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Results for “QTL mapping”

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PubMed2005

Identification of molecular markers for aluminium tolerance in diploid oat through comparative mapping and QTL analysis.

The degree of aluminium tolerance varies widely across cereal species, with oats (Avena spp.) being among the most tolerant. The objective of this study was to identify molecular markers linked to aluminium tolerance in the diploid oat A. strigosa. Restriction fragment length polymorphism markers were tested in regions where comparative mapping indicated the potential for orthologous quantitative trait loci (QTL) for aluminium tolerance in other grass species. Amplified fragment length polymorphism (AFLP) and sequence-characterized amplified region (SCAR) markers were used to provide additional coverage of the genome. Four QTL were identified. The largest QTL explained 39% of the variation and is possibly orthologous to the major gene found in the Triticeae as well as Alm1 in maize and a minor gene in rice. A second QTL may be orthologous to the Alm2 gene in maize. Two other QTL were associated with anonymous markers. Together, these QTL accounted for 55% of the variation. A SCAR marker linked to the major QTL identified in this study could be used to introgress the aluminium tolerance trait from A. strigosa into cultivated oat germplasm.

Aluminum↗

PubMed2005

Comparison of quantitative trait loci for adaptive traits between oak and chestnut based on an expressed sequence tag consensus map.

A comparative genetic and QTL mapping was performed between Quercus robur L. and Castanea sativa Mill., two major forest tree species belonging to the Fagaceae family. Oak EST-derived markers (STSs) were used to align the 12 linkage groups of the two species. Fifty-one and 45 STSs were mapped in oak and chestnut, respectively. These STSs, added to SSR markers previously mapped in both species, provided a total number of 55 orthologous molecular markers for comparative mapping within the Fagaceae family. Homeologous genomic regions identified between oak and chestnut allowed us to compare QTL positions for three important adaptive traits. Colocation of the QTL controlling the timing of bud burst was significant between the two species. However, conservation of QTL for height growth was not supported by statistical tests. No QTL for carbon isotope discrimination was conserved between the two species. Putative candidate genes for bud burst can be identified on the basis of colocations between EST-derived markers and QTL.

Adaptation, Physiological↗

PubMed2004

Quantitative trait loci mapping for ethanol sensitivity and neurotensin receptor density in an F2 intercross derived from inbred high and low alcohol sensitivity selectively bred rat lines.

BACKGROUND: Genetic variance in initial sensitivity to ethanol has been implicated as a risk factor for the development of alcoholism. Identification of the genes that confer differential initial sensitivity is an important goal for the development of new treatment strategies and for a comprehensive understanding of the mechanism of ethanol's action. Quantitative trait loci (QTL) mapping for initial sensitivity and other ethanol-related behavioral traits in model organisms has become an important first step for the ultimate identification of genes that contribute to variation in ethanol responses. METHODS: An F(2) intercross was made from the Inbred High and Low Alcohol Sensitivity rat lines (IHAS and ILAS). The F(2) rats were tested for duration of the loss of righting reflex test (LORR); blood ethanol concentration at regain of righting reflex (BECrrr); BEC at the first time to reach criterion on the rotarod after 1.6 g/kg of ethanol (BEC1); acute functional tolerance on the rotarod (AFT); and high-affinity neurotensin receptor (NTR1) density in the nucleus accumbens (NAc), caudate putamen (CP), and ventral midbrain (VMB). A full genome scan with an average marker spacing of 16.8 cM for interval QTL mapping was conducted on the F(2) rats (N = 363). RESULTS: Seven significant or suggestive QTL were detected for LORR, one for BECrrr, three for BEC1, two for NTR1 binding in the CP, and one for binding in the NAc, but none were mapped for AFT or NTR1 binding density in the VMB. Effect size of the seven LORR QTL, the trait for which the parental strains were selected, ranged from 3 to 4%, with all accounting for approximately 22% of the total phenotypic variation. One of the LORR QTL on chromosome 2 (approximately 87 cM) was significant, and a second QTL on chromosome 5 (approximately 37 cM) was suggestive for both LORR and BECrrr. CONCLUSIONS: The results indicate that segregating populations derived from the IHAS and ILAS strains can be used for mapping ethanol sensitivity QTL. The chromosome 2 LORR QTL may confer variation in ethanol metabolism, whereas the chromosome 5 LORR/BECrrr QTL likely mediates central nervous system ethanol sensitivity. The small number or absence of QTL for BEC1, AFT, and NTR1 receptor density suggests that genetic variation for these traits is minimal in the IHAS/ILAS strains and/or the effect size of QTL for these traits is too small to be mapped efficiently in this sample of F(2) rats. The ultimate identification of genes underlying these alcohol sensitivity QTL will contribute to our understanding of the actions of alcohol in the central nervous system if not to a deeper understanding of the genetic risk factors for alcoholism.

