Power calculations for selective genotyping in QTL mapping in backcross mice

Selective genotyping concerns the genotyping of a portion of individuals chosen on the basis of their phenotypic values. Often individuals are selected for genotyping from the high and low extremes of the phenotypic distribution. This procedure yields savings in cost and time by decreasing the total number of individuals genotyped. Previous work by Darvasi et al. (1993) has shown that the power to detect a QTL by genotyping 40-50 % of a population is roughly equivalent to genotyping the entire sample. However, these power studies have not accounted for different strategies of analysing the data when phenotypes of individuals in the middle are excluded, nor have they investigated the genome-wide type I error rate under these different strategies or different selection percentages. Further, these simulation studies have not considered markers over the entire genome. In this paper, we present simulation studies of power for the maximum likelihood approach to QTL mapping by Lander & Botstein (1989) in the context of selective genotyping. We calculate the power of selectively genotyping the individuals from the middle of the phenotypic distribution when performing QTL mapping over the whole mouse genome.     

Optimal designs for linkage disequilibrium mapping and candidate gene association tests in livestock populations

Simulation was used to evaluate the performance of different selective genotyping strategies when using linkage disequilibrium across large half-sib families to position a QTL within a previously defined genomic region. Strategies examined included standard selective genotyping and different approaches of discordant and concordant sib selection applied to arbitrary or selected families. Strategies were compared as a function of effect and frequency of QTL alleles, heritability, and phenotypic expression of the trait. Large half-sib families were simulated for 100 generations and 2% of the population was genotyped in the final generation. Simple ANOVA was applied and the marker with the greatest F-value was considered the most likely QTL position. For traits with continuous phenotypes, genotyping the most divergent pairs of half-sibs from all families was the best strategy in general, but standard selective genotyping was somewhat more precise when heritability was low. When the phenotype was distributed in ordered categories, discordant sib selection was the optimal approach for positioning QTL for traits with high heritability and concordant sib selection was the best approach when genetic effects were small. Genotyping of a few selected sibs from many families was generally more efficient than genotyping many individuals from a few highly selected sires.     

Molecular marker-assisted selection for malting quality traits in barley

Selection for malting quality in breeding programs by micromalting and micromashing is time-consuming, and resource-intensive. More efficient and feasible approaches for identifying genotypes with good malting quality would be highly desirable. With the advent of molecular markers, it is possible to map and tag the loci affecting malting quality. The objective of this study was to assess the effectiveness of molecular marker assisted selection for malting quality traits. Two major quantitative trait loci (QTL) regions in six-row barley for malt extract percentage, -amylase activity, diastatic power, and malt -glucan content on chromosomes 1 (QTL1) and 4 (QTL2) have been previously identified. The flanking markers, Brz and Amy2, and WG622 and BCD402B, for these two major QTL regions were used in marker-assisted selection. Four alternative selection strategies; phenotypic selection, genotypic selection, tandem genotypic and phenotypic selection, and combined phenotypic and genotypic selection, were compared for both single and multiple trait selection in a population consisting of 92 doubled haploid lines derived from Steptoe × Morex crosses. Marker assisted selection for QTL1 (tandem genotypic and phenotypic selection, and combined phenotypic and genotypic selection) was more effective than phenotypic selection, but for QTL2 was not as effective as phenotypic selection due to a lack of QTL2 effects in the selection population. The effectiveness of tandem genotypic and phenotypic selection makes marker assisted selection practical for traits which are extremely difficult or expensive to measure such as most malting quality traits. It can substantially eliminate undesirable genotypes by early genotyping and keeping only desirable genotypes for later phenotypic selection.     

