Multipoint genetic mapping of quantitative trait loci with dominant markers in outbred populations

We present a multipoint algorithm for mapping quantitative trait loci (QTLs) using dominant markers. The algorithm is designed for outbred populations and is particularly suited for large families. The algorithm works with either codominant or dominant markers, either of which may be interspersed within the same linkage map. Concurrently, the algorithm also partitions dominance variance at the QTL. Computer simulations show that with large families, QTL mapping with dominant markers can be almost as powerful as with bi-allelic, codominant markers. Yet despite this, other situations show a large standard deviation in the estimate of the QTL position, thus making QTL mapping with dominant markers in outbred populations a useful detection tool, albeit limited in its resolution. This revised version was published online in July 2006 with corrections to the Cover Date.     

Mapping quantitative trait loci with dominant markers in four-way crosses

 A common problem in mapping quantitative trait loci (QTLs) is that marker data are often incomplete. This includes missing data, dominant markers, and partially informative markers, arising in outbred populations. Here we briefly present an iteratively re-weighted least square method (IRWLS) to incorporate dominant and missing markers for mapping QTLs in four-way crosses under a heterogeneous variance model. The algorithm uses information from all markers in a linkage group to infer the QTL genotype. Monte Carlo simulations indicate that with half dominant markers, QTL detection is almost as efficient as with all co-dominant markers. However, the precision of the estimated QTL parameters generally decreases as more markers become missing or dominant. Notable differences are observed on the standard deviation of the estimated QTL position for varying levels of marker information content. The method is relatively simple so that more complex models including multiple QTLs or fixed effects can be fitted. Finally, the method can be readily extended to QTL mapping in full-sib families. Received: 16 June 1998 / Accepted: 29 September 1998     

Detection and parameter estimation for quantitative trait loci using regression models and multiple markers

A strategy of multi-step minimal conditional regression analysis has been developed to determine the existence of statistical testing and parameter estimation for a quantitative trait locus (QTL) that are unaffected by linked QTLs. The estimation of marker-QTL recombination frequency needs to consider only three cases: 1) the chromosome has only one QTL, 2) one side of the target QTL has one or more QTLs, and 3) either side of the target QTL has one or more QTLs. Analytical formula was derived to estimate marker-QTL recombination frequency for each of the three cases. The formula involves two flanking markers for case 1), two flanking markers plus a conditional marker for case 2), and two flanking markers plus two conditional markers for case 3). Each QTL variance and effect, and the total QTL variance were also estimated using analytical formulae. Simulation data show that the formulae for estimating marker-QTL recombination frequency could be a useful statistical tool for fine QTL mapping. With 1 000 observations, a QTL could be mapped to a narrow chromosome region of 1.5 cM if no linked QTL is present, and to a 2.8 cM chromosome region if either side of the target QTL has at least one linked QTL.     

Identification of quantitative trait loci controlling heading date in rice using a high-density linkage map

 Quantitative trait locus (QTL) analysis has been carried out to identify genes conferring heading date in rice. One hundred and eighty six F₂ plants derived from a cross between a japonica variety, Nipponbare, and an indica variety, Kasalath, were used as a segregating population for QTL mapping and more than 850 markers were employed to identify QTLs. Scan-analysis revealed the existence of two QTLs with large effects, Hd-1 and Hd-2, one in the middle of chromosome 6 and one at the end of chromosome 7, respectively. For both loci, the Kasalath alleles reduced days-to-heading. In addition, three QTLs with minor effects, Hd-3, Hd-4 and Hd-5, were found to be located on chromosomes 6, 7 and 8 based on a secondary scan analysis which was carried out by removing the phenotypic effects of Hd-1 and Hd-2. For the three secondary loci, the Nipponbare alleles reduced days-to-heading. The five QTLs explained 84% of the total phenotypic variation in the F₂ population based on a multiple-QTL model. The presence of a digenic interaction between Hd-1 and Hd-2 was clearly suggested. Received: 18 March 1997 / Accepted: 24 June 1997     

Microsatellite markers associated with genes expressed in developing wings of Bicyclus anynana butterflies

Deriving useful microsatellite markers in lepidopterans has been challenging when relying on scans of genomic DNA libraries, presumably due to repetitiveness in their genomes. We assayed 96 of 320 microsatellites identified in silico from a collection of Bicyclus anynana ESTs, in 11 independent individuals from a laboratory population. From the 68 successful assays, we identified 40 polymorphic markers including 22 with BLAST-based annotation. Nine of 12 selected polymorphic markers tested in a panel of 24 wild-caught individuals converted to successful assays and were all polymorphic. We discuss how microsatellite discovery in ESTs is an efficient strategy with important attendant advantages.     

