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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

Comparison of QTL controlling seedling vigour under different temperature conditions using recombinant inbred lines in rice (Oryza sativa).

BACKGROUND AND AIMS: Seedling vigour is one of the major determinants for stable stand establishment in rice (Oryza sativa), especially in a direct seeding cropping system. The objectives of this study were to identify superior alleles with consistent effects on seedling vigour across different temperature conditions and to investigate genotype x environmental temperature interactions for seedling vigour QTL. METHODS: A set of 282 F13 recombinant inbred lines (RILs) derived from a rice cross were assessed for four seedling vigour traits at three temperatures (25 degrees C, 20 degrees C and 15 degrees C). Using a linkage map with 198 marker loci, the main-effect QTL for the traits were mapped by composite interval mapping. KEY RESULTS: A total of 34 QTL for the four seedling vigour traits were identified. Of these QTL, the majority (82%) were clustered within five genomic regions, designated as QTL qSV-3-1, qSV-3-2, qSV-5, qSV-8-1 and qSV-8-2. All of these five QTL had small individual effects on the traits, explaining 3.1-15.8 % of the phenotypic variation with a mean of 7.3 %. QTL qSV-3-1, qSV-3-2 and qSV-8-1 showed almost consistent effects on the traits across all three temperatures while qSV-5 and qSV-8-2 had effects mainly at the 'normal' temperatures of 20 degrees C and 25 degrees C. Among the five QTL identified, all and four showed additive effects on shoot length and germination rate, respectively. The contributions of these five QTL to shoot length and germination rate were also much larger than those to the other two traits. CONCLUSIONS: A few of genomic regions (or QTL) were identified as showing effects on seedling vigour. For these QTL, significant genotype x environmental temperature interactions were found and these interactions appeared to be QTL-specific. Among the four seedling vigour traits measured, shoot length and germination rate could be used as relatively good indicators to evaluate the level of seedling vigour in rice.

Chromosome Mapping↗

PubMed2001

Multiple blood pressure QTL on rat chromosome 1 defined by Dahl rat congenic strains.

A series of congenic strains were constructed in which segments of chromosome (chr) 1 from Lewis (LEW) rats were introgressed into the Dahl salt-sensitive (S) strain. Three blood pressure quantitative trait loci (QTL) were defined. Two of these (QTL 1a and QTL 1b) were closely linked in the region between 1q31 and 1q35. The third blood pressure QTL (QTL region 2) was close to the centromere between 1p11 and 1q12, which includes the candidate gene Slc9a3 for sodium/hydrogen exchange. The blood pressure QTL 1a and QTL 1b defined here overlap significantly with QTL for disease phenotypes of renal failure, stroke, ventricular mass, and salt susceptibility defined in other rat strains, implying that these disease phenotypes and our blood pressure phenotype have causes in common. QTL 1b also corresponded approximately with a blood pressure QTL described on human chr 15. The QTL region 2 corresponded approximately with blood pressure QTL described on mouse chr 10 and human chr 6.

Animals↗

PubMed2004

[Comparative analyses of QTL for important agronomic traits between maize and rice].

The objective of this study was to assess syntenic relationships of quantitative trait loci (QTL) for important agronomic traits between maize and rice based on the comparative genomic map of maize and rice using two F(2:3) populations. Through the comparisons, it was observed that there were extensive conserved relationships of maize QTL affected plant height, row number, and kernels per row with rice QTL affected plant height, tillers per plant, and grains per panicle respectively. Sixteen of 45 QTL affecting five different maize traits were conserved compared with 12 of 38 QTL affecting five different rice traits, which provided some useful information for locating, isolating and cloning maize QTL by using the rice genomic data. In this study, one QTL in rice usually had two conserved QTL in maize, further supporting the hypothesis that there is a polyploidization event during maize evolution. It was interested in observing that there were QTL rich regions on chromosomes in maize and rice, where QTL affecting different traits were usually clustered. These results revealed that the QTL affected the same or similar traits in maize and rice may have the common origin. These results will be helpful to map, isolate and clone QTL in large genome crops, such as maize, by using rice genome information, as well as to understand the evolutionary forces that structured the organization of the grass genomes.

