Quantitative Trait Loci In Population Research Articles

Detection of QTL for panicle architecture in $$\hbox {F}_{2}$$ population of rice

Panicle traits are the most important agronomic characters which directly relate to yield in rice. Panicle length (PL) being one of the major components of rice panicle structure is controlled by quantitative trait loci (QTLs). In our research, conducted at Research Farm of SKUAST-J, crosses of parental lines K343 and DHMAS were made for generating F2 mapping population, which were then transplanted into the field using augmented design-I. The F2 population was used for phenotypic evaluation, development of linkage map and identification of QTLs on the chromosomes by using SSR markers. A total of 450 SSR markers were used for screening both the parents of which 53 highly polymorphic markers were selected and used for genotyping of 233 genotypes of F2population. Linkage map was generated using MAPMAKER/EXP3.0 software, seven linkage groups were found distributed on 11 chromosomes of rice. QTLs were detected using QTL Cartographer (v2.5) software. Based on 1000 permutation tests, a logarithm of odds (LOD) threshold value 2.0 and 3.0 was set. Composite interval mapping was used to map QTLs in populations derived from bi-parental crosses. The phenotypic data, genotypic data and the genetic linkage map generated identified total three QTLs of which one was identified for PL qPL2, located at 85.01 cM position with 2.1 LOD value and in between the marker intervals RM324-RM208, this QTL explained the phenotype variation by 4.36%. The other two QTLs were identified for spikelet density (SD) qSD3.1 and qSD3.2, located at 28.91 and 39.51 cM, respectively, both with a flanking marker RM6832 on chromosome 3. The LOD value and phenotypic variation explained for qSD3.1 and qSD3.2 was 3.00 and 3.25; 9.70 and 12.34% respectively. The reported QTLs identified in the study suggested a less diversity in the parents used and also the rejection of not so useful markers from the used set of markers for PL and SD.

An investigation of the power for separating closely linked QTL in experimental populations

SummaryHu & Xu (2008) developed a statistical method for computing the statistical power for detecting a quantitative trait locus (QTL) located in a marker interval. Their method is based on the regression interval mapping method and allows experimenters to effectively investigate the power for detecting a QTL in a population. This paper continues to work on the power analysis of separating multiple-linked QTLs. We propose simple formulae to calculate the power of separating closely linked QTLs located in marker intervals. The proposed formulae are simple functions of information numbers, variance inflation factors and genetic parameters of a statistical model in a population. Both regression and maximum likelihood interval mappings suitable for detecting QTL in the marker intervals are considered. In addition, the issue of separating linked QTLs in the progeny populations from an F2 subject to further self and/or random mating is also touched upon. One of the primary keys to our approach is to derive the genotypic distributions of three and four loci for evaluating the correlation structures between pairwise unobservable QTLs in the model across populations. The proposed formulae allow us to predict the power of separation when several factors, such as sample sizes, sizes and directions of QTL effects, distances between QTLs, interval sizes and relative QTL positions in the intervals, are considered together at a time in different experimental populations. Numerical justifications and Monte Carlo simulations were provided for confirmation and illustration.

Optimizing Selection on Multiple Identified Quantitative Trait Loci in Population with Overlapping Generations

A method was developed to model and optimize selection on multiple identified quantitative trait loci (QTLs) and polygenic estimated breeding value, in order to maximize a weighted sum of cumulative response to selection over multiple years in a population with overlapping generations. The model allows for a population with multiple sex-age classes, different number of age class between sires and dams, and varied genetic contribution of the age class. The optimization problem was formulated as a multiple-stage optimal control problem and solved by a forward and backward iteration loop. The practical utility of this method was illustrated in an example of pig breeding population with overlapping generations. The selection response of this method was compared with standard QTL selection and conventional best linear unbiased prediction (BLUP) selection. Simulation results show that optimal selection achieved greater selection response than either standard QTL or conventional BLUP selections. The influence of population structure on optimal selection was significant. Optimal QTL selection and standard QTL selection were more favorable in a population with overlapping generations than discrete generations, and obtained more benefits relative to conventional BLUP selection in a population with overlapping generations. Optimal QTL selection relative to conventional BLUP selection is also more favorable following increase of genetic contribution of two-year-old boars and sows in a population with overlapping generations.

Mapping quantitative trait loci in complex pedigrees: a two-step variance component approach.

There is a growing need for the development of statistical techniques capable of mapping quantitative trait loci (QTL) in general outbred animal populations. Presently used variance component methods, which correctly account for the complex relationships that may exist between individuals, are challenged by the difficulties incurred through unknown marker genotypes, inbred individuals, partially or unknown marker phases, and multigenerational data. In this article, a two-step variance component approach that enables practitioners to routinely map QTL in populations with the aforementioned difficulties is explored. The performance of the QTL mapping methodology is assessed via its application to simulated data. The capacity of the technique to accurately estimate parameters is examined for a range of scenarios.