Identification of quantitative trait loci controlling sucrose content in soybean (Glycine max)

Sucrose is a primary constituent of soybean (Glycine max) seed; however, little information concerning the inheritance of seed sucrose in soybean is available. The objective of this research was to use molecular markers to identify genomic regions significantly associated with quantitative trait loci (QTL) controlling sucrose content in a segregating F₂ population. DNA samples from 149 F₂ individuals were analyzed with 178 polymorphic genetic markers, including RFLPs, SSRs, and RAPDs. Sucrose content was measured on seed harvested from each of 149 F_2:3 lines from replicated field experiments in 1993 and 1995. Seventeen marker loci, mapping to seven different genomic regions, were significantly associated with sucrose variation at P<0.01. Individually, these markers explained from 6.1% to 12.4% of the total phenotypic variation for sucrose content in this population. In a combined analysis these genomic regions; explained 53% of total variation for sucrose content. No significant evidence of epistasis among QTLs was observed. Comparison of our QTL mapping results for sucrose content and those previously reported for protein and oil content (the other major seed constituents in soybean), suggests that seed quality traits are inherited as clusters of linked loci or that `major' QTLs with pleiotropic effects may control all three traits. Of the seven genomic regions having significant effects on sucrose content, three were associated with significant variation for protein content and three were significantly associated with oil content.     

Estimation of the contribution of quantitative trait loci (QTL) to the variance of a quantitative trait by means of genetic markers

The estimation of the contribution of an individual quantitative trait locus (QTL) to the variance of a quantitative trait is considered in the framework of an analysis of variance (ANOVA). ANOVA mean squares expectations which are appropriate to the specific case of QTL mapping experiments are derived. These expectations allow the specificities associated with the limited number of genotypes at a given locus to be taken into account. Discrepancies with classical expectations are particularly important for two-class experiments (backcross, recombinant inbred lines, doubled haploid populations) and F₂ populations. The result allows us firstly to reconsider the power of experiments (i.e. the probability of detecting a QTL with a given contribution to the variance of the trait). It illustrates that the use of classical formulae for mean squares expectations leads to a strong underestimation of the power of the experiments. Secondly, from the observed mean squares it is possible to estimate directly the variance associated with a locus and the fraction of the total variance associated to this locus (r _l ² ). When compared to other methods, the values estimated using this method are unbiased. Considering unbiased estimators increases in importance when (1) the experimental size is limited; (2) the number of genotypes at the locus of interest is large; and (3) the fraction of the variation associated with this locus is small. Finally, specific mean squares expectations allows us to propose a simple analytical method by which to estimate the confidence interval of r _l ² . This point is particularly important since results indicate that 95% confidence intervals for r _l ² can be rather wide:2–23% for a 10% estimate and 8–34% for a 20% estimate if 100 individuals are considered.     

Sequential sampling in determining linkage between marker loci and quantitative trait loci

Summary As compared to classical, fixed sample size techniques, simulation studies showed that a proposed sequential sampling procedure can provide a substantial decrease (up to 50%, in some cases) in the mean sample size required for the detection of linkage between marker loci and quantitative trait loci. Sequential sampling with truncation set at the required sample size for the non-sequential test, produced a modest further decrease in mean sample size, accompanied by a modest increase in error probabilities. Sequential sampling with observations taken in groups produced a noticeable increase in mean sample size, with a considerable decrease in error probabilities, as compared to straightforward sequential sampling. It is concluded that sequential sampling has a particularly useful application to experiments aimed at investigating the genetics of differences between lines or strains that differ in some single outstanding trait.     

Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines

Dominant phenotype of a genetic marker provides incomplete information about the marker genotype of an individual. A consequence of using this incomplete information for mapping quantitative trait loci (QTL) is that the inference of the genotype of a putative QTL flanked by a marker with dominant phenotype will depend on the genotype or phenotype of the next marker. This dependence can be extended further until a marker genotype is fully observed. A general algorithm is derived to calculate the probability distribution of the genotype of a putative QTL at a given genomic position, conditional on all observed marker phenotypes in the region with dominant and missing marker information for an individual. The algorithm is implemented for various populations stemming from two inbred lines in the context of mapping QTL. Simulation results show that if only a proportion of markers contain missing or dominant phenotypes, QTL mapping can be almost as efficient as if there were no missing information in the data. The efficiency of the analysis, however, may decrease substantially when a very large proportion of markers contain missing or dominant phenotypes and a genetic map has to be reconstructed first on the same data as well. So it is important to combine dominant markers with codominant markers in a QTL mapping study. This revised version was published online in July 2006 with corrections to the Cover Date.     

