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.

     

A major QTL associated with Fusarium oxysporum race 1 resistance identified in genetic populations derived from closely related watermelon lines using selective genotyping and genotyping-by-sequencing for SNP discovery

Key message

A major quantitative trait locus (QTL) for Fusarium oxysporum Fr. f. sp. niveum race 1 resistance was identified by employing a “selective genotyping” approach together with genotyping-by-sequencing technology to identify QTLs and single nucleotide polymorphisms associated with the resistance among closely related watermelon genotypes.

Abstract

Fusarium wilt is a major disease of watermelon caused by the soil-borne fungus Fusarium oxysporum Schlechtend.:Fr. f. sp. niveum (E.F. Sm.) W.C. Snyder & H.N. Hans (Fon). In this study, a genetic population of 168 F₃ families (24 plants in each family) exhibited continuous distribution for Fon race 1 response. Using a “selective genotyping” approach, DNA was isolated from 91 F₂ plants whose F₃ progeny exhibited the highest resistance (30 F₂ plants) versus highest susceptibility (32 F₂ plants), or moderate resistance to Fon race 1 (29 F₂ plants). Genotyping-by-sequencing (GBS) technology was used on these 91 selected F₂ samples to produce 266 single nucleotide polymorphism (SNP) markers, representing the 11 chromosomes of watermelon. A major quantitative trait locus (QTL) associated with resistance to Fon race 1 was identified with a peak logarithm of odds (LOD) of 33.31 and 1-LOD confidence interval from 2.3 to 8.4 cM on chromosome 1 of the watermelon genetic map. This QTL was designated “Fo-1.1” and is positioned in a genomic region where several putative pathogenesis-related or putative disease-resistant gene sequences were identified. Additional independent, but minor QTLs were identified on chromosome 1 (LOD 4.16), chromosome 3 (LOD 4.36), chromosome 4 (LOD 4.52), chromosome 9 (LOD 6.8), and chromosome 10 (LOD 5.03 and 4.26). Following the identification of a major QTL for resistance using the “selective genotyping” approach, all 168 plants of the F ₂ population were genotyped using the SNP nearest the peak LOD, confirming the association of this SNP marker with Fon race 1 resistance. The results in this study should be useful for further elucidating the mechanism of resistance to Fusarium wilt and in the development of molecular markers for use in breeding programs of watermelon.     

Lack of evidence for a genetic association between FGF20 and Parkinson's disease in Finnish and Greek patients

Background

Fibroblast growth factor 20 (FGF20) is a neurotrophic factor preferentially expressed in the substantia nigra of rat brain and could be involved in dopaminergic neurons survival. Recently, a strong genetic association has been found between FGF20 gene and the risk of suffering from Parkinson's disease (PD). Our aim was to replicate this association in two independent populations.

Methods

Allelic, genotypic, and haplotype frequencies of four biallelic polymorphisms were assessed in 151 sporadic PD cases and 186 controls from Greece, and 144 sporadic PD patients and 135 controls from Finland.

Results

No association was found in any of the populations studied.

Conclusion

Taken together, these findings suggest that common genetic variants in FGF20 are not a risk factor for PD in, at least, some European populations.     

Multiallelic models of genetic effects and variance decomposition in non-equilibrium populations

Quantitative genetics stems from the theoretical models of genetic effects, which are re-parameterizations of the genotypic values into parameters of biological (genetic) relevance. Different formulations of genetic effects are adequate to address different subjects. We thus need to generalize and unify them under a common framework for enabling researchers to easily transform genetic effects between different biological meanings. The Natural and Orthogonal Interactions (NOIA) model of genetic effects has been developed to achieve this aim. Here, we further implement the statistical formulation of NOIA with multiple alleles under Hardy–Weinberg departures (HWD). We show that our developments are straightforwardly connected to the decomposition of the genetic variance and we point out several emergent properties of multiallelic quantitative genetic models, as compared to the biallelic ones. Further, NOIA entails a natural extension of one-locus developments to multiple epistatic loci under linkage equilibrium. Therefore, we present an extension of the orthogonal decomposition of the genetic variance to multiple epistatic, multiallelic loci under HWD. We illustrate this theory with a graphical interpretation and an analysis of published data on the human acid phosphatase (ACP1) polymorphism.     

