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PubMed2001

[QTL mapping of the root traits in rice seedling].

Drought resistance is one of the most important traits in rice abiotic study. The report here analyzed several major root traits affecting drought resistance expression with the double haploid population (DH) from ZYQ8 (indica)/JX17 (japonica) containing 127 lines. After 10 days solution culturing, three rice root traits, Maximum Root Length (MRL), Dry Root Weight (DRW) and Root/Shoot Ratio of Dry Weight (RSR), were revealed existing significant difference among the DH lines. Using the constructed molecular linkage map from this segregating population, the QTL mapping was conducted among these three root parameters. MRL, DRW, and RSR were found being controlled by 2/1/2 QTLs respectively in JX17, 2/0/1 QTLs respectively in ZYQ8. Phenotype variance could be explained by 16.4% and 17.0% for MRL, 16.4% for DRW, 10.4% and 19.9% for RSR in JX17, 19.6% and 13.0% for MRL, 13.2% for RSR in ZYQ8. All these QTLs identified were distributed on rice chromosome 2, 3, 4, 5, 6, 9 and 10. Comparing with the other mapping results, one QTL for each trait (L169-CT106A for MRL, G45-G1314A for DRW, G62-G144 for RSR) was identical with the results reported previously.

Chromosome Mapping↗
PubMed2000

[The effect of marker density on QTL mapping in a grand-daughter design].

Using Monte Carlo method, the effects of marker density on the accuracy of QTL mapping, measured with mean squared error (MSE) of QTL position estimates, were investigated for a grand-daughter design under different population structures, heritabilities, sizes of QTL effect, and QTL locations. The optimal marker densities for QTL mapping for applying to marker assisted selection, (MAS) were analyzed from an economic point of view. The results show that, in general, with the increase of marker density MSEs decrease much more rapidly when all other factors are at a high level than at a low level. When the sample size reaches a certain level (e.g. 40 families with 100 sons per family), or when the QTL effect is large (e.g. the QTL explains 50% of the genetic variance), the decrease of MSE will be only slightly affected by other factors. For the best benefits of MAS, the optimal marker distances under different combinations of various factors were 5cM (small sample and large QTL, moderate sample, heritability and QTL, or large sample and small QTL), 10cM (moderate sample and high heritability or large QTL, or large sample and moderate heritability and QTL), and 20cM (large sample and high heritability or large QTL).

Chromosome Mapping↗
PubMed2005

QTLs mapping for fruit size and shape in chromosomes 2 and 4 in pepper and a comparison of the pepper QTL map with that of tomato.

Quantitative trait locus (QTL) mapping for fruit weight and shape in pepper (Capsicum spp.) was performed using C. chinense and C. frutescens introgression lines of chromosomes 2 and 4. In chromosome 2, a single major fruit-weight QTL, fw2.1, was detected in both populations that explained 62% of the trait variation. This QTL, as well as a fruit-shape QTL, fs2.1, which had a more minor effect, were localized to the tomato fruit-shape gene ovate. The cloned tomato fruit-weight QTL, fw2.2, did not play a major role in controlling fruit size variations in pepper. In chromosome 4, two fruit-weight QTLs, fw4.1 and fw4.2, were detected in the same genomic regions in both mapping populations. In addition, a single fruit-shape QTL was detected in each of the mapping populations that co-localized with one of the fruit-weight QTLs, suggesting pleiotropy or close linkage of the genes controlling size and shape. fw2.1 and fw4.2 represent major fruit-weight QTLs that are conserved in the three Capsicum species analyzed to date for fruit-size variations. Co-localization of the pepper QTLs with QTLs identified for similar traits in tomato suggests that the pepper and tomato QTLs are orthologous. Compared to fruit-shape QTLs, fruit-weight QTLs were more often conserved between pepper and tomato. This implies that different modes of selection were employed for these traits during domestication of the two Solanaceae species.

Capsicum↗
PubMed2004

MCQTL: multi-allelic QTL mapping in multi-cross design.