Trait-based analyses for the detection of linkage between marker loci and quantitative trait loci in crosses between inbred lines

Summary Methods are presented for determining linkage between a marker locus and a nearby locus affecting a quantitative trait (quantitative trait locus=QTL), based on changes in the marker allele frequencies in selection lines derived from the F-2 of a cross between inbred lines, or in the high and low phenotypic classes of an F-2 or BC population. The power of such trait-based (TB) analyses was evaluated and compared with that of methods for determining linkage based on the mean quantitative trait value of marker genotypes in F-2 or BC populations [marker-based (MB) analyses]. TB analyses can be utilized for marker-QTL linkage determination in situations where the MB analysis is not applicable, including analysis of polygenic resistance traits where only a part of the population survives exposure to the Stressor and analysis of marker-allele frequency changes in selection lines. TB analyses may be a useful alternative to MB analyses when interest is centered on a single quantitative trait only and costs of scoring for markers are high compared with costs of raising and obtaining quantitative trait information on F-2 or BC individuals. In this case, a TB analysis will enable equivalent power to be obtained with fewer individuals scored for the marker, but more individuals scored for the quantitative trait. MB analyses remain the method of choice when more than one quantitative trait is to be analyzed in a given population.Contribution from the ARO, Bet Dagan, Israel. No. 1698-E, 1986 series     

Mapping quantitative trait loci controlling silking date in a diallel cross among four lines of maize

 We describe and apply an interval mapping method for quantitative trait locus (QTL) detection using F₃ and testcross progenies derived from F₂ populations obtained from a diallel cross among four elite lines of maize. Linear model-based procedures were used for the test and estimation of putative QTL effects together with genetic interactions including epistasis. We mapped QTL associated with silking date and explored their genetic effects. Ten QTL were detected, and these explained more than 40% of the phenotypic variance. Most of these QTL had consistent and stable effects among genetic backgrounds and did not show significant epistasis. QTL-by-environment interaction was important for four QTL and was essentially due to changes in magnitude of allelic effects. These results show the efficiency of our method in several genetic situations as well as the power of the diallel design in detecting QTL simultaneously over several populations. Received: 2 September 1996 / Accepted: 20 December 1996     

Association of transforming growth factor beta genes with quantitative trait loci for antibody response kinetics in hens

Antibody responses (primary and secondary phases) were measured in an F2 population. The resource population was derived from grandsires of two highly inbred major histocompatibility complex (MHC)-congenic Fayoumi chicken lines (named M5.1 and M15.2) mated with highly inbred Leghorn G-B1 hens. Secondary phase parameters of maximum titres (Ymax) and time required to achieve Ymax (Tmax) were estimated from post-secondary titres by using a non-linear regression model. Associations of single nucleotide polymorphisms (SNP) in transforming growth factor beta2 (TGFB2), 3, and 4 genes with antibody response parameters were evaluated. Multiple immune response parameters were significantly associated with the TGFB2 gene primarily in the lineage of the M5.1 grandsire, suggesting that TGFB2 or linked genes affect antibody response in hens. Significant main effects of the three genes were mostly found in the lineage of the M5.1 grandsire. Significant two-way interactions on antibody response were primarily detected between TGFB3 and TGFB4 genes, and in the lineage of the M15.2 grandsire. Effects preferentially detectable in only one of the MHC-congenic lineages suggest that there was interaction between the MHC and TGFB genes. The characterized TGFB SNPs might be applied in marker-assisted selection to improve antibody production.     

