The analysis of quantitative trait loci in multi-environment trials using a multiplicative mixed model

This book describes the theory and practice of methods for quantitative trait loci (QTL) detection and analysis, as well as marker assisted selection, in animal genetics and breeding. It is divided into 16 chapters: historical overview; principles of parameter estimation; random and fixed effects (the mixed model); experimental designs to detect QTL (generation of linkage disequilibrium); QTL parameter estimation for crosses between inbred lines; advanced statistical methods for QTL detection and parameter estimation; analysis of QTL as random effects; statistical power to detect QTL and parameter confidence interval; optimization of experimental designs; fine mapping of QTL; complete genome QTL scans (the problem of multiple comparisons); multiple trait QTL analysis; principles of selection index and traditional breeding programmes; marker-assisted selection (theory); marker-assisted selection (results of simulation studies); and marker-assisted introgression. The book is aimed at graduate students and research workers in quantitative genetics and animal breeding. There is an author index and a detailed subject index.

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 …

Thirty-nine chromosome segment substitution lines (CSSLs) population derived from a Koshihikari / Kasalath cross was used for quantitative trait locus (QTL) analysis of plant type in rice (Oryza sativa L.). Putative rough QTLs (26.2∼60.3cM of Kasalath chromosomal segments) for culm length, plant height, panicle number, chlorophyll content of flag leaf blade at heading and specific leaf weight, were mapped on the several chromosomal segments based on the comparison of CSSLs with Koshihikari in the field experiment for 3 years. In order to verify and narrow QTLs detected in CSSLs, we conducted QTL analyses using F2 populations derived from a cross between Koshihikari and target CSSL holding a putative rough QTL. The qPN-2, QTL for panicle number was mapped on chromosome 2. In traits of flag leaf, the qCHL-4-1 and qCHL-4-2 for chlorophyll content was mapped on chromosome 4, and the qSLW-7 for specific leaf weight on chromosome 7. All QTLs were detected in narrow marker intervals, compared with rough QTLs in CSSLs. The qPN-2, qCHL-4-1 and qCHL-4-2 had only additive effect. On the other hand, the qSLW-7 showed over-dominance. It could be emphasized that QTL analysis in the present study with the combination of CSSLs and backcross progeny F2 population can not only verify the rough QTLs detected in CSSLs but also estimate allelic effects on the QTL.

BackgroundThe Ay allele at the agouti locus causes obesity and promotes linear growth in mice. The effect of the Ay allele on obesity has been extensively investigated, whereas its effect on body length is only poorly analyzed. To gain insight into the genetic control of body length, quantitative trait locus (QTL) analysis was performed in F2 female mice produced by crossing C57BL/6 J females and DDD.Cg-Ay males. A congenic DDD.Cg-Ay strain was established by introgressing the Ay allele from the B6.Cg-Ay strain by backcrossing for 12 generations. DDD.Cg-Ay females were longer than B6.Cg-Ay females; therefore, QTLs that interact with the Ay allele may be identified for body length. In addition, QTL analysis was also performed for plasma insulin-like growth factor 1 (IGF1) levels because IGF1 is known to play essential roles in growth and development. If QTLs for IGF1 levels coincide with those for body length, we can gain endocrinological insight into the QTLs for body length.ResultsCorrelations between body length and IGF1 levels were statistically significant in F2 populations. For body length, two significant QTLs were identified on chromosomes 15 and 17. For IGF1 levels, three significant QTLs were identified on chromosomes 10, 12, and 19. QTLs on chromosomes 12 and 19 appeared to be novel, and the latter interacted with the Ay allele.ConclusionQTLs for body length and IGF1 levels contained candidate genes that were components of the growth hormone/insulin-like growth factor axis. However, there was no overlap between QTLs for these two traits. Contrary to our expectations, QTLs that interacted with the Ay allele were identified not for body length but for IGF1 levels. Body length and IGF1 levels were, thus, controlled by different sets of genes.