Alcohol Drinking↗

PubMed2002

Quantitative trait loci for inflorescence development in Arabidopsis thaliana.

Variation in inflorescence development patterns is a central factor in the evolutionary ecology of plants. The genetic architectures of 13 traits associated with inflorescence developmental timing, architecture, rosette morphology, and fitness were investigated in Arabidopsis thaliana, a model plant system. There is substantial naturally occurring genetic variation for inflorescence development traits, with broad sense heritabilities computed from 21 Arabidopsis ecotypes ranging from 0.134 to 0.772. Genetic correlations are significant for most (64/78) pairs of traits, suggesting either pleiotropy or tight linkage among loci. Quantitative trait locus (QTL) mapping indicates 47 and 63 QTL for inflorescence developmental traits in Ler x Col and Cvi x Ler recombinant inbred mapping populations, respectively. Several QTL associated with different developmental traits map to the same Arabidopsis chromosomal regions, in agreement with the strong genetic correlations observed. Epistasis among QTL was observed only in the Cvi x Ler population, and only between regions on chromosomes 1 and 5. Examination of the completed Arabidopsis genome sequence in three QTL regions revealed between 375 and 783 genes per region. Previously identified flowering time, inflorescence architecture, floral meristem identity, and hormone signaling genes represent some of the many candidate genes in these regions.

Arabidopsis↗

PubMed2001

Investigation of quantitative measures related to reading disability in a large sample of sib-pairs from the UK.

We describe a family-based sample of individuals with reading disability collected as part of a quantitative trait loci (QTL) mapping study. Eighty-nine nuclear families (135 independent sib-pairs) were identified through a single proband using a traditional discrepancy score of predicted/actual reading ability and a known family history. Eight correlated psychometric measures were administered to each sibling, including single word reading, spelling, similarities, matrices, spoonerisms, nonword and irregular word reading, and a pseudohomophone test. Summary statistics for each measure showed a reduced mean for the probands compared to the co-sibs, which in turn was lower than that of the population. This partial co-sib regression back to the mean indicates that the measures are influenced by familial factors and therefore, may be suitable for a mapping study. The variance of each of the measures remained largely unaffected, which is reassuring for the application of a QTL approach. Multivariate genetic analysis carried out to explore the relationship between the measures identified a common factor between the reading measures that accounted for 54% of the variance. Finally the familiality estimates (range 0.32-0.73) obtained for the reading measures including the common factor (0.68) supported their heritability. These findings demonstrate the viability of this sample for QTL mapping, and will assist in the interpretation of any subsequent linkage findings in an ongoing genome scan.

Child↗

PubMed2004

What proportion of declared QTL in plants are false?