Financial feasibility of marker-aided selection in Douglas-fir

The land area required for a marker-aided selection (MAS) program to break-even (i.e., have equal costs and benefits) was estimated using computer simulation for coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) in the Pacific Northwestern United States. We compared the selection efficiency obtained when using an index that included the phenotype and marker score with that obtained using only the phenotype. It was assumed that MAS was restricted to within-family selection, that the rotation age was 50 years, and that growth rate (h2= 0.25), tree form (h2= 0.25), and (or) wood density (h2= 0.45) were the objects of improvement. Several population quantitative trait loci (QTL) models, selection population sizes, and interest rates were considered. When large selection population sizes were employed (500 trees per family) MAS gave considerable increases in efficiency of within-family selection; however, results showed that the combination of small selection population sizes (100 trees per family) and many QTL of moderate effect could lead to losses in gain from MAS compared with phenotypic selection. For many reasonable selection scenarios and the simplified assumptions in our model, the land base required for breeding programs to break-even is smaller or near to the limit of those in place under operational breeding programs in the region. Considerably more research is needed to reasonably predict whether MAS would be cost-effective in practice. However, before some of the basic research needed to implement MAS can be done, organizations need to establish large blocks of full-sib families to allow for QTL identification.

Financial feasibility of marker-aided selection in Douglas-fir

The land area required for a marker-aided selection (MAS) program to break-even (i.e., have equal costs and benefits) was estimated using computer simulation for coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) in the Pacific Northwestern United States. We compared the selection efficiency obtained when using an index that included the phenotype and marker score with that obtained using only the phenotype. It was assumed that MAS was restricted to within-family selection, that the rotation age was 50 years, and that growth rate ( h 2 = 0.25), tree form (h 2 = 0.25), and (or) wood density (h 2 = 0.45) were the objects of improvement. Several population quantitative trait loci (QTL) models, selection population sizes, and interest rates were considered. When large selection population sizes were em - ployed (500 trees per family) MAS gave considerable increases in efficiency of within-family selection; however, re- sults showed that the combination of small selection population sizes (100 trees per family) and many QTL of moderate effect could lead to losses in gain from MAS compared with phenotypic selection. For many reasonable se- lection scenarios and the simplified assumptions in our model, the land base required for breeding programs to break- even is smaller or near to the limit of those in place under operational breeding programs in the region. Considerably more research is needed to reasonably predict whether MAS would be cost-effective in practice. However, before some of the basic research needed to implement MAS can be done, organizations need to establish large blocks of full-sib families to allow for QTL identification.

Linkage analysis of molecular markers and quantitative trait loci in populations of inbred backcross lines of Brassica napus L.

Backcross populations are often used to study quantitative trait loci (QTL) after they are initially discovered in balanced populations, such as F(2), BC(1), or recombinant inbreds. While the latter are more powerful for mapping marker loci, the former have the reduced background genetic variation necessary for more precise estimation of QTL effects. Many populations of inbred backcross lines (IBLs) have been developed in plant and animal systems to permit simultaneous study and dissection of quantitative genetic variation introgressed from one source to another. Such populations have a genetic structure that can be used for linkage estimation and discovery of QTL. In this study, four populations of IBLs of oilseed Brassica napus were developed and analyzed to map genomic regions from the donor parent (a winter-type cultivar) that affect agronomic traits in spring-type inbreds and hybrids. Restriction fragment length polymorphisms (RFLPs) identified among the IBLs were used to calculate two-point recombination fractions and LOD scores through grid searches. This information allowed the enrichment of a composite genetic map of B. napus with 72 new RFLP loci. The selfed and hybrid progenies of the IBLs were evaluated during two growing seasons for several agronomic traits. Both pedigree structure and map information were incorporated into the QTL analysis by using a regression approach. The number of QTL detected for each trait and the number of effective factors calculated by using biometrical methods were of similar magnitude. Populations of IBLs were shown to be valuable for both marker mapping and QTL analysis.

Power analysis for quantitative trait locus mapping in populations derived by multiple backcrosses

Populations derived by multiple backcrosses are potentially useful for quantitative trait locus (QTL) mapping studies. Comparisons of relative power to detect QTL using populations derived by multiple back-crosses are needed to make decisions when mapping projects are initiated. The objective of this study was to theoretically compare the power to detect QTL in populations derived by multiple backcrosses relative to mapping in a recombinant inbred population of equal size. Backcrossing results in a reduction in genetic variance with each generation and also results in an increasing frequency of the recurrent parent marker genotype. The relevant outcome for QTL mapping is a reduction in genetic variance to partition between marker genotype classes and increasing unbalance of the number of individuals contributing to the mean of the marker genotypes. Both of these factors lead to a decrease in the power to detect a QTL as the number of backcross generations increases. Experimental error was held constant with the populations compared. From a theoretical standpoint, backcross-derived populations offer few advantages for QTL detection. If, however, a backcrossing approach is the most efficient method to achieve a desired breeding objective and if QTL detection is an objective of equal or less importance, backcross-derived populations are a reasonable approach to QTL detection.