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PubMed2005

Identification of Glu-B1-1 as a candidate gene for the quantity of high-molecular-weight glutenin in bread wheat (Triticum aestivum L.) by means of an association study.

A previous study in wheat (Triticum aestivum L.) identified two candidate genes controlling a quantitative trait locus (QTL) for high-molecular-weight glutenin subunit (HMW-GS) GluBx. These candidates were Glu-B1-1, the structural gene coding for Glu1Bx, and the B homoeologous gene coding for SPA (spa-B), a seed storage protein activator. The goal of this study was to identify the best candidate gene for this QTL. Single nucleotide polymorphisms (SNPs) are an abundant source of DNA polymorphisms that have been successfully used to identify loci associated with particular phenotypes. As no linkage disequilibrium was detected between Glu-B1-1 and spa-B, we performed an association study to identify the individual gene responsible for the QTL. Six SNPs, three located in Glu-B1-1 and three in spa-B, were genotyped by mass spectrometry in a collection of 113 bread wheat lines. These lines were also evaluated for protein content as well as the total quantity of HMW-GSs and of each HMW-GS in seed samples from two harvest years. Significant associations were detected only between Glu-B1-1 polymorphism and most of the traits evaluated. Spa-B was unambiguously discarded as a candidate. To our knowledge, this is the first report on an association study that was successfully used to discriminate between two candidate genes.

DNA, Plant↗

PubMed2003

Molecular marker dissection of rice (Oryza sativa L.) plant architecture under temperate and tropical climates.

Rice ( Oryza sativa L.) plants develop vertically with shoot elongation and horizontally with tillering. The purpose of this study was to identify and characterize genomic regions influencing the rice plant architecture by quantitative trait locus (QTL) analysis for the component traits: culm length (CL), panicle length (PnL), panicle number (PnN) and tiller number (TN). For this QTL analysis, 191 recombinant inbred lines (F(7)) derived from a cross of Milyang 23 (M23) and Akihikari (AK) were grown in 1995, 1996 and 1997 (May-Oct) in Joetsu, Japan (temperate climate), and in the 2000 dry season (Jan-Apr), the 2000 wet season (Jun-Oct) and the 2001 dry season in Los Baños, The Philippines (tropical climate). Results showed that rice plant architecture was influenced by 19 genomic regions categorized into five groups. In Group I, two regions (on chrs. 6 and 11) affected shoot elongation (CL and PnL) and tillering (PnN and TN) in opposite directions more significantly in Los Baños than in Joetsu. In Group II, two regions (chrs. 3 and 12) affected shoot elongation, whereas in Group III, five regions [chrs. 1 (two), 2, 3 and 9] affected only culm length (CL). Expressions of four regions of Group III were influenced by either tropical or temperate environments. In Group IV, seven regions (chrs. 1, 2, 4, 5, 6, 8 and 9) controlled panicle development (PnN or PnL), and in Group V, three regions (chrs. 1, 2 and 3) regulated tillering (PnN or TN). Characterizing these 19 genomic regions provided a detailed analysis of rice plant architecture with emphasis on the multiple effect and environmental responsive regions.

Genetic Markers↗

PubMed2002

Heterozygosity for genes influencing a quantitative trait.

Choosing families to sample for a quantitative trait locus mapping experiment is a critical component of experimental design because only heterozygous families contribute information to the analysis. Additive genetic variance of a paternal half-sib family can be partitioned into two parts: a variance component of maternal source that is constant across different families and a variance component of paternal source that is defined as an index of heterozygosity of a sire. This index is shown to be an upper limit of variance among marker genotypes of a half-sib family and can be used to identify highly heterozygous sires, thus improving the power of detecting QTL in detection studies. Simulated progeny phenotypic data were used to estimate sire's heterozygosity index via an ANOVA method, and accuracy of the estimation was evaluated with the correlation coefficient between the true and estimated index summarized both as the correlation and by the correct ranking of results as measured by the ratio of the true average heterozygosity index of experimentally selected parents to average heterozygosity of all sires. Positive but small correlation can be achieved in the estimation of a sire's heterozygosity when based on the daughters' phenotypic data, and accuracy was improved when progeny-tested sons were used to estimate their grandsire's heterozygosity index, depending on the genetic model of a trait and the size and structure of families.

