GENETIC ARCHITECTURE AND POSTZYGOTIC REPRODUCTIVE ISOLATION: EVOLUTION OF BATESON–DOBZHANSKY–MULLER INCOMPATIBILITIES IN A POLYGENIC MODEL

The Bateson–Dobzhansky–Muller model predicts that postzygotic isolation evolves due to the accumulation of incompatible epistatic interactions, but few studies have quantified the relationship between genetic architecture and patterns of reproductive divergence. We examined how the direction and magnitude of epistatic interactions in a polygenic trait under stabilizing selection influenced the evolution of hybrid incompatibilities. We found that populations evolving independently under stabilizing selection experienced suites of compensatory allelic changes that resulted in genetic divergence between populations despite the maintenance of a stable, high‐fitness phenotype. A small number of loci were then incompatible with multiple alleles in the genetic background of the hybrid and the identity of these incompatibility loci changed over the evolution of the populations. For F₁ hybrids, reduced fitness evolved in a window of intermediate strengths of epistatic interactions, but F₂ and backcross hybrids evolved reduced fitness across weak and moderate strengths of epistasis due to segregation variance. Strong epistatic interactions constrained the allelic divergence of parental populations and prevented the development of reproductive isolation. Because many traits with varying genetic architectures must be under stabilizing selection, our results indicate that polygenetic drift is a plausible hypothesis for the evolution of postzygotic reproductive isolation.     

Epistatic QTL pairs associated with meat quality and carcass composition traits in a porcine Duroc × Pietrain population

Background

Quantitative trait loci (QTL) analyses in pig have revealed numerous individual QTL affecting growth, carcass composition, reproduction and meat quality, indicating a complex genetic architecture. In general, statistical QTL models consider only additive and dominance effects and identification of epistatic effects in livestock is not yet widespread. The aim of this study was to identify and characterize epistatic effects between common and novel QTL regions for carcass composition and meat quality traits in pig.

Methods

Five hundred and eighty five F₂ pigs from a Duroc × Pietrain resource population were genotyped using 131 genetic markers (microsatellites and SNP) spread over the 18 pig autosomes. Phenotypic information for 26 carcass composition and meat quality traits was available for all F₂ animals. Linkage analysis was performed in a two-step procedure using a maximum likelihood approach implemented in the QxPak program.

Results

A number of interacting QTL was observed for different traits, leading to the identification of a variety of networks among chromosomal regions throughout the porcine genome. We distinguished 17 epistatic QTL pairs for carcass composition and 39 for meat quality traits. These interacting QTL pairs explained up to 8% of the phenotypic variance.

Conclusions

Our findings demonstrate the significance of epistasis in pigs. We have revealed evidence for epistatic relationships between different chromosomal regions, confirmed known QTL loci and connected regions reported in other studies. Considering interactions between loci allowed us to identify several novel QTL and trait-specific relationships of loci within and across chromosomes.     

Dissection of the genetic architecture of body weight in chicken reveals the impact of epistasis on domestication traits

In this contribution, we study the genetic mechanisms leading to differences in the observed growth patterns of domesticated White Leghorn chickens and their wild ancestor the red jungle fowl. An epistatic QTL analysis for several body-weight measures from hatch to adulthood confirms earlier findings that polymorphisms at >15 loci contribute to body-weight determination in an F(2) intercross between these populations and that many loci are involved in complex genetic interactions. Here, we use a new genetic model to decompose the genetic effects of this multilocus epistatic genetic network. The results show how the functional modeling of genetic effects provides new insights into how genetic interactions in a large set of loci jointly contribute to phenotypic expression. By exploring the functional effects of QTL alleles, we show that some alleles can display temporal shifts in the expression of genetic effects due to their dependencies on the genetic background. Our results demonstrate that the effects of many genes are dependent on genetic interactions with other loci and how their involvement in the domestication process relies on these interactions.     

Influence of dent corn genetic backgrounds on QTL detection for plant-height traits and their relationships in high-oil maize

QTL mapping for plant-height traits has not been hitherto reported in high-oil maize. A high-oil maize inbred ‘GY220’ was crossed with two dent maize inbreds (‘8984’ and ‘8622’) to generate two connected F_2:3 populations. Four plant-height traits were evaluated in 284 and 265 F_2:3 families. Single-trait QTL mapping and multiple-trait joint QTL mapping was used to detect QTLs for the traits and the genetic relationship between plant height (PH) and two other plant-height traits. A total of 28 QTLs and 12 pairs of digenic interactions among detected QTLs for four traits were detected in the two F_2:3 families. Only one marker was shared between the two populations. Joint analysis of PH with ear height (EH) and PH with top height (TH) detected 32 additional QTLs. Our results showed that QTL detection for PH was dependent on the genetic background of dent corn inbreds. Multiple-trait joint QTL analysis could increase the number of detected QTLs.     