UNLABELLED: The aim of the MCQTL software package is to perform QTL mapping in multi-cross designs. It allows the analysis of the usual populations derived from inbred lines and can link the families by assuming that the QTL locations are the same in all of them. Moreover, a diallel modelling of the QTL genotypic effects is allowed in multiple related families. The implemented model is a linear regression model. A composite interval mapping and an iterative QTL mapping are implemented to deal with multiple QTL models. Marker cofactor selections by forward or backward stepwise methods are implemented as well as computation of threshold test value by permutation. AVAILABILITY: The program is available on request after signing a licence agreement; free of charge for academic and non-profit organizations at http://www.genoplante.org (Bioinformatics products).

Algorithms↗
PubMed2001

Multi-trait QTL mapping in barley using multivariate regression.

Many studies of QTL locations record several different traits on the same population, but most analyses look at this information on a trait-by-trait basis. In this paper we show how the regression approach to QTL mapping of Haley & Knott (1992) may be extended to a multi-trait analysis via multivariate regression, easily programmed in statistical packages. A procedure for identifying QTL locations using forward selection and bootstrapping is proposed. The method is applied to examine the locations for QTLs for six yield characters (the number of fertile stems, the grain number of the main stem, the main stem grain weight, the single plant yield, the plot yield and the thousand grain weight) in a doubled haploid population of spring barley. Several chromosomal locations with effects on more than one trait are found. The method is also suitable for examining a single trait measured in different years or environments, and is used here to examine data on heading date, a highly heritable trait, and plot yield, a trait with moderate heritability and showing QTL-environment interactions.

Chromosome Mapping↗
PubMed2006

QTL mapping of grain length in rice (Oryza sativa L.) using chromosome segment substitution lines.

Chromosome segment substitution (CSS) lines have the potential for use in QTL fine mapping and map-based cloning. The standard t-test used in the idealized case that each CSS line has a single segment from the donor parent is not suitable for non-idealized CSS lines carrying several substituted segments from the donor parent. In this study, we present a likelihood ratio test based on stepwise regression (RSTEP-LRT) that can be used for QTL mapping in a population consisting of non-idealized CSS lines. Stepwise regression is used to select the most important segments for the trait of interest, and the likelihood ratio test is used to calculate the LOD score of each chromosome segment. This method is statistically equivalent to the standard t-test with idealized CSS lines. To further improve the power of QTL mapping, a method is proposed to decrease multicollinearity among markers (or chromosome segments). QTL mapping with an example CSS population in rice consisting of 65 non-idealized CSS lines and 82 chromosome segments indicated that a total of 18 segments on eight of the 12 rice chromosomes harboured QTLs affecting grain length under the LOD threshold of 2.5. Three major stable QTLs were detected in all eight environments. Some minor QTLs were not detected in all environments, but they could increase or decrease the grain length constantly. These minor genes are also useful in marker-assisted gene pyramiding.

Chromosome Mapping↗
PubMed2003

QTL mapping reveals a striking coincidence in the positions of genomic regions associated with adaptive variation in body size in parallel clines of Drosophila melanogaster on different continents.

Latitudinal genetic clines in body size are common in many ectotherm species and are attributed to climatic adaptation. Here, we use Quantitative Trait Loci (QTL) mapping to identify genomic regions associated with adaptive variation in body size in natural populations of Drosophila melanogaster from extreme ends of a cline in South America. Our results show that there is a significant association between the positions of QTL with strong effects on wing area in South America and those previously reported in a QTL mapping study of Australian cline end populations (P < 0.05). In both continents, the right arm of the third chromosome is associated with QTL with the strongest effect on wing area. We also show that QTL peaks for wing area and thorax length are associated with the same genomic regions, indicating that the clinal variation in the body size traits may have a similar genetic basis. The consistency of the results found for the South American and Australian cline end populations indicate that the genetic basis of the two clines may be similar and future efforts to identify the genes producing the response to selection should be focused on the genomic regions highlighted by the present work.