The false discovery rate (FDR) is the probability that a quantitative trait locus (QTL) is false, given that a QTL has been declared. A misconception in QTL mapping is that the FDR is equal to the comparison-wise significance level, alpha(C). The objective of this simulation study was to determine the FDR in an F(2) mapping population, given different numbers of QTL, population sizes, and trait heritabilities. Markers linked to QTL were detected by multiple regression of phenotype on marker genotype. Phenotypic selection and marker-based recurrent selection were compared. The FDR increased as alpha(C) increased. Notably, the FDR was often 10-30 times higher than the alpha(C) level used. Regardless of the number of QTL, heritability, or size of the genome, the FDR was < or =0.01 when alpha(C) was 0.0001. The FDR increased to 0.82 when alpha(C) was 0.05, heritability was low, and only one QTL controlled the trait. An alpha(C) of 0.05 led to a low FDR when many QTL (30 or 100) controlled the trait, but this lower FDR was accompanied by a diminished power to detect QTL. Larger mapping populations led to both lower a FDR and increased power. Relaxed significance levels of alpha(C)=0.1 or 0.2 led to the largest responses to marker-based recurrent selection, despite the high FDR. To prevent false QTL from confusing the literature and databases, a detected QTL should, in general, be reported as a QTL only if it was identified at a stringent significance level, e.g., alpha(C) congruent with 0.0001.

Computer Simulation↗

PubMed2005

Joint analysis of two breed cross populations in pigs to improve detection and characterization of quantitative trait loci.

The purpose of this study was to develop and implement least squares interval-mapping models for joint analysis of breed cross QTL mapping populations and to evaluate the effect of joint analysis on QTL detected for economic traits in data from two breed crosses in pigs. Data on 26 growth, carcass composition, and meat quality traits from F2 crosses between commercially relevant pig breeds were used: a Berkshire x Yorkshire cross at Iowa State University (ISU) and a Berkshire x Duroc cross at the University of Illinois (UOI). All animals were genotyped for a total of 39 (ISU) and 32 (UOI) markers on chromosomes 2, 6, 13, and 18. Marker linkage maps derived from the individual and joint data were similar with regard to order and relative position, but some differences in absolute distances existed. Maps from the joint data were used in all analyses. The individual and joint data sets were analyzed using several least squares interval-mapping models: line-cross (LC) models with Mendelian and parent-of-origin effects; halfsib models (HS); and combined models (CB) that included LC and HS effects. Lack-of-fit tests between the models were used to characterize QTL for mode of expression and to identify segregation of QTL within parental breeds. A total of 26 (8), 47 (18), and 53 (16) QTL were detected at the 5% chromosome (genome)-wise level in the ISU, UOI, and joint data for the 26 analyzed traits. Of the 53 QTL detected in the joint data, only six were detected in both populations and for many, allele effects differed between the two crosses. Despite the lack of overlap between the two populations, joint analysis resulted in an increase in significance for many QTL, including detection of ten QTL that did not reach significance in either population. Confidence intervals for position also were smaller for several QTL. In contrast, 24 QTL, most of which were detected at chromosome-wise levels in the ISU or UOI population, were not detected in the joint data. Presence of paternally expressed QTL near the IGF2 region of SSC2 was confirmed, with major effects on backfat and loin muscle area, particularly in the UOI population, as well as one or more QTL for carcass composition in the distal arm of Chromosome 6. Results of this study suggest that joint analysis using a range of QTL models increases the power of QTL mapping and QTL characterization, which helps to identify genes for subsequent marker-assisted selection.

Animals↗

PubMed1998

Approximate analysis of QTL-environment interaction with no limits on the number of environments.

An approach is presented here for quantitative trait loci (QTL) mapping analysis that allows for QTL x environment (E) interaction across multiple environments, without necessarily increasing the number of parameters. The main distinction of the proposed model is in the chosen way of approximation of the dependence of putative QTL effects on environmental states. We hypothesize that environmental dependence of a putative QTL effect can be represented as a function of environmental mean value of the trait. Such a description can be applied to take into account the effects of any cosegregating QTLs from other genomic regions that also may vary across environments. The conducted Monte-Carlo simulations and the example of barley multiple environments experiment demonstrate a high potential of the proposed approach for analyzing QTL x E interaction, although the results are only approximated by definition. However, this drawback is compensated by the possibility to utilize information from a potentially unlimited number of environments with a remarkable reduction in the number of parameters, as compared to previously proposed mapping models with QTL x E interactions.

Chromosome Mapping↗

PubMed2003

Genomic approaches to analyzing natural variation in Arabidopsis thaliana.