Oryza↗

PubMed2002

Substitution mapping of Pup1: a major QTL increasing phosphorus uptake of rice from a phosphorus-deficient soil.

A major QTL for P uptake had previously been mapped to a 13-cM marker interval on the long arm of chromosome 12. To map that major QTL with higher precision and certainty, a secondary mapping population was developed by backcrossing a near-isogenic line containing the QTL from the donor parent to the recurrent parent of low P uptake. Two different mapping strategies have been followed in this study. A conventional QTL mapping approach was based on individual F(2) RFLP data and the phenotypic evaluation of family means in the F(3). The second strategy employed a substitution-mapping approach. Phenotypic and marker data were obtained for 160 F(3) individuals of six highly informative families that differed in the size of donor chromosomal segments in the region of the putative QTL. QTL mapping showed that close to 80% of the variation between families was due to a single QTL, hereafter referred to as Pup1 (Phosphorus uptake 1). Pup1 was placed in a 3-cM interval flanked by markers S14025 and S13126, which is within 1 cM of the position identified in the original QTL mapping experiment. Other chromosomal regions and epistatic effects were not significant. Substitution mapping revealed that Pup1 co-segregated with marker S13126 and that the flanking markers, S14025 and S13752, were outside the interval containing Pup1. The two mapping strategies therefore yielded almost identical results and, in combining the advantages of both, Pup1 could be mapped with high certainty. The QTL mapping appoach showed that the phenotypic variation between families was due to only one QTL without any additional epistacic interactions, whereas the advantage of substitution mapping was to place clearly defined borders around the QTL.

Journal Article↗

PubMed2004

Optimal sampling of a population to determine QTL location, variance, and allelic number.

In a population intended for breeding and selection, questions of interest relative to a specific segregating QTL are the variance it generates in the population, and the number and effects of its alleles. One approach to address these questions is to extract several inbreds from the population and use them to generate multiple mapping families. Given random sampling of parents, sampling strategy may be an important factor determining the power of the analysis and its accuracy in estimating QTL variance and allelic number. We describe appropriate multiple-family QTL mapping methodology and apply it to simulated data sets to determine optimal sampling strategies in terms of family number versus family size. Genomes were simulated with seven chromosomes, on which 107 markers and six QTL were distributed. The total heritability was 0.60. Two to ten alleles were segregating at each QTL. Sampling strategies ranged from sampling two inbreds and generating a single family of 600 progeny to sampling 40 inbreds and generating 40 families of 15 progeny each. Strategies involving only one to five families were subject to variation due to the sampling of inbred parents. For QTL where more than two alleles were segregating, these strategies did not sample QTL alleles representative of the original population. Conversely, strategies involving 30 or more parents were subject to variation due to sampling of QTL genotypes within the small families obtained. Given these constraints, greatest QTL detection power was obtained for strategies involving five to ten mapping families. The most accurate estimation of the variance generated by the QTL, however, was obtained with strategies involving 20 or more families. Finally, strategies with an intermediate number of families best estimated the number of QTL alleles. We conclude that no overall optimal sampling strategy exists but that the strategy adopted must depend on the objective.

Alleles↗

PubMed2004

Use of trial clustering to study QTL x environment effects for grain yield and related traits in maize.

A population of 300 F(3:4) lines derived from the cross between maize inbred lines F2 and F252 was evaluated for testcross value in a large range of environmental conditions (11 different locations in 2 years: 1995 and 1996) in order to study (1) the magnitude of genotype x environment and (2) the stability of quantitative trait loci (QTL) effects. Several agronomic traits were measured: dry grain yield (DGY), kernel weight, average number of kernels per plant, silking date (SD) and grain moisture at harvest. A large genotype x environment interaction was found, particularly for DGY. A hierarchical classification of trials and an additive main effects and multiplicative interaction (AMMI) model were carried out. Both methods led to the conclusion that trials could be partitioned into three groups consistent with (1) the year of experiment and (2) the water availability (irrigated vs non-irrigated) for the trials sown in 1995. QTL detection was carried out for all the traits in the different groups of trials. Between 9 and 15 QTL were detected for each trait. QTL x group and QTL x trial effects were tested and proved significant for a large proportion of QTL. QTL detection was also performed on coordinates on the first two principal components (PC) of the AMMI model. PC QTL were generally detected in areas where QTL x group and QTL x trial interactions were significant. A region located on chromosome 8 near an SD QTL seemed to play a key role in DGY stability. Our results confirm the key role of water availability and flowering earliness on grain yield stability in maize.