Quantitative trait loci and molecular markers associated with wheat allelopathy

Wheat (Triticum aestivum L.) has been examined for allelopathic potential against annual ryegrass (Lolium rigidum). The bioassay technique, 'equal-compartment-agar-method', was employed to evaluate seedling allelopathy in a doubled-haploid (DH) population derived from cv Sunco (weakly allelopathic) and cv Tasman (strongly allelopathic). A significant difference in allelopathic activity was found among the DH lines, which inhibited the root length of ryegrass across a range from 23.7 to 88.3%. The phenotypic data showed that wheat allelopathic activity was distributed normally within this DH population and a substantial transgressive segregation for seedling allelopathic activity was also found. Analysis of restriction fragment length polymorphism (RFLP), amplified fragment length polymorphism (AFLP) and microsatellite (SSRs) markers identified two major QTLs on chromosome 2B associated with wheat allelopathy. The linkage analysis of genetic markers and the QTLs may improve genetic gains for the allelopathic activity through marker-assisted selection in wheat breeding. The development of wheat allelopathic cultivars could reduce the over-reliance of weed control on synthetic herbicides.Communicated by J. Dvorak     

DNA fingerprint bands applied to linkage analysis with quantitative trait loci in chickens

Summary
An efficient approach to detect association between quantitative traits and bands of DNA fingerprint patterns uses intra-family tail analysis, which compares fingerprints of DNA mixes from individuals at the two tails of a phenotypic distribution. In analysis of 67 paternal half-sibs of a meat-type chicken family, of 57 sire bands generated by two probes, one sire-specific band (S6–6) was associated with abdominal fat deposition. The band effect was estimated by a linear model analysis to be 0–88 standard deviations, or about 30% of the family mean. The association between band S6–6 and abdominal fat was further examined by testing progeny of paternal half-sibs of the chickens which were used in the tail analysis, establishing genetic linkage between the DNA marker and a genetic locus affecting abdominal fat deposition.     

Mapping of quantitative trait loci controlling lifespan in the short-lived fish Nothobranchius furzeri--a new vertebrate model for age research

The African annual fish Nothobranchius furzeri emerged as a new model for age research over recent years. Nothobranchius furzeri show an exceptionally short lifespan, age-dependent cognitive/behavioral decline, expression of age-related biomarkers, and susceptibility to lifespan manipulation. In addition, laboratory strains differ largely in lifespan. Here, we set out to study the genetics of lifespan determination. We crossed a short- to a long-lived strain, recorded lifespan, and established polymorphic markers. On the basis of genotypes of 411 marker loci in 404 F(2) progeny, we built a genetic map comprising 355 markers at an average spacing of 5.5 cM, 22 linkage groups (LGs) and 1965 cM. By combining marker data with lifespan values, we identified one genome-wide highly significant quantitative trait locus (QTL) on LG 9 (P < 0.01), which explained 11.3% of the F(2) lifespan variance, and three suggestive QTLs on LG 11, 14, and 17. We characterized the highly significant QTL by synteny analysis, because a genome sequence of N. furzeri was not available. We located the syntenic region on medaka chromosome 5, identified candidate genes, and performed fine mapping, resulting in a c. 40% reduction of the initial 95% confidence interval. We show both that lifespan determination in N. furzeri is polygenic, and that candidate gene detection is easily feasible by cross-species analysis. Our work provides first results on the way to identify loci controlling lifespan in N. furzeri and illustrates the potential of this vertebrate species as a genetic model for age research.     

Analyses of quantitative trait loci associated with resistance to shape Sclerotinia sclerotiorum in sunflowers (shape Helianthus annuus L.) using molecular markers

Restriction fragment length polymorphism and isoenzyme markers were used to investigate quantitative trait loci involved in sunflower resistance to mycelial extension of Sclerotinia sclerotiorum on leaves and capitula. Seed weight, oil content and flowering data were also evaluated. Four quantitative trait loci were demonstrated for leaf resistance and two for capitulum resistance. One of these zones appears involved in resistance to both types of S. sclerotiorum attack while the others appear specific for resistance of one part of the plant. Two quantitative trait loci were detected for seed weight, three for oil content and three for flowering date. Individual quantitative trait loci explained 9% to 48% of the phenotypic variability, confirming the polygenic basis of the quantitative traits studied. Overall, the quantitative trait loci explain 60% of the genetic variation for leaf resistance and 38% for capitulum resistance to S. sclerotiorum. One linkage group is particularly interesting since it includes quantitative trait loci for all the five quantitative traits measured. Hypotheses for linkage versus pleiotropy and consequences of all the results in resistance breeding are discussed.     