The genomics tools available for studying Arabidopsis thaliana are a great resource for researchers trying to characterize and understand the genetic basis of natural variation. Abundant polymorphic markers aid quantitative trait locus (QTL) mapping, the fully sequenced genome provides rapid identification of candidate loci, and extensive knockout collections allow those candidate loci to be tested. Combining QTL mapping of classic phenotypic traits with biochemical or expression analysis is providing mechanistic insight into the traits of interest. Conversely, natural variation studies are now being done on genomic traits such as methylation or chiasma frequency.

Arabidopsis↗

PubMed2004

The inheritance and evolution of leaf pigmentation and pubescence in teosinte.

To investigate the genetic mechanisms that underlie morphological evolution in natural populations, we employed QTL mapping to dissect the inheritance of leaf sheath characters that distinguish Chalco from Balsas teosinte. Abundant macrohairs (trichomes) and intense anthocyanin accumulation are found in Chalco teosinte sheaths whereas Balsas teosinte leaf sheaths are green and glabrous. These character states may represent adaptations to the cooler highland (Chalco) vs. warmer middle-elevation (Balsas) climates. QTL mapping in multiple populations revealed a mix of major- and minor-effect QTL affecting both sheath color (anthocyanin) and macrohair abundance. The major QTL for macrohairs accounts for 52% of the parental difference. Epistatic interactions were detected between the major-effect QTL and multiple other QTL for both traits, accounting for substantial portions of phenotypic variance. Developmental analyses suggest that regulatory program changes underlie the phenotypic differences. Sheath anthocyanin QTL are clearly associated with b1 and a3, both of which are regulators of anthocyanin biosynthesis. Our findings suggest that changes in a small number of QTL can lead to morphological evolution by modulating existing developmental programs.

Chlorophyll↗

PubMed2006

Mapping quantitative trait loci for hearing loss in Black Swiss mice.

In common inbred mouse strains, hearing loss is a highly prevalent quantitative trait, which is mainly controlled by the Cdh23(753A) variant and alleles at numerous other strain-specific loci. Here, we investigated the genetic basis of hearing loss in non-inbred strains. Mice of Swiss Webster, CF-1, NIH Swiss, ICR, and Black Swiss strains exhibited hearing profiles characteristic of progressive, sensorineural hearing impairment. In particular, CF-1, Black Swiss, and NIH Swiss mice showed early-onset hearing impairment, ICR and Swiss Webster mice expressed a delayed-onset hearing loss, and NMRI mice had normal hearing. By quantitative trait locus (QTL) mapping, two significant QTLs were identified underlying hearing loss in Black Swiss mice: one QTL mapped to chromosome (chr) 10 (named ahl5, LOD 8.9, peak association 35-42 cM) and a second QTL localized to chr 18 (ahl6, LOD 3.8, 38-44 cM). Ahl5 and ahl6 account for 61% and 32% of the variation in the backcross, respectively. Cadherin 23 (Cdh23) and protocadherin 15 (Pcdh15), mapping within the 95% confidence interval of ahl5, bear nucleotide polymorphisms in coding exons, but these appear to be unrelated to the hearing phenotype. Haplotype analyses across the Cdh23 locus demonstrated the phylogenetic relationship between Black Swiss and common inbred strains.

Age Factors↗

PubMed2004

Genomics tools for QTL analysis and gene discovery.

In recent years, several new genomics resources and tools have become available that will greatly assist quantitative trait locus (QTL) mapping and cloning of the corresponding genes. Genome sequences, tens of thousands of molecular markers, microarrays, and knock-out collections are being applied to QTL mapping, facilitating the use of natural accessions for gene discovery.

Genes, Plant↗

PubMed2004

Multigenerational selection and detection of altered histone acetylation and methylation patterns: toward a quantitative epigenetics in Drosophila.

