Impact of QTL properties on the accuracy of multi-breed genomic prediction
Wientjes, Y.C.J. ; Calus, M.P.L. ; Goddard, M.E. ; Hayes, B.J. - \ 2015
Genetics, Selection, Evolution 47 (2015). - ISSN 0999-193X
dairy-cattle populations - residual feed-intake - complex traits - linkage disequilibrium - genotype imputation - data sets - selection - values - animals - reliability
Background - Although simulation studies show that combining multiple breeds in one reference population increases accuracy of genomic prediction, this is not always confirmed in empirical studies. This discrepancy might be due to the assumptions on quantitative trait loci (QTL) properties applied in simulation studies, including number of QTL, spectrum of QTL allele frequencies across breeds, and distribution of allele substitution effects. We investigated the effects of QTL properties and of including a random across- and within-breed animal effect in a genomic best linear unbiased prediction (GBLUP) model on accuracy of multi-breed genomic prediction using genotypes of Holstein-Friesian and Jersey cows. Methods - Genotypes of three classes of variants obtained from whole-genome sequence data, with moderately low, very low or extremely low average minor allele frequencies (MAF), were imputed in 3000 Holstein-Friesian and 3000 Jersey cows that had real high-density genotypes. Phenotypes of traits controlled by QTL with different properties were simulated by sampling 100 or 1000 QTL from one class of variants and their allele substitution effects either randomly from a gamma distribution, or computed such that each QTL explained the same variance, i.e. rare alleles had a large effect. Genomic breeding values for 1000 selection candidates per breed were estimated using GBLUP modelsincluding a random across- and a within-breed animal effect. Results - For all three classes of QTL allele frequency spectra, accuracies of genomic prediction were not affected by the addition of 2000 individuals of the other breed to a reference population of the same breed as the selection candidates. Accuracies of both single- and multi-breed genomic prediction decreased as MAF of QTL decreased, especially when rare alleles had a large effect. Accuracies of genomic prediction were similar for the models with and without a random within-breed animal effect, probably because of insufficient power to separate across- and within-breed animal effects. Conclusions - Accuracy of both single- and multi-breed genomic prediction depends on the properties of the QTL that underlie the trait. As QTL MAF decreased, accuracy decreased, especially when rare alleles had a large effect. This demonstrates that QTL properties are key parameters that determine the accuracy of genomic prediction.
Empirical and deterministic accuracies of across-population genomic prediction
Wientjes, Y.C.J. ; Veerkamp, R.F. ; Bijma, P. ; Bovenhuis, H. ; Schrooten, C. ; Calus, M.P.L. - \ 2015
Genetics, Selection, Evolution 47 (2015). - ISSN 0999-193X
dairy-cattle breeds - linkage disequilibrium - relationship matrix - complex traits - multi-breed - selection - values - markers - heritability - models
Background: Differences in linkage disequilibrium and in allele substitution effects of QTL (quantitative trait loci) may hinder genomic prediction across populations. Our objective was to develop a deterministic formula to estimate the accuracy of across-population genomic prediction, for which reference individuals and selection candidates are from different populations, and to investigate the impact of differences in allele substitution effects across populations and of the number of QTL underlying a trait on the accuracy. Methods: A deterministic formula to estimate the accuracy of across-population genomic prediction was derived based on selection index theory. Moreover, accuracies were deterministically predicted using a formula based on population parameters and empirically calculated using simulated phenotypes and a GBLUP (genomic best linear unbiased prediction) model. Phenotypes of 1033 Holstein-Friesian, 105 Groninger White Headed and 147 Meuse-Rhine-Yssel cows were simulated by sampling 3000, 300, 30 or 3 QTL from the available high-density SNP (single nucleotide polymorphism) information of three chromosomes, assuming a correlation of 1.0, 0.8, 0.6, 0.4, or 0.2 between allele substitution effects across breeds. The simulated heritability was set to 0.95 to resemble the heritability of deregressed proofs of bulls. Results: Accuracies estimated with the deterministic formula based on selection index theory were similar to empirical accuracies for all scenarios, while accuracies predicted with the formula based on population parameters overestimated empirical accuracies by ~25 to 30%. When the between-breed genetic correlation differed from 1, i.e. allele substitution effects differed across breeds, empirical and deterministic accuracies decreased in proportion to the genetic correlation. Using a multi-trait model, it was possible to accurately estimate the genetic correlation between the breeds based on phenotypes and high-density genotypes. The number of QTL underlying the simulated trait did not affect the accuracy. Conclusions: The deterministic formula based on selection index theory estimated the accuracy of across-population genomic predictions well. The deterministic formula using population parameters overestimated the across-population genomic accuracy, but may still be useful because of its simplicity. Both formulas could accommodate for genetic correlations between populations lower than 1. The number of QTL underlying a trait did not affect the accuracy of across-population genomic prediction using a GBLUP method
Understanding the genetic basis of potato development using a multi-trait QTL analysis
Hurtado-Lopez, P.X. ; Tessema, B.B. ; Schnabel, S.K. ; Maliepaard, C.A. ; Linden, C.G. van der; Eilers, P.H.C. ; Jansen, J. ; Eeuwijk, F.A. van; Visser, R.G.F. - \ 2015
Euphytica 204 (2015)1. - ISSN 0014-2336 - p. 229 - 241.
