Discovery and validation of genomic regions associated with resistance to maize lethal necrosis in four biparental populations

In sub-Saharan Africa, maize is the key determinant of food security for smallholder farmers. The sudden outbreak of maize lethal necrosis (MLN) disease is seriously threatening the maize production in the region. Understanding the genetic basis of MLN resistance is crucial. In this study, we used four biparental populations applied linkage mapping and joint linkage mapping approaches to identify and validate the MLN resistance-associated genomic regions. All populations were genotyped with low to high density markers and phenotyped in multiple environments against MLN under artificial inoculation. Phenotypic variation for MLN resistance was significant and heritability was moderate to high in all four populations for both early and late stages of disease infection. Linkage mapping revealed three major quantitative trait loci (QTL) on chromosomes 3, 6, and 9 that were consistently detected in at least two of the four populations. Phenotypic variance explained by a single QTL in each population ranged from 3.9% in population 1 to 43.8% in population 2. Joint linkage association mapping across three populations with three biometric models together revealed 16 and 10 main effect QTL for MLN-early and MLN-late, respectively. The QTL identified on chromosomes 3, 5, 6, and 9 were consistent with the QTL identified by linkage mapping. Ridge regression best linear unbiased prediction with five-fold cross-validation revealed high accuracy for prediction across populations for both MLN-early and MLN-late. Overall, the study discovered and validated the presence of major effect QTL on chromosomes 3, 6, and 9 which can be potential candidates for marker-assisted breeding to improve the MLN resistance.     

Mapping quantitative trait loci in multiple populations of Arabidopsis thaliana identifies natural allelic variation for trichome density

The majority of biological traits are genetically complex. Mapping the quantitative trait loci (QTL) that determine these phenotypes is a powerful means for estimating many parameters of the genetic architecture for a trait and potentially identifying the genes responsible for natural variation. Typically, such experiments are conducted in a single mapping population and, therefore, have only the potential to reveal genomic regions that are polymorphic between the progenitors of the population. What remains unclear is how well the QTL identified in any one mapping experiment characterize the genetics that underlie natural variation in traits. Here we provide QTL mapping data for trichome density from four recombinant inbred mapping populations of Arabidopsis thaliana. By aligning the linkage maps for these four populations onto a common physical map, the results from each experiment were directly compared. Seven of the nine QTL identified are population specific while two were mapped in all four populations. Our results show that many lineage-specific alleles that either increase or decrease trichome density persist in natural populations and that most of this genetic variation is additive. More generally, these findings suggest that the use of multiple populations holds great promise for better understanding the genetic architecture of natural variation.     

Mapping genetic loci for iron deficiency chlorosis in soybean

The objective of this study was to map genes controlling iron deficiency chlorosis in two intraspecific soybean [Glycine max (L.) Merrill] populations. Chlorosis symptoms were evaluated by visual scores and spectrometric chlorophyll determinations at the V4 stage (third trifoliolate leaf fully developed) in the field in 1993, and at V2 (first trifoliolate leaf fully developed) and V4 stages in 1994. A total of 89 RFLP and 10 SSR markers in the Pride B216 x A15 population, and 82 RFLP, 14 SSR and 1 morphological I (hilum color) markers in the Anoka x A7 population were used to map quantitative trait loci (QTL) affecting iron deficiency chlorosis. QTL with minor effects were detected on six linkage groups of the Pride B216 x A15 population, suggesting a typical polygene mechanism. In contrast, in the Anoka x A7 population, one QTL contributed an average of 72.7% of the visual score variation and 68.8% of the chlorophyll concentration variation and was mapped on linkage group N. Another QTL for visual score variation, and one for chlorophyll concentration variation were detected on linkage groups A1 and I, respectively. Due to the large LOD score and major genetic effect of the QTL on linkage group N, the quantitative data was reclassified into qualitative data fitting a one major gene model according to the means of the QTL genotypic classes. The major gene was mapped in the same interval of linkage group N using both visual scores and chlorophyll concentrations, thus verifying that one major gene is involved in segregation for iron chlorosis deficiency in the Anoka x A7 population. This study supported a previous hypothesis that two separate genetic mechanisms control iron deficiency in soybean.     