Quantitative trait locus analysis for obesity reveals multiple networks of interacting loci

Obesity is a highly heritable and genetically complex trait with hundreds of potential loci identified. An intercross of 513 F₂ progeny between the SM/J × NZB/BINJ inbred mouse strains was generated to identify quantitative trait loci (QTL) that are involved in the weight of four fat pads: mesenteric, inguinal, gonadal, and retroperitoneal. Sex and lean body weight were treated as covariates in the analysis of these fat pads. This analysis uncoupled genetic effects related to overall body size from those influencing the adiposity of a mouse. We identified multiple significant QTL. QTL alleles associated with increased lean body weight and individual fat pad weights are contributed by the NZB background. Adiposity loci are distinct from these body size QTLs and high-adiposity alleles are contributed by the SM background. An extended network of epistatic QTL is also observed. A QTL on Chr 19 is the center of a network of eight interacting QTL, Chr 4 is the center of six, and Chr 17 the center of four interacting QTL. We conclude that interacting networks of multiple genes characterize the regulation of fat pad depots and body weight. Haplotype patterns and a literature-driven approach were used to generate hypotheses regarding the identity of the genes and pathways underlying the QTL.     

Theory for identification of marker locus-QTL associations in population by line crosses

The objective of this paper is to present genetic theory demonstrating the conditions under which it should be possible to identify molecular marker-quantitative trait locus (QTL) associations in crosses of random-mating populations to inbreds. Using as an example the cross of a corn (Zea mays L.) population to an inbred, the expected disequilibrium for testcross and per se performance of F₂, F₃, BC₁ (to the inbred) and recombinant inbred generations was derived for cases where a marker allele is linked to an unfavorable QTL allele in the inbred and where the marker allele is linked to a favorable QTL allele in the inbred. Disequilibrium in segregating generations was shown to be a function of disequilibrium in the parent population, the frequency of marker and QTL alleles in the parent population, and the recombination distance between the marker and the QTL. To maximize the opportunity to identify a favorable QTL the following procedures are suggested:

Multi-QTL Mapping for Quantitative Traits Using Epistatic Distorted Markers

The interaction between segregation distortion loci (SDL) has been often observed in all kinds of mapping populations. However, little has been known about the effect of epistatic SDL on quantitative trait locus (QTL) mapping. Here we proposed a multi-QTL mapping approach using epistatic distorted markers. Using the corrected linkage groups, epistatic SDL was identified. Then, these SDL parameters were used to correct the conditional probabilities of QTL genotypes, and these corrections were further incorporated into the new QTL mapping approach. Finally, a set of simulated datasets and a real data in 304 mouse F₂ individuals were used to validate the new method. As compared with the old method, the new one corrects genetic distance between distorted markers, and considers epistasis between two linked SDL. As a result, the power in the detection of QTL is higher for the new method than for the old one, and significant differences for estimates of QTL parameters between the two methods were observed, except for QTL position. Among two QTL for mouse weight, one significant difference for QTL additive effect between the above two methods was observed, because epistatic SDL between markers C66 and T93 exists (P = 2.94e-4).     

Characterization and detection of epistatic interactions of 3 QTLs, Hd1, Hd2, and Hd3, controlling heading date in rice using nearly isogenic lines

To characterize quantitative trait loci (QTLs), we used marker-assisted selection (MAS) to develop three nearly isogenic lines (NILs) differing only for the presence of a single, specific QTL (QTL-NILs) –Hd1, Hd2, and Hd3 – for heading date in rice. The three lines contained the chromosomal region of the target QTL from donor variety Kasalath(indica) in the genetic background of var. Nipponbare (japonica). To analyze epistatic interactions in pairs of these QTLs, we also used MAS to develop four combined QTL-NILs with 2 of the 3 QTLs or with all 3. Different daylength treatment testing of the QTL-NILs revealed that the three QTLs control photoperiod sensitivity. Genetic analysis of F₂ populations derived from crosses between the three QTL-NILs with a single QTL using molecular markers revealed the existence of epistatic interactions between Hd1 and Hd2, and Hd2 and Hd3. These interactions were also confirmed by the analysis of combined QTL-NILs under different daylength conditions. The existence of an epistatic interaction between Hd1 and Hd3 was also clarified. Based on these results, we suggest that the Kasalath allele of Hd3 does not affect photoperiod sensitivity by itself but that it is involved in enhancement of the expression of the Nipponbare alleles of Hd1 and Hd2. Received: 22 October 1999 / Accepted: 21 March 2000     