Identification of quantitative trait loci for growth and carcass composition in cattle

A genomic screening to detect quantitative trait loci (QTL) affecting growth, carcass composition and meat quality traits was pursued. Two hundred nineteen microsatellite markers were genotyped on 176 of 620 (28%) progeny from a Brahman x Angus sire mated to mostly MARC III dams. Selective genotyping, based on retail product yield (%) and fat yield (%), was used to select individuals to be genotyped. Traits included in the study were birth weight (kg), hot carcass weight (kg), retail product yield, fat yield, marbling score (400 = slight00 and 500 = small00), USDA yield grade, and estimated kidney, heart and pelvic fat (%). The QTL were classified as significant when the expected number of false positives (ENFP) was less than 0.05 (F-statistic greater than 17.3), and suggestive when the ENFP was <1 (F-statistic between 10.2 and 17.3). A significant QTL (F = 19; ENFP = 0.02) was detected for marbling score at centimorgan (cM) 54 on chromosome 2. Suggestive QTL were detected for fat yield at 50 cM, for retail product yield at 53 cM, and for USDA yield grade at 63 cM on chromosome 1, for marbling score at 56 cM, for retail product yield at 70 cM, and for estimated kidney, heart and pelvic fat at 79 cM on chromosome 3, for marbling score at 44 cM, for hot carcass weight at 49 cM, and for estimated kidney, heart and pelvic fat at 62 cM on chromosome 16, and for fat yield at 35 cM on chromosome 17. Two suggestive QTL for birth weight were identified, one at 12 cM on chromosome 20 and the other at 56 cM on chromosome 21. An additional suggestive QTL was detected for retail product yield, for fat yield, and for USDA yield grade at 26 cM on chromosome 26. Results presented here represent the initial search for quantitative trait loci in this family. Validation of detected QTL in other populations will be necessary.     

Identification of quantitative trait loci associated with resistance to cucumber mosaic virus in Capsicum annuum

QTL analysis for resistance to cucumber mosaic virus (CMV) was performed in an intraspecific Capsicum annuum population. A total of 180 F3 families were derived from a cross between the susceptible bell-type cultivar Maor and the resistant small-fruited Indian line Perennial and inoculated with CMV in three experiments carried out in the USA and Israel using two virus isolates. Mostly RFLP and AFLP markers were used to construct the genetic map, and interval analysis was used for QTL detection. Four QTL were significantly associated with resistance to CMV. Two digenic interactions involving markers with and without an individual effect on CMV resistance were also detected. The QTL controlling the largest percentage (16–33%) of the observed phenotypic variation (cmv11.1) was detected in all three experiments and was also involved in one of the digenic interactions. This QTL is linked to the L locus that confers resistance to tobacco mosaic virus (TMV), confirming earlier anecdotal observations of an association between resistance to CMV and susceptibility to TMV in Perennial. An advanced backcross breeding line from an unrelated population, 3990, selected for resistance to CMV was analyzed for markers covering the genome, allowing the identification of genomic regions introgressed from Perennial. Four of these introgressions included regions associated with QTL for CMV resistance. Markers in two genomic regions that were identified as linked to QTL for CMV resistance were also linked to QTL for fruit weight, confirming additional breeding observations of an association between resistance to CMV originating from Perennial and small fruit weight. Received: 17 July 2000 / Accepted: 16 October 2000     

A genome-screen experiment to detect quantitative trait loci affecting resistance to facial eczema disease in sheep

Facial eczema (FE) is a secondary photosensitization disease arising from liver cirrhosis caused by the mycotoxin sporidesmin. The disease affects sheep, cattle, deer and goats, and costs the New Zealand sheep industry alone an estimated NZ$63M annually. A long-term sustainable solution to this century-old FE problem is to breed for disease-resistant animals by marker-assisted selection. As a step towards finding a diagnostic DNA test for FE sensitivity, we have conducted a genome-scan experiment to screen for quantitative trait loci (QTL) affecting this trait in Romney sheep. Four F₁ sires, obtained from reciprocal matings of FE resistant and susceptible selection-line animals, were used to generate four outcross families. The resulting half-sib progeny were artificially challenged with sporidesmin to phenotype their FE traits measured in terms of their serum levels of liver-specific enzymes, namely gamma-glutamyl transferase and glutamate dehydrogenase. In a primary screen using selective genotyping on extreme progeny of each family, a total of 244 DNA markers uniformly distributed over all 26 ovine autosomes (with an autosomal genome coverage of 79–91%) were tested for linkage to the FE traits. Data were analysed using Haley–Knott regression. The primary screen detected one significant and one suggestive QTL on chromosomes 3 and 8 respectively. Both the significant and suggestive QTL were followed up in a secondary screen where all progeny were genotyped and analysed; the QTL on chromosome 3 was significant in this analysis.