Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize

Quantitative trait loci (QTL) detection experiments have often been restricted to large biallelic populations. Use of connected multiparental crosses has been proposed to increase the genetic variability addressed and to test for epistatic interactions between QTL and the genetic background. We present here the results of a QTL detection performed on six connected F₂ populations of 150 F_2:3 families each, derived from four maize inbreds and evaluated for three traits of agronomic interest. The QTL detection was carried out by composite interval mapping on each population separately, then on the global design either by taking into account the connections between populations or not. Epistatic interactions between loci and with the genetic background were tested. Taking into account the connections between populations increased the number of QTL detected and the accuracy of QTL position estimates. We detected many epistatic interactions, particularly for grain yield QTL (R ² increase of 9.6%). Use of connections for the QTL detection also allowed a global ranking of alleles at each QTL. Allelic relationships and epistasis both contribute to the lack of consistency for QTL positions observed among populations, in addition to the limited power of the tests. The potential benefit of assembling favorable alleles by marker-assisted selection are discussed.     

Interactions between markers can be caused by the dominance effect of quantitative trait loci

F₂ populations are commonly used in genetic studies of animals and plants. For simplicity, most quantitative trait locus or loci (QTL) mapping methods have been developed on the basis of populations having two distinct genotypes at each polymorphic marker or gene locus. In this study, we demonstrate that dominance can cause the interactions between markers and propose an inclusive linear model that includes marker variables and marker interactions so as to completely control both additive and dominance effects of QTL. The proposed linear model is the theoretical basis for inclusive composite-interval QTL mapping (ICIM) for F₂ populations, which consists of two steps: first, the best regression model is selected by stepwise regression, which approximately identifies markers and marker interactions explaining both additive and dominance variations; second, the interval mapping approach is applied to the phenotypic values adjusted by the regression model selected in the first step. Due to the limited mapping population size, the large number of variables, and multicollinearity between variables, coefficients in the inclusive linear model cannot be accurately determined in the first step. Interval mapping is necessary in the second step to fine tune the QTL to their true positions. The efficiency of including marker interactions in mapping additive and dominance QTL was demonstrated by extensive simulations using three QTL distribution models with two population sizes and an actual rice F₂ population.     

Detection of putative quantitative trait loci in line crosses under infinitesimal genetic models

Quantitative trait locus (QTL) mapping studies often employ segregating generations derived from a cross between genetically divergent inbred lines. In the analysis of such data it is customary to fit a single QTL and use a null hypothesis which assumes that the genomic region under study contributes no genetic variance. To explore the situation in which multiple linked genes contribute to the genetic variance, we simulated an F₂-mapping experiment in which the genetic difference between the two original inbred strains was caused by a large number of loci, each having equal effect on the quantitative trait. QTLs were either in coupling, dispersion or repulsion phase in the base population of inbred lines, with the expected F₂ genetic variance explained by the QTLs being equivalent in the three models. Where QTLs were in coupling phase, one inbred line was fixed for all plus alleles, and the other line was fixed for minus alleles. Where QTLs were in dispersion phase, they were assumed to be randomly fixed for one or other allele (as if the inbred lines had evolved from a common ancestor by random drift). Where QTLs were in repulsion phase alleles within an inbred line were alternating plus and minus at adjacent loci, and alternative alleles were fixed in the two inbred lines. In all these genetic models a standard interval mapping test statistic used to determine whether there is a QTL of large effect segregating in the population was inflated on average. Furthermore, the use of a threshold for QTL detection derived under the assumption that no QTLs were segregating would often lead to spurious conclusions regards the presence of genes of large effects (i.e. type I errors). The employment of an alternative model for the analysis, including linked markers as cofactors in the analysis of a single interval, reduced the problem of type I error rate, although test statistics were still inflated relative to the case of no QTLs. It is argued that in practice one should take into account the difference between the strains or the genetic variance in the F₂ population when setting significance thresholds. In addition, tests designed to probe the adequacy of a single-QTL model or of an alternative infinitesimal coupling model are described. Such tests should be applied in QTL mapping studies to help dissect the true nature of genetic variation.     