Detecting QTL for feed intake traits and other performance traits in growing pigs in a Piétrain-Large White backcross

Knowing the large difference in daily feed intake (DFI) between Large White (LW) and Piétrain (PI) growing pigs, a backcross (BC) population has been set up to map QTL that could be used in marker assisted selection strategies. LW × PI boars were mated with sows from two LW lines to produce 16 sire families. A total of 717 BC progeny were fed ad libitum from 30 to 108 kg BW using single-place electronic feeders. A genome scan was conducted using genotypes for the halothane gene and 118 microsatellite markers spread on the 18 porcine autosomes. Interval mapping analyses were carried out, assuming different QTL alleles between sire families to account for within breed variability using the QTLMap software. The effects of the halothane genotype and of the dam line on the QTL effect estimates were tested. One QTL for DFI (P < 0.05 at the chromosome-wide (CW) level) and one QTL for feed conversion ratio (P < 0.01 at the CW level) were mapped to chromosomes SSC6 - probably due to the halothane alleles - and SSC7, respectively. Three putative QTL for feed intake traits were detected (P < 0.06 at the CW level) on SSC2, SSC7 and SSC9. QTL on feeding traits had effects in the range of 0.20 phenotypic s.d. The relatively low number of QTL detected for these traits suggests a large QTL allele variability within breeds and/or low effects of individual loci. Significant QTL were detected for traits related to carcass composition on chromosomes SSC6, SSC15 and SSC17, and to meat quality on chromosome SSC6 (P < 0.01 at the genome-wide level). QTL effects for body composition on SSC13 and SSC17 differed according to the LW dam line, which confirmed that QTL alleles were segregating in the LW breed. An epistatic effect involving the halothane locus and a QTL for loin weight on SSC7 was identified, the estimated substitution effects for the QTL differing by 200 g between Nn and NN individuals. The interactions between QTL alleles and genetic background or particular genes suggest further work to validate QTL segregations in the populations where marker assisted selection for the QTL would be applied.     

Two-trait selection response with marker-based assortative mating

 Marker-based assortative mating (MAM) – the mating of individuals that have similar genotypes at random marker loci – can increase selection response for a single trait by 3–8% over random mating (RM). Genetic gain is usually desired for multiple traits rather than for a single trait. My objectives in this study were to (1) compare MAM, phenotypic assortative mating (PAM), and RM of selected individuals for improving two traits and (2) determine when MAM will be most useful for improving two traits. I simulated 20 generations of selecting 32 out of 200 individuals in an F₂ population. The individuals were selected based on an index (SI) of two traits and were intermated by MAM, PAM, or RM. I studied eight genetic models that differed in three contrasts: (1) weight, number of quantitative trait loci (QTL), and heritability (h ²) for each trait; (2) linkage of QTL for each trait; and (3) trait means of the inbred parents of the F₂. For SI and the two component traits, MAM increased short-term selection response by 5–8% in six out of the eight genetic models. The MAM procedure was least effective in two genetic models, wherein the QTL for one trait were unlinked to the QTL for the other trait and the parents of the F₂ had divergent means for each trait. The loss of QTL heterozygosity was much greater with MAM than with PAM or RM. Consequently, the advantage of MAM over RM dissipated after 5–7 generations. Differences were small between selection responses with PAM and RM. The MAM procedure can enhance short-term selection response for two traits when selection is not stringent, h ² is low, and the means of the parents of the F₂ are equal for each trait. Received: 10 June 1998 / Accepted: 5 August 1998     

Verification of yield QTL through realized molecular marker- assisted selection responses in a barley cross

Verification of putative quantitative trait loci (QTL) is an essential step towards implementing the use of marker-assisted selection (MAS) in cultivar improvement. In a previous study with 150 doubled haploid lines derived from the 6-row cross Steptoe/Morex (S/M), four regions (QTL1–4) of the barley genome were associated with differential genotypic expression for grain yield across environments. The objectives of this study were to verify the value of these four QTL for selection and to compare the efficiency of alternative MAS strategies using these QTL vs. conventional phenotypic selection for grain yield. A total of 92 DHLs derived from the S/M cross that were not used in the original mapping efforts were used for QTL verification. Confirmation of QTL effects was first accomplished by assessing yield differences between individuals carrying alternative alleles at each putative locus in three environments. QTL1 on chromosome 3 was confirmed as the most important and consistent locus to determine yield across sites, with the S allele being favorable. The M allele at QTL3 on chromosome 6 was beneficial for grain yield across sites, but to a lesser degree than QTL1. Magnitudes of allele effects at QTL2 (chromosome 2) and QTL4 (chromosome 7) were highly influenced by the environment where the genotypes were grown. Verification of QTL effects was best achieved by comparing realized selection response. Genotypic (MAS) and tandem genotypic and phenotypic selection were at least as good as phenotypic selection. Consistent selection responses were detected for QTL1 alone and together with QTL3. Genotypic selection for lines carrying the S allele at QTL1 resulted in the identification of high-yielding genotypes. Selection responses increased when the M allele at QTL3 was combined with the S allele at QTL1. Significant qualitative QTL × environment interactions for QTL2 and QTL4 were detected through differential realized selection responses at different sites. Without a thorough understanding of the physiological and agronomic particulars of any QTL and the target environment, MAS for QTL showing qualitative interactions should be minimized This revised version was published online in June 2006 with corrections to the Cover Date.     