Chromosome Mapping↗

PubMed2005

Mapping of a major QTL for pre-harvest sprouting tolerance on chromosome 3A in bread wheat.

Quantitative trait loci (QTL) analysis was conducted for pre-harvest sprouting tolerance (PHST) in bread wheat for a solitary chromosome 3A, which was shown to be important for this trait in earlier studies. An inter-varietal mapping population in the form of recombinant inbred lines (RILs) developed from a cross between SPR8198 (a PHS tolerant genotype) and HD2329 (a PHS susceptible cultivar) was used for this purpose. The parents and the RIL population were grown in six different environments and the data on PHS were collected in each case. A framework linkage map of chromosome 3A with 13 markers was prepared and used for QTL analysis. A major QTL (QPhs.ccsu-3A.1) was detected on 3AL at a genetic distance of approximately 183 cM from centromere, the length of the map being 279.1 cM. The QTL explained 24.68% to 35.21% variation in individual environments and 78.03% of the variation across the environments (pooled data). The results of the present study are significant on two counts. Firstly, the detected QTL is a major QTL, explaining up to 78.03% of the variation and, secondly, the QTL showed up in all the six environments and also with the pooled data, which is rather rare in QTL analysis. The positive additive effects in the present study suggest that a superior allele of the QTL is available in the superior parent (SPR8198), which can be used for marker-aided selection for the transfer of this QTL allele to obtain PHS-tolerant progeny. It has also been shown that the red-coloured grain of PHS tolerant parent is not associated with the QTL for PHST identified during the present study, suggesting that PHS tolerant white-grained cultivars can be developed.

Chromosome Mapping↗

PubMed2006

QTL influencing baseline hematocrit in the C57BL/6J and DBA/2J lineage: age-related effects.

Baseline serum hematocrit varies substantially in the population. While additive genetic factors account for a large part of this variability, little is known about the genetic architecture underlying the trait. Because hematocrit levels vary with age, it is plausible that quantitative trait loci (QTL) that influence the phenotype also show an age-specific profile. To investigate this possibility, hematocrit was measured in three different age cohorts of mice (150, 450, and 750 days) of the C57BL/6J (B6) and the DBA2/J (D2) lineage. QTL were searched in the B6D2F(2) intercross and the BXD recombinant inbred (RI) strains. The effects of these QTL were explored across the different age groups. On the phenotypic level, baseline serum hematocrit declines with age in a sex-specific manner. In the B6D2F(2) intercross, suggestive QTL that influence the phenotype were located on Chromosomes (Chr) 1, 2, 7, 11, 13, and 16. With the exception of the QTL on Chr 2, all of these QTL exerted their largest effect at 750 days. The QTL on Chr 1, 2, 7, 11 and 16 were confirmed in the BXD RIs in a sex- and age-specific manner. Linkage analysis in the BXD RIs revealed an additional significant QTL on Chr 19. Baseline serum hematocrit is influenced by several QTL that appear to vary with the age and sex of the animal. These QTL primarily overlap with QTL that have been shown to regulate hematopoietic stem cell phenotypes.

Age Factors↗

PubMed2006

Gene-environment interactions in complex diseases: genetic models and methods for QTL mapping in multiple half-sib populations.