Characterization and molecular mapping of stripe rust resistance gene Yr61 in winter wheat cultivar Pindong 34

Key message

We report a new stripe rust resistance gene on chromosome 7AS in wheat and molecular markers useful for transferring it to other wheat genotypes.

Abstract

Several new races of the stripe rust pathogen have established throughout the wheat growing regions of China in recent years. These new races are virulent to most of the designated seedling resistance genes limiting the resistance sources. It is necessary to identify new genes for diversification and for pyramiding different resistance genes in order to achieve more durable resistance. We report here the identification of a new resistance gene, designated as Yr61, in Chinese wheat cultivar Pindong 34. A mapping population of 208 F₂ plants and 128 derived F_2:3 lines in a cross between Mingxian 169 and Pindong 34 was evaluated for seedling stripe rust response. A genetic map consisting of eight resistance gene analog polymorphism (RGAP), two sequence-tagged site (STS) and four simple sequence repeat (SSR) markers was constructed. Yr61 was located on the short arm of chromosome 7A and flanked by RGAP markers Xwgp5467 and Xwgp5765 about 1.9 and 3.9 cM in distance, which were successfully converted into STS markers STS5467 and STS5765b, respectively. The flanking STS markers could be used for marker-assisted selection of Yr61 in breeding programs.     

Pathway analysis reveals functional convergence of gene expression profiles in breast cancer

Background

Angiotensin I-converting enzyme (ACE) plays an important role in cardiovascular homeostasis. There is evidence from different ethnic groups that circulating ACE levels are influenced by a quantitative trait locus (QTL) at the ACE gene on chromosome 17. The finding of significant residual familial correlations in different ethnic groups, after accounting for this QTL, and the finding of support for linkage to a locus on chromosome 4 in Mexican-American families strongly suggest that there may well be QTLs for ACE unlinked to the ACE gene.

Methods

A genome-wide panel of microsatellite markers, and a panel of biallelic polymorphisms in the ACE gene were typed in Nigerian families. Single locus models with fixed parameters were used to test for linkage to circulating ACE with and without adjustment for the effects of the ACE gene polymorphisms.

Results

Strong evidence was found for D17S2193 (Z_max = 3.5); other nearby markers on chromosome 17 also showed modest support. After adjustment for the effects of the ACE gene locus, evidence of "suggestive linkage" to circulating ACE was found for D4S1629 (Z_max = 2.2); this marker is very close to a locus previously shown to be linked to circulating ACE levels in Mexican-American families.

Conclusion

In this report we have provided further support for the notion that there are QTLs for ACE unlinked to the ACE gene; our findings for chromosome 4, which appear to replicate the findings of a previous independent study, should be considered strong grounds for a more detailed examination of this region in the search for genes/variants which influence ACE levels. The poor yields, thus far, in defining the genetic determinants of hypertension risk suggest a need to look beyond simple relationships between genotypes and the ultimate phenotype. In addition to incorporating information on important environmental exposures, a better understanding of the factors which influence the building blocks of the blood pressure homeostatic network is also required. Detailed studies of the genetic determinants of ACE, an important component of the renin-angiotensin system, have the potential to contribute to this strategic objective.     