Quantitative genetics and functional-structural plant growth models: simulation of quantitative trait loci detection for model parameters and application to potential yield optimization

BACKGROUND AND AIMS: Prediction of phenotypic traits from new genotypes under untested environmental conditions is crucial to build simulations of breeding strategies to improve target traits. Although the plant response to environmental stresses is characterized by both architectural and functional plasticity, recent attempts to integrate biological knowledge into genetics models have mainly concerned specific physiological processes or crop models without architecture, and thus may prove limited when studying genotype x environment interactions. Consequently, this paper presents a simulation study introducing genetics into a functional-structural growth model, which gives access to more fundamental traits for quantitative trait loci (QTL) detection and thus to promising tools for yield optimization. METHODS: The GREENLAB model was selected as a reasonable choice to link growth model parameters to QTL. Virtual genes and virtual chromosomes were defined to build a simple genetic model that drove the settings of the species-specific parameters of the model. The QTL Cartographer software was used to study QTL detection of simulated plant traits. A genetic algorithm was implemented to define the ideotype for yield maximization based on the model parameters and the associated allelic combination. KEY RESULTS AND CONCLUSIONS: By keeping the environmental factors constant and using a virtual population with a large number of individuals generated by a Mendelian genetic model, results for an ideal case could be simulated. Virtual QTL detection was compared in the case of phenotypic traits--such as cob weight--and when traits were model parameters, and was found to be more accurate in the latter case. The practical interest of this approach is illustrated by calculating the parameters (and the corresponding genotype) associated with yield optimization of a GREENLAB maize model. The paper discusses the potentials of GREENLAB to represent environment x genotype interactions, in particular through its main state variable, the ratio of biomass supply over demand.     

Targeted resequencing of a genomic region influencing tameness and aggression reveals multiple signals of positive selection

The identification of the causative genetic variants in quantitative trait loci (QTL) influencing phenotypic traits is challenging, especially in crosses between outbred strains. We have previously identified several QTL influencing tameness and aggression in a cross between two lines of wild-derived, outbred rats (Rattus norvegicus) selected for their behavior towards humans. Here, we use targeted sequence capture and massively parallel sequencing of all genes in the strongest QTL in the founder animals of the cross. We identify many novel sequence variants, several of which are potentially functionally relevant. The QTL contains several regions where either the tame or the aggressive founders contain no sequence variation, and two regions where alternative haplotypes are fixed between the founders. A re-analysis of the QTL signal showed that the causative site is likely to be fixed among the tame founder animals, but that several causative alleles may segregate among the aggressive founder animals. Using a formal test for the detection of positive selection, we find 10 putative positively selected regions, some of which are close to genes known to influence behavior. Together, these results show that the QTL is probably not caused by a single selected site, but may instead represent the joint effects of several sites that were targets of polygenic selection.     

Efficient marker-based recurrent selection for multiple quantitative trait loci

We studied the efficiency of recurrent selection based solely on marker genotypes (marker-based selection), in order to increase favourable allele frequency at 50 previously detected quantitative trait loci (QTLs). Two selection procedures were investigated, using computer simulations: (1) Truncation Selection (MTS), in which individuals are ranked based on marker score, and best individuals are selected for recombination; and (2) QTL Complementation Selection (QCS), in which individuals are selected such that their QTL composition complements those individuals already selected. Provided QTL locations are accurate, marker-based selection with a population size of 200 was very effective in rapidly increasing frequencies of favourable QTL alleles. QCS methods were more effective than MTS for improving the mean frequency and fixation of favourable QTL alleles. Marker-based selection was not very sensitive to a reduction in population size, and appears valuable to optimize the use of molecular markers in recurrent selection programmes.     