Molecular Dissection of a Genomic Region Governing Root Traits Associated with Drought Tolerance Employing a Combinatorial Approach of QTL Mapping and RNA-seq in Rice

Drought is considered as one of the major obstacles for progressive yield enhancement and stability in rice, especially in rain-fed conditions. Being a complex trait, drought is regulated by numerous quantitative trait loci (QTL), of which, however, very few underlying genes have been cloned. In the present investigation, we made an attempt to uncover the candidate gene(s) behind a major QTL, rdw8.1 governing drought tolerance traits viz., root dry weight and root length. The targeted QTL has been delimited to 366.75 kb from 10.17 Mb by QTL mapping in BC₁F₂ population. Further, the targeted region was delineated employing next-generation sequencing based RNA-seq. Based on the QTL mapping and RNA-seq approaches, the plausible candidate gene underlying the QTL region was identified as a wound inducible protein (LOC_Os08g08090). This gene can be of potential value to enhance the drought tolerance of the elite rice varieties through molecular breeding.     

Linkage disequilibrium in related breeding lines of chickens

High-density genotyping of single-nucleotide polymorphisms (SNPs) enables detection of quantitative trait loci (QTL) by linkage disequilibrium (LD) mapping using LD between markers and QTL and the subsequent use of this information for marker-assisted selection (MAS). The success of LD mapping and MAS depends on the extent of LD in the populations of interest and the use of associations across populations requires LD between loci to be consistent across populations. To assess the extent and consistency of LD in commercial broiler breeding populations, we used genotype data for 959 and 398 SNPs on chromosomes 1 and 4 on 179-244 individuals from each of nine commercial broiler chicken breeding lines. Results show that LD measured by r(2) extends over shorter distances than reported previously in other livestock breeding populations. The LD at short distance (within 1 cM) tended to be consistent across related populations; correlations of LD measured by r for pairs of lines ranged from 0.17 to 0.94 and closely matched the line relationships based on marker allele frequencies. In conclusion, LD-based correlations are good estimates of line relationships and the relationship between a pair of lines a good predictor of LD consistency between the lines.     

Linkage disequilibrium mapping of quantitative trait loci under truncation selection

As a dense map of single nucleotide polymorphism (SNP) markers are available, population-based linkage disequilibrium (LD) mapping or association study is becoming one of the major tools for identifying quantitative trait loci (QTL) and for fine gene mapping. However, in many cases, LD between the marker and trait locus is not very strong. Approaches that maximize the potential of detecting LD will be essential for the success of LD mapping of QTL. In this paper, we propose two strategies for increasing the probability of detecting LD: (1) phenotypic selection and (2) haplotype LD mapping. To provide the foundations for LD mapping of QTL under selection, we develop analytic tools for assessing the impact of phenotypic selection on allele and haplotype frequencies, and LD under three trait models: single trait locus, two unlinked trait loci, and two linked trait loci with or without epistasis. In addition to a traditional chi(2) test, which compares the difference in allele or haplotype frequencies in the selected sample and population sample, we present multiple regression methods for LD mapping of QTL, and investigate which methods are effective in employing phenotypic selection for QTL mapping. We also develop a statistical framework for investigating and comparing the power of the single marker and multilocus haplotype test for LD mapping of QTL. Finally, the proposed methods are applied to mapping QTL influencing variation in systolic blood pressure in an isolated Chinese population.     

QTL detection for stover yield and quality traits using two connected populations in high-oil maize

Both yield and quality traits for stover portion were important for forage and biofuel production utility in maize. A high-oil maize inbred GY220 was crossed with two normal-oil dent maize inbred lines 8984 and 8622 to generate two connected F_2:3 populations with 284 and 265 F_2:3 families. Seven yield and quality traits were evaluated under two environments. The variance components of genotype (σ_g²), environment (σ_e²) and genotype × environment interactions (σ_ge²) were all significant for most traits in both populations. Different levels of correlations were observed for all traits. QTL mapping was conducted using composite interval mapping (CIM) for data under each environment and in combined analysis in both populations. Totally, 45 and 42 QTL were detected in the two populations. Only five common QTL across the two populations, and one and three common QTL across the two environments in the two populations were detected, reflecting substantial influence of genetic backgrounds and environments on the results of QTL detection for stover traits. Combined analysis across two environments failed to detect most QTL mapped using individual environmental data in both populations. Few of the detected QTL displayed digenic epistatic interactions. Common QTL among all traits were consistent with their correlations. Some QTL herein have been detected in previous researches, and linked with candidate genes for enzymes postulated to have direct and indirect roles in cell wall components biosynthesis.     