The complex resistance to cucumber mosaic virus (CMV) present in the exotic melon accession Sonwang Charmi PI161375 (SC) has been studied using two populations, a near-isogenic line (NIL) collection and a doubled haploid line (DHL) collection, both generated from a cross between SC and the cultivar Piel de Sapo as resistant and susceptible parents, respectively. The NIL collection had previously allowed us to describe a single recessive gene, cmv1, which conferred full resistance to CMV strains P9 and P104.82. Screening of the whole DHL population followed by quantitative trait locus (QTL) analysis revealed that resistance to the strains M6 and TL, both non-responsive to cmv1, was quantitative and governed by at least three QTLs. One of them, cmvqw12.1, co-located with cmv1 in linkage group (LG) XII. The QTL analysis mapped another two QTLs in LGIII (cmvqw3.1) and LGX (cmvqw10.1) and showed interaction between cmvqw12.1 and cmvqw3.1. Progeny of crosses between resistant DHLs carrying the three main QTLs showed complete resistance to the strain M6, validating the accuracy of the QTL analysis. However, in our screening, there were resistant DHLs carrying only two QTLs, suggesting that there are other regions involved in resistance to M6 and required when one of the main QTLs is missing. Therefore, resistance to CMV in melon SC is qualitative for some strains and quantitative for the rest. For this late resistance, cmv1 is necessary and explains most of the phenotypic variance, but it is not sufficient, and needs the interaction with other loci.

The productivity of sorghum is mainly determined by quantitative traits such as grain yield and stem sugar-related characteristics. Substantial crop improvement has been achieved by breeding in the last decades. Today, genetic mapping and characterization of quantitative trait loci (QTLs) is considered a valuable tool for trait enhancement. We have investigated QTL associated with the sugar components (Brix, glucose, sucrose, and total sugar content) and sugar-related agronomic traits (flowering date, plant height, stem diameter, tiller number per plant, fresh panicle weight, and estimated juice weight) in four different environments (two locations) using a population of 188 recombinant inbred lines (RILs) from a cross between grain (M71) and sweet sorghum (SS79). A genetic map with 157 AFLP, SSR, and EST-SSR markers was constructed, and several QTLs were detected using composite interval mapping (CIM). Further, additive x additive interaction and QTL x environmental interaction were estimated. CIM identified more than five additive QTLs in most traits explaining a range of 6.0-26.1% of the phenotypic variation. A total of 24 digenic epistatic locus pairs were identified in seven traits, supporting the hypothesis that QTL analysis without considering epistasis can result in biased estimates. QTLs showing multiple effects were identified, where the major QTL on SBI-06 was significantly associated with most of the traits, i.e., flowering date, plant height, Brix, sucrose, and sugar content. Four out of ten traits studied showed a significant QTL x environmental interaction. Our results are an important step toward marker-assisted selection for sugar-related traits and biofuel yield in sorghum.

Proof of concept of Bayesian integrated QTL analyses across pedigree-related families from breeding programs of an outbreeding species. Results include QTL confidence intervals, individuals' genotype probabilities and genomic breeding values. Bayesian QTL linkage mapping approaches offer the flexibility to study multiple full sib families with known pedigrees simultaneously. Such a joint analysis increases the probability of detecting these quantitative trait loci (QTL) and provide insight of the magnitude of QTL across different genetic backgrounds. Here, we present an improved Bayesian multi-QTL pedigree-based approach on an outcrossing species using progenies with different (complex) genetic relationships. Different modeling assumptions were studied in the QTL analyses, i.e., the a priori expected number of QTL varied and polygenic effects were considered. The inferences include number of QTL, additive QTL effect sizes and supporting credible intervals, posterior probabilities of QTL genotypes for all individuals in the dataset, and QTL-based as well as genome-wide breeding values. All these features have been implemented in the FlexQTL(™) software. We analyzed fruit firmness in a large apple dataset that comprised 1,347 individuals forming 27 full sib families and their known ancestral pedigrees, with genotypes for 87 SSR markers on 17 chromosomes. We report strong or positive evidence for 14 QTL for fruit firmness on eight chromosomes, validating our approach as several of these QTL were reported previously, though dispersed over a series of studies based on single mapping populations. Interpretation of linked QTL was possible via individuals' QTL genotypes. The correlation between the genomic breeding values and phenotypes was on average 90 %, but varied with the number of detected QTL in a family. The detailed posterior knowledge on QTL of potential parents is critical for the efficiency of marker-assisted breeding.