Relevance of genetic relationship in GWAS and genomic prediction

The objective of this study was to analyze the relevance of relationship information on the identification of low heritability quantitative trait loci (QTLs) from a genome-wide association study (GWAS) and on the genomic prediction of complex traits in human, animal and cross-pollinating populations. The simulation-based data sets included 50 samples of 1000 individuals of seven populations derived from a common population with linkage disequilibrium. The populations had non-inbred and inbred progeny structure (50 to 200) with varying number of members (5 to 20). The individuals were genotyped for 10,000 single nucleotide polymorphisms (SNPs) and phenotyped for a quantitative trait controlled by 10 QTLs and 90 minor genes showing dominance. The SNP density was 0.1 cM and the narrow sense heritability was 25%. The QTL heritabilities ranged from 1.1 to 2.9%. We applied mixed model approaches for both GWAS and genomic prediction using pedigree-based and genomic relationship matrices. For GWAS, the observed false discovery rate was kept below the significance level of 5%, the power of detection for the low heritability QTLs ranged from 14 to 50%, and the average bias between significant SNPs and a QTL ranged from less than 0.01 to 0.23 cM. The QTL detection power was consistently higher using genomic relationship matrix. Regardless of population and training set size, genomic prediction provided higher prediction accuracy of complex trait when compared to pedigree-based prediction. The accuracy of genomic prediction when there is relatedness between individuals in the training set and the reference population is much higher than the value for unrelated individuals.     

Empirical and deterministic accuracies of across-population genomic prediction

Background

Differences in linkage disequilibrium and in allele substitution effects of QTL (quantitative trait loci) may hinder genomic prediction across populations. Our objective was to develop a deterministic formula to estimate the accuracy of across-population genomic prediction, for which reference individuals and selection candidates are from different populations, and to investigate the impact of differences in allele substitution effects across populations and of the number of QTL underlying a trait on the accuracy.

Methods

A deterministic formula to estimate the accuracy of across-population genomic prediction was derived based on selection index theory. Moreover, accuracies were deterministically predicted using a formula based on population parameters and empirically calculated using simulated phenotypes and a GBLUP (genomic best linear unbiased prediction) model. Phenotypes of 1033 Holstein-Friesian, 105 Groninger White Headed and 147 Meuse-Rhine-Yssel cows were simulated by sampling 3000, 300, 30 or 3 QTL from the available high-density SNP (single nucleotide polymorphism) information of three chromosomes, assuming a correlation of 1.0, 0.8, 0.6, 0.4, or 0.2 between allele substitution effects across breeds. The simulated heritability was set to 0.95 to resemble the heritability of deregressed proofs of bulls.

Results

Accuracies estimated with the deterministic formula based on selection index theory were similar to empirical accuracies for all scenarios, while accuracies predicted with the formula based on population parameters overestimated empirical accuracies by ~25 to 30%. When the between-breed genetic correlation differed from 1, i.e. allele substitution effects differed across breeds, empirical and deterministic accuracies decreased in proportion to the genetic correlation. Using a multi-trait model, it was possible to accurately estimate the genetic correlation between the breeds based on phenotypes and high-density genotypes. The number of QTL underlying the simulated trait did not affect the accuracy.

Conclusions

The deterministic formula based on selection index theory estimated the accuracy of across-population genomic predictions well. The deterministic formula using population parameters overestimated the across-population genomic accuracy, but may still be useful because of its simplicity. Both formulas could accommodate for genetic correlations between populations lower than 1. The number of QTL underlying a trait did not affect the accuracy of across-population genomic prediction using a GBLUP method.     

Quantitative trait loci associated with red foliage in <Emphasis Type="Italic">Cornus florida</Emphasis> L.