Genetic control of soybean seed oil: I. QTL and genes associated with seed oil concentration in RIL populations derived from crossing moderately high-oil parents

Soybean seed is a major source of oil for human consumption worldwide and the main renewable feedstock for biodiesel production in North America. Increasing seed oil concentration in soybean [Glycine max (L.) Merrill] with no or minimal impact on protein concentration could be accelerated by exploiting quantitative trait loci (QTL) or gene-specific markers. Oil concentration in soybean is a polygenic trait regulated by many genes with mostly small effects and which is negatively associated with protein concentration. The objectives of this study were to discover and validate oil QTL in two recombinant inbred line (RIL) populations derived from crosses between three moderately high-oil soybean cultivars, OAC Wallace, OAC Glencoe, and RCAT Angora. The RIL populations were grown across several environments over 2 years in Ontario, Canada. In a population of 203 F_3:6 RILs from a cross of OAC Wallace and OAC Glencoe, a total of 11 genomic regions on nine different chromosomes were identified as associated with oil concentration using multiple QTL mapping and single-factor ANOVA. The percentage of the phenotypic variation accounted for by each QTL ranged from 4 to 11 %. Of the five QTL that were tested in a population of 211 F_3:5 RILs from the cross RCAT Angora × OAC Wallace, a “trait-based” bidirectional selective genotyping analysis validated four QTL (80 %). In addition, a total of seven two-way epistatic interactions were identified for oil concentration in this study. The QTL and epistatic interactions identified in this study could be used in marker-assisted introgression aimed at pyramiding high-oil alleles in soybean cultivars to increase oil concentration for biodiesel as well as edible oil applications.     

QTL consistency and meta-analysis for grain yield components in three generations in maize

Grain yield is the most important and complex trait in maize. In this study, a total of 258 F₉ recombinant inbred lines (RIL), derived from a cross between dent corn inbred Dan232 and popcorn inbred N04, were evaluated for eight grain yield components under four environments. Quantitative trait loci (QTL) and their epistatic interactions were detected for all traits under each environment and in combined analysis. Meta-analysis was used to integrate genetic maps and detected QTL across three generations (RIL, F_2:3 and BC₂F₂) derived from the same cross. In total, 103 QTL, 42 pairs of epistatic interactions and 16 meta-QTL (mQTL) were detected. Twelve out of 13 QTL with contributions (R ²) over 15% were consistently detected in 3–4 environments (or in combined analysis) and integrated in mQTL. Only q100GW-7-1 was detected in all four environments and in combined analysis. 100qGW-1-1 had the largest R ² (19.3–24.6%) in three environments and in combined analysis. In contrast, 35 QTL for 6 grain yield components were detected in the BC₂F₂ and F_2:3 generations, no common QTL across three generations were located in the same marker intervals. Only 100 grain weight (100GW) QTL on chromosome 5 were located in adjacent marker intervals. Four common QTL were detected across the RIL and F_2:3 generations, and two between the RIL and BC₂F₂ generations. Each of five important mQTL (mQTL7-1, mQTL10-2, mQTL4-1, mQTL5-1 and mQTL1-3) included 7–12 QTL associated with 2–6 traits. In conclusion, we found evidence of strong influence of genetic structure and environment on QTL detection, high consistency of major QTL across environments and generations, and remarkable QTL co-location for grain yield components. Fine mapping for five major QTL (q100GW-1-1, q100GW-7-1, qGWP-4-1, qERN-4-1 and qKR-4-1) and construction of single chromosome segment lines for genetic regions of five mQTL merit further studies and could be put into use in marker-assisted breeding.     

Identification and reciprocal introgression of a QTL affecting body mass in mice

The aim of this study was to examine the effects of a QTL in different genetic backgrounds. A QTL affecting body mass on chromosome 6 was identified in an F₂ cross between two lines of mice that have been divergently selected for this trait. The effect of the QTL on mass increased between 6 and 10 weeks of age and was not sex-specific. Body composition analysis showed effects on fat-free dry body mass and fat mass. To examine the effect of this QTL in different genetic backgrounds, the high body mass sixth chromosome was introgressed into the low body mass genetic background and vice versa by repeated marker-assisted backcrossing. After three generations of backcrossing, new F₂ populations were established within each of the introgression lines by crossing individuals that were heterozygous across the sixth chromosome. The estimated additive effect of the QTL on 10-week body mass was similar in both genetic backgrounds and in the original F₂ population (i.e., ~0.4 phenotypic standard deviations); no evidence of epistatic interaction with the genetic background was found. The 95% confidence interval for the location of the QTL was refined to a region of approximately 7 cM between D6Mit268 and D6Mit123.     