Adaptation, Biological↗
PubMed2004

QTL mapping for a trade-off between leaf and bud production in a recombinant inbred population of Microseris douglasii and M. bigelovii (Asteraceae, Lactuceae): a potential preadaptation for the colonization of serpentine soils.

The different response to growth on serpentine soil is a major autecological difference between the annual asteracean species Microseris douglasii and M. bigelovii, with nearly non-overlapping distribution ranges in California. Early flowering and seed set is regarded as a crucial character contributing to escape drought and thus is strongly correlated with survival and reproductive success on serpentine as naturally toxic soil. M. bigelovii (strain C94) from non-serpentine soil produces more leaves at the expense of bud production in the first growing phase than M. douglasii (B14) from serpentine soil. A QTL mapping study for this trade-off and for other growth-related traits was performed after six generations of inbreeding (F7) from a single interspecific hybrid between B14 and C94 on plants that were grown on serpentine and alternatively on normal potting soil. The trade-off is mainly correlated with markers on one map region on linkage group 03a (lg03a) with major phenotypic effects (phenotypic variance explained [PVE] = 18.8 - 31.7 %). Plants with the M. douglasii allele in QTL-B1 (QTL-NL1) produce more buds but fewer leaves in the first 119 days on both soil types. Three modifier QTL could be mapped for bud and leaf production. In one modifier (QTL-B2 = QTL-NL4) the M. douglasii allele is again associated with more buds but fewer leaves. QTL mapped for bud set in the F6 co-localize with QTL-B1 (major QTL) and QTL-B3. Two additional QTL for leaf length and red coloration of leaves could be mapped to one map region on lg03a. Co-localization of the two QTL loci with major phenotypic effects on bud and leaf production strongly suggests that a major genetic locus controls the trade-off between the two adaptive traits. The importance of mutational changes in major genes for the adaptation to stressful environments is discussed.

Adaptation, Physiological↗
PubMed2004

Power calculations for selective genotyping in QTL mapping in backcross mice.

Selective genotyping concerns the genotyping of a portion of individuals chosen on the basis of their phenotypic values. Often individuals are selected for genotyping from the high and low extremes of the phenotypic distribution. This procedure yields savings in cost and time by decreasing the total number of individuals genotyped. Previous work by Darvasi et al. (1993) has shown that the power to detect a QTL by genotyping 40-50 % of a population is roughly equivalent to genotyping the entire sample. However, these power studies have not accounted for different strategies of analysing the data when phenotypes of individuals in the middle are excluded, nor have they investigated the genome-wide type I error rate under these different strategies or different selection percentages. Further, these simulation studies have not considered markers over the entire genome. In this paper, we present simulation studies of power for the maximum likelihood approach to QTL mapping by Lander & Botstein (1989) in the context of selective genotyping. We calculate the power of selectively genotyping the individuals from the middle of the phenotypic distribution when performing QTL mapping over the whole mouse genome.

Animals↗
PubMed2006

QTL mapping analysis of plant height and ear height of maize (Zea mays L.).

Genetic map containing 103 microsatellite loci obtained on 200 F2 plants derived from the cross R15 x 478 was used for quantitative trait loci (QTL) mapping in maize. QTL were characterized in a population of 200 F2:4 lines, derived from selfing the F2 plants, and were evaluated with two replications in two environments. QTL determinations were made from the mean of these two environments. Plant height (PH) and ear height (EH) were measured. Using composite interval mapping (CIM) method, a total of 14 distinct QTLs were identified: nine for PH and five for EH. Additive, partial dominance, dominance, and overdominance actions existed among all detected QTL affecting plant height and ear height. The QTL explained 78.27% of the phenotypic variance of PH and 41.50% of EH. The 14 QTLs displayed mostly dominance or partial dominance gene action and mapped to chromosomes 2, 3, 4, 8 and 9.

Chromosome Mapping↗
PubMed2004

[Founding and genetic analysis of Landrace x Lantang pig resource population for QTL mapping].