A multi-marker test based on family data in genome-wide association study

Background

Requirements for successful implementation of multivariate animal threshold models including phenotypic and genotypic information are not known yet. Here simulated horse data were used to investigate the properties of multivariate estimators of genetic parameters for categorical, continuous and molecular genetic data in the context of important radiological health traits using mixed linear-threshold animal models via Gibbs sampling. The simulated pedigree comprised 7 generations and 40000 animals per generation. Additive genetic values, residuals and fixed effects for one continuous trait and liabilities of four binary traits were simulated, resembling situations encountered in the Warmblood horse. Quantitative trait locus (QTL) effects and genetic marker information were simulated for one of the liabilities. Different scenarios with respect to recombination rate between genetic markers and QTL and polymorphism information content of genetic markers were studied. For each scenario ten replicates were sampled from the simulated population, and within each replicate six different datasets differing in number and distribution of animals with trait records and availability of genetic marker information were generated. (Co)Variance components were estimated using a Bayesian mixed linear-threshold animal model via Gibbs sampling. Residual variances were fixed to zero and a proper prior was used for the genetic covariance matrix.

Results

Effective sample sizes (ESS) and biases of genetic parameters differed significantly between datasets. Bias of heritability estimates was -6% to +6% for the continuous trait, -6% to +10% for the binary traits of moderate heritability, and -21% to +25% for the binary traits of low heritability. Additive genetic correlations were mostly underestimated between the continuous trait and binary traits of low heritability, under- or overestimated between the continuous trait and binary traits of moderate heritability, and overestimated between two binary traits. Use of trait information on two subsequent generations of animals increased ESS and reduced bias of parameter estimates more than mere increase of the number of informative animals from one generation. Consideration of genotype information as a fixed effect in the model resulted in overestimation of polygenic heritability of the QTL trait, but increased accuracy of estimated additive genetic correlations of the QTL trait.

Conclusion

Combined use of phenotype and genotype information on parents and offspring will help to identify agonistic and antagonistic genetic correlations between traits of interests, facilitating design of effective multiple trait selection schemes.     

The influence of genetic variation and nitrogen source on nitrate accumulation and iso-osmotic regulation by lettuce

Background and aims

Characterisation of genetic variation in nitrate accumulation by lettuce will inform strategies for selecting low-nitrate varieties more capable of meeting EU legislation on harvested produce. This study uses a population of recombinant inbred lines (RILs) of lettuce to determine how genotypic differences influence N uptake, N assimilation and iso-osmotic regulation, and to identify key related traits prior to future genetic analysis.

Methods

Measurements were made on plants grown to maturity in soil fertilised with ammonium nitrate, and in a complete nutrient solution containing only nitrate-N. A simple osmotic balance model was developed to estimate variations in shoot osmotic concentration between RILs.

Results

There were significant genotypic variations in nitrate accumulation when plants were grown either with nitrate alone or in combination with ammonium. Ammonium-N significantly reduced nitrate in the shoot but had no effect on its relative variability, or on the ranking of genotypes. Shoot nitrate-N was correlated positively with total-N and tissue water, and negatively with assimilated-C in both experiments. Corresponding relationships with assimilated-N and shoot weight were weaker. Estimated concentrations of total osmotica in shoot sap were statistically identical in all RILs, despite variations in nitrate concentration across the population.

Conclusions

Approximately 73% of the genotypic variability in nitrate accumulation within the population of RILs arose from differences in nitrate uptake and only 27% from differences in nitrate assimilated, irrespective of whether or not part of the N was recovered as ammonium, or whether the plants were grown in soil or solution culture. Genotypic variability in nitrate accumulation was associated with changes in concentrations of other endogenous solutes (especially carboxylates and soluble carbohydrates) and of tissue water, which minimised differences in osmotic potential of shoot sap between RILs. This offers the opportunity of using the regulation of these solutes as additional traits to manipulate nitrate accumulation.     

Molecular characterization of Nicaraguan Pinus tecunumanii Schw. ex Eguiluz et Perry populations for in situ conservation

Key message

We conducted molecular characterization of Nicaraguan Pinus tecunumanii populations using microsatellite markers. Populations possess considerable genetic variation but there are risks associated with inbreeding and population fragmentation.