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.     

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.     

Unraveling epistasis with triple testcross progenies of near-isogenic lines

Libraries of near-isogenic lines (NILs) are a powerful plant genetic resource to map quantitative trait loci (QTL). Nevertheless, QTL mapping with NILs is mostly restricted to genetic main effects. Here we propose a two-step procedure to map additive-by-additive digenic epistasis with NILs. In the first step, a generation means analysis of parents, their F₁ hybrid, and one-segment NILs and their triple testcross (TTC) progenies is used to identify in a one-dimensional scan loci exhibiting QTL-by-background interactions. In a second step, one-segment NILs with significant additive-by-additive background interactions are used to produce particular two-segment NILs to test for digenic epistatic interactions between these segments. We evaluated our approach by analyzing a random subset of a genomewide Arabidopsis thaliana NIL library for growth-related traits. The results of our experimental study illustrated the potential of the presented two-step procedure to map additive-by-additive digenic epistasis with NILs. Furthermore, our findings suggested that additive main effects as well as additive-by-additive digenic epistasis strongly influence the genetic architecture underlying growth-related traits of A. thaliana.     

Simultaneous mapping of epistatic QTL in DU6i × DBA/2 mice

We have mapped epistatic quantitative trait loci (QTL) in an F₂ cross between DU6i × DBA/2 mice. By including these epistatic QTL and their interaction parameters in the genetic model, we were able to increase the genetic variance explained substantially (8.8%–128.3%) for several growth and body composition traits. We used an analysis method based on a simultaneous search for epistatic QTL pairs without assuming that the QTL had any effect individually. We were able to detect several QTL that could not be detected in a search for marginal QTL effects because the epistasis cancelled out the individual effects of the QTL. In total, 23 genomic regions were found to contain QTL affecting one or several of the traits and eight of these QTL did not have significant individual effects. We identified 44 QTL pairs with significant effects on the traits, and, for 28 of the pairs, an epistatic QTL model fit the data significantly better than a model without interactions. The epistatic pairs were classified by the significance of the epistatic parameters in the genetic model, and visual inspection of the two-locus genotype means identified six types of related genotype–phenotype patterns among the pairs. Five of these patterns resembled previously published patterns of QTL interactions.     

Contribution of genetic effects to genetic variance components with epistasis and linkage disequilibrium

Background

Cockerham genetic models are commonly used in quantitative trait loci (QTL) analysis with a special feature of partitioning genotypic variances into various genetic variance components, while the F_∞ genetic models are widely used in genetic association studies. Over years, there have been some confusion about the relationship between these two type of models. A link between the additive, dominance and epistatic effects in an F_∞ model and the additive, dominance and epistatic variance components in a Cockerham model has not been well established, especially when there are multiple QTL in presence of epistasis and linkage disequilibrium (LD).

Results

In this paper, we further explore the differences and links between the F_∞ and Cockerham models. First, we show that the Cockerham type models are allelic based models with a special modification to correct a confounding problem. Several important moment functions, which are useful for partition of variance components in Cockerham models, are also derived. Next, we discuss properties of the F_∞ models in partition of genotypic variances. Its difference from that of the Cockerham models is addressed. Finally, for a two-locus biallelic QTL model with epistasis and LD between the loci, we present detailed formulas for calculation of the genetic variance components in terms of the additive, dominant and epistatic effects in an F_∞ model. A new way of linking the Cockerham and F_∞ model parameters through their coding variables of genotypes is also proposed, which is especially useful when reduced F_∞ models are applied.

Conclusion

The Cockerham type models are allele-based models with a focus on partition of genotypic variances into various genetic variance components, which are contributed by allelic effects and their interactions. By contrast, the F_∞ regression models are genotype-based models focusing on modeling and testing of within-locus genotypic effects and locus-by-locus genotypic interactions. When there is no need to distinguish the paternal and maternal allelic effects, these two types of models are transferable. Transformation between an F_∞ model's parameters and its corresponding Cockerham model's parameters can be established through a relationship between their coding variables of genotypes. Genetic variance components in terms of the additive, dominance and epistatic genetic effects in an F_∞ model can then be calculated by translating formulas derived for the Cockerham models.