A transmission/disequilibrium test approach to screen for quantitative trait loci in two selected lines of large white pigs

Pedigree and marker data from a multiple-generation pig selection experiment have been analysed to screen for loci affecting quantitative traits (QTL). Pigs from a base population were selected either for low backfat thickness at fixed live weight (L-line) or high live weight at fixed age (F-line). Selection was based on single-trait own performance and DNA was available on selected individuals only. Genotypes for three marker loci with known positions on chromosome 4 were available. The transmission/disequilibrium test (TDT) was originally described in human genetics to test for linkage between a genetic marker and a disease-susceptibility locus, in the presence of association. Here, we adapt the TDT to test for linkage between a marker and QTL favoured by selection, and for linkage disequilibrium between them in the base population. The a priori unknown distribution of the test statistic under the null hypothesis, no linkage, was obtained via Monte Carlo simulation. Significant TDT statistics were found for markers AFABP and SW818 in the F-line, indicating the presence of a closely linked QTL affecting growth performance. In the L-line, none of the markers studied showed significance. This study emphasizes the potential of the TDT as a quick and simple approach to screen for QTL in situations where marker genotypes are available on selected individuals. The results suggest that previously identified QTL in crosses of genetically diverse breeds may also segregate in commercial selection lines.     

Some practical considerations for using RFLP markers to aid in selection during inbreeding of maize

Summary If molecular markers are to be routinely used in maize (Zea mays L.) breeding for selection of quantitative trait loci (QTL), then consistent marker-trait associations across breeding populations are needed, as are efficient methods for weighting information from different markers. Given 15 restriction fragment length polymorphism (RFLP) markers associated with grain yield in testcrosses of 220 [BS11(FR)C7 x FRMol7] F₂ individuals to FRB73, separate weighting schemes were attempted in order to maximize the frequency of favorable marker genotypes associated with increased grain yield in selected F₂ individuals and F₂:S₄ Unes. The following principles were apparent: (1) Differential weighting among markers, in addition to weighting individual marker genotypes on the basis of associated mean effects, should be emphasized when using markers to select in breeding populations. This is due to limited population sizes that can readily be handled. (2) Relatively few markers may need to be used to screen segregating populations (e.g., F₂) of limited size for loci affecting complex traits, such as combining ability for grain yield, assuming prior knowledge of marker-QTL associations. Markers given greatest weight (largest estimates of associated effects) will determine most selections. (3) When marker-based selection is among individuals at higher levels of inbreeding (e.g., S₄) within selected families, more markers need to be used in screening because those associated with relatively small effects have an increased chance of affecting selection.These results suggest a qualitative approach for utilizing RFLP markers to aid in selection of complex traits in commercial hybrid maize breeding programs. Commercial research programs produce thousands of crosses each year aimed at inbred line development. Discovery of molecular markers with consistent QTL associations across breeding populations and close QTL linkages would allow for rapid screening of new F₂ populations at a few key markers. Early elimination of individuals with undesirable genotypes would reduce the extent of hybrid performance testing necessary during later stages of inbreeding.     

Selection of parents for crossing based on genotyping and phenotyping for stripe rust (Puccinia striiformis) resistance and agronomic traits in bread wheat breeding