Animals↗

PubMed2001

Genetic factors in addiction: QTL mapping and candidate gene studies implicate GABAergic genes in alcohol and barbiturate withdrawal in mice.

Quantitative trait locus (QTL) mapping has allowed dramatic progress toward the detection and chromosome mapping of minor and major gene loci involved in murine responses to alcohol and other drugs of abuse. Here we focus on the identification of QTLs for one particular trait relevant to addiction, drug withdrawal following acute or chronic drug administration. To date, five significant QTLs (p < 5 x 10(-5)) and six suggestive QTLs (p < 0.001) have been mapped to specific murine chromosomes for alcohol and pentobarbital withdrawal, indicating the presence of a relevant gene or genes at each location. Overlapping QTLs for alcohol withdrawal and pentobarbital withdrawal are identified on murine chromosomes 1, 4, and 11, and may detect the influence of common genes. For many QTLs, candidate genes with relevant neurobiological function lie within the mapped region. Notably, several QTLs for alcohol and pentobarbital withdrawal are in proximity to genes that directly or indirectly affect GABAA receptor-mediated transmission, which has been implicated in some of the actions of alcohol and other drugs. These include a cluster of GABAA receptor genes and genes encoding the enzymes steroid 5 alpha-reductase-1 (involved in biosynthesis of the neuroactive steroid allopregnanolone) and glutamic acid decarboxylase-1 (involved in GABA biosynthesis). This paper will discuss data that examines the involvement of GABAergic genes in withdrawal and other drug responses, including genetic variation in gene sequence, expression and function.

Animals↗

PubMed2025

From genetic causality to druggable targets: A multiomics framework identifies ZSCAN16 in gout pathogenesis.

ObjectiveGout is a prevalent form of inflammatory arthritis in which many patients respond suboptimally to current therapies. Drug development is hampered by a lack of genetically validated targets, leading to high clinical trial attrition. This study aimed to systematically identify and prioritize novel, druggable targets for gout via a multilayered genetic and functional genomics approach.MethodsWe performed two-sample Mendelian randomization (MR) using cis-expression quantitative trait locus (cis-eQTL) data and dual independent gout genome-wide association study (GWAS) cohorts (openGWAS and FinnGen). The candidate genes were subjected to a rigorous validation pipeline including Bayesian colocalization, phenome-wide association studies (PheWASs) to assess pleiotropy and on-target safety, and single-cell RNA sequencing (scRNA-seq) to delineate the cellular context. Molecular docking was used to evaluate the structural druggability of prioritized targets.ResultsMR analysis revealed 15 genes causally associated with gout. Colocalization analysis (PPH4 > 0.8) prioritized two targets: ZSCAN16 (risk-increasing, OR = 1.04, 95% CI [1.02-1.06]) and TRIM10 (protective, OR = 0.96, 95% CI [0.94-0.98]). Crucially, PheWAS revealed that ZSCAN16 is highly specific to gout, whereas TRIM10 exhibited extensive pleiotropy with hematological and cardiometabolic traits, indicating significant safety risks. Single-cell analysis provided orthogonal validation, demonstrating flare-specific upregulation of ZSCAN16 in cytotoxic T/NK cells. Molecular docking confirmed ZSCAN16 as a structurally druggable target, showing high-affinity binding with known compounds (e.g. digoxin, binding energy = -9.6 kcal/mol).ConclusionsOur study identifies ZSCAN16 as a high-potential, druggable therapeutic target for gout, highlighting its genetic influence on specific immune cell activities during acute flares. Conversely, TRIM10 was deprioritized owing to substantial pleiotropic liabilities and poor chemical tractability. These findings suggest that ZSCAN16 could play a crucial role in the pathogenesis of gout and may provide a valuable lead for future drug discovery efforts.

