Identification of QTLs influencing wood property traits in loblolly pine (Pinus taeda L.). I. Physical wood properties

Physical wood property traits for loblolly pine (Pinus taeda L.) were analyzed for the presence of quantitative trait loci (QTLs) in a three-generation outbred pedigree. These traits include wood specific gravity (wsg), volume percentage of latewood (vol%), and microfibril angle (mfa). Phenotypic data were collected for rings 2–11 for wsg and vol%, and rings 3, 5 and 7 for mfa. Both earlywood and latewood were analyzed for each trait. An interval mapping approach designed for an outbred pedigree was used to estimate the number of QTLs, the magnitude of QTL effects, and their genomic position. Nine unique QTLs were detected for composite traits (average of all rings) for wsg, five for vol%, and five for mfa. The majority of these QTLs were verified by analyses of individual-ring traits. Additional QTLs for each trait were also detected by these individual-ring analyses. Most QTLs for wsg were specific to either earlywood or latewood, whereas each QTL for mfa was detected for both earlywood and latewood. Before these QTLs are utilized in a breeding program, they should be verified in larger experiments and in different genetic and environmental backgrounds. Received: 15 February 2000 / Accepted: 31 March 2000     

A Random Model Approach to Interval Mapping of Quantitative Trait Loci

Mapping quantitative trait loci in outbred populations is important because many populations of organisms are noninbred. Unfortunately, information about the genetic architecture of the trait may not be available in outbred populations. Thus, the allelic effects of genes can not be estimated with ease. In addition, under linkage equilibrium, marker genotypes provide no information about the genotype of a QTL (our terminology for a single quantitative trait locus is QTL while multiple loci are referred to as QTLs). To circumvent this problem, an interval mapping procedure based on a random model approach is described. Under a random model, instead of estimating the effects, segregating variances of QTLs are estimated by a maximum likelihood method. Estimation of the variance component of a QTL depends on the proportion of genes identical-by-descent (IBD) shared by relatives at the locus, which is predicted by the IBD of two markers flanking the QTL. The marker IBD shared by two relatives are inferred from the observed marker genotypes. The procedure offers an advantage over the regression interval mapping in terms of high power and small estimation errors and provides flexibility for large sibships, irregular pedigree relationships and incorporation of common environmental and fixed effects.     

Identification of QTLs influencing wood property traits in loblolly pine ( Pinus taeda L.). II. Chemical wood properties

Chemical wood property traits were analyzed for the presence of quantitative trait loci (QTLs) in a three-generation outbred pedigree of loblolly pine (Pinus taeda L.). These traits were assayed using pyrolysis molecular beam mass spectrometry and include mass spectrum peak intensities associated with carbohydrates, α-cellulose and hemicellulose sugars, and lignin. Models for projection to latent structures (PLS) were used to also estimate the chemical composition of cell walls (i.e., α-cellulose, galactan and lignin) from mass spectrum data using multivariate regression. Both earlywood and latewood fractions from the fifth annual ring were analyzed for each trait. An interval mapping approach designed for an outbred pedigree was used to estimate the number of QTLs, the magnitude of QTL effects, and their genomic position. Eight unique QTLs influencing cell wall chemistry were detected from multiple peak intensities and/or PLS estimates using the one- and two-QTL models. Significant differences in chemical contents were observed among the populations from North Carolina vs Oklahoma, and results from QTL×environment analyses suggest that QTLs interact with environmental location. QTLs should be verified in larger experiments and in different genetic and environmental backgrounds. QTL mapping will help towards eventually identifying genes having a major effect on chemical wood properties. Received: 19 January 2001 / Accepted: 31 May 2001     

Identification of Quantitative Trait Loci Influencing Wood Specific Gravity in an Outbred Pedigree of Loblolly Pine