Abstract

We carried out a molecular characterization of three natural populations of Pinus tecunumanii using nine microsatellite markers. All studied populations occur in Nicaragua, where the species has declined primarily due to human-influenced factors. The results showed that there is a high amount of genetic variation in populations (expected heterozygosities 0.775–0.841), populations do not show significant differentiation (mean F _ST 0.0073), apparently due to frequent gene flow or a more continuous distribution and homogenous genetic composition in the past, and inbreeding is common in all populations (F _IS 0.705–0.780). The Structure analysis revealed that there is no evident clustering pattern among P. tecunumanii individuals. Although all studied populations possess a considerable amount of genetic variation, risks associated with inbreeding and population fragmentation should be acknowledged and a conservation strategy developed to safeguard the genetic resources of P. tecunumanii.     

QTL analysis in multiple sorghum populations facilitates the dissection of the genetic and physiological control of tillering

Key message

A QTL model for the genetic control of tillering in sorghum is proposed, presenting new opportunities for sorghum breeders to select germplasm with tillering characteristics appropriate for their target environments.

Abstract

Tillering in sorghum can be associated with either the carbon supply–demand (S/D) balance of the plant or an intrinsic propensity to tiller (PTT). Knowledge of the genetic control of tillering could assist breeders in selecting germplasm with tillering characteristics appropriate for their target environments. The aims of this study were to identify QTL for tillering and component traits associated with the S/D balance or PTT, to develop a framework model for the genetic control of tillering in sorghum. Four mapping populations were grown in a number of experiments in south east Queensland, Australia. The QTL analysis suggested that the contribution of traits associated with either the S/D balance or PTT to the genotypic differences in tillering differed among populations. Thirty-four tillering QTL were identified across the populations, of which 15 were novel to this study. Additionally, half of the tillering QTL co-located with QTL for component traits. A comparison of tillering QTL and candidate gene locations identified numerous coincident QTL and gene locations across populations, including the identification of common non-synonymous SNPs in the parental genotypes of two mapping populations in a sorghum homologue of MAX1, a gene involved in the control of tiller bud outgrowth through the production of strigolactones. Combined with a framework for crop physiological processes that underpin genotypic differences in tillering, the co-location of QTL for tillering and component traits and candidate genes allowed the development of a framework QTL model for the genetic control of tillering in sorghum.     

Genome-wide investigation of genetic changes during modern breeding of Brassica napus

Key message

Considerable genome variation had been incorporated within rapeseed breeding programs over past decades.

Abstract

In past decades, there have been substantial changes in phenotypic properties of rapeseed as a result of extensive breeding effort. Uncovering the underlying patterns of allelic variation in the context of genome organisation would provide knowledge to guide future genetic improvement. We assessed genome-wide genetic changes, including population structure, genetic relatedness, the extent of linkage disequilibrium, nucleotide diversity and genetic differentiation based on F _ST outlier detection, for a panel of 472 Brassica napus inbred accessions using a 60 k Brassica Infinium^® SNP array. We found genetic diversity varied in different sub-groups. Moreover, the genetic diversity increased from 1950 to 1980 and then remained at a similar level in China and Europe. We also found ~6–10 % genomic regions revealed high F _ST values. Some QTLs previously associated with important agronomic traits overlapped with these regions. Overall, the B. napus C genome was found to have more high F _ST signals than the A genome, and we concluded that the C genome may contribute more valuable alleles to generate elite traits. The results of this study indicate that considerable genome variation had been incorporated within rapeseed breeding programs over past decades. These results also contribute to understanding the impact of rapeseed improvement on available genome variation and the potential for dissecting complex agronomic traits.     

A complete classification of epistatic two-locus models

Background

The study of epistasis is of great importance in statistical genetics in fields such as linkage and association analysis and QTL mapping. In an effort to classify the types of epistasis in the case of two biallelic loci Li and Reich listed and described all models in the simplest case of 0/1 penetrance values. However, they left open the problem of finding a classification of two-locus models with continuous penetrance values.