Humans↗

PubMed2002

In silico discovery of gene-coding variants in murine quantitative trait loci using strain-specific genome sequence databases.

BACKGROUND: The identification of genes underlying complex traits has been aided by quantitative trait locus (QTL) mapping approaches, which in turn have benefited from advances in mammalian genome research. Most recently, whole-genome draft sequences and assemblies have been generated for mouse strains that have been used for a large fraction of QTL mapping studies. Here we show how such strain-specific mouse genome sequence databases can be used as part of a high-throughput pipeline for the in silico discovery of gene-coding variations within murine QTLs. As a test of this approach we focused on two QTLs on mouse chromosomes 1 and 13 that are involved in physical dependence on alcohol. RESULTS: Interstrain alignment of sequences derived from the relevant mouse strain genome sequence databases for 199 QTL-localized genes spanning 210,020 base-pairs of coding sequence identified 21 genes with different coding sequences for the progenitor strains. Several of these genes, including four that exhibit strong phenotypic links to chronic alcohol withdrawal, are promising candidates to underlie these QTLs. CONCLUSIONS: This approach has wide general utility, and should be applicable to any of the several hundred mouse QTLs, encompassing over 60 different complex traits, that have been identified using strains for which relatively complete genome sequences are available.

Alcohol Withdrawal Seizures↗

PubMed2003

Effect of Box-Cox transformation on power of Haseman-Elston and maximum-likelihood variance components tests to detect quantitative trait Loci.

Non-normality of the phenotypic distribution can affect power to detect quantitative trait loci in sib pair studies. Previously, we observed that Winsorizing the sib pair phenotypes increased the power of quantitative trait locus (QTL) detection for both Haseman-Elston (HE) least-squares tests [Hum Hered 2002;53:59-67] and maximum likelihood-based variance components (MLVC) analysis [Behav Genet (in press)]. Winsorizing the phenotypes led to a slight increase in type 1 error in H-E tests and a slight decrease in type I error for MLVC analysis. Herein, we considered transforming the sib pair phenotypes using the Box-Cox family of transformations. Data were simulated for normal and non-normal (skewed and kurtic) distributions. Phenotypic values were replaced by Box-Cox transformed values. Twenty thousand replications were performed for three H-E tests of linkage and the likelihood ratio test (LRT), the Wald test and other robust versions based on the MLVC method. We calculated the relative nominal inflation rate as the ratio of observed empirical type 1 error divided by the set alpha level (5, 1 and 0.1% alpha levels). MLVC tests applied to non-normal data had inflated type I errors (rate ratio greater than 1.0), which were controlled best by Box-Cox transformation and to a lesser degree by Winsorizing. For example, for non-transformed, skewed phenotypes (derived from a chi2 distribution with 2 degrees of freedom), the rates of empirical type 1 error with respect to set alpha level=0.01 were 0.80, 4.35 and 7.33 for the original H-E test, LRT and Wald test, respectively. For the same alpha level=0.01, these rates were 1.12, 3.095 and 4.088 after Winsorizing and 0.723, 1.195 and 1.905 after Box-Cox transformation. Winsorizing reduced inflated error rates for the leptokurtic distribution (derived from a Laplace distribution with mean 0 and variance 8). Further, power (adjusted for empirical type 1 error) at the 0.01 alpha level ranged from 4.7 to 17.3% across all tests using the non-transformed, skewed phenotypes, from 7.5 to 20.1% after Winsorizing and from 12.6 to 33.2% after Box-Cox transformation. Likewise, power (adjusted for empirical type 1 error) using leptokurtic phenotypes at the 0.01 alpha level ranged from 4.4 to 12.5% across all tests with no transformation, from 7 to 19.2% after Winsorizing and from 4.5 to 13.8% after Box-Cox transformation. Thus the Box-Cox transformation apparently provided the best type 1 error control and maximal power among the procedures we considered for analyzing a non-normal, skewed distribution (chi2) while Winzorizing worked best for the non-normal, kurtic distribution (Laplace). We repeated the same simulations using a larger sample size (200 sib pairs) and found similar results.