With F2 design, 16 Chinese Lantang sows crossbred with eight highly improved Landrace boars to establish a resource population including 40 F1 sows, eight F1 boars, and 232 F2 pigs. Genetic analysis of the resource population showed that the 32 performance traits displayed some degree of variation, and coefficients of variation of the majority of economic traits exceeded 10%. The variance component analysis revealed that the ratios of additive genetic variance to phenotypic variance of the majority of economic traits were high. Of the 22 microsatellite DNA markers, only 12 microsatellite DNA markers are polymorphisic in this populations. The average heterozygosity of these markers and PIC were 0.53 and 0.46 respectively. The results showed that these markers can provide enough information for QTL mapping. In conclusion,the F2 pigs were sufficiently segregated, and were capable of serving as a resource population for QTL mapping.

Animals↗
PubMed2005

[QTL mapping and interaction analysis of genotype x environment (Fe2+ -concentrations ) for mesocotyl length in rice (Oryza sativa L.)].

A recombinant inbred lines (RILs) population derived from a cross between Zhenshan97B and Miyang46 was used for detecting QTLs with additive effects and additive-by-additive epistasis for rice mesocotyl length. A linkage map consisting of 207 DNA markers,distributing on the 12 chromosomes of rice,was employed for QTL mapping by using software QTL Mapper 1.6 of mixed linear model. Rice mesocotyl length under germination conditions with 4 different FeSO4 concentrations (0, 1.79, 7.16, 14.32 mmol/L) was measured 7 days after planting. A total of 6 QTLs with significant additive effects on chromosome 1, 5 and 9, with variance explained of 3.5%-11.4%, eleven QTLs with significant additive x additive epistatic effects on chromosome 1, 2, 3, 4, 5, 8 were detected, with variance explained of 4.5%-8.1%. In addition, one QTL for environmental interaction (Fe2+ -concentrations) was detected.

Alleles↗
PubMed2025

Combining QTL mapping and RNA-Seq reveals candidate genes controlling flag leaf width in foxtail millet.

BACKGROUND: The flag leaf, a crucial component of plant architecture, significantly influences final grain yield in crops, including foxtail millet (Setaria italica L.). Optimizing flag leaf size is considered an effective strategy for enhancing grain yield potential under higher planting densities. However, the genetic mechanism underlying flag leaf size, particularly flag leaf width (FLW), remains largely unknown under varying planting densities in foxtail millet. RESULTS: An FLW phenotype variation analysis was conducted across multiple planting densities using a recombinant inbred line (RIL) population derived from Heizhigu (narrow leaf) and Changnong 35 (wide leaf). Based on a high-density genetic map with 3795 Bin markers, 11 flag leaf width (FLW) QTLs were identified on chromosomes 3, 5, and 6, explaining 2.35%-36.06%. Among these, qFLW5-2 was a major QTL, detected consistently across 3 environments and explaining a large proportion of FLW variation. The QTL was further validated with 9 InDel markers with its candidate region across different planting densities. Moreover, RNA-seq revealed 2,293 and 2,338 differentially expressed genes (DEGs) between biparents at heading stage and grain filling stage, respectively. There were 11 and 9 DEGs within the location range of qFLW5-2 among 2 comparison groups (HZG-H_vs_CN35-H and HZG-G_vs_CN35-G). Combining QTL mapping and RNA-seq, we speculated that Seita.5g134600 (encoding an auxin responsive protein Aux/IAA) and Seita.5G123900 (encoding a cytochrome P450 family protein) as key candidate genes for qFLW5-2. Furthermore, variation analysis confirmed that the lines or germplasm with Seita.5G1346005UTR277+ allele, both within the RIL population and natural populations, exhibited significantly wider leaves than those with Seita.5G1346005UTR277- allele. These findings advance our understanding of the genetic and molecular regulatory mechanisms governing flag leaf growth. CONCLUSIONS: This study elucidates genetic and molecular mechanism regulating flag leaf growth and development in foxtail millet. The results provide a theoretical foundation for improving plant architecture and facilitating molecular marker-assisted breeding in this crop.