The objective of our investigation was to acquire information on the association between molecular markers and foliage color in flowering dogwood in order to improve our understanding of the inheritance of this trait and to make possible early selection of red foliage genotypes in breeding programs. A segregating pseudo-F₂ population of 94 individuals of flowering dogwood (Cornus florida L.), together with 255 simple sequence repeat markers, was used to identify putative quantitative trait loci (QTL) for foliage color. Foliage color segregated into green- and red-leaved phenotypes and was visually rated for color on five spring dates over 3 years (2007–2009). Repeated measures single-marker categorical analysis of variance (ANOVA) identified four putative QTL (CF309C, CF792A, CF367B, and CF367C) on three linkage groups. Single-marker categorical ANOVA was then used to determine stability of QTL across dates. We identified different QTL, found a low percentage of phenotypic variance explained by the QTL, and detected QTL instability over time, providing evidence of the complex genetics for red pigment expression in flowering dogwood.     

Identification of quantitative trait loci for wood quality and growth across eight full-sib coastal Douglas-fir families

Typical linkage and quantitative trait locus (QTL) analyses in forest trees have been conducted in single pedigrees with sex-averaged linkage maps. The results of a QTL analysis for wood quality and growth traits of coastal Douglas-fir using eight full-sib families, each consisting of 40 progeny, replicated on four sites are presented. The resulting map of segregating genetic markers consisted of 120 amplified fragment length polymorphism (AFLP) loci distributed across 19 linkage groups. The wood quality traits represent the widest suite of traits yet examined for QTL analysis in a tree species in a single study. Wood fiber traits showed the lowest number of QTLs (3) with relatively small effect (ca. 4%); wood density traits also showed just three QTLs but with slightly larger effect; wood chemistry traits showed more QTLs (7), while ring density traits showed many QTLs with large numbers of QTLs (78) and interesting patterns of temporal variation. Growth traits gave just five QTLs but of major effect (10–16%). Trees, with their long generation times, provide a rich resource for studies of temporal variation of QTL expression.     

Identification of genes for complex disease using longitudinal phenotypes

Using the simulated data set from Genetic Analysis Workshop 13, we explored the advantages of using longitudinal data in genetic analyses. The weighted average of the longitudinal data for each of seven quantitative phenotypes were computed and analyzed. Genome screen results were then compared for these longitudinal phenotypes and the results obtained using two cross-sectional designs: data collected near a single age (45 years) and data collected at a single time point. Significant linkage was obtained for nine regions (LOD scores ranging from 5.5 to 34.6) for six of the phenotypes. Using cross-sectional data, LOD scores were slightly lower for the same chromosomal regions, with two regions becoming nonsignificant and one additional region being identified. The magnitude of the LOD score was highly correlated with the heritability of each phenotype as well as the proportion of phenotypic variance due to that locus. There were no false-positive linkage results using the longitudinal data and three false-positive findings using the cross-sectional data. The three false positive results appear to be due to the kurtosis in the trait distribution, even after removing extreme outliers. Our analyses demonstrated that the use of simple longitudinal phenotypes was a powerful means to detect genes of major to moderate effect on trait variability. In only one instance was the power and heritability of the trait increased by using data from one examination. Power to detect linkage can be improved by identifying the most heritable phenotype, ensuring normality of the trait distribution and maximizing the information utilized through novel longitudinal designs for genetic analysis.     

Fine mapping of quantitative trait loci for mastitis resistance on bovine chromosome 11

Quantitative trait loci (QTL) affecting clinical mastitis (CM) and somatic cell score (SCS) were mapped on bovine chromosome 11. The mapping population consisted of 14 grandsire families belonging to three Nordic red cattle breeds: Finnish Ayrshire (FA), Swedish Red and White (SRB) and Danish Red. The families had previously been shown to segregate for udder health QTL. A total of 524 progeny tested bulls were included in the analysis. A linkage map including 33 microsatellite and five SNP markers was constructed. We performed combined linkage disequilibrium and linkage analysis (LDLA) using the whole data set. Further analyses were performed for FA and SRB separately to study the origin of the identified QTL/haplotype and to examine if it was common in both populations. Finally, different two-trait models were fitted. These postulated either a pleiotropic QTL affecting both traits; two linked QTL, each affecting one trait; or one QTL affecting a single trait. A QTL affecting CM was fine-mapped. In FA, a haplotype having a strong association with a high negative effect on mastitis resistance was identified. The mapping precision of an earlier detected SCS-QTL was not improved by the LDLA analysis because of lack of linkage disequilibrium between the markers used and the QTL in the region.     