Chromosome Mapping↗

PubMed1997

Quantitative trait loci affecting methamphetamine responses in BXD recombinant inbred mouse strains.

Individual differences in most behavioral and pharmacological responses to abused drugs are dependent on both genetic and environmental factors. The genetic influences on the complex phenotypes related to drug abuse have been difficult to study using classical genetic analyses. Quantitative trait locus (QTL) mapping is a method that has been used successfully to examine genetic contributions to some of these traits by correlating allelic variation in polymorphic genetic markers of known chromosomal location with variation in drug-response phenotypes. We evaluated several behavioral responses to multiple doses of methamphetamine (METH) in C57BL/6J (B6), DBA/2J (D2), and 25 of their recombinant inbred (BXD RI) strains. Stereotyped chewing, horizontal home cage activity, and changes in body temperature after 0, 4, 8, or 16 mg/kg METH, as well as stereotyped climbing behavior after 16 mg/kg METH, were examined. Associations (p < 0.01) between METH sensitivity and allelic status at multiple microsatellite genetic markers were subsequently determined for each response. QTLs were provisionally identified for each phenotype, some unique to a particular behavior and others that appeared to influence multiple phenotypes. Candidate genes suggested by these analyses included several that mapped near genes relevant for the neurotransmitters acetylcholine and glutamate. The locations of QTLs provisionally identified by this analysis were compared with QTLs hypothesized in other studies to influence methamphetamine- and cocaine-related phenotypes. In several instances, QTLs appeared to overlap, which is consistent with idea that common neural substrates underlie some responses to psychostimulants.

Alleles↗

PubMed2005

Integrative genetic analysis of transcription modules: towards filling the gap between genetic loci and inherited traits.

Genetic loci that regulate inherited traits are routinely identified using quantitative trait locus (QTL) mapping methods. However, the genotype-phenotype associations do not provide information on the gene expression program through which the genetic loci regulate the traits. Transcription modules are 'self-consistent regulatory units' and are closely related to the modular components of gene regulatory network [Ihmels, J., Friedlander, G., Bergmann, S., Sarig, O., Ziv, Y. and Barkai, N. (2002) Revealing modular organization in the yeast transcriptional network. Nat. Genet., 31, 370-377; Segal, E., Shapira, M., Regev, A., Pe'er, D., Botstein, D., Koller, D. and Friedman, N. (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet., 34, 166-176]. We used genome-wide genotype and gene expression data of a genetic reference population that consists of mice of 32 recombinant inbred strains to identify the transcription modules and the genetic loci regulating them. Twenty-nine transcription modules defined by genetic variations were identified. Statistically significant associations between the transcription modules and 18 classical physiological and behavioral traits were found. Genome-wide interval mapping showed that major QTLs regulating the transcription modules are often co-localized with the QTLs regulating the associated classical traits. The association and the possible co-regulation of the classical trait and transcription module indicate that the transcription module may be involved in the gene pathways connecting the QTL and the classical trait. Our results show that a transcription module may associate with multiple seemingly unrelated classical traits and a classical trait may associate with different modules. Literature mining results provided strong independent evidences for the relations among genes of the transcription modules, genes in the regions of the QTLs regulating the transcription modules and the keywords representing the classical traits.

Animals↗

PubMed1998

Multiple marker mapping of quantitative trait loci in a cross between outbred wild boar and large white pigs.