Mapping minor QTL for increased stearic acid content in sunflower seed oil

Increased stearic acid (C18:0) content in the seed oil of sunflower would improve the oil quality for some edible uses. The sunflower line CAS-20 (C18:0 genotype Es1Es1es2es2), developed from the high C18:0 mutant line CAS-3 (C18:0 genotype es1es1es2es2; 25% C18:0), shows increased C18:0 levels in its seed oil (8.6%). The objective of this research was to map quantitative trait loci (QTL) conferring increased C18:0 content in CAS-20 in an F₂ mapping population developed from crosses between HA-89 (wild type Es1Es1Es2Es2; low C18:0) and CAS-20, which segregates independently of the macromutation Es1 controlling high C18:0 content in CAS-3. Seed oil fatty acid composition was measured in the F₂ population by gas-liquid chromatography. A genetic linkage map of 17 linkage groups (LGs) comprising 80 RFLP and 19 SSR marker loci from this population was used to identify QTL controlling fatty acid composition. Three QTL affecting C18:0 content were identified on LG3, LG11, and LG13, with all alleles for increased C18:0 content inherited from CAS-20. In total, these QTL explained 43.6% of the C18:0 phenotypic variation. Additionally, four candidate genes (two stearate desaturase genes, SAD6 and SAD17, and a FatA and a FatB thioesterase gene) were placed on the QTL map. On the basis of positional information, QTL on LG11 was suggested to be a SAD6 locus. The results presented show that increased C18:0 content in sunflower seed oil is not a simple trait, and the markers flanking these QTL constitute a powerful tool for plant breeding programs.     

Mapping Fusarium wilt race 1 resistance genes in cotton by inheritance,QTL and sequencing composition

Knowledge of the inheritance of disease resistance and genomic regions housing resistance (R) genes is essential to prevent expanding pathogen threats such as Fusarium wilt [Fusarium oxysporum f.sp. vasinfectum (FOV) Atk. Sny & Hans] in cotton (Gossypium spp.). We conducted a comprehensive study combining conventional inheritance, genetic and quantitative trait loci (QTL) mapping, QTL marker-sequence composition, and genome sequencing to examine the distribution, structure and organization of disease R genes to race 1 of FOV in the cotton genome. Molecular markers were applied to F₂ and recombinant inbred line (RIL) interspecific mapping populations from the crosses Pima-S7 (G. barbadense L.) × ‘Acala NemX’ (G. hirsutum L.) and Upland TM-1 (G. hirsutum) × Pima 3-79 (G. barbadense), respectively. Three greenhouse tests and one field test were used to obtain sequential estimates of severity index (DSI) of leaves, and vascular stem and root staining (VRS). A single resistance gene model was observed for the F₂ population based on inheritance of phenotypes. However, additional inheritance analyses and QTL mapping indicated gene interactions and inheritance from nine cotton chromosomes, with major QTLs detected on five chromosomes [Fov1-C06, Fov1-C08, (Fov1-C11 ₁ and Fov1-C11 ₂₎ , Fov1-C16 and Fov1-C19 loci], explaining 8–31% of the DSI or VRS variation. The Fov1-C16 QTL locus identified in the F₂ and in the RIL populations had a significant role in conferring FOV race 1 resistance in different cotton backgrounds. Identified molecular markers may have important potential for breeding effective FOV race 1 resistance into elite cultivars by marker-assisted selection. Reconciliation between genetic and physical mapping of gene annotations from marker-DNA and new DNA sequences of BAC clones tagged with the resistance-associated QTLs revealed defenses genes induced upon pathogen infection and gene regions rich in disease-response elements, respectively. These offer candidate gene targets for Fusarium wilt resistance response in cotton and other host plants.     