Quantitative epigenetics (QE) is a new area of research that combines some of the techniques developed for global quantitative trait loci (QTL) mapping analyses with epigenetic analyses. Quantitative traits such as height vary, not in a discrete or discontinuous fashion, but continuously, usually in a normal distribution. QTL analyses assume that allelic DNA sequence variation in a population is partly responsible for the trait variation, and the aim is to deduce the locations of the contributing genes. QE analyses assume that epigenetic variation in a population is partly responsible for the trait variation, and the aim is to associate inheritance of the trait with segregation of informative epigenetic polymorphisms, or epialleles. QTL and QE analyses are thus complementary, but the latter has several advantages. QTL mapping is limited in resolution because of meiotic recombination and population size, placing quantitative traits on genomic regions that are each typically several megabase-pairs long, and requires DNA sequence variation. In contrast, QE analysis can make use of powerful emerging mapping techniques that allow the positioning of epialleles defined by chromatin variation to individual genes or chromosomal regions, even in the absence of DNA sequence variation. In this chapter, we present a case study for QE analysis-epigenetic mapping of enhancers of the KrIf-1 ectopic eye bristle phenotype in an isogenic strain of Drosophila melanogaster.

Acetylation↗

PubMed2004

Mapping QTLs for traits measured as percentages.

Many quantitative traits are measured as percentages. As a result, the assumption of a normal distribution for the residual errors of such percentage data is often violated. However, most quantitative trait locus (QTL) mapping procedures assume normality of the residuals. Therefore, proper data transformation is often recommended before statistical analysis is conducted. We propose the probit transformation to convert percentage data into variables with a normal distribution. The advantage of the probit transformation is that it can handle measurement errors with heterogeneous variance and correlation structure in a statistically sound manner. We compared the results of this data transformation with other transformations and found that this method can substantially increase the statistical power of QTL detection. We develop the QTL mapping procedure based on the maximum likelihood methodology implemented via the expectation-maximization algorithm. The efficacy of the new method is demonstrated using Monte Carlo simulation.

Chromosome Mapping↗

PubMed2005

Selection bias in quantitative trait loci mapping.

A simulation study was performed to see whether selection affected quantitative trait loci (QTL) mapping. Populations under random selection, under selection among full-sib families, and under selection within a full-sib family were simulated each with heritability of 0.3, 0.5, and 0.7. They were analyzed with the marker spacing of 10 cM and 20 cM. The accuracy for QTL detection decreased for the populations under selection within full-sib family. Estimates of QTL effects and positions differed (P < .05) from their input values. The problems could be ignored when mapping a QTL for the populations under selection among full-sib families. A large heritability helped reduction of such problems. When the animals were selected within a full-sib family, the QTL was detected for the populations with heritability of 0.5 or larger using the marker spacing of 10 cM, and with heritability of 0.7 using the marker spacing of 20 cM. This study implied that when selection was introduced, the accuracy for QTL detection decreased and the estimates of QTL effects were biased. A caution was warranted on the decision of data (including selected animals to be genotyped) for QTL mapping.

Animals↗

PubMed2000

A multivariate approach to the problem of QTL localization.

QTL mapping with statistical likelihood-based procedures or asymptotically equivalent regression methods is usually carried out in a univariate way, even if many traits were observed in the experiment. Some proposals for multivariate QTL mapping by an extension of the maximum likelihood method for mixture models or by an application of the canonical transformation have been given in the literature. This paper describes a method of analysis of multitrait data sets, aimed at localization of QTLs contributing to many traits simultaneously, which is based on the linear model of multivariate multiple regression. A special form of the canonical analysis is employed to decompose the test statistic for the general no-QTL hypothesis into components pertaining to individual traits and individual, putative QTLs. Extended linear hypotheses are used to formulate conjectures concerning pleiotropy. A practical mapping algorithm is described. The theory is illustrated with the analysis of data from a study of maize drought resistance.

Genes, Plant↗

PubMed2002

Identification of QTLs for early blight ( Alternaria solani) resistance in tomato using backcross populations of a Lycopersicon esculentum x L. hirsutum cross.