Quantitative Trait Loci↗
PubMed2005

QTL mapping in a mouse model of cardiomyopathy reveals an ancestral modifier allele affecting heart function and survival.

The progression from myocardial hypertrophy to heart failure is a complex process, involving genetic and environmental factors. Elucidating the genetic components contributing to heart failure has been difficult, largely because of the heterogeneity of human populations. We have employed a strategy to map genetic loci that modify the heart failure phenotype in a transgenic mouse model of cardiomyopathy caused by cardiac-specific overexpression of calsequestrin. Strain-specific differences in both cardiac function and survival are observed when the transgene is moved into different inbred mouse strains. We have previously reported linkage results from mapping in reciprocal backcrosses between C57/BL6 (BL6) and DBA/2J (DBA) and a backcross between DBA/AKR and AKR. Here we report the results of a genome-wide linkage scan in the reciprocal backcross between DBA/AKR and DBA. We identified one novel locus on Chromosome (Chr) 18 that affects heart function and a second on Chr 3 that shows significant linkage to both survival and heart function. Intriguingly, the Chr 3 allele of AKR shows a susceptibility effect on phenotype, whereas the overall effect of the AKR genetic background is protective. The Chr 3 locus also completely overlaps the Hrtfm2 locus, which was previously mapped in crosses between DBA and BL6. Mapping the same QTL in two different crosses allowed us to use ancestral haplotypes to narrow the candidate gene interval from 9 to 2 Mb. Identification of the genes at these QTLs in the mouse will provide novel candidate genes that can be evaluated for their role in human heart failure.

Alleles↗
PubMed1998

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

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

Chromosome Mapping↗
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↗
PubMed2006

[A simulated study on mapping QTL in a segregating sub-population].

For mapping QTLs, phenotypes of the traits in segregating population derived from the cross between two isogenic lines of the targeted QTL may reflect its genotype if the effect of the QTL is relatively large. In order to map the QTL, it is necessary to use a large sample under the high density of markers around the QTL. However, it increases experimental costs. In order to save the costs, it is possible to map the QTL using the sub-population that consists of plants with homogenous recessive. In this paper, the sub-population was used to estimate the recombination fraction between the marker and the QTL, and its standard error for F2, backcross (BC), double haploid (DH) and recombinant inbred lines (RIL) populations, respectively. The results from Monte Carlo simulation showed that the estimation of recombination fraction based on the sub-population is consistent with that obtained from the full population, and the precision of the former is same as that of the later under the same sample size.

Genetic Markers↗
PubMed1997

Short-term selective breeding as a tool for QTL mapping: ethanol preference drinking in mice.

Short-term selective breeding starting from an F2 intercross of two inbred strains is a largely unexploited but potentially useful tool for quantitative trait locus (QTL) mapping. The selection lines can also serve as a valuable confirmation test of recombinant inbred (RI) QTL results when the same two progenitor strains are used. Starting from an F2 from a C57BL/6J (B6) X DBA/2J (D2) cross (B6D2F2), this approach was used in a population of approximately 72 mice per generation bidirectionally selected for two-bottle choice 10% ethanol (alcohol) preference for four generations. The high-preference line diverged significantly from the low line in the first generation with a realized heritability of .32. By generation 4, the preference ratios in the high line were double those seen in the low line. Regions of the genome previously implicated by BXD RI QTL analysis as containing QTLs were searched using microsatellite markers. The test for the presence of QTLs was based on the divergence of marker allele frequencies in the two oppositely selected lines significantly exceeding that expected from random (genetic) drift and allele frequency estimation error. Combining the BXD and two-way selection line results, the most probable QTL was found on chromosome 3 (near the adhl locus; LOD approximately 2.9), other probable QTLs were found with LOD 2.4-2.6.

Alcohol Drinking↗