We report the identification of quantitative trait loci (QTL) influencing wood specific gravity (WSG) in an outbred pedigree of loblolly pine (Pinus taeda L.). QTL mapping in an outcrossing species is complicated by the presence of multiple alleles (>2) at QTL and marker loci. Multiple alleles at QTL allow the examination of interaction among alleles at QTL (deviation from additive gene action). Restriction fragment length polymorphism (RFLP) marker genotypes and wood specific gravity phenotypes were determined for 177 progeny. Two RFLP linkage maps were constructed, representing maternal and paternal parent gamete segregations as inferred from diploid progeny RFLP genotypes. RFLP loci segregating for multiple alleles were vital for aligning the two maps. Each RFLP locus was assayed for cosegregation with WSG QTL using analysis of variance (ANOVA). Five regions of the genome contained one or more RFLP loci showing differences in mean WSG at or below the P = 0.05 level for progeny as grouped by RFLP genotype. One region contained a marker locus (S6a) whose QTL-associated effects were highly significant (P > 0.0002). Marker S6a segregated for multiple alleles, a prerequisite for determining the number of alleles segregating at the linked QTL and analyzing the interactions among QTL alleles. The QTL associated with marker S6a appeared to be segregating for multiple alleles which interacted with each other and with environments. No evidence for digenic epistasis was found among the five QTL.     

Multiple-marker mapping of wood density loci in an outbred pedigree of radiata pine

The objective of this study was to determine the genetic location and effects of genomic regions controlling wood density at three stages, i.e., rings corresponding to ages 1–5 (WD1_5), rings corresponding to ages 6–10 (WD6_10), and outer wood density (WD14) in a full-sib pedigree (850.055×850.096) of Pinus radiata. The number of offspring measured at these three stages were 80, 93 and 93, respectively. Only a single linkage group of the parent 850.55 was considered for mapping quantitative trait loci (QTLs). A multiple-marker least-squares approach was employed for mapping QTLs for each of the three traits, using a single-QTL model. Logistic regression was used for multiple-trait QTL mapping. Critical values for test-statistic were calculated empirically by ’shuffling’ the data. A putative QTL with large effect on WD1_5 appears to be segregating at the 73 cM position (experimentwise P<0.01). The width of the 95% bootstrap confidence interval for this putative QTL was 40 cM (i.e. 56–96 cM). The effect of this QTL on the expression of wood density at later stages was diminished. From multiple-trait analysis, two marker locations (at 66 cM and 91 cM) were found to be significantly associated (experimentwise P <0.05) with the expression of wood density at different ages. These results are encouraging for the application of marker information to early selection in order to increase juvenile wood density, although the putative QTLs detected in this study need to be verified in an independent population. Received: 23 July 1999 / Accepted: 27 August 1999     

Identification of quantitative trait loci for wood and fibre properties in two full-sib pedigrees of Eucalyptus globulus

Regions of the genome influencing wood and fibre traits in Eucalyptus globulus Labill. have been identified in two full-sib pedigrees that share a common male parent. The first pedigree, cross A, contains 148 progeny, and the second pedigree, cross B, contains 135 progeny. Subsets of progeny of these two controlled crosses were planted at seven sites throughout Australia in 1990. Wood cores were taken at 0.9 m above ground in 1997, and wood and fibre traits were analysed for each individual. Three quantitative trait loci (QTL) affecting wood density, one QTL affecting pulp yield and one QTL affecting microfibril angle have been located in both pedigrees, using single-factor analysis of variance. Other QTLs affecting these traits, as well as fibre length and cellulose content were located in cross A only.     

QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat

In bread wheat, single-locus and two-locus QTL analyses were conducted for seven yield and yield contributing traits using two different mapping populations (P I and P II). Single-locus QTL analyses involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated yield traits to detect the pleiotropic QTLs. Two-locus analyses were conducted to detect main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions (QE and QQE). Only a solitary QTL for spikelets per spike was common between the above two populations. HomoeoQTLs were also detected, suggesting the presence of triplicate QTLs in bread wheat. Relatively fewer QTLs were detected in P I than in P II. This may be partly due to low density of marker loci on P I framework map (173) than in P II (521) and partly due to more divergent parents used for developing P II. Six QTLs were important which were pleiotropic/coincident involving more than one trait and were also consistent over environments. These QTLs could be utilized efficiently for marker assisted selection (MAS).     

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.     

