Validating a QTL region characterized by multiple haplotypes

Due to the universal presence of genotype by environment interactions, understanding the pattern of quantitative trait loci (QTL)-by-environment interactions is a prerequisite for effective marker-assisted selection. In this report, we describe a biplot approach for investigating QTL-by-environment patterns. This approach involves two steps. It starts with a two-way table containing effects of individual QTLs in individual environments for the trait under investigation. This table is decomposed into principal components via singular value decomposition, and the first two principal components are plotted for both QTLs and environments to form a biplot. The resulting ‘QQE biplot’ contains information on QTL main effects (Q) and QTL-by-environment interactions (QE). A QQE biplot displays the QTL-by-environment patterns and allows visualization of: (1) the magnitude of the effect of a QTL, (2) the average effect of a QTL and its stability across environments, (3) the effects of a QTL in individual environments, (4) the similarity/dissimilarity among QTLs in effect and response to the environments, (5) the similarity/dissimilarity among environments in modulating QTL effects, (6) any differentiation of mega-environments, and (7) the combination of QTL alleles for maximum/minimum expression of the trait for each environment or mega-environment. A case study is provided using the QTL-by-environment two-way table for barley yield.

To the Editor: Familial combined hyperlipidemia (FCHL), associated with premature cardiovascular disease, is the most common genetic hyperlipidemia with an estimated prevalence of 1%.1 The complex genetic background of FCHL is slowly being dissected through genome screens and positional candidate association studies.2–6 However, it has also been argued that although numerous studies have been conducted and reported, there are many conflicting results and a true understanding of the genetics remains largely unknown.7 Previously in this journal, we reported 4 quantitative trait loci (QTL) for apolipoprotein B (apoB), cholesterol, and triglycerides with P <0.001 in 22 FCHL pedigrees from Maastricht, in The Netherlands.8 The original QTL study was based on the premise that because FCHL is defined by increased serum levels of cholesterol, apoB, and triglycerides in families, QTL analyses of these quantitative traits in FCHL families might identify important genetic loci for this disorder. This letter reports QTL analyses of 5-year follow-up data in the same individuals. Longitudinal studies show that the lipid …

Regression interval mapping and multiple interval mapping are compared with regard to mapping linked quantitative trait loci (QTL) in inbred-line cross experiments. For that purpose, a simulation study was performed using genetic models with two linked QTL. Data were simulated for F(2) populations of different sizes and with all QTL and marker alleles fixed for alternative alleles in the parental lines. The criteria for comparison are power of QTL identification and the accuracy of the QTL position and effect estimates. Further, the estimates of the relative QTL variance are assessed. There are distinct differences in the QTL position estimates between the two methods. Multiple interval mapping tends to be more powerful as compared to regression interval mapping. Multiple interval mapping further leads to more accurate QTL position and QTL effect estimates. The superiority increased with wider marker intervals and larger population sizes. If QTL are in repulsion, the differences between the two methods are very pronounced. For both methods, the reduction of the marker interval size from 10 to 5 cM increases power and greatly improves QTL parameter estimates. This contrasts with findings in the literature for single QTL scenarios, where a marker density of 10 cM is generally considered as sufficient. The use of standard (asymptotic) statistical theory for the computation of the standard errors of the QTL position and effect estimates proves to give much too optimistic standard errors for regression interval mapping as well as for multiple interval mapping.

Multiple-trait analysis typically employs models that associate a quantitative trait locus (QTL) with all of the traits. As a result, statistical power for QTL detection may not be optimal if the QTL contributes to the phenotypic variation in only a small proportion of the traits. Excluding QTL effects that contribute little to the test statistic can improve statistical power. In this article, we show that an optimal power can be achieved when the number of QTL effects is best estimated, and that a stringent criterion for QTL effect selection may improve power when the number of QTL effects is small but can reduce power otherwise. We investigate strategies for excluding trivial QTL effects, and propose a method that improves statistical power when the number of QTL effects is relatively small, and fairly maintains the power when the number of QTL effects is large. The proposed method first uses resampling techniques to determine the number of nontrivial QTL effects, and then selects QTL effects by the backward elimination procedure for significance test. We also propose a method for testing QTL-trait associations that are desired for biological interpretation in applications. We validate our methods using simulations and Arabidopsis thaliana transcript data.