Quantitative trait loci for thermotolerance phenotypes in Drosophila melanogaster

For insects, temperature is a major environmental variable that can influence an individual's behavioral activities and fitness. Drosophila melanogaster is a cosmopolitan species that has had great success in adapting to and colonizing diverse thermal niches. This adaptation and colonization has resulted in complex patterns of genetic variation in thermotolerance phenotypes in nature. Although extensive work has been conducted documenting patterns of genetic variation, substantially less is known about the genomic regions or genes that underlie this ecologically and evolutionarily important genetic variation. To begin to understand and identify the genes controlling thermotolerance phenotypes, we have used a mapping population of recombinant inbred (RI) lines to map quantitative trait loci (QTL) that affect variation in both heat- and cold-stress resistance. The mapping population was derived from a cross between two lines of D. melanogaster (Oregon-R and 2b) that were not selected for thermotolerance phenotypes, but exhibit significant genetic divergence for both phenotypes. Using a design in which each RI line was backcrossed to both parental lines, we mapped seven QTL affecting thermotolerance on the second and third chromosomes. Three of the QTL influence cold-stress resistance and four affect heat-stress resistance. Most of the QTL were trait or sex specific, suggesting that overlapping but generally unique genetic architectures underlie resistance to low- and high-temperature extremes. Each QTL explained between 5 and 14% of the genetic variance among lines, and degrees of dominance ranged from completely additive to partial dominance. Potential thermotolerance candidate loci contained within our QTL regions are identified and discussed.     

High‐density linkage map reveals QTL underlying growth traits in AP13×VS16 biparental population of switchgrass

Switchgrass (Panicum virgatum L.), a native warm‐season perennial grass, is being considered as a feedstock for biofuel production in the United States. To expedite its genetic improvement and enhance genetic gain per selection cycle, application of marker‐assisted selection is indispensable. A high‐density linkage map was constructed in a pseudo‐F₁ testcross mapping population of AP13×VS16, consisting of 349 progenies. A total of 8,757 single nucleotide polymorphism (SNP) markers generated through genotype‐by‐sequencing (GBS) were used to construct the linkage map. The total map length spans up to 2,540.2 cM with the marker density of one marker in every 0.25–0.34 cM. Spring green‐up (SG), days to flowering (FL), and the vegetative growth period (VP) data were analyzed and used for quantitative trait loci (QTL) mapping. The population showed significant variations and exhibited transgressive segregation for SG, FL, and VP. QTL analyses were performed using trait mean of each year and location along with BLUP (best linear unbiased prediction) values of the traits. A total of 35, 37, and 34 QTL for SG, FL, and VP, respectively, were identified. Phenotypic variability explained by each QTL ranged from 11.29% to 27.85%. The additive genetic effects of individual QTL ranged from ?1.81 to 2.40, ?6.12 to 7.58, and ?16.01 to 6.38 for SG, FL, and VP, respectively. Comparing major QTL regions in the switchgrass genome, 20 candidate genes were identified which were reported to be involved in growth‐, development‐, and flowering‐related traits in switchgrass.     

Refinement of the 6p21.3 quantitative trait locus influencing dyslexia: linkage and association analyses

Reading disability (RD), or dyslexia, is the most common learning disability with a prevalence rate of ~5%–10% in school-age children. RD is highly heritable with evidence of a neurobiological origin. Linkage studies have identified several quantitative trait loci (QTLs) for RD. The QTL on chromosome 6p21.3 has been independently replicated by several groups and spans a 16.4-Mb (13.8 cM) interval from D6S109 to D6S291. In this study, we performed sib-pair linkage analyses with Haseman–Elston and DeFries–Fulker methods to define more accurately the QTL interval. Linkage was assessed by using five quantitative phenotypes, including a composite measure of reading performance and four component phenotypes. When probands were selected for severe scores, single- and multi-point analyses showed significant linkage with all five phenotypes, converging over an interval of ~3.24 Mb spanning D6S1597 to D6S1571. Maximal linkage converged at marker D6S1554 across phenotypes. Out of 12 genes in the linkage interval, ten clustered within ~680 kb and were selected for association analysis based on central nervous system expression and putative function. Marker-trait associations were assessed by using QTDT (a general test of association for quantitative traits) and the family-based association test (FBAT), and haplotype analysis was performed by using FBAT and the GeneHunter Transmission/Disequilibrium Test TDT. Marker associations were detected in five of the ten genes, results that were corroborated by our haplotype TDT analysis. The results of the association study have thereby allowed us to significantly reduce the number of possible candidate genes and to prioritize genes for further mutation screening.     