Most commercial cultivars of tomato, Lycopersicon esculentum Mill., are susceptible to early blight (EB), a devastating fungal ( Alternaria solani Sorauer) disease of tomato in the northern and eastern parts of the U.S. and elsewhere in the world. The disease causes plant defoliation, which reduces yield and fruit quality, and contributes to significant crop loss. Sources of resistance have been identified within related wild species of tomato. The purpose of this study was to identify and validate quantitative trait loci (QTLs) for EB resistance in backcross populations of a cross between a susceptible tomato breeding line (NC84173; maternal and recurrent parent) and a resistant Lycopersicon hirsutum Humb. and Bonpl. accession (PI126445). Sixteen hundred BC(1) plants were grown to maturity in a field in 1998. Plants that were self-incompatible, indeterminant in growth habit, and/or extremely late in maturity, were discarded in order to eliminate confounding effects of these factors on disease evaluation, QTL mapping, and future breeding research. The remaining 145 plants (referred to as the BC(1) population) were genotyped for 141 restriction fragment length polymorphism (RFLP) markers and 23 resistance gene analogs (RGAs), and a genetic linkage map was constructed. BC(1) plants were evaluated for disease symptoms throughout the season, and the area under the disease progress curve (AUDPC) and the final percent defoliation (disease severity) were determined for each plant. BC(1) plants were self-pollinated and produced BC(1)S(1) seed. The BC(1)S(1) population, consisting of 145 BC(1)S(1) families, was grown and evaluated for disease symptoms in replicated field trials in two subsequent years (1999 and 2000) and AUDPC and/or final percent defoliation were determined for each family in each year. Two QTL mapping approaches, simple interval mapping (SIM) and composite interval mapping (CIM), were used to identify QTLs for EB resistance in the BC(1) and BC(1)S(1) populations. QTL results were highly consistent across generations, years and mapping approaches. Approximately ten significant QTLs (LOD >/= 2.4, P </= 0.001) were identified (and validated) for EB resistance, with individual effects ranging from 8.4% to 25.9% and with combined effects of >57% of the total phenotypic variation. All QTLs had the positive alleles from the disease-resistant parent. The good agreement between results of the BC(1) and 2 years of the BC(1)S(1) generations indicated the stability of the identified QTLs and their potential usefulness for improving tomato EB resistance using marker-assisted selection (MAS). Further inspections using SIM and CIM indicated that six of the ten QTLs had independent additive effects and together could account for up to 56.4% of the total phenotypic variation. These complementary QTLs, which were identified in two generations and 3 years, should be the most useful QTLs for MAS and improvement of tomato EB resistance using PI126445 as a gene resource. Furthermore, the chromosomal locations of 10 of the 23 RGAs coincided with the locations of three QTLs, suggesting possible involvement of these RGAs with EB resistance and a potential for identifying and cloning genes which confer EB resistance in tomato.

Journal Article↗

PubMed2000

Maximum likelihood analysis of quantitative trait loci under selective genotyping.

Selective genotyping is a cost-saving strategy in mapping quantitative trait loci (QTLs). When the proportion of individuals selected for genotyping is low, the majority of the individuals are not genotyped, but their phenotypic values, if available, are still included in the data analysis to correct the bias in parameter estimation. These ungenotyped individuals do not contribute much information about linkage analysis and their inclusion can substantially increase the computational burden. For multiple trait analysis, ungenotyped individuals may not have a full array of phenotypic measurements. In this case, unbiased estimation of QTL effects using current methods seems to be impossible. In this study, we develop a maximum likelihood method of QTL mapping under selective genotyping using only the phenotypic values of genotyped individuals. Compared with the full data analysis (using all phenotypic values), the proposed method performs well. We derive an expectation-maximization (EM) algorithm that appears to be a simple modification of the existing EM algorithm for standard interval mapping. The new method can be readily incorporated into a standard QTL mapping software, e.g. MAPMAKER. A general recommendation is that whenever full data analysis is possible, the full maximum likelihood analysis should be performed. If it is impossible to analyse the full data, e.g. sample sizes are too large, phenotypic values of ungenotyped individuals are missing or composite interval mapping is to be performed, the proposed method can be applied.

Algorithms↗