Generalized Linear Model for Mapping Discrete Trait Loci Implemented with LASSO Algorithm

Generalized estimating equation (GEE) algorithm under a heterogeneous residual variance model is an extension of the iteratively reweighted least squares (IRLS) method for continuous traits to discrete traits. In contrast to mixture model-based expectation–maximization (EM) algorithm, the GEE algorithm can well detect quantitative trait locus (QTL), especially large effect QTLs located in large marker intervals in the manner of high computing speed. Based on a single QTL model, however, the GEE algorithm has very limited statistical power to detect multiple QTLs because of ignoring other linked QTLs. In this study, the fast least absolute shrinkage and selection operator (LASSO) is derived for generalized linear model (GLM) with all possible link functions. Under a heterogeneous residual variance model, the LASSO for GLM is used to iteratively estimate the non-zero genetic effects of those loci over entire genome. The iteratively reweighted LASSO is therefore extended to mapping QTL for discrete traits, such as ordinal, binary, and Poisson traits. The simulated and real data analyses are conducted to demonstrate the efficiency of the proposed method to simultaneously identify multiple QTLs for binary and Poisson traits as examples.     

Quantitative genetic dissection of complex traits in a QTL-mapping pedigree

This paper summarizes and modifies quantitative genetic analyses on a pedigree used to map genetic factors (i.e., QTLs) underlying a complex trait. The total genetic variance can be exactly estimated based on the F₂ family derived from two homozygous parents for alternative alleles at all QTLs of interest. The parents, F₁ hybrids, and two backcrosses are combined to each parent, and the total number of QTLs and the number of dominant QTLs are estimated under the assumptions of gene association with the two parents, equal gene effect, no linkage, and no epistasis among QTLs. Further relaxation for each of the assumptions are made in detail. The biometric estimator for the QTL number and action mode averaged over the entire genome could provide some basic and complementary information to QTL mapping designed to detect the effect and location of specific genetic factors.     

Novel linkage mapping approach using DNA pooling in human and animal genetics. II. Detection of quantitative traits loci in dairy cattle

Selective DNA pooling is an advanced methodology for linkage mapping of quantitative trait loci (QTL) in farm animals. The principle is based on densitometric estimates of marker allele frequency in pooled DNA samples of phenotypically extreme individuals from half-sib, backcross and F(2) experimental designs in farm animals. This methodology provides a rapid and efficient analysis of a large number of individuals with short tandem repeat markers that are essential to detect QTL through the genome - wide searching approach. Several strategies involving whole genome scanning with a high statistical power have been developed for systematic search to detect the quantitative traits loci and linked loci of complex traits. In recent studies, greater success has been achieved in mapping several QTLs in Israel-Holstein cattle using selective DNA pooling. This paper outlines the currently emerged novel strategies of linkage mapping to identify QTL based on selective DNA pooling with more emphasis on its theoretical pre-requisite to detect linked QTLs, applications, a general theory for experimental half-sib designs, the power of statistics and its feasibility to identify genetic markers linked QTL in dairy cattle. The study reveals that the application of selective DNA pooling in dairy cattle can be best exploited in the genome-wide detection of linked loci with small and large QTL effects and applied to a moderately sized half-sib family of about 500 animals.     

Bayesian association mapping of multiple quantitative trait loci and its application to the analysis of genetic variation among <Emphasis Type="Italic">Oryza sativa</Emphasis> L. germplasms

One way to use a crop germplasm collection directly to map QTLs without using line-crossing experiments is the whole genome association mapping. A major problem with association mapping is the presence of population structure, which can lead to both false positives and failure to detect genuine associations (i.e., false negatives). Particularly in highly selfing species such as Asian cultivated rice, high levels of population structure are expected and therefore the efficiency of association mapping remains almost unknown. Here, we propose an approach that combines a Bayesian method for mapping multiple QTLs with a regression method that directly incorporates estimates of population structure. That is, the effects due to both multiple QTLs and population structure were included in our statistical model. We evaluated the efficiency of our approach in simulated- and real-trait analyses of a rice germplasm collection. Simulation analyses based on real marker data showed that our model could suppress both false-positive and false-negative rates and the error of estimation of genetic effects over single QTL models, indicating that our model has statistically desirable attributes over single QTL models. As real traits, we analyzed the size and shape of milled rice grains and found significant markers that may be linked to QTLs reported previously. Association mapping should have good prospects in highly selfing species such as rice if proper methods are adopted. Our approach will be useful for the whole genome association mapping of various selfing crop species.     