Quantitative trait loci for the circadian clock in Neurospora crassa

Neurospora crassa has been a model organism for the study of circadian clocks for the past four decades. Among natural accessions of Neurospora crassa, there is significant variation in clock phenotypes. In an attempt to investigate natural allelic variants contributing to quantitative variation, we used a quantitative trait loci mapping approach to analyze three independent mapping populations whose progenitors were collected from geographically isolated locations. Two circadian clock phenotypes, free-running period and entrained phase, were evaluated in the 188 F(1) progeny of each mapping population. To identify the clock QTL, we applied two QTL mapping analyses: composite interval mapping (CIM) and Bayesian multiple QTL analysis (BMQ). When controlling false positive rates < or =0.05, BMQ appears to be the more sensitive of the two approaches. BMQ confirmed most of the QTL from CIM (18 QTL) and identified 23 additional QTL. While 13 QTL colocalize with previously identified clock genes, we identified 30 QTL that were not linked with any previously characterized clock genes. These are candidate regions where clock genes may be located and are expected to lead to new insights in clock regulation.     

Power of QTL mapping experiments in commercial Atlantic salmon populations, exploiting linkage and linkage disequilibrium and effect of limited recombination in males

Whereas detection and positioning of genes that affect quantitative traits (quantitative trait loci (QTL)) using linkage mapping uses only information from recombinants in the genotyped generations, linkage disequilibrium (LD) mapping uses historical recombinants. Thus, whereas linkage mapping requires large family sizes to detect and accurately position QTL, LD mapping is more dependent on the number of families sampled from the population. In commercial Atlantic salmon breeding programmes, only a small number of individuals per family are routinely phenotyped for traits such as disease resistance and meat colour. In this paper, we assess the power and accuracy of combined linkage disequilibrium linkage analysis (LDLA) to detect QTL in the commercial population using simulation. When 15 half-sib sire families (each sire mated to 30 dams, each dam with 10 progeny) were sampled from the population for genotyping, we were able to detect a QTL explaining 10% of the phenotypic variance in 85% of replicates and position this QTL within 3 cM of the true position in 70% of replicates. When recombination was absent in males, a feature of the salmon genome, power to detect QTL increased; however, the accuracy of positioning the QTL was decreased. By increasing the number of sire families sampled from the population to be genotyped to 30, we were able to increase both the proportion of QTL detected and correctly positioned (even with no recombination in males). QTL with much smaller effect could also be detected. The results suggest that even with the existing recording structure in commercial salmon breeding programmes, there is considerable power to detect and accurately position QTL using LDLA.     

Genetic characterisation of seed yield and fertility traits in perennial ryegrass (<Emphasis Type="Italic">Lolium perenne</Emphasis> L.)

Seed yield is a trait of major interest for the key grassland species Lolium perenne L. An F2 mapping population of perennial ryegrass (VrnA), recently characterised for vernalisation response, was assessed in a glasshouse for traits related to seed yield based on a lattice design with four replications over 2 years. The traits heading date, plant height, length of panicles, number of panicles per plant, seed yield per panicle, flag leaf length, flag leaf width and seed yield per plant revealed repeatabilities ranging from 41 to 76% and a considerable amount of genetic variation in the VrnA population. Path analysis partitioned the direct and indirect effects of seed yield components on seed yield per plant. Seed yield per panicle showed the highest effect on total seed yield. The adjusted mean values of each trait and a genetic linkage map consisting of 97 anonymous and 85 gene associated DNA markers were used for quantitative trait loci (QTL) analysis. Of particular interest were two QTL on linkage group (LG) 1 and LG 2, explaining 41 and 18%, respectively, of the observed phenotypic variation for the trait seed yield per panicle. Both QTL co-located with two major QTL for total seed yield per plant possibly representing the S and Z loci of the gametophytic self incompatibility (SI) system of perennial ryegrass. The diversity of SI alleles in mapping parents and the degree of heterozygosity at SI loci in the full sib progeny determines the interference of self incompatibility with seed production.     