An interval quantitative trait locus (QTL) mapping method for complex polygenic diseases (as binary traits) showing QTL by environment interactions (QEI) was developed for outbred populations on a within-family basis. The main objectives, within the above context, were to investigate selection of genetic models and to compare liability or generalized interval mapping (GIM) and linear regression interval mapping (RIM) methods. Two different genetic models were used: one with main QTL and QEI effects (QEI model) and the other with only a main QTL effect (QTL model). Over 30 types of binary disease data as well as six types of continuous data were simulated and analysed by RIM and GIM. Using table values for significance testing, results show that RIM had an increased false detection rate (FDR) for testing interactions which was attributable to scale effects on the binary scale. GIM did not suffer from a high FDR for testing interactions. The use of empirical thresholds, which effectively means higher thresholds for RIM for testing interactions, could repair this increased FDR for RIM, but such empirical thresholds would have to be derived for each case because the amount of FDR depends on the incidence on the binary scale. RIM still suffered from higher biases (15-100% over- or under-estimation of true values) and high standard errors in QTL variance and location estimates than GIM for QEI models. Hence GIM is recommended for disease QTL mapping with QEI. In the presence of QEI, the model including QEI has more power (20-80% increase) to detect the QTL when the average QTL effect is small (in a situation where the model with a main QTL only is not too powerful). Top-down model selection is proposed in which a full test for QEI is conducted first and then the model is subsequently simplified. Methods and results will be applicable to human, plant and animal QTL mapping experiments.

Animals↗

PubMed2003

Identification of quantitative trait loci influencing wood property traits in loblolly pine (Pinus taeda L.). III. QTL Verification and candidate gene mapping.

A long-term series of experiments to map QTL influencing wood property traits in loblolly pine has been completed. These experiments were designed to identify and subsequently verify QTL in multiple genetic backgrounds, environments, and growing seasons. Verification of QTL is necessary to substantiate a biological basis for observed marker-trait associations, to provide precise estimates of the magnitude of QTL effects, and to predict QTL expression at a given age or in a particular environment. Verification was based on the repeated detection of QTL among populations, as well as among multiple growing seasons for each population. Temporal stability of QTL was moderate, with approximately half being detected in multiple seasons. Fewer QTL were common to different populations, but the results are nonetheless encouraging for restricted applications of marker-assisted selection. QTL from larger populations accounted for less phenotypic variation than QTL detected in smaller populations, emphasizing the need for experiments employing much larger families. Additionally, 18 candidate genes related to lignin biosynthesis and cell wall structure were mapped genetically. Several candidate genes colocated with wood property QTL; however, these relationships must be verified in future experiments.

Chromosome Mapping↗

PubMed2005

Genetic characterization of QTL associated with resistance to Fusarium head blight in a doubled-haploid spring wheat population.

Fusarium head blight (FHB) is one of the most important fungal wheat diseases worldwide. Understanding the genetics of FHB resistance is the key to facilitating the introgression of different FHB resistance genes into adapted wheat. The objectives of the present study were to detect and map quantitative trait loci (QTL) associated with FHB resistance genes and characterize the genetic components of the QTL in a doubled-haploid (DH) spring wheat population using both single-locus and two-locus analysis. A mapping population, consisting of 174 DH lines from the cross between DH181 (resistant) and AC Foremost (susceptible), was evaluated for type I resistance to initial infection during a 2-year period in spray-inoculated field trials, for Type II resistance to fungal spread within the spike in 3 greenhouse experiments using single-floret inoculation, and for resistance to kernel infection in a 2001 field trial. One-locus QTL analysis revealed 7 QTL for type I resistance on chromosome arms 2DS, 3AS, 3BS, 3BC (centromeric), 4DL, 5AS, and 6BS, 4 QTL for type II resistance on chromosomes 2DS, 3BS, 6BS, and 7BL, and 6 QTL for resistance to kernel infection on chromosomes 1DL, 2DS, 3BS, 3BC, 4DL, and 6BS. Two-locus QTL analysis detected 8 QTL with main effects and 4 additive by additive epistatic interactions for FHB resistance and identified novel FHB resistance genes for the first time on chromosomes 1DL, 4AL, and 4DL. Neither significant QTL by environment interactions nor epistatic QTL by environment interactions were found for either type I or type II resistance. The additive effects of QTL explained most of the phenotypic variance for FHB resistance. Marker-assisted selection for the favored alleles at multiple genomic regions appears to be a promising tool to accelerate the introgression and pyramiding of different FHB resistance genes into adapted wheat genetic backgrounds.

Chromosome Mapping↗

PubMedDate not supplied

Mapping multiple QTL using linkage disequilibrium and linkage analysis information and multitrait data.