Mapping of quantitative trait loci controlling timing of bud flush in Salix

Dormancy release is an important phenological stage, which determines plant growth and survival in northern temperate regions. Spring bud flushing was studied in a Salix pedigree (n=82) derived from a cross between the male hybrid clone "Bj?rn" (Salix viminalis x Salix schwerinii) and the female clone "78183" (Salix viminalis). The timing of bud flush was recorded outdoors in two consecutive years (1998, 1999) and indoor in the spring of 1998. Timing of bud flush was found to be under moderately strong genetic control (clonal mean heritabilities ranging from 0.43 to 0.72). Phenotypic correlations between height growth and bud flushing were negative but non-significant (r=0.1-0.3). Using a Salix linkage map composed of 325 AFLP and 38 RFLP markers, six quantitative trait loci (QTLs) and three unmapped marker loci associated with timing of bud flush were detected. Four QTLs were detected in the field experiment while two QTLs and three unmapped marker loci were identified in the indoor experiment. One QTL associated with indoor bud flushing coincided with one of the QTL detected from the field data. Individual QTL explained 6-16% of the phenotypic variance [corrected]. None of the bud flush QTLs coincided with QTLs controlling height growth identified previously in the same pedigree.     

Quantitative trait loci mapping for fatty acid contents in the backfat on porcine chromosomes 1, 13, and 18

A partial genome scan using microsatellite markers was conducted in order to detect quantitative trait loci (QTLs) for 10 fatty acid contents of the backfat in a pig reference population. Two QTLs were found by studying SSC1, SSC13, and SSC18, where QTLs had already been identified for backfat thickness. A QTL was located between marker loci S0113 and SW974 on chromosome 1; this QTL was only significantly detected (P < 0.05) for linoleic acid. The other QTL was discovered between markers S0062 and S0120 on chromosome 18, and its significance only showed (P < 0.05) for myristic acid. The two QTLs mapped to the same location as the backfat thickness QTL. A third of the phenotypic variation was explained for linoleic acid by the QTL on chromosome 1, and a quarter for myristic acid by the QTL on chromosome 18. Further studies on fine mapping and positional comparative candidate gene analyses will be the next step toward a better understanding of the genetic architecture of fatty acid contents.     

Effectiveness of selective genotyping for detection of quantitative trait loci: an analysis of grain and malt quality traits in three barley populations.

Marker genotype data and grain and malt quality phenotype data from three barley (Hordeum vulgare L.) mapping populations were used to investigate the feasibility of selective genotyping for detection of quantitative trait loci (QTLs). With selective genotyping, only individuals with high and low phenotypic values for the trait of interest are genotyped. Here, genotyping of 10 to 70% of each population (i.e., 5 to 35% in each tail of the phenotypic distribution) was considered. Genomic positions detected by selective genotyping were compared to QTL position estimates from interval mapping analysis using marker genotype data from the entire population. Selective genotyping reliably detected almost all of the mapped QTLs, often with only 10% of the population genotyped. Selective genotyping also detected spurious QTLs in regions of the genome where no significant QTL had been mapped. Even with additional genotyping to verify putative QTLs, the total genotyping effort for detection of QTLs for a single trait by selective genotyping was usually less than 30% of that required for conventional interval mapping. Simultaneous investigation of two or more traits by selective genotyping would require additional genotyping effort, but could still be worthwhile.     

QTL‐seq: rapid mapping of quantitative trait loci in rice by whole genome resequencing of DNA from two bulked populations

The majority of agronomically important crop traits are quantitative, meaning that they are controlled by multiple genes each with a small effect (quantitative trait loci, QTLs). Mapping and isolation of QTLs is important for efficient crop breeding by marker‐assisted selection (MAS) and for a better understanding of the molecular mechanisms underlying the traits. However, since it requires the development and selection of DNA markers for linkage analysis, QTL analysis has been time‐consuming and labor‐intensive. Here we report the rapid identification of plant QTLs by whole‐genome resequencing of DNAs from two populations each composed of 20–50 individuals showing extreme opposite trait values for a given phenotype in a segregating progeny. We propose to name this approach QTL‐seq as applied to plant species. We applied QTL‐seq to rice recombinant inbred lines and F₂ populations and successfully identified QTLs for important agronomic traits, such as partial resistance to the fungal rice blast disease and seedling vigor. Simulation study showed that QTL‐seq is able to detect QTLs over wide ranges of experimental variables, and the method can be generally applied in population genomics studies to rapidly identify genomic regions that underwent artificial or natural selective sweeps.     