Potential of a tomato MAGIC population to decipher the genetic control of quantitative traits and detect causal variants in the resequencing era

Identification of the polymorphisms controlling quantitative traits remains a challenge for plant geneticists. Multiparent advanced generation intercross (MAGIC) populations offer an alternative to traditional linkage or association mapping populations by increasing the precision of quantitative trait loci (QTL) mapping. Here, we present the first tomato MAGIC population and highlight its potential for the valorization of intraspecific variation, QTL mapping and causal polymorphism identification. The population was developed by crossing eight founder lines, selected to include a wide range of genetic diversity, whose genomes have been previously resequenced. We selected 1536 SNPs among the 4 million available to enhance haplotype prediction and recombination detection in the population. The linkage map obtained showed an 87% increase in recombination frequencies compared to biparental populations. The prediction of the haplotype origin was possible for 89% of the MAGIC line genomes, allowing QTL detection at the haplotype level. We grew the population in two greenhouse trials and detected QTLs for fruit weight. We mapped three stable QTLs and six specific of a location. Finally, we showed the potential of the MAGIC population when coupled with whole genome sequencing of founder lines to detect candidate SNPs underlying the QTLs. For a previously cloned QTL on chromosome 3, we used the predicted allelic effect of each founder and their genome sequences to select putative causal polymorphisms in the supporting interval. The number of candidate polymorphisms was reduced from 12 284 (in 800 genes) to 96 (in 54 genes), including the actual causal polymorphism. This population represents a new permanent resource for the tomato genetics community.     

Genetic Control of the Leaf Angle and Leaf Orientation Value as Revealed by Ultra-High Density Maps in Three Connected Maize Populations

Plant architecture is a key factor for high productivity maize because ideal plant architecture with an erect leaf angle and optimum leaf orientation value allow for more efficient light capture during photosynthesis and better wind circulation under dense planting conditions. To extend our understanding of the genetic mechanisms involved in leaf-related traits, three connected recombination inbred line (RIL) populations including 538 RILs were genotyped by genotyping-by-sequencing (GBS) method and phenotyped for the leaf angle and related traits in six environments. We conducted single population quantitative trait locus (QTL) mapping and joint linkage analysis based on high-density recombination bin maps constructed from GBS genotype data. A total of 45 QTLs with phenotypic effects ranging from 1.2% to 29.2% were detected for four leaf architecture traits by using joint linkage mapping across the three populations. All the QTLs identified for each trait could explain approximately 60% of the phenotypic variance. Four QTLs were located on small genomic regions where candidate genes were found. Genomic predictions from a genomic best linear unbiased prediction (GBLUP) model explained 45±9% to 68±8% of the variation in the remaining RILs for the four traits. These results extend our understanding of the genetics of leaf traits and can be used in genomic prediction to accelerate plant architecture improvement.     

Bayesian prediction of breeding values for multivariate binary and continuous traits in simulated horse populations using threshold-linear models with Gibbs sampling