Bread wheat (Triticum aestivum L.) germplasm consisting of 45 genotypes were clustered phenotypically using ten morphological traits and Area Under Disease Progress Curve (AUDPC) as measure of stripe rust resistance. The clustering was ratified by using twenty three molecular markers (SSR, EST and STS) linked to stripe rust (Puccinia striiformis f. sp. tritici) resistant QTLs. The aim was to asses the extent of genetic variability among the genotypes in order to select the parents for crossing between the resistant and susceptible genotypes with respect to stripe rust. The Euclidian dissimilarity values resulted from phenotypic data regarding morphological traits and AUDPC were used to construct a dendrogram for clustering the accessions. Using un-weighted pair group method with arithmetic means, another dendrogram resulted from the similarity coefficient values was used to distinguish the genotypes with respect to stripe rust. Clustering based on phenotypic data produced two major groups and five clusters (with Euclidian dissimilarity ranging from 244 to 16.16) whereas genotypic data yielded two major groups and four clusters (with percent similarity coefficient values ranging from 0.1 to 46.0) to separate the gene pool into highly resistant, resistant, moderately resistant, moderately susceptible and susceptible genotypes. With few exceptions, the outcome of both type of clustering was almost similar and resistant as well as susceptible genotypes came in the same clusters of molecular genotyping as yielded by phenotypic clustering. As a result seven genotypes (Bakhtawar-92, Frontana, Saleem 2000, Tatara, Inqilab-91, Fakhre Sarhad and Karwan) of diverse genetic background were selected for pyramiding stripe rust resistant genes as well as some other agronomic traits after hybridization.     

The effect of missing marker genotypes on the accuracy of gene-assisted breeding value estimation: a comparison of methods

In livestock populations, missing genotypes on a large proportion of the animals is a major problem when implementing gene-assisted breeding value estimation for genes with known effect. The objective of this study was to compare different methods to deal with missing genotypes on accuracy of gene-assisted breeding value estimation for identified bi-allelic genes using Monte Carlo simulation. A nested full-sib half-sib structure was simulated with a mixed inheritance model with one bi-allelic quantitative trait loci (QTL) and a polygenic effect due to infinite number of polygenes. The effect of the QTL was included in gene-assisted BLUP either by random regression on predicted gene content, i.e. the number of positive alleles, or including haplotype effects in the model with an inverse IBD matrix to account for identity-by-descent relationships between haplotypes using linkage analysis information (IBD-LA). The inverse IBD matrix was constructed using segregation indicator probabilities obtained from multiple marker iterative peeling. Gene contents for unknown genotypes were predicted using either multiple marker iterative peeling or mixed model methodology. For both methods, gene-assisted breeding value estimation increased accuracies of total estimated breeding value (EBV) with 0% to 22% for genotyped animals in comparison to conventional breeding value estimation. For animals that were not genotyped, the increase in accuracy was much lower (0% to 5%), but still substantial when the heritability was 0.1 and when the QTL explained at least 15% of the genetic variance. Regression on predicted gene content yielded higher accuracies than IBD-LA. Allele substitution effects were, however, overestimated, especially when only sires and males in the last generation were genotyped. For juveniles without phenotypic records and traits measured only on females, the superiority of regression on gene content over IBD-LA was larger than when all animals had phenotypes. Missing gene contents were predicted with higher accuracy using multiple-marker iterative peeling than with using mixed model methodology, but the difference in accuracy of total EBV was negligible and mixed model methodology was computationally much faster than multiple iterative peeling. For large livestock populations it can be concluded that gene-assisted breeding value estimation can be practically best performed by regression on gene contents, using mixed model methodology to predict missing marker genotypes, combining phenotypic information of genotyped and ungenotyped animals in one evaluation. This technique would be, in principle, also feasible for genomic selection. It is expected that genomic selection for ungenotyped animals using predicted single nucleotide polymorphism gene contents might be beneficial especially for low heritable traits.     

QTL for body weight,morphometric traits and stress response in European sea bass Dicentrarchus labrax

Natural mating and mass spawning in the European sea bass (Dicentrarchus labrax L., Moronidae, Teleostei) complicate genetic studies and the implementation of selective breeding schemes. We utilized a two‐step experimental design for detecting QTL in mass‐spawning species: 2122 offspring from natural mating between 57 parents (22 males, 34 females and one missing) phenotyped for body weight, eight morphometric traits and cortisol levels, had been previously assigned to parents based on genotypes of 31 DNA microsatellite markers. Five large full‐sib families (five sires and two dams) were selected from the offspring (570 animals), which were genotyped with 67 additional markers. A new genetic map was compiled, specific to our population, but based on the previously published map. QTL mapping was performed with two methods: half‐sib regression analysis (paternal and maternal) and variance component analysis accounting for all family relationships. Two significant QTL were found for body weight on linkage group 4 and 6, six significant QTL for morphometric traits on linkage groups 1B, 4, 6, 7, 15 and 23 and three suggestive QTL for stress response on linkage groups 3, 14 and 23. The QTL explained between 8% and 38% of phenotypic variance. The results are the first step towards identifying genes involved in economically important traits like body weight and stress response in European sea bass.     