Three novel quantitative trait loci for skin thickness in swine identified by linkage and genome‐wide association studies

Skin is the largest organ in the pig body and plays a key role in protecting the body against pathogens and excessive water loss. Deciphering the genetic basis of swine skin thickness would enrich our knowledge about the skin. To identify the loci for porcine skin thickness, we first performed a genome scan with 194 microsatellite markers in a White Duroc × Erhualian F₂ intercross. We identified three genome‐wide significant QTL on pig chromosomes (SSC) 4, 7 and 15 using linkage analysis. The most significant QTL was found on SSC7 with a small confidence interval of ~5 cM, explaining 23.9 percent of phenotypic variance. Further, we conducted a genome‐wide association study (GWAS) using Illumina PorcineSNP60 Beadchips for the F₂ pedigree and a population of Chinese Sutai pigs. We confirmed significant QTL in the F₂ pedigree and replicated QTL on SSC15 in Chinese Sutai pigs. A meta‐analysis of GWASs on both populations detected a genomic region associated with skin thickness on SSC4. GWAS results were generally consistent with QTL mapping. Identical‐by‐descent analysis defined QTL on SSC7 in a 683‐kb region harboring an interesting candidate gene: HMGA1. On SSC15, the linkage disequilibrium analysis showed a haplotype block of 2.20 Mb that likely harbors the gene responsible for skin thickness. Our findings provide novel insights into the genetic basis of swine skin thickness, which would benefit further understanding of porcine skin function.     

Identification of QTLs Underlying Water-Logging Tolerance in Soybean

Soil water-logging can cause severe damage to soybean [Glycine max (L.) Merr.] and results in significant yield reduction. The objective of this study was to identify quantitative trait loci (QTL) that condition water-logging tolerance (WLT) in soybean. Two populations with 103 and 67 F_6:11 recombinant inbred lines (RILs) from A5403 × Archer (Population 1) and P9641 × Archer (Population 2), respectively, were used as the mapping populations. The populations were evaluated for WLT in manually flooded fields in 2001, 2002, and 2003. Significant variation was observed for WLT among the lines in the two populations. No transgressive tolerant segregants were observed in either population. Broad-sense heritability of WLT for populations 1 and 2 were 0.59 and 0.43, respectively. The tolerant and sensitive RILs from each population were selected to create a tolerant bulk and a sensitive bulk, respectively. The two bulks and the parents of each population were tested with 912 simple sequence repeat (SSR) markers to select candidate regions on the linkage map that were associated with WLT. Markers from the candidate regions were used to genotype the RILs in both populations. Both single marker analysis (SMA) and composite interval mapping (CIM) were used to identify QTL for WLT. Seventeen markers in Population 1 and 15 markers in Population 2 were significantly (p <0.0001) associated with WLT in SMA. Many of these markers were linked to Rps genes or QTL conferring resistance to Phytophthora sojae Kaufmann and Gerdemann. Five markers, Satt599 on linkage group (LG) A1, Satt160, Satt269, and Satt252 on LG F, and Satt485 on LG N, were significant (p <0.0001) for WLT in both populations. With CIM, a WLT QTL was found close to the marker Satt385 on LG A1 in Population 1 in 2003. This QTL explained 10% of the phenotypic variation and the allele that increased WLT came from Archer. In Population 2 in 2002, a WLT QTL was located near the marker Satt269 on LG F. This QTL explained 16% of the phenotypic variation and the allele that increased WLT also came from Archer.     

Linkage disequilibrium fine mapping of quantitative trait loci: A simulation study

Recently, the use of linkage disequilibrium (LD) to locate genes which affect quantitative traits (QTL) has received an increasing interest, but the plausibility of fine mapping using linkage disequilibrium techniques for QTL has not been well studied. The main objectives of this work were to (1) measure the extent and pattern of LD between a putative QTL and nearby markers in finite populations and (2) investigate the usefulness of LD in fine mapping QTL in simulated populations using a dense map of multiallelic or biallelic marker loci. The test of association between a marker and QTL and the power of the test were calculated based on single-marker regression analysis. The results show the presence of substantial linkage disequilibrium with closely linked marker loci after 100 to 200 generations of random mating. Although the power to test the association with a frequent QTL of large effect was satisfactory, the power was low for the QTL with a small effect and/or low frequency. More powerful, multi-locus methods may be required to map low frequent QTL with small genetic effects, as well as combining both linkage and linkage disequilibrium information. The results also showed that multiallelic markers are more useful than biallelic markers to detect linkage disequilibrium and association at an equal distance.     