In a previous study on maize (Zea mays, L.) several quantitative trait loci (QTL) showing high dominance-additive ratio for agronomic traits were identified in a population of recombinant inbred lines derived from B73 × H99. For four of these mapped QTL, namely 3.05, 4.10, 7.03 and 10.03 according to their chromosome and bin position, families of near-isogenic lines (NILs) were developed, i.e., couples of homozygous lines nearly identical except for the QTL region that is homozygote either for the allele provided by B73 or by H99. For two of these QTL (3.05 and 4.10) the NILs families were produced in two different genetic backgrounds. The present research was conducted in order to: (i) characterize these QTL by estimating additive and dominance effects; (ii) investigate if these effects can be affected by genetic background, inbreeding level and environmental growing conditions (low vs. high plant density). The six NILs’ families were tested across three years and in three Experiments at different inbreeding levels as NILs per se and their reciprocal crosses (Experiment 1), NILs crossed to related inbreds B73 and H99 (Experiment 2) and NILs crossed to four unrelated inbreds (Experiment 3). Experiment 2 was conducted at two plant densities (4.5 and 9.0 plants m-2). Results of Experiments 1 and 2 confirmed previous findings as to QTL effects, with dominance-additive ratio superior to 1 for several traits, especially for grain yield per plant and its component traits; as a tendency, dominance effects were more pronounced in Experiment 1. The QTL effects were also confirmed in Experiment 3. The interactions involving QTL effects, families and plant density were generally negligible, suggesting a certain stability of the QTL. Results emphasize the importance of dominance effects for these QTL, suggesting that they might deserve further studies, using NILs’ families and their crosses as base materials.

BackgroundVariance component (VC) models are commonly used for Quantitative Trait Loci (QTL) mapping in outbred populations. Here, the QTL effect is given as a random effect and a critical part of the model is the relationship between the phenotypic values and the random effect. In the traditional VC model, each individual has a unique QTL effect and the relationship between these random effects is given as a covariance structure (known as the identity-by-descent (IBD) matrix).ResultsWe present an alternative notation of the variance component model, where the elements of the random effect are independent base generation allele effects and sampling term effects. The relationship between the phenotypic vales and the random effect is given by an incidence matrix, which results in a novel, but statistically equivalent, version of the traditional VC model. A general algorithm to estimate this incidence matrix is presented. Since the model is given in terms of base generation allele effects and sampling term effects, these effects can be estimated separately using best linear unbiased prediction (BLUP). From simulated data, we showed that biallelic QTL effects could be accurately clustered using the BLUP obtained from our model notation when markers are fully informative, and that the accuracy increased with the size of the QTL effect. We also developed a measure indicating whether a base generation marker homozygote is a QTL heterozygote or not, by comparing the variances of the sampling term BLUP and the base generation allele BLUP. A ratio greater than one gives strong support for a QTL heterozygote.ConclusionWe developed a simple presentation of the VC QTL model for identification of base generation allele effects in QTL linkage analysis. The base generation allele effects and sampling term effects were separated in our model notation. This clarifies the assumptions of the model and should also enhance the development of genome scan methods.

Wheat cultivar Madsen has a new gene on the short arm of chromosome 1A and two QTL for all-stage resistance and three QTL for high-temperature adult-plant resistance that in combination confer high-level, durable resistance to stripe rust. Wheat cultivar Madsen has maintained a high-level resistance to stripe rust over 30years. To map quantitative trait loci (QTL) underlying the high-level, durable resistance, 156 recombinant inbred lines (RILs) developed from cross Avocet S × Madsen were phenotyped with selected races of Puccinia striiformis f. sp. tritici in the greenhouse seedling tests, and in naturally infected fields during 2015-2017. The RILs were genotyped by SSR and SNP markers from genotyping by sequencing and the 90K wheat SNP chip. Three QTL for all-stage resistance were mapped on chromosomes 1AS, 1BS and 2AS, and two QTL for high-temperature adult-plant (HTAP) resistance were mapped on 3BS and 6BS. The most effective QTL on 2AS, explaining 8.97-23.10% of the phenotypic variation in seedling tests and 8.60-71.23% in field tests, contained Yr17 for all-stage resistance and an additional gene for HTAP resistance. The 6BS QTL, detected in all field tests, was identified as Yr78. The 1AS QTL, conferring all-stage resistance, was identified as a new gene, which explained 20.45 and 30.23% of variation in resistance to races PSTv-37 and PSTv-40, respectively, and contributed significantly to field resistance at Pullman in 2015-2017, but was not detected at Mount Vernon. The interactions among QTL were mostly additive, and RILs with all five QTL had the highest level of resistance in the field, similar to Madsen. Genotyping 148 US Pacific Northwest wheat cultivars with markers for the 1AS, 2AS and 6BS QTL validated the genes and markers, and indicated their usefulness for marker-assisted selection.