A quantitative trait locus (QTL) analysis of growth and fatness data from a three generation pig experiment is presented. The population of 199 F2 animals was derived from a cross between wild boar and Large White pigs. Animals were typed for 240 markers spanning 23 Morgans of 18 autosomes and the X chromosome. A series of analyses are presented within a least squares framework. First, these identify chromosomes containing loci controlling trait variation and subsequently attempt to map QTLs to locations within chromosomes. This population gives evidence for a large QTL affecting back fat and another for abdominal fat segregating on chromosome 4. The best locations for these QTLs are within 4 cM of each other and, hence, this is likely to be a single QTL affecting both traits. The allele inherited from the wild boar causes an increase in fat deposition. A QTL for intestinal length was also located in the same region on chromosome 4 and could be the same QTL with pleiotropic effects. Significant effects, owing to multiple QTLs, for intestinal length were identified on chromosomes 3 and 5. A single QTL affecting growth rate to 30 kg was located on chromosome 13 such that the Large White allele increased early growth rate, another QTL on chromosome 10 affected growth rate from 30 to 70 kg and another on chromosome 4 affected growth rate to 70 kg.

Animals↗

PubMed2006

Pleiotropic QTL on chromosome 12q23-q24 influences triglyceride and high-density lipoprotein cholesterol levels: the HERITAGE family study.

To determine whether a common quantitative trait locus (QTL) influences the variation of fasting triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) levels, we used a bivariate multipoint linkage analysis with 654 polymorphic markers in 99 white and 101 black families. The phenotypes were investigated under two conditions: at baseline and after a 20-week exercise training intervention. A maximum genome-wide bivariate LOD score of 3.0 (p = 0.00010) was found on chromosome 12q23-q24, located within the IGF1 gene (insulin-like growth factor 1, at 107 cM) for TG and HDL-C at baseline in whites. This bivariate linkage peak is considerably higher than the univariate linkage results at the same chromosome location for either trait (for TG, LOD = 2.07, p = 0.00108; for HDL-C, LOD = 2.04, p = 0.00101). The genetic correlations between baseline TG and HDL-C levels were -0.14 for the residual and -0.33 for the QTL components. Moreover, association analysis showed that TG, HDL-C, and IGF1 are significantly associated (p = 0.04). In conclusion, these results suggest that a QTL on chromosome 12q23-q24 influences the variation of plasma TG and HDL-C levels. Further investigation should confirm whether IGF1 or another nearby gene is responsible for the concomitant variation in TG and HDL-C levels.

Adolescent↗

PubMed2006

Heritability and tissue specificity of expression quantitative trait loci.

Variation in gene expression is heritable and has been mapped to the genome in humans and model organisms as expression quantitative trait loci (eQTLs). We applied integrated genome-wide expression profiling and linkage analysis to the regulation of gene expression in fat, kidney, adrenal, and heart tissues using the BXH/HXB panel of rat recombinant inbred strains. Here, we report the influence of heritability and allelic effect of the quantitative trait locus on detection of cis- and trans-acting eQTLs and discuss how these factors operate in a tissue-specific context. We identified several hundred major eQTLs in each tissue and found that cis-acting eQTLs are highly heritable and easier to detect than trans-eQTLs. The proportion of heritable expression traits was similar in all tissues; however, heritability alone was not a reliable predictor of whether an eQTL will be detected. We empirically show how the use of heritability as a filter reduces the ability to discover trans-eQTLs, particularly for eQTLs with small effects. Only 3% of cis- and trans-eQTLs exhibited large allelic effects, explaining more than 40% of the phenotypic variance, suggestive of a highly polygenic control of gene expression. Power calculations indicated that, across tissues, minor differences in genetic effects are expected to have a significant impact on detection of trans-eQTLs. Trans-eQTLs generally show smaller effects than cis-eQTLs and have a higher false discovery rate, particularly in more heterogeneous tissues, suggesting that small biological variability, likely relating to tissue composition, may influence detection of trans-eQTLs in this system. We delineate the effects of genetic architecture on variation in gene expression and show the sensitivity of this experimental design to tissue sampling variability in large-scale eQTL studies.

Alleles↗

PubMed1998

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.