Quantitative trait locus (QTL) analysis of wood quality traits in <Emphasis Type="Italic">Eucalyptus nitens</Emphasis>

To identify the chromosomal regions affecting wood quality traits, we conducted a genome-wide quantitative trait locus (QTL) analysis of wood quality traits in Eucalyptus nitens. This information is important to exploit the full potential of the impending Eucalyptus genome sequence. A three generational mapping population consisting of 296 progeny trees was used to identify QTL associated with several wood quality traits in E. nitens. Thirty-six QTL positions for cellulose content, pulp yield, lignin content, density, and microfibril angle (MFA) were identified across different linkage groups. On linkage groups (LG)2 and 8, cellulose QTL cluster with pulp yield and extractives QTL while on LG4 and 10 cellulose and pulp yield QTLs cluster together. Similarly, on LG4, 5, and 6 QTL for lignin traits were clustered together. At two positions, QTL for MFA, a physical trait related to wood stiffness, were clustered with QTL for lignin traits. Several cell wall candidate genes were co-located to QTL positions affecting different traits. Comparative QTL analysis with Eucalyptus globulus revealed two common QTL regions for cellulose and pulp yield. The QTL positions identified in this study provide a resource for identifying wood quality genes using the impending Eucalyptus genome sequence. Candidate genes identified in this study through co-location to QTL regions may be useful in association studies.     

A consensus linkage map of common carp (Cyprinus carpio L.) to compare the distribution and variation of QTLs associated with growth traits

The ability to detect and identify quantitative trait loci (QTLs) in a single population is often limited. Analyzing multiple populations in QTL analysis improves the power of detecting QTLs and provides a better understanding of their functional allelic variation and distribution. In this study, a consensus map of the common carp was constructed, based on four populations, to compare the distribution and variation of QTLs. The consensus map spans 2371.6 cM across the 42 linkage groups and comprises 257 microsatellites and 421 SNPs, with a mean marker interval of 3.7 cM/marker. Sixty-seven QTLs affecting four growth traits from the four populations were mapped to the consensus map. Only one QTL was common to three populations, and nine QTLs were detected in two populations. However, no QTL was common to all four populations. The results of the QTL comparison suggest that the QTLs are responsible for the phenotypic variability observed for these traits in a broad array of common carp germplasms. The study also reveals the different genetic performances between major and minor genes in different populations.     

Mixed model approaches for the identification of QTLs within a maize hybrid breeding program

Two outlines for mixed model based approaches to quantitative trait locus (QTL) mapping in existing maize hybrid selection programs are presented: a restricted maximum likelihood (REML) and a Bayesian Markov Chain Monte Carlo (MCMC) approach. The methods use the in-silico-mapping procedure developed by Parisseaux and Bernardo (2004) as a starting point. The original single-point approach is extended to a multi-point approach that facilitates interval mapping procedures. For computational and conceptual reasons, we partition the full set of relationships from founders to parents of hybrids into two types of relations by defining so-called intermediate founders. QTL effects are defined in terms of those intermediate founders. Marker based identity by descent relationships between intermediate founders define structuring matrices for the QTL effects that change along the genome. The dimension of the vector of QTL effects is reduced by the fact that there are fewer intermediate founders than parents. Furthermore, additional reduction in the number of QTL effects follows from the identification of founder groups by various algorithms. As a result, we obtain a powerful mixed model based statistical framework to identify QTLs in genetic backgrounds relevant to the elite germplasm of a commercial breeding program. The identification of such QTLs will provide the foundation for effective marker assisted and genome wide selection strategies. Analyses of an example data set show that QTLs are primarily identified in different heterotic groups and point to complementation of additive QTL effects as an important factor in hybrid performance.