Grain yield and associated agronomic traits are important factors in wheat (Triticum aestivum L.) improvement. Knowledge regarding the number, genomic location, and effect of quantitative trait loci (QTL) would facilitate marker-assisted selection and the development of cultivars with desirable characteristics. Our objectives were to identify QTLs directly and indirectly affecting grain yield expression. A population of 132 F12 recombinant inbred lines (RILs) was derived by single-seed descent from a cross between the Chinese facultative wheat Ning7840 and the US soft red winter wheat Clark. Phenotypic data were collected for 15 yield and other agronomic traits in the RILs and parental lines from three locations in Oklahoma from 2001 to 2003. Twenty-nine linkage groups, consisting of 363 AFLP and 47 SSR markers, were identified. Using composite interval mapping (CIM) analysis, 10, 16, 30, and 14 QTLs were detected for yield, yield components, plant adaptation (shattering and lodging resistance, heading date, and plant height), and spike morphology traits, respectively. The QTL effects ranged from 7 to 23%. Marker alleles from Clark were associated with a positive effect for the majority of QTLs for yield and yield components, but gene dispersion was the rule rather than the exception for this RIL population. Often, QTLs were detected in proximal positions for different traits. Consistent, co-localized QTLs were identified in linkage groups 1AL, 1B, 4B, 5A, 6A, and 7A, and less consistent but unique QTLs were identified on 2BL, 2BS, 2DL, and 6B. Results of this study provide a benchmark for future efforts on QTL identification for yield traits.

BackgroundThe genetic architecture of egg production and egg quality traits, i.e. the quantitative trait loci (QTL) that influence these traits, is still poorly known. To date, 33 studies have focused on the detection of QTL for laying traits in chickens, but less than 10 genes have been identified. The availability of a high-density SNP (single nucleotide polymorphism) chicken array developed by Affymetrix, i.e. the 600K Affymetrix® Axiom® HD genotyping array offers the possibility to narrow down the localization of previously detected QTL and to detect new QTL. This high-density array is also anticipated to take research beyond the classical hypothesis of additivity of QTL effects or of QTL and environmental effects. The aim of our study was to search for QTL that influence laying traits using the 600K SNP chip and to investigate whether the effects of these QTL differed between diets and age at egg collection.ResultsOne hundred and thirty-one QTL were detected for 16 laying traits and were spread across all marked chromosomes, except chromosomes 16 and 25. The percentage of variance explained by a QTL varied from 2 to 10 % for the various traits, depending on diet and age at egg collection. Chromosomes 3, 9, 10 and Z were overrepresented, with more than eight QTL on each one. Among the 131 QTL, 60 had a significantly different effect, depending on diet or age at egg collection. For egg production traits, when the QTL × environment interaction was significant, numerous inversions of sign of the SNP effects were observed, whereas for egg quality traits, the QTL × environment interaction was mostly due to a difference of magnitude of the SNP effects.ConclusionsOur results show that numerous QTL influence egg production and egg quality traits and that the genomic regions, which are involved in shaping the ability of layer chickens to adapt to their environment for egg production, vary depending on the environmental conditions. The next question will be to address what the impact of these genotype × environment interactions is on selection.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-015-0160-2) contains supplementary material, which is available to authorized users.

Consistent action of two partially effective loci conferring resistance to <i>Globodera pallida</i> Pa2/3 across multiple nematode field populations

Late flowering sometimes occurs in F1 hybrids between rice varieties (Oryza sativa L.), although the parental varieties show similar days-to-flowering (DTF). The genetic architecture prompting the occurrence of such late flowering is poorly understood. To clarify the genetic architecture of late flowering in F1 hybrids from a cross between rice varieties, 'Koshihikari' and 'IR64', we performed quantitative trait locus (QTL) analysis using an F2 population (selfed progeny of an F1 plant), in which heterozygous genotypes should segregate in a certain proportion in a Mendelian manner. The QTL analysis detected three significant QTLs. At one QTL (putatively Heading date 1), the 'Koshihikari' allele increased DTF, and at the other two QTLs (putatively Heading date 6 and Oryza sativa Pseudo-Response Regulator 37/Heading date 2), the 'IR64' alleles increased DTF. All alleles at these three QTLs showed partial dominance. The combination of the QTLs explained 82.2% of the total phenotypic variance of DTF in the F2 population, with contribution from epistasis between QTLs. There was no difference between DTFs of F1 hybrids and heterozygous genotypes for the three QTLs. Our results demonstrated that the complementary effects accompanied by epistasis of at least three QTLs were responsible for late flowering in F1 hybrids.