Results

We provide a complete classification of biallelic two-locus models. In addition to solving the classification problem for dichotomous trait disease models, our results apply to any instance where real numbers are assigned to genotypes, and provide a complete framework for studying epistasis in QTL data. Our approach is geometric and we show that there are 387 distinct types of two-locus models, which can be reduced to 69 when symmetry between loci and alleles is accounted for. The model types are defined by 86 circuits, which are linear combinations of genotype values, each of which measures a fundamental unit of interaction.

Conclusion

The circuits provide information on epistasis beyond that contained in the additive × additive, additive × dominance, and dominance × dominance interaction terms. We discuss the connection between our classification and standard epistatic models and demonstrate its utility by analyzing a previously published dataset.     

High-resolution mapping of zym, a recessive gene for Zucchini yellow mosaic virus resistance in cucumber

Key message

Using a high-resolution mapping approach, we identified a candidate gene for ZYMV resistance in cucumber. Our findings should assist the development of high-versatility molecular markers for MAS for ZYMV resistance.

Abstract

Zucchini yellow mosaic virus (ZYMV) causes significant disease, which leads to fruit yield loss in cucurbit crops. Since ZYMV resistance is often inherited recessively in cucumber, marker-assisted selection (MAS) is a useful tool for the development of resistant cucumber cultivars. Using 128 families of an F_2:3 population derived from a cross between susceptible ‘CS-PMR1’ and resistant ‘A192-18’ cucumber inbred lines, we confirmed that ZYMV resistance is conferred by a single recessive locus: zym ^A192-18 . We constructed a cucumber genetic linkage map that included 125 simple sequence repeat (SSR) markers segregating into 7 linkage groups (chromosomes). The zym ^A192-18 locus was mapped to chromosome 6, at genetic distances of 0.9 and 1.3 cM from two closely linked SSR markers. For high-resolution genetic mapping, we identified new molecular markers cosegregating with the zym ^A192-18 locus; using cucumber genomic and molecular marker resources and screening an F₂ population of 2,429 plants, we narrowed down the zym ^A192-18 locus to a <50-kb genomic region flanked by two SSR markers, which included six candidate genes. Sequence analysis of the candidate genes’ coding regions revealed that the vacuolar protein sorting-associated protein 4-like (VPS4-like) gene had two SNPs between the parental lines. Based on SNPs of the VPS-4-like gene, we developed zym ^A192-18 -linked DNA markers and found that genotypes associated with these markers were correlated with the ZYMV resistance phenotype in 48 cucumber inbred lines. According to our data, the gene encoding VPS4-like protein is a candidate for the zym ^A192-18 locus. These results may be valuable for MAS for ZYMV resistance in cucumber.     

Interval estimation of genetic susceptibility for retrospective case-control studies

Background

This article describes classical and Bayesian interval estimation of genetic susceptibility based on random samples with pre-specified numbers of unrelated cases and controls.

Results

Frequencies of genotypes in cases and controls can be estimated directly from retrospective case-control data. On the other hand, genetic susceptibility defined as the expected proportion of cases among individuals with a particular genotype depends on the population proportion of cases (prevalence). Given this design, prevalence is an external parameter and hence the susceptibility cannot be estimated based on only the observed data. Interval estimation of susceptibility that can incorporate uncertainty in prevalence values is explored from both classical and Bayesian perspective. Similarity between classical and Bayesian interval estimates in terms of frequentist coverage probabilities for this problem allows an appealing interpretation of classical intervals as bounds for genetic susceptibility. In addition, it is observed that both the asymptotic classical and Bayesian interval estimates have comparable average length. These interval estimates serve as a very good approximation to the "exact" (finite sample) Bayesian interval estimates. Extension from genotypic to allelic susceptibility intervals shows dependency on phenotype-induced deviations from Hardy-Weinberg equilibrium.