Linkage analysis of quantitative trait loci in multiple line crosses

Simple line crosses, for example, backcross and F₂, are commonly used in mapping quantitative trait loci (QTL). However, these simple crosses are rarely used alone in commercial plant breeding; rather, crosses involving multiple inbred lines or several simple crosses but connected by shared inbred lines may be common in plant breeding. Mapping QTL using crosses of multiple lines is more relevant to plant breeding. Unfortunately, current statistical methods and computer programs of QTL mapping are all designed for simple line crosses or multiple line crosses but under a regular mating system. It is not straightforward to extend the existing methods to handle multiple line crosses under irregular and complicated mating designs. The major hurdle comes from irregular inbreeding, multiple generations, and multiple alleles. In this study, we develop a Bayesian method implemented via the Markov chain Monte Carlo (MCMC) algorithm for mapping QTL using complicated multiple line crosses. With the MCMC algorithm, we are able to draw a complete path of the gene flow from founder alleles to their descendents via a recursive process. This has greatly simplified the problem caused by irregular mating and inbreeding in the mapping population. Adopting the reversible jump MCMC algorithm, we are able to simultaneously search for multiple QTL along the genome. We can even infer the posterior distribution of the number of QTL, one of the most important parameters in QTL study. Application of the new MCMC based QTL mapping procedure is demonstrated using two different mating designs. Design I involves two inbred lines and their derived F₁, F₂, and BC populations. Design II is a half-diallel cross involving three inbred lines. The two designs appear different, but can be handled with the same robust computer program.     

Fine‐mapping of Obesity‐related Quantitative Trait Loci in an F9/10 Advanced Intercross Line

Obesity develops in response to a combination of environmental effects and multiple genes of small effect. Although there has been significant progress in characterizing genes in many pathways contributing to metabolic disease, knowledge about the relationships of these genes to each other and their joint effects upon obesity lags behind. The LG,SM advanced intercross line (AIL) model of obesity has been used to characterize over 70 loci involved in fatpad weight, body weight, and organ weights. Each of these quantitative trait loci (QTLs) encompasses large regions of the genome and require fine‐mapping to isolate causative sequence changes and possible mechanisms of action as indicated by the genetic architecture. In this study we fine‐map QTLs first identified in the F₂ and F_2/3 populations in the combined F_9/10 advanced intercross generations. We observed significantly narrowed QTL confidence regions, identified many single QTL that resolve into multiple QTL peaks, and identified new QTLs that may have been previously masked due to opposite gene effects at closely linked loci. We also present further characterization of the pleiotropic and epistatic interactions underlying these obesity‐related traits.     

Four loci differentially expressed in muscle tissue depending on water-holding capacity are associated with meat quality in commercial pig herds

Four genes, VTN, KERA, LYZ, and a non-annotated EST (Affymetrix probe set ID: Ssc.25503.1.S1_at), whose candidacy for traits related to water-holding capacity of meat arises from their trait-dependent differential expression, were selected for candidate gene analysis. Based on in silico analysis SNPs were detected, confirmed by sequencing and used to genotype animals of 4 pig populations including 3 commercial herds of Pietrain (PI), Pietrain × (German Large White × German Landrace) (PIF1), German Landrace (DL) and 1 experimental F₂ population Duroc × Pietrain (DUPI). Comparative and genetic mapping established the location of VTN on SSC12, of LYZ and KERA on SSC5 and of UN on SSC7, coinciding with QTL regions for meat quality traits. VTN showed association with pH1, pH24 and drip loss. LYZ revealed association with conductivity 24, pH1 and drip loss. KERA was associated with pH. UN showed association with pH24 and drip loss, respectively. However, none of the candidate genes showed significant associations for a particular trait across all populations. This may be due to breed specific effects that are related to the differences in meat quality of theses pig breeds. The studies revealed statistic evidence for a link of genetic variation at these loci or close to them and promoted those four candidate genes as functional and/or positional candidate genes for meat quality traits.     