Simulated data were used to determine the properties of multivariate prediction of breeding values for categorical and continuous traits using phenotypic, molecular genetic and pedigree information by mixed linear-threshold animal models via Gibbs sampling. Simulation parameters were chosen such that the data resembled situations encountered in Warmblood horse populations. Genetic evaluation was performed in the context of the radiographic findings in the equine limbs. The simulated pedigree comprised seven generations and 40 000 animals per generation. The simulated data included additive genetic values, residuals and fixed effects for one continuous trait and liabilities of four binary traits. For one of the binary traits, quantitative trait locus (QTL) effects and genetic markers were simulated, with three different scenarios with respect to recombination rate (r) between genetic markers and QTL and polymorphism information content (PIC) of genetic markers being studied: r = 0.00 and PIC = 0.90 (r0p9), r = 0.01 and PIC = 0.90 (r1p9), and r = 0.00 and PIC = 0.70 (r0p7). For each scenario, 10 replicates were sampled from the simulated horse population, and six different data sets were generated per replicate. Data sets differed in number and distribution of animals with trait records and the availability of genetic marker information. Breeding values were predicted via Gibbs sampling using a Bayesian mixed linear-threshold animal model with residual covariances fixed to zero and a proper prior for the genetic covariance matrix. Relative breeding values were used to investigate expected response to multi- and single-trait selection. In the sires with 10 or more offspring with trait information, correlations between true and predicted breeding values ranged between 0.89 and 0.94 for the continuous traits and between 0.39 and 0.77 for the binary traits. Proportions of successful identification of sires of average, favourable and unfavourable genetic value were 81% to 86% for the continuous trait and 57% to 74% for the binary traits in these sires. Expected decrease of prevalence of the QTL trait was 3% to 12% after multi-trait selection for all binary traits and 9% to 17% after single-trait selection for the QTL trait. The combined use of phenotype and genotype data was superior to the use of phenotype data alone. It was concluded that information on phenotypes and highly informative genetic markers should be used for prediction of breeding values in mixed linear-threshold animal models via Gibbs sampling to achieve maximum reduction in prevalences of binary traits.     

Genetic variability at neutral markers,quantitative trait land trait in a subdivided population under selection

Genetic variability in a subdivided population under stabilizing and diversifying selection was investigated at three levels: neutral markers, QTL coding for a trait, and the trait itself. A quantitative model with additive effects was used to link genotypes to phenotypes. No physical linkage was introduced. Using an analytical approach, we compared the diversity within deme (H(S)) and the differentiation (F(ST)) at the QTL with the genetic variance within deme (V(W)) and the differentiation (Q(ST)) for the trait. The difference between F(ST) and Q(ST) was shown to depend on the relative amounts of covariance between QTL within and between demes. Simulations were used to study the effect of selection intensity, variance of optima among demes, and migration rate for an allogamous and predominantly selfing species. Contrasting dynamics of the genetic variability at markers, QTL, and trait were observed as a function of the level of gene flow and diversifying selection. The highest discrepancy among the three levels occurred under highly diversifying selection and high gene flow. Furthermore, diversifying selection might cause substantial heterogeneity among QTL, only a few of them showing allelic differentiation, while the others behave as neutral markers.     

QTL mapping of yield, agronomic and quality traits in tetraploid potato (Solanum tuberosum subsp. tuberosum)

Interval mapping of quantitative trait loci (QTL) for 16 yield, agronomic and quality traits in potato was performed on a tetraploid full-sib family comprising 227 clones from a cross between processing clone 12601ab1 and table cultivar Stirling. Thirty-eight AFLP primer combinations and six SSRs provided 514 informative markers which formed a molecular marker map comprising 12 linkage groups (LGs) in 12601ab1 (nine with four homologous chromosomes) which were aligned with 12 in Stirling (11 with four homologous chromosomes), with four partial groups remaining in 12601ab1. Two LGs were identified unequivocally as chromosomes IV and V and eight others were tentatively assigned with chromosomes VII and X unidentified. All of the traits scored had moderately high heritabilities with 54–92% of the variation in clone means over 3 years and two replicates being due to genetic differences. A total of 39 QTLs were identified. A QTL for maturity was identified on chromosome V which explained 56% of the phenotypic variance, whereas the other QTLs individually explained between 5.4 and 16.5%. However, six QTLs were detected for after-cooking blackening and four for each of regularity of tuber shape, fry colour both after storage at 4 and 10°C and sprouting. Just two QTLs were found for each of yield, the two ‘overall’ scores, crop emergence, tuber size and common scab and just one QTL was detected for each of dry matter content, keeping quality, growth cracks and internal condition. The implications for practical potato breeding and for practical linkage and QTL analysis in autotetraploids are discussed.