Phosphorus (P) deficiency is one of the major constraints to wheat production worldwide. In this study, conditional and unconditional quantitative trait locus (QTL) analyses for 12 traits of wheat seedlings under P-normal and P-deficient conditions were conducted using three F9 recombinant inbred line (RIL) populations with a common female parent in wheat. Of the total 110 QTLs identified by unconditional QTL analysis, 29 were detected in multiple populations or under different P treatment, with 28 major QTLs. Notably, QRPU-4B on chromosome 4B in interval wPt-3608-wPt-667817 was detected in two populations and at two P levels, accounting for 44.08 % average value of phenotypic variance. Of 49 specific QTLs related to P-deficient tolerance detected by conditional QTL analysis, only nine were detected by unconditional QTL mapping under P-deficient condition, indicating that most QTLs directly concerned with P-deficient tolerance could be detected only by conditional QTL analysis. Eighteen important QTL clusters were mapped on 12 chromosomes 1D, 2A, 2B, 3A, 3B, 4B, 4D, 5A, 5D, 6A, 6B, and 7B, involving 85 QTLs that accounted for 53.4 % of total numbers of QTLs detected by unconditional and conditional mapping. Of these 18 QTL clusters, 14 were detected in multiple populations and 11 were detected by both unconditional and conditional analyses. Thus, these chromosome intervals were important areas controlling the expression of seedling traits related to P-deficient tolerance.

We provide a theoretical framework for quantitative trait locus (QTL) analysis of a crossed population where parental lines may be outbred and dominance as well as inbreeding are allowed for. It can be applied to any pedigree. A biallelic QTL is assumed, and the QTL allele frequencies can be different in each breed. The genetic covariance between any two individuals is expressed as a nonlinear function of the probability of up to 15 possible identity modes and of the additive and dominance effects, together with the allelic frequencies in each of the two parental breeds. The probabilities of each identity mode are obtained at the desired genome positions using a Monte Carlo Markov chain method. Unbiased estimates of the actual genetic parameters are recovered in a simulated F(2) cross and in a six-generation complex pedigree under a variety of genetic models (allele fixed or segregating in the parental populations and additive or dominance action). Results from analyzing an F(2) cross between Meishan and Large White pigs are also presented.

Drought is a major stress impacting forest ecosystems worldwide. We utilized quantitative trait loci (QTL) analysis to study the genetic basis of variation in (a) drought resistance and recovery and (b) candidate traits that may be associated with this variation in the forest tree Eucalyptus globulus. QTL analysis was performed using a large outcrossed F2 mapping population from which 300 trees were phenotyped based on the mean performance of their open-pollinated F3 progeny. Progenies were grown in a glasshouse in a randomized complete block design. A subset of seedlings was subjected to a drought treatment after which they were rewatered and scored for damage and growth postdrought. Nondroughted seedlings were assessed for growth traits as well as lignotuber size and resprouting following severe damage to the main stem. QTL were detected for most traits. Importantly, independent QTL were detected for (a) drought damage and plant size, (b) drought damage and growth recovery, and (c) lignotuber size and resprouting capacity. Such independence argues that trade-offs are unlikely to be a major limitation to the response to selection and at the early life history stage studied; there are opportunities to improve resilience to drought without adverse effects on productivity.

Ramie fiber extracted from ramie stem bark (RSB) is a highly important natural fiber, and therefore, RSB is an economically important plant organ. The genetic basis of RSB traits is poorly understood. In the present study, fiber yield and three RSB traits (bark thickness, bark weight, and fiber output ratio) were subject to quantitative trait locus (QTL) analysis using an F2 agamous line population derived from two ramie varieties (Qingdaye and Zhongzhu 1). A total of 4338 high-quality single nucleotide polymorphisms were identified using the genotyping-by-sequencing technique and were subsequently used to construct a high-density genetic map spanning 1942.9 cM. Thereafter, QTL analysis identified five, two, four, and four QTLs for bark thickness, bark weight, fiber output ratio, and fiber yield, respectively. A 5.1 cM region that corresponded to a QTL for bark thickness (qBT4a) contained 106 candidate genes, and the Zhongzhu 1 allele of one of the genes, a putative MYB gene (evm.model.scaffold7373.133_D1), included a 760-bp insertion that caused premature termination, thereby producing a protein that lacked part of the MYB domain. Because MYB transcription factors play central roles in regulating the development of secondary cellular walls and fiber biosynthesis, we propose evm.model.scaffold7373.133_D1 as a likely candidate gene for qBT4a.