Pooled analysis of data from multiple quantitative trait locus mapping populations

Quantitative trait locus (QTL) analysis on pooled data from multiple populations (pooled analysis) provides a means for evaluating, as a whole, evidence for existence of a QTL from different studies and examining differences in gene effect of a QTL among different populations. Objectives of this study were to: (1) develop a method for pooled analysis and (2) conduct pooled analysis on data from two soybean mapping populations. Least square interval mapping was extended for pooled analysis by inclusion of populations and cofactor markers as indicator variables and covariate variables separately in the multiple linear models. The general linear test approach was applied for detecting a QTL. Single population-based and pooled analyses were conducted on data from two F_2:3 mapping populations, Hamilton (susceptible) × PI 90763 (resistant) and Magellan (susceptible) × PI 404198A (resistant), for resistance to soybean cyst nematode (SCN) in soybean. It was demonstrated that where a QTL was shared among populations, pooled analysis showed increased LOD values on the QTL candidate region over single population analyses. Where a QTL was not shared among populations, however, the pooled analysis showed decreased LOD values on the QTL candidate region over single population analyses. Pooled analysis on data from genetically similar populations may have higher power of QTL detection than single population-based analyses. QTLs were identified by pooled analysis on linkage groups (LGs) G, B1 and J for resistance to SCN race 2 whereas QTLs on LGs G, B1 and E for resistance to SCN race 5 in soybean PI 90763 and PI 404198A. QTLs on LG G and B1 were identified in both PI 90763 and PI 404198A whereas QTLs on LG E and J were identified in PI 90763 only. QTLs on LGs G and B1 for resistance to race 2 may be the same or closely linked with QTLs on LG G and B1 for resistance to race 5, respectively. It was further demonstrated that QTLs on G and B1 carried by PI 90763 were not significantly different in gene effect from QTLs on LGs G and B1 in PI 404198A, respectively.     

Simultaneous selection of major and minor genes: use of QTL to increase selection efficiency of coleoptile length of wheat (Triticum aestivum L.)

Plant breeders simultaneously select for qualitative traits controlled by one or a small number of major genes, as well as for polygenic traits controlled by multiple genes that may be detected as quantitative trait loci (QTL). In this study, we applied computer simulation to investigate simultaneous selection for alleles at both major and minor gene (as QTL) loci in breeding populations of two wheat parental lines, HM14BS and Sunstate. Loci targeted for selection included six major genes affecting plant height, disease resistance, and grain quality, plus 6 known and 11 “unidentified” QTL affecting coleoptile length (CL). Parental line HM14BS contributed the target alleles at two of the major gene loci, while parental line Sunstate contributed target alleles at four loci. The parents have similar plant height, but HM14BS has a longer coleoptile, a desirable attribute for deep sowing in rainfed environments. Including the wild-type allele at the major reduced-height locus Rht-D1, HM14BS was assumed to have 13 QTL for increased CL, and Sunstate four; these assumptions being derived from mapping studies and empirical data from an actual HM14BS/Sunstate population. Simulation indicated that compared to backcross populations, a single biparental F₁ cross produced the highest frequency of target genotypes (six desired alleles at major genes plus desired QTL alleles for long CL). From 1,000 simulation runs, an average of 2.4 individuals with the target genotype were present in unselected F₁-derived doubled haploid (DH) or recombinant inbred line (RIL) populations of size 200. A selection scheme for the six major genes increased the number of target individuals to 19.1, and additional marker-assisted selection (MAS) for CL increased the number to 23.0. Phenotypic selection (PS) of CL outperformed MAS in this study due to the high heritability of CL, incompletely linked markers for known QTL, and the existence of unidentified QTL. However, a selection scheme combining MAS and PS was equally as efficient as PS and would result in net savings in production and time to delivery of long coleoptile wheats containing the six favorable alleles.     