Alcohol Drinking↗

PubMed1998

Identification of quantitative trait loci for prolificacy and growth in mice.

Marker-quantitative trait locus (QTL) linkage was evaluated in F2 intercross and backcross mouse populations derived from stocks differing dramatically in prolificacy and mature weight. A highly prolific outbred Quackenbush-Swiss mouse line, or an inbred line derived from it (16.62 +/- 0.22 and 14.64 +/- 0.27 pups per litter, respectively) were used as one of the grandparents in these populations. The less prolific C57BL/6J inbred mouse line (6.67 +/- 0.37 pups per litter) was used as the other grandparent. Linkage was evaluated in a three-step process that involved selective genotyping of F2 intercross progeny representing extremes for prolificacy, genotyping of the full F2 for chromosomal regions potentially associated with prolificacy, and genotyping of the backcross for genomic regions significantly associated with prolificacy in the F2. Segments of Chromosomes (Chrs) 2 and 4 were significantly (P < 0.05, experiment-wise error rate) associated with prolificacy, and LOD scores suggestive of linkage were observed for litter size on Chr 9 and growth on Chrs 4 and 11. Existence of growth QTL was also supported by marker effects that were significant (P < 0.05) or approaching significance (P < 0.10) in the backcross. Additive litter size QTL effects ranged from 0.56 to 0.79 pups per litter, and dominance deviations ranged from -0.56 to 1.19 pups per litter, suggesting overdominance as a possible mode of gene action in some cases. The observation of pleiotropic or linked QTL for growth and prolificacy corresponds well with results from many selection experiments identifying positively correlated responses to selection for these two traits.

Analysis of Variance↗

PubMed2001

Selection and subsequent analysis of sib pair data for QTL detection.

Haseman and Elston (1972) developed a robust regression method for the detection of linkage between a marker and a quantitative trait locus (QTL) using sib pair data. The principle underlying this method is that the difference in phenotypes between pairs of sibs becomes larger as they share a decreasing number of alleles at a particular QTL identical by descent (IBD) from their parents. In this case, phenotypically very different sibs will also on average share a proportion of alleles IBD at any marker linked to the QTL that is lower than the expected value of 0.5. Thus, the deviation of the proportion of marker alleles IBD from the expected value in pairs of sibs selected to be phenotypically different (i.e. discordant) can provide a test for the presence of a QTL. A simple regression method for QTL detection in sib pairs selected for high phenotypic differences is presented here. The power of the analytical method was found to be greater than the power obtained using the standard analysis when samples of sib pairs with high phenotypic differences were used. However, the use of discordant sib pairs was found to be less powerful for QTL detection than alternative selective genotyping schemes based on the phenotypic values of the sibs except with intense selection, when its advantage was only marginal. The most effective selection scheme overall was the use of sib pairs from entire families selected on the basis of high within-family variance for the trait in question. There is little effect of selection on QTL position estimates, which are in good agreement with the simulated values. However, QTL variance estimates are biased to a greater or lesser degree, depending on the selection method.

Biological Evolution↗

PubMedDate not supplied

Detection of quantitative trait loci for carcass composition traits in pigs.

A quantitative trait locus (QTL) analysis of carcass composition data from a three-generation experimental cross between Meishan (MS) and Large White (LW) pig breeds is presented. A total of 488 F2 males issued from six F1 boars and 23 F1 sows, the progeny of six LW boars and six MS sows, were slaughtered at approximately 80 kg live weight and were submitted to a standardised cutting of the carcass. Fifteen traits, i.e. dressing percentage, loin, ham, shoulder, belly, backfat, leaf fat, feet and head weights, two backfat thickness and one muscle depth measurements, ham + loin and back + leaf fat percentages and estimated carcass lean content were analysed. Animals were typed for a total of 137 markers covering the entire porcine genome. Analyses were performed using a line-cross (LC) regression method where founder lines were assumed to be fixed for different QTL alleles and a half/full sib (HFS) maximum likelihood method where allele substitution effects were estimated within each half-/full-sib family. Additional analyses were performed to search for multiple linked QTL and imprinting effects. Significant gene effects were evidenced for both leanness and fatness traits in the telomeric regions of SSC 1q and SSC 2p, on SSC 4, SSC 7 and SSC X. Additional significant QTL were identified for ham weight on SSC 5, for head weight on SSC 1 and SSC 7, for feet weight on SSC 7 and for dressing percentage on SSC X. LW alleles were associated with a higher lean content and a lower fat content of the carcass, except for the fatness trait on SSC 7. Suggestive evidence of linked QTL on SSC 7 and of imprinting effects on SSC 6, SSC 7, SSC 9 and SSC 17 were also obtained.