Uncovering QTL for resistance and survival time to Philasterides dicentrarchi in turbot (Scophthalmus maximus)

Disease resistance‐related traits have received increasing importance in aquaculture breeding programs worldwide. Currently, genomic information offers new possibilities in breeding to address the improvement of this kind of traits. The turbot is one of the most promising European aquaculture species, and Philasterides dicentrarchi is a scuticociliate parasite causing fatal disease in farmed turbot. An appealing approach to fight against disease is to achieve a more robust broodstock, which could prevent or diminish the devastating effects of scuticociliatosis on farmed individuals. In the present study, a genome scan for quantitative trait loci (QTL) affecting resistance and survival time to P. dicentrarchi in four turbot families was carried out. The objectives were to identify QTL using different statistical approaches [linear regression (LR) and maximum likelihood (ML)] and to locate significantly associated markers for their application in genetic breeding strategies. Several genomic regions controlling resistance and survival time to P. dicentrarchi were detected. When analyzing each family separately, significant QTL for resistance were identified by the LR method in two linkage groups (LG1 and LG9) and for survival time in LG1, while the ML methodology identified QTL for resistance in LG9 and LG23 and for survival time in LG6 and LG23. The analysis of the total data set identified an additional significant QTL for resistance and survival time in LG3 with the LR method. Significant association between disease resistance‐related traits and genotypes was detected for several markers, a single one explaining up to 22% of the phenotypic variance. Obtained results will be essential to identify candidate genes for resistance and to apply them in marker‐assisted selection programs to improve turbot production.     

Many QTLs with minor additive effects are associated with a large difference in growth between two selection lines in chickens

Two growth-selected lines in chickens have been developed from a single founder population by divergent selection for body weight at 56 days of age. After more than 40 generations of selection they show a nine-fold difference in body weight at selection age and large differences in growth rate, appetite, fat deposition and metabolic characteristics. We have generated a large intercross between these lines comprising more than 800 F2 birds. QTL mapping revealed 13 loci affecting growth. The most striking observation was that the allele in the high weight line in all cases was associated with enhanced growth, but each locus explained only a small proportion of the phenotypic variance using a standard QTL model (1.3-3.1%). This result is in sharp contrast to our previous study where we reported that the two-fold difference in adult body size between the red junglefowl and White Leghorn domestic chickens is explained by a small number of QTLs with large additive effects. Furthermore, no QTLs for anorexia or antibody response were detected despite large differences for these traits between the founder lines. The result is an excellent example where a large phenotypic difference between populations occurs in the apparent absence of any single locus with large phenotypic effects. The study underscores the need for powerful experimental designs in genetic studies of multifactorial traits. No QTL at all would have reached genome-wide significance using a less powerful design (e.g. approx. 200 F2 individuals) regardless of the nine-fold phenotypic difference between the founder lines for the selected trait.     

A molecular selection index method based on eigenanalysis

The traditional molecular selection index (MSI) employed in marker-assisted selection maximizes the selection response by combining information on molecular markers linked to quantitative trait loci (QTL) and phenotypic values of the traits of the individuals of interest. This study proposes an MSI based on an eigenanalysis method (molecular eigen selection index method, MESIM), where the first eigenvector is used as a selection index criterion, and its elements determine the proportion of the trait's contribution to the selection index. This article develops the theoretical framework of MESIM. Simulation results show that the genotypic means and the expected selection response from MESIM for each trait are equal to or greater than those from the traditional MSI. When several traits are simultaneously selected, MESIM performs well for traits with relatively low heritability. The main advantages of MESIM over the traditional molecular selection index are that its statistical sampling properties are known and that it does not require economic weights and thus can be used in practical applications when all or some of the traits need to be improved simultaneously.