Conclusions

The suggested classical and Bayesian interval estimates appear to perform reasonably well. Generally, the use of exact Bayesian interval estimation method is recommended for genetic susceptibility, however the asymptotic classical and approximate Bayesian methods are adequate for sample sizes of at least 50 cases and controls.     

Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress

Background and Aims

Genetic markers can be used in combination with ecophysiological crop models to predict the performance of genotypes. Crop models can estimate the contribution of individual markers to crop performance in given environments. The objectives of this study were to explore the use of crop models to design markers and virtual ideotypes for improving yields of rice (Oryza sativa) under drought stress.

Methods

Using the model GECROS, crop yield was dissected into seven easily measured parameters. Loci for these parameters were identified for a rice population of 94 introgression lines (ILs) derived from two parents differing in drought tolerance. Marker-based values of ILs for each of these parameters were estimated from additive allele effects of the loci, and were fed to the model in order to simulate yields of the ILs grown under well-watered and drought conditions and in order to design virtual ideotypes for those conditions.

Key Results

To account for genotypic yield differences, it was necessary to parameterize the model for differences in an additional trait ‘total crop nitrogen uptake’ (N_max) among the ILs. Genetic variation in N_max had the most significant effect on yield; five other parameters also significantly influenced yield, but seed weight and leaf photosynthesis did not. Using the marker-based parameter values, GECROS also simulated yield variation among 251 recombinant inbred lines of the same parents. The model-based dissection approach detected more markers than the analysis using only yield per se. Model-based sensitivity analysis ranked all markers for their importance in determining yield differences among the ILs. Virtual ideotypes based on markers identified by modelling had 10–36 % more yield than those based on markers for yield per se.

Conclusions

This study outlines a genotype-to-phenotype approach that exploits the potential value of marker-based crop modelling in developing new plant types with high yields. The approach can provide more markers for selection programmes for specific environments whilst also allowing for prioritization. Crop modelling is thus a powerful tool for marker design for improved rice yields and for ideotyping under contrasting conditions.     

Mapping quantitative trait loci using molecular marker linkage maps

Summary High-density restriction fragment length polymorphism (RFLP) and allozyme linkage maps have been developed in several plant species. These maps make it technically feasible to map quantitative trait loci (QTL) using methods based on flanking marker genetic models. In this paper, we describe flanking marker models for doubled haploid (DH), recombinant inbred (RI), backcross (BC), F₁ testcross (F₁TC), DH testcross (DHTC), recombinant inbred testcross (RITC), F₂, and F₃ progeny. These models are functions of the means of quantitative trait locus genotypes and recombination frequencies between marker and quantitative trait loci. In addition to the genetic models, we describe maximum likelihood methods for estimating these parameters using linear, nonlinear, and univariate or multivariate normal distribution mixture models. We defined recombination frequency estimators for backcross and F₂ progeny group genetic models using the parameters of linear models. In addition, we found a genetically unbiased estimator of the QTL heterozygote mean using a linear function of marker means. In nonlinear models, recombination frequencies are estimated less efficiently than the means of quantitative trait locus genotypes. Recombination frequency estimation efficiency decreases as the distance between markers decreases, because the number of progeny in recombinant marker classes decreases. Mean estimation efficiency is nearly equal for these methods.     

QTLRel: an R Package for Genome-wide Association Studies in which Relatedness is a Concern

Background

Existing software for quantitative trait mapping is either not able to model polygenic variation or does not allow incorporation of more than one genetic variance component. Improperly modeling the genetic relatedness among subjects can result in excessive false positives. We have developed an R package, QTLRel, to enable more flexible modeling of genetic relatedness as well as covariates and non-genetic variance components.

Results

We have successfully used the package to analyze many datasets, including F₃₄ body weight data that contains 688 individuals genotyped at 3105 SNP markers and identified 11 QTL. It took 295 seconds to estimate variance components and 70 seconds to perform the genome scan on an Linux machine equipped with a 2.40GHz Intel(R) Core(TM)2 Quad CPU.