This report describes a dynamic QTL analysis for plant height at various stages using a large doubled haploid population and performs a QTL comparison between different populations in Brassica napus. Plant height (PH) not only plays an important role in determining plant architecture, but is also an important character related to yield. The process of determining PH occurs through a series of steps; however, no studies have focused on developmental behavior factors affecting PH in Brassica napus. In the present study, KN DH, a large doubled haploid population containing 348 lines was used for a dynamic quantitative trait locus (QTL) analysis for PH in six experiments. In all, 20 QTLs were identified at maturity, whereas 50 QTLs were detected by conditional m apping method and the same number was identified by unconditional mapping strategies. Interestingly, five unconditional QTLs ucPH.A2-2, ucPH.A3-2, ucPH.C5-1, ucPH.C6-2 and ucPH.C6-3 were identified that were consistent over the all growth stages of one or two particular experiments, and one conditional QTL cPH.A2-3 was expressed throughout the entire growth process in one experiment. A total of 70 QTLs were obtained after combining QTLs identified at maturity, by conditional and unconditional mapping strategies, in which 25 showed opposite genetic effects in different periods/stages and experiments. A consensus map containing 1357 markers was constructed to compare QTLs identified in the KN population with five previously mapped populations. Alignment of the QTLs detected in different populations onto the consensus map showed that 27 were repeatedly detected in different genetic backgrounds. These findings will enhance our understanding of the genetic control of PH regulation in B. napus, and will be useful for rapeseed genetic manipulation through molecular marker-assisted selection.

A recombinant inbred line (RILs) population with 157 lines derived from an inter-subspecies cross of Daguandao/IR28 by the single seed descent method was used to detect quantitative trait loci (QTLs) conferring resistance to rice false smut (caused by Ustilaginoidea virens (Cooke) Takahashi) in 2007 and 2009. The disease rate index of the two parents and 157 RILs caused by rice false smut were scored and the QTLs for rice false smut resistance were detected accordingly by QTL Cartographer software. Seven QTLs controlling false smut resistance were detected on chromosomes 1, 2, 4, 8, 10, 11, and 12, respectively, with the phenotypic variance of 9.8-22.5%. There were five and four QTLs detected in 2007 and 2009, respectively, and only two QTLs were found in both two years, the phenotypic variation was explained by individual QTL ranged from 18.0% to 19.3% for these two QTLs, and the additive effects of these two QTLs contributed to the 8.0-16.3% decrease of disease index and therefore the disease resistance increased. The direction of the additive effects at six loci qFsr1, qFsr2, qFsr8, qFsr10, qFsr11, and qFsr12 coincided with that predicted by phenotypes of the parents, and the IR28 alleles at these loci had positive effect against rice false smut while the negative effects were found in Daguandao alleles at qFsr4. Both qFsr11 and qFsr12 should be useful in rice breeding for resistance to rice false smut in marker-assisted selection (MAS) program.

A recombinant inbred line(RILs) population with 157 lines derived from an inter-subspecies cross of Daguandao/IR28 by the single seed descent method was used to detect quantitative trait loci (QTLs) conferring resistance to rice false smut [caused by <italic>U stilaginoidea virens</italic> (Cooke) Takahashi] in 2007 and 2009. The disease rate index of the two parents and 157 RILs caused by rice false smut were scored and the QTLs for rice false smut resistance were detected accordingly by QTL Cartographer software. Seven QTLs controlling false smut resistance were detected on chromosomes 1, 2, 4, 8, 10, 11, and 12, respectively, with the phenotypic variance of 9.8%–22.5%. There were five and four QTLs detected in 2007 and 2009, respectively, and only two QTLs were found in both two years, the phenotypic variation was explained by individual QTL ranged from 18.0% to 19.3% for these two QTLs,and the additive effects of these two QTLs contributed to the 8.0%–16.5% decrease of disease index and therefore the disease resistance increased. The direction of the additive effects at six loci <italic>qFsr1</italic>, <italic>qFsr2</italic>, <italic>qFsr8</italic>, <italic>qFsr10</italic>, <italic>qFsr11</italic>, and <italic>qFsr12</italic> coincided with that predicted by phenotypes of the parents, and the IR28 alleles at these loci had positive effect against rice false smut while the negative effects were found in Daguandao alleles at <italic>qFsr4.</italic> Both <italic>qFsr11</italic> and <italic>qFsr12</italic> should be useful in rice breeding for resistance to rice false smut in marker-assisted selection (MAS) program.

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.