Role of seed germination in adaptation and reproductive isolation in Arabidopsis lyrata

Seed germination is an important developmental and life history stage. Yet, the evolutionary impact of germination has mainly been studied in the context of dormancy, or for its role in reproductive isolation between species. Here, we aim to examine multiple consequences of genetic divergence on germination traits between two Arabidopsis lyrata subspecies: ssp. petraea (Eurasia) and ssp. lyrata (North America). Postdormancy germination time, a potentially adaptive trait, showed differentiation between the populations, and quantitative trait loci (QTL) mapping revealed that the trait variation is mainly controlled by two antagonistic loci. These QTL areas contain several candidate genes with known function in postdormancy germination in A. thaliana. The sequence variation of three genes was consistent with differential selection, and they also included fixed nonsynonymous substitutions with potential to account for the phenotypic differentiation. We further show that the divergence between the subspecies has led to a slight but significant reduction in hybrid germination proportions, indicating incipient reproductive isolation. Comparison of reciprocal F₁ and F₂ progenies suggests that Bateson–Dobzhansky–Muller incompatibilities likely act through uniparentally inherited factors. Examination of genomewide transmission ratio distortion further revealed that cytonuclear interactions cause substantial pregermination inviability in the hybrids. These results confirm that seed germination has adaptive potential beyond the dormancy stage and that hybrid seed inviability can be one of the first reproductive barriers to arise during divergence.     

QTL fine mapping by measuring and testing for Hardy-Weinberg and linkage disequilibrium at a series of linked marker loci in extreme samples of populations

It has recently been demonstrated that fine-scale mapping of a susceptibility locus for a complex disease can be accomplished on the basis of deviations from Hardy-Weinberg (HW) equilibrium at closely linked marker loci among affected individuals. We extend this theory to fine-scale localization of a quantitative-trait locus (QTL) from extreme individuals in populations, by means of HW and linkage-disequilibrium (LD) analyses. QTL mapping and/or linkage analyses can establish a large genomic region ( approximately 30 cM) that contains a QTL. The QTL can be fine mapped by examination of the degree of deviation from HW and LD at a series of closely linked marker loci. The tests can be performed for samples of individuals belonging to either high or low percentiles of the phenotype distribution or for combined samples of these extreme individuals. The statistical properties (the power and the size) of the tests of this fine-mapping approach are investigated and are compared extensively, under various genetic models and parameters for the QTL and marker loci. On the basis of the results, a two-stage procedure that uses extreme samples and different tests (for HW and LD) is suggested for QTL fine mapping. This two-step procedure is economic and powerful and can accurately narrow a genomic region containing a QTL from approximately 30-1 cM, a range that renders physical mapping feasible for identification of the QTL. In addition, the relationship between parameterizations of complex diseases, by means of penetrance, and those of complex quantitative traits, by means of genotypic values, is outlined. This means that many statistical genetic methods developed for searching for susceptibility loci of complex diseases can be directly adopted and/or extended to QTL mapping for quantitative traits.     

Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars

Association mapping is a method for detection of gene effects based on linkage disequilibrium (LD) that complements QTL analysis in the development of tools for molecular plant breeding. In this study, association mapping was performed on a selected sample of 95 cultivars of soft winter wheat. Population structure was estimated on the basis of 36 unlinked simple-sequence repeat (SSR) markers. The extent of LD was estimated on chromosomes 2D and part of 5A, relative to the LD observed among unlinked markers. Consistent LD on chromosome 2D was <1 cM, whereas in the centromeric region of 5A, LD extended for approximately 5 cM. Association of 62 SSR loci on chromosomes 2D, 5A, and 5B with kernel morphology and milling quality was analyzed through a mixed-effects model, where subpopulation was considered as a random factor and the marker tested was considered as a fixed factor. Permutations were used to adjust the threshold of significance for multiple testing within chromosomes. In agreement with previous QTL analysis, significant markers for kernel size were detected on the three chromosomes tested, and alleles potentially useful for selection were identified. Our results demonstrated that association mapping could complement and enhance previous QTL information for marker-assisted selection.