Conclusions

QTLRel provides a toolkit for genome-wide association studies that is capable of calculating genetic incidence matrices from pedigrees, estimating variance components, performing genome scans, incorporating interactive covariates and genetic and non-genetic variance components, as well as other functionalities such as multiple-QTL mapping and genome-wide epistasis.     

Bayesian estimation and use of high-throughput remote sensing indices for quantitative genetic analyses of leaf growth

Key message

We develop Bayesian function-valued trait models that mathematically isolate genetic mechanisms underlying leaf growth trajectories by factoring out genotype-specific differences in photosynthesis. Remote sensing data can be used instead of leaf-level physiological measurements.

Abstract

Characterizing the genetic basis of traits that vary during ontogeny and affect plant performance is a major goal in evolutionary biology and agronomy. Describing genetic programs that specifically regulate morphological traits can be complicated by genotypic differences in physiological traits. We describe the growth trajectories of leaves using novel Bayesian function-valued trait (FVT) modeling approaches in Brassica rapa recombinant inbred lines raised in heterogeneous field settings. While frequentist approaches estimate parameter values by treating each experimental replicate discretely, Bayesian models can utilize information in the global dataset, potentially leading to more robust trait estimation. We illustrate this principle by estimating growth asymptotes in the face of missing data and comparing heritabilities of growth trajectory parameters estimated by Bayesian and frequentist approaches. Using pseudo-Bayes factors, we compare the performance of an initial Bayesian logistic growth model and a model that incorporates carbon assimilation (A _max) as a cofactor, thus statistically accounting for genotypic differences in carbon resources. We further evaluate two remotely sensed spectroradiometric indices, photochemical reflectance (pri2) and MERIS Terrestrial Chlorophyll Index (mtci) as covariates in lieu of A _max, because these two indices were genetically correlated with A _max across years and treatments yet allow much higher throughput compared to direct leaf-level gas-exchange measurements. For leaf lengths in uncrowded settings, including A _max improves model fit over the initial model. The mtci and pri2 indices also outperform direct A _max measurements. Of particular importance for evolutionary biologists and plant breeders, hierarchical Bayesian models estimating FVT parameters improve heritabilities compared to frequentist approaches.

     

Comparative genetic mapping and genomic region collinearity analysis of the powdery mildew resistance gene Pm41

Key message

By applying comparative genomics analyses, a high-density genetic linkage map narrowed the powdery mildew resistance gene Pm41 originating from wild emmer in a sub-centimorgan genetic interval.

Abstract

Wheat powdery mildew, caused by Blumeria graminis f. sp. tritici, results in large yield losses worldwide. A high-density genetic linkage map of the powdery mildew resistance gene Pm41, originating from wild emmer (Triticum turgidum var. dicoccoides) and previously mapped to the distal region of chromosome 3BL bin 0.63–1.00, was constructed using an F_5:6 recombinant inbred line population derived from a cross of durum wheat cultivar Langdon and wild emmer accession IW2. By applying comparative genomics analyses, 19 polymorphic sequence-tagged site markers were developed and integrated into the Pm41 genetic linkage map. Ultimately, Pm41 was mapped in a 0.6 cM genetic interval flanked by markers XWGGC1505 and XWGGC1507, which correspond to 11.7, 19.2, and 24.9 kb orthologous genomic regions in Brachypodium, rice, and sorghum, respectively. The XWGGC1506 marker co-segregated with Pm41 and could be served as a starting point for chromosome landing and map-based cloning as well as marker-assisted selection of Pm41. Detailed comparative genomics analysis of the markers flanking the Pm41 locus in wheat and the putative orthologous genes in Brachypodium, rice, and sorghum suggests that the gene order is highly conserved between rice and sorghum. However, intra-chromosome inversions and re-arrangements are evident in the wheat and Brachypodium genomic regions, and gene duplications are also present in the orthologous genomic regions of Pm41 in wheat, indicating that the Brachypodium gene model can provide more useful information for wheat marker development.