A multiple marker least squares approach is presented for the analysis of a single three-generation pedigree for quantitative trait locus (QTL) characterisation. It is an extension of the approach by Haley et al. (1994) to the situation where grandparents cannot be assumed to be homozygous at QTLs for the trait of interest. The method is applied to the analysis of wood specific gravity in loblolly pine (Pinus taeda L.). Within a similar framework, a series of preliminary analyses are carried out, followed by a more detailed search of the genome for one or more QTLs. The preliminary analyses provide information about whether the contribution from each linkage group appears to be polygenic, localised to a small region (e.g. a single QTL) or oligogenic (i.e. several QTLs). Significance levels are obtained using a permutation test that uses the observed phenotypes and marker genotypes. The conclusion of these analyses is that in this pedigree single QTLs with very large effect on wood specific gravity do not appear to be segregating, although there is evidence for QTLs with small effect. Finally, in order to assess the potential power of this pedigree, we simulated QTLs within the framework of the actual marker data. As expected, QTL effects would need to be large to be reliably detected in this study, and the power to detect QTLs varies at different positions in the genome depending on the level of information in the local markers.

Low temperature affects the rice plants at all stages of growth. It can cause severe seedling injury and male sterility resulting in severe yield losses. Using a mini core collection of 174 Chinese rice accessions and 273 SSR markers we investigated cold tolerance at the germination and booting stages, as well as the underlying genetic bases, by association mapping. Two distinct populations, corresponding to subspecies indica and japonica showed evident differences in cold tolerance and its genetic basis. Both subspecies were sensitive to cold stress at both growth stages. However, japonica was more tolerant than indica at all stages as measured by seedling survival and seed setting. There was a low correlation in cold tolerance between the germination and booting stages. Fifty one quantitative trait loci (QTLs) for cold tolerance were dispersed across all 12 chromosomes; 22 detected at the germination stage and 33 at the booting stage. Eight QTLs were identified by at least two of four measures. About 46% of the QTLs represented new loci. The only QTL shared between indica and japonica for the same measure was qLTSSvR6-2 for SSvR. This implied a complicated mechanism of old tolerance between the two subspecies. According to the relative genotypic effect (RGE) of each genotype for each QTL, we detected 18 positive genotypes and 21 negative genotypes in indica, and 19 positive genotypes and 24 negative genotypes in japonica. In general, the negative effects were much stronger than the positive effects in both subspecies. Markers for QTL with positive effects in one subspecies were shown to be effective for selection of cold tolerance in that subspecies, but not in the other subspecies. QTL with strong negative effects on cold tolerance should be avoided during MAS breeding so as to not cancel the effect of favorable QTL at other loci.

Inbred strains of mice exhibit large differences in their susceptibility to various complex quantitative genetic traits, among which is the susceptibility to lung cancer. These differences are caused by the combined effects of multiple quantitative trait loci (QTLs). Due to their multiplicity, it is relatively difficult and laborious to study the effects of individual QTLs. To dissect complex genetic traits the authors make use of recombinant congenic strains (RCS), a system of mouse inbred strains in which the genetic complexity is reduced. The susceptibility to lung cancer is studied by using the series of O20-congenic-B10.O20 (OcB) RC strains. They are derived from the parental background strain O20 and the parental donor strain B10.O20, two mouse inbred strains that differ from each other in both quantitative and qualitative aspects of lung tumorigenesis. This study describes the segregation of lung tumor number, size, and histology among the OcB RC strains, and indicates that these traits are influenced by multiple interacting QTLs with considerable individual effects. The results suggest that some of the susceptibility loci to lung cancer affect the susceptibility to other types of cancer as well, possibly by functioning systematically.

QTL Mapping for Wheat Flour Color with Additive, Epistatic, and QTL×Environmental Interaction Effects