A multi-locus QTL mapping method is presented, which combines linkage and linkage disequilibrium (LD) information and uses multitrait data. The method assumed a putative QTL at the midpoint of each marker bracket. Whether the putative QTL had an effect or not was sampled using Markov chain Monte Carlo (MCMC) methods. The method was tested in dairy cattle data on chromosome 14 where the DGAT1 gene was known to be segregating. The DGAT1 gene was mapped to a region of 0.04 cM, and the effects of the gene were accurately estimated. The fitting of multiple QTL gave a much sharper indication of the QTL position than a single QTL model using multitrait data, probably because the multi-locus QTL mapping reduced the carry over effect of the large DGAT1 gene to adjacent putative QTL positions. This suggests that the method could detect secondary QTL that would, in single point analyses, remain hidden under the broad peak of the dominant QTL. However, no indications for a second QTL affecting dairy traits were found on chromosome 14.

Acyltransferases↗

PubMedDate not supplied

Detection of multiple QTL with epistatic effects under a mixed inheritance model in an outbred population.

A quantitative trait depends on multiple quantitative trait loci (QTL) and on the interaction between two or more QTL, named epistasis. Several methods to detect multiple QTL in various types of design have been proposed, but most of these are based on the assumption that each QTL works independently and epistasis has not been explored sufficiently. The objective of the study was to propose an integrated method to detect multiple QTL with epistases using Bayesian inference via a Markov chain Monte Carlo (MCMC) algorithm. Since the mixed inheritance model is assumed and the deterministic algorithm to calculate the probabilities of QTL genotypes is incorporated in the method, this can be applied to an outbred population such as livestock. Additionally, we treated a pair of QTL as one variable in the Reversible jump Markov chain Monte Carlo (RJMCMC) algorithm so that two QTL were able to be simultaneously added into or deleted from a model. As a result, both of the QTL can be detected, not only in cases where either of the two QTL has main effects and they have epistatic effects between each other, but also in cases where neither of the two QTL has main effects but they have epistatic effects. The method will help ascertain the complicated structure of quantitative traits.

Algorithms↗

PubMed2005

Microsatellite mapping of QTL affecting growth, feed consumption, egg production, tonic immobility and body temperature of Japanese quail.

BACKGROUND: The Japanese quail (Coturnix japonica) is both an animal model in biology and a commercial bird for egg and meat production. Modern research developments with this bird, however, have been slowed down by the limited information that is available on the genetics of the Japanese quail. Recently, quail genetic maps with microsatellites and AFLP have been produced which open the way to comparative works with the chicken (Gallus gallus), and to QTL detection for a variety of traits. The purpose of this work was to detect for the first time QTL for commercial traits and for more basic characters in an F2 experiment with 434 female quail, and to compare the nature and the position of the detected QTL with those from the first chicken genome scans carried out during the last few years. RESULTS: Genome-wide significant or suggestive QTL were found for clutch length, body weight and feed intake on CJA01, age at first egg and egg number on CJA06, and eggshell weight and residual feed intake on CJA20, with possible pleiotropy for the QTL affecting body weight and feed intake, and egg number and age at first egg. A suggestive QTL was found for tonic immobility on CJA01, and chromosome-wide significant QTL for body temperature were detected on CJA01 and CJA03. Other chromosome-wide significant QTL were found on CJA02, CJA05, CJA09 and CJA14. Parent-of-origin effects were found for QTL for body weight and feed intake on CJA01. CONCLUSION: Despite its limited length, the first quail microsatellite map was useful to detect new QTL for rarely reported traits, like residual feed intake, and to help establish some correspondence between the QTL for feed intake, body weight and tonic immobility detected in the present work and those reported on GGA01 in the chicken. Further comparative work is now possible in order to better estimate and understand the genetic similarities and differences of these two Phasianidae species.

Animals↗

PubMed2002

Genetic dissection of femur breaking strength in a large population (MRL/MpJ x SJL/J) of F2 Mice: single QTL effects, epistasis, and pleiotropy.

