of the entire genome. The original interval-mapping approach and various modified versions of it may have limited use in evaluating the genetic effects of the entire genome because they require evaluation of multiple models and model selection. Here we present a Bayesian regression method to simultaneously estimate genetic effects associated with markers of the entire genome. With the Bayesian method, we were able to handle situations in which the number of effects is even larger than the number of observations. The key to the success is that we allow each marker effect to have its own variance parameter, which in turn has its own prior distribution so that the variance can be estimated from the data. Under this hierarchical model, we were able to handle a large number of markers and most of the markers may have negligible effects. As a result, it is possible to evaluate the distribution of the marker effects. Using data from the North American Barley Genome Mapping Project in double-haploid barley, we found that the distribution of gene effects follows closely an L-shaped Gamma distribution, which is in contrast to the bell-shaped Gamma distribution when the gene effects were estimated from interval mapping. In addition, we show that the Bayesian method serves as an alternative or even better QTL mapping method because it produces clearer signals for QTL. Similar results were found from simulated data sets of F(2) and backcross (BC) families.

Bayes Theorem↗
PubMed2004

Quantitative trait loci that determine lipoprotein cholesterol levels in an intercross of 129S1/SvImJ and CAST/Ei inbred mice.

To identify genetic determinants of lipoprotein levels, we are performing quantitative trait locus (QTL) analysis on a series of mouse intercrosses in a "daisy chain" experimental design, to increase the power of detecting QTL and to identify common variants that should segregate in multiple intercrosses. In this study, we intercrossed strains CAST/Ei and 129S1/SvImJ, determined HDL, total, and non-HDL cholesterol levels, and performed QTL mapping using Pseudomarker software. For HDL cholesterol, we identified two significant QTL on chromosome (Chr) 1 (Hdlq5, 82 cM, 60-100 cM) and Chr 4 (Hdlq10, 20 cM, 10-30 cM). For total cholesterol, we identified three significant QTL on Chr 1 (Chol7, 74 cM, 65-80 cM), Chr 4 (Chol8, 12 cM, 0-30 cM), and Chr 17 (Chol9, 54 cM, 20-60 cM). For non-HDL cholesterol, we identified significant QTL on Chr 8 (Nhdlq1, 34 cM, 20-60 cM) and Chr X (Nhdlq2, 6 cM, 0-18 cM). Hdlq10 was the only QTL detected in two intercrosses involving strain CAST/Ei. Hdlq5, Hdlq10, Nhdlq1, and two suggestive QTL at D7Mit246 and D15Mit115 coincided with orthologous human lipoprotein QTL. Our analysis furthers the knowledge of the genetic control of lipoprotein levels and points to the importance of Hdlq10, which was detected repeatedly in multiple studies.

ATP Binding Cassette Transporter 1↗