     

Quantitative trait loci (QTL) for lean body mass and body length in MRL/MPJ and SJL/J F(2) mice

Studies on the genetic mechanisms involved in the regulation of lean body mass (LBM) in mammals are minimal, although LBM is associated with a competent immune system and an overall good (healthy) body functional status. In this study, we performed a high-density genome-wide scan using 633 (MRL/MPJ × SJL/J) F₂ intercross to identify the quantitative trait loci (QTL) involved in the regulation of LBM. We hypothesized that additional QTL can be identified using a different mouse cross (MRL/SJL cross). Ten QTL were identified for LBM on chromosomes (chrs) 2, 6, 7, 9,13 and 14. Of those ten, QTL on chrs 6, 7 and 14 were exclusive to LBM, while QTL on chrs 4 and 11 were exclusively body length. LBM QTL on chrs 2 and 9 overlap with those of size. Altogether, the ten LBM QTL explained 41.2% of phenotypic variance in F₂ mice. Five significantly interacting loci that may be involved in the regulation of LBM were identified and accounted for 24.4% of phenotypic variance explained by the QTL. Five epistatic interactions, contributing 22.9% of phenotypic variance, were identified for body length. Interacting loci on chr 2 may influence LBM by regulating body length. Therefore, epistatic interactions as well as single QTL effects play an important role in the regulation of LBM. Electronic Publication     

Mapping single-locus and epistatic quantitative trait loci for plant architectural traits in chrysanthemum

Plant architecture is important for chrysanthemum cultivation and breeding. To determine the genetic basis of plant architectural traits in chrysanthemum, a population of 142 F1 plants derived from a cross between the creeping ground-cover chrysanthemum cultivar Yuhualuoying and the erect potted cultivar Aoyunhanxiao was used to detect quantitative trait loci (QTL) associated with plant height, plant width, inter-node length and flower neck length. The broad-sense heritability h _B ² for the four plant architectural traits ranged from 0.33 to 0.83, and transgressive segregation was observed. Single-locus QTL analysis revealed a total of five QTL, accounting for 6.0?C16.1% of the phenotypic variation. Additionally, 11 pairs of epistatic QTL were identified, explaining 3.5?C14.5% of the phenotypic variations. The majority of the interactions detected occurred between background loci. These results indicate that both additive and epistatic effects contribute to phenotypic variation in the plant architecture of chrysanthemum. It is expected that the identified markers associated with the additive QTL and epistatic QTL detected in this study will be of importance in future breeding programs to develop chrysanthemum cultivars exhibiting desirable plant architecture.     

Congruence in QTL for adventitious rooting in Pinus elliottii × Pinus caribaea hybrids resolves between and within-species effects

Targeting between-species effects for improvement in synthetic hybrid populations derived from outcrossing parental tree species may be one way to increase the efficacy and predictability of hybrid breeding. We present a comparative analysis of the quantitative trait loci (QTL) which resolved between from within-species effects for adventitious rooting in two populations of hybrids between Pinus elliottii and P. caribaea, an outbred F₁ (n=287) and an inbred-like F₂ family (n=357). Most small to moderate effect QTL (each explaining 2–5% of phenotypic variation, PV) were congruent (3 out of 4 QTL in each family) and therefore considered within-species effects as they segregated in both families. A single large effect QTL (40% PV) was detected uniquely in the F₂ family and assumed to be due to a between-species effect, resulting from a genetic locus with contrasting alleles in each parental species. Oligogenic as opposed to polygenic architecture was supported in both families (60% and 20% PV explained by 4 QTL in the F₂ and F₁ respectively). The importance of adventitious rooting for adaptation to survive water-logged environments was thought in part to explain oligogenic architecture of what is believed to be a complex trait controlled by many hundreds of genes.     

QTL Mapping for Plant Architecture Traits in Upland Cotton Using RILs and SSR Markers

Xiangzamian 2 (XZM2) is the most widely cultivated cotton hybrid in China. By crossing two parents Zhongmiansuo 12 and 8891 and upon subsequent selfings, we got F₈ and F₉ populations having 180 recombinant inbred lines. Ten plant architecture traits were investigated in two years with this population. A genetic map was constructed mainly with SSR markers. Quantitative trait loci (QTL) conditioning plant architecture traits were determined at the single-locus and double-locus levels. The results showed that epistastic effects as well as additive effects of QTL played an important role as the genetic basis of cotton plant architecture. The QTL detected in our research might provide new information on improving plant architecture traits. The polymorphism of molecular markers between ZMS12 and 8891 were quite limited, while significant differences between their phenotypes were found and the hybrid XZM2 expressed high heterosis in yield. All these could be partly explained by the effect of epistatic QTL.