Animals↗

PubMed2004

Marker-evaluated selection in rice: shifts in allele frequency among bulks selected in contrasting agricultural environments identify genomic regions of importance to rice adaptation and breeding.

Conventional methods for quantitative trait locus (QTL) mapping require the selection of particular traits to be measured based on assumptions as to their importance. We have tested an alternative approach for the location of QTLs-marker-evaluated selection-that makes no prior assumptions as to which traits are important. The results of phenotype selection were evaluated in the products of modified bulk-population breeding that was replicated across a range of rice ecosystems. Selection was carried out in close collaboration with farmers in bulk populations that were all derived from a cross between an Indian upland variety (Kalinga III) and a high-yielding semi-dwarf variety (IR64).Twenty-seven diverse bulks were produced that were screened with molecular markers in order to determine whether shifts could be detected in marker allele frequency as a result of selection and if such changes varied by genomic region across ecosystems. Marker loci linked to important traits for adaptation to specific environments were identified without making any prior assumptions about which traits might be important. Genomic regions from Kalinga III were strongly selected in the upland environments and regions from IR64 in the lowland ones.However, exceptions occurred where the upland parent contributed positively to lowland adaptation and vice versa. The results can be used as a basis for the development of second-cycle varieties, using marker-assisted selection to produce genotypic ideotypes for specific target environments. The very strong selection for genomic regions from the adapted parents of the wide(upland x lowland) cross indicates that, in non-marker-assisted breeding, where genetically distant parents have been used, modified backcross breeding should be efficient. A single backcross to the adapted parent for aspecific ecosystem will result in a higher frequency of segregants with the desired high genetic contribution from the adapted parent.

Acclimatization↗

PubMed2004

Identification and characterization of a QTL on chromosome 2 for cytosolic glutamine synthetase content and panicle number in rice.

A quantitative trait locus (QTL) associated with the protein content of cytosolic glutamine synthetase (GS1; EC 6.3.1.2) in senescing leaves, panicle number, and panicle weight was characterized in rice (Oryza sativa L.). A near-isogenic line (NIL), C-22, developed by marker-assisted selection was grown under different nitrogen levels in the greenhouse and in a paddy field. Chromosome 2 of C-22 had an approximately 50-cM segment substituted from the Kasalath (indica) chromosome in a Koshihikari (japonica) genetic background. C-22 showed a 12-37% lower content of GS1 protein in leaf blades than Koshihikari, which was in good agreement with a QTL region positively affected by the japonica chromosome. At an early vegetative stage, C-22 had more active tillers than Koshihikari in the greenhouse. At the reproductive stage, both panicle number and total panicle weight of C-22 were significantly higher than those of Koshihikari, particularly when the plants were grown under a low-nitrogen condition. These traits of C-22 were further confirmed in a paddy field. Thus, tiller development was positively affected by the Kasalath chromosome at an early vegetative stage, which resulted in an increased panicle number and panicle weight at the mature stage in C-22. These data indicate that the target QTL (Pnn1; panicle number 1) is important in the development of tillers and panicles in rice. Linkage analyses for panicle number and ratio of developing tiller formation in the second axil (RDT) revealed that Pnn1 was delimited at the 6.7-cM region.

Alleles↗