Bone breaking strength is an ultimate measurement of the risk of fracture. For a practical reason, bone mineral density (BMD) has been commonly used for predicting the risk instead. To identify genetic loci influencing femur-breaking strength (FBS), which was measured by three-point bending using an Instron DynaMight Low-Force Testing System, the whole-genome scan was carried out using 119 polymorphic markers in 633 (MRLxSJL) F2 female mice. We identified six significant quantitative trait loci (QTL) affecting bone breaking strength on chromosomes 1, 2, 8, 9, 10, and 17, which together explained 23% of F2 variance. Of those, the QTL on chromosomes 2, 8, and 10 seem to be unique to bone breaking strength, whereas the remaining three QTL are concordant with femur BMD QTL. Genetic analysis suggests that, of these six FBS QTL, three influence BMD, two influence bone quality, and one influences bone size. We detected multiple significant epistatic interactions for FBS, which accounts for half (14.6%) of F2 variance compared with significant single QTL effects. We found evidence that pleiotropic effect might represent a common genetic mechanism to coordinately regulate bone-related phenotypes. Pleiotropic analysis also suggests that our current threshold level for significant QTL may be too high to detect biologically significant QTL with small effect. Together with epistatic interactions, these undetected small QTL could explain 30% of genetic variance that remains unaccounted for in this study (heritability estimate for FBS is 68%). Our findings in single QTL effects, epistasis, and pleiotropy demonstrate that partially overlapped but distinct combinations of genetic loci in MRL/MpJ and SJL/J inbred strains of mice regulate bone strength and bone density. Identification of the genes unique to FBS may have an impact on prediction of osteoporosis in human.

Animals↗

PubMed2006

Genetic and QTL analysis of maize tassel and ear inflorescence architecture.

Maize (Zea mays L.) ear inflorescence architecture is directly relevant to grain yield components, and tassel architecture is relevant to hybrid seed production. The objectives of this study were to (1) determine heritabilities and correlations of a comprehensive set of tassel and ear inflorescence architecture traits in a set of (Illinois Low ProteinxB73) B73 S1 families, (2) identify chromosomal positions of QTL affecting tassel and ear architecture, and (3) identify possible candidate genes associated with these QTL. For tassel traits, the number of detected QTL ranged from one to five, and explained between 6.5 and 35.9% of phenotypic variation. For ear traits, the number of detected QTL ranged from one to nine and phenotypic variation explained by those QTL varied between 7.9 and 53.0%. We detected QTL for tassel architecture traits that required calculation of ratios from measured traits. Some of these calculated traits QTL were detected in regions that did not show QTL for the measured traits, suggesting that calculation of ratios may reveal developmentally relevant patterns of tassel architecture. We detected a QTL on chromosome 7 for tassel branch number near the gene ramosa1 (ra1), which is known to control tassel branch number, making ra1 a candidate gene for tassel branch number. We detected QTL for several traits on chromosomes 6, 8, and 9, where no inflorescence architecture genes have been mapped, thus providing initial information towards new gene discovery for control of inflorescence architecture.

Chromosome Mapping↗

PubMed2004

A large-sample QTL study in mice: III. Reproduction.

Using lines of mice having undergone long-term selection for high and low growth, a large-sample (n approximately to 1000 F2) experiment was conducted to gain further understanding of the genetic architecture of complex polygenic traits. Composite interval mapping on data from 10-week-old F2 females (n = 439) detected 15 quantitative trait loci (QTLs) on 5 chromosomes that influence reproduction traits characterized at day 16 of gestation. These QTL are broadly categorized into two groups: those where effects on the number of live fetuses (LF) were accompanied by parallel effects on the number of dead fetuses (DF), and those free of such undesirable effects. QTL for ovulation rate (OR) did not overlap with QTL for litter size, potentially indicating the importance of uterine capacity. Large dominance effects were identified for most QTL detected, and overdominance was also present. The QTL of largest effects were detected in regions of Chromosome 2, where large QTL effects for growth and fatness have also been found and where corroborating evidence from other studies exists. Considerable overlap between locations of QTL for reproductive traits and for growth traits corresponds well with the positive correlations usually observed among these sets of phenotypes. Some support for the relevance of QTL x genetic background interactions in reproduction was detected. Traits with low heritability demand considerably larger sample sizes to achieve effective power of QTL detection. This is unfortunate as traits with low heritability are among those that could most benefit from QTL-complemented breeding and selection strategies in food animal production.

Animals↗