solanum-tuberosum - complex traits - night temperature - linkage maps - mixed-model - loci - architecture - population - maturity - growth
Understanding the genetic basis of plant development in potato requires a proper characterization of plant morphology over time. Parameters related to different aging stages can be used to describe the developmental processes. It is attractive to map these traits simultaneously in a QTL analysis; because the power to detect a QTL will often be improved and it will be easier to identify pleiotropic QTLs. We included complex, agronomic traits together with plant development parameters in a multi-trait QTL analysis. First, the results of our analysis led to coherent insight into the genetic architecture of complex traits in potato. Secondly, QTL for parameters related to plant development were identified. Thirdly, pleiotropic regions for various types of traits were identified. Emergence, number of main stems, number of tubers and yield were explained by 9, 5, 4 and 6 QTL, respectively. These traits were measured once during the growing season. The genetic control of flowering, senescence and plant height, which were measured at regular time intervals, was explained by 9, 10 and 12 QTL, respectively. Genetic relationships between aboveground and belowground traits in potato were observed in 14 pleiotropic QTL. Some of our results suggest the presence of QTL-by-Environment interactions. Therefore, additional studies comparing development under different photoperiods are required to investigate the plasticity of the crop.
Marker-Based Estimation of Heritability in Immortal Populations
Kruijer, W.T. ; Boer, M.P. ; Malosetti, M. ; Flood, P.J. ; Engel, B. ; Kooke, R. ; Keurentjes, J.J.B. ; Eeuwijk, F.A. van - \ 2015
Genetics 199 (2015)2. - ISSN 0016-6731 - p. 379 - 398.
genome-wide association - multi-environment trials - quantitative trait loci - plant-breeding trials - linear mixed models - arabidopsis-thaliana - missing heritability - complex traits - selection - prediction
Heritability is a central parameter in quantitative genetics, both from an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within and between genotype variability. This approach estimates broad-sense heritability, and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here, is to use mixed models at individual plant or plot level. Using statistical arguments, simulations and real data we investigate the feasibility of both approaches, and how these affect genomic prediction with G-BLUP and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For GWAS on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.
Genomic relationships computed from either next- generation sequence or array SNP data
Perez Enciso, M. - \ 2014
Journal of Animal Breeding and Genetics 131 (2014)2. - ISSN 0931-2668 - p. 85 - 96.
genetic-variation - complex traits - selection - pig - predictions - genotype - samples - cattle
The use of sequence data in genomic prediction models is a topic of high interest, given the decreasing prices of current next'-generation sequencing technologies (NGS) and the theoretical possibility of directly interrogating the genomes for all causal mutations. Here, we compare by simulation how well genetic relationships (G) could be estimated using either NGS or ascertained SNP arrays. DNA sequences were simulated using the coalescence according to two scenarios: a cattle' scenario that consisted of a bottleneck followed by a split in two breeds without migration, and a pig' model where Chinese introgression into international pig breeds was simulated. We found that introgression results in a large amount of variability across the genome and between individuals, both in differentiation and in diversity. In general, NGS data allowed the most accurate estimates of G, provided enough sequencing depth was available, because shallow NGS (4x) may result in highly distorted estimates of G elements, especially if not standardized by allele frequency. However, high-density genotyping can also result in accurate estimates of G. Given that genotyping is much less noisy than NGS data, it is suggested that specific high-density arrays (similar to 3M SNPs) that minimize the effects of ascertainment could be developed in the population of interest by sequencing the most influential animals and rely on those arrays for implementing genomic selection.
Positive Selection of Deleterious Alleles through Interaction with a Sex-Ratio Suppressor Gene in African Buffalo: A Plausible New Mechanism for a High Frequency Anomaly
Hooft, W.F. van; Greyling, B.J. ; Getz, W.M. ; Helden, P.D. van; Zwaan, B.J. ; Bastos, A.D.S. - \ 2014
PLoS ONE 9 (2014)11. - ISSN 1932-6203
heterozygosity-fitness correlations - bovine tuberculosis - syncerus-caffer - inbreeding depression - population-levels - complex traits - software - disease - wild - flow
Although generally rare, deleterious alleles can become common through genetic drift, hitchhiking or reductions in selective constraints. Here we present a possible new mechanism that explains the attainment of high frequencies of deleterious alleles in the African buffalo (Syncerus caffer) population of Kruger National Park, through positive selection of these alleles that is ultimately driven by a sex-ratio suppressor. We have previously shown that one in four Kruger buffalo has a Y-chromosome profile that, despite being associated with low body condition, appears to impart a relative reproductive advantage, and which is stably maintained through a sex-ratio suppressor. Apparently, this sex-ratio suppressor prevents fertility reduction that generally accompanies sex-ratio distortion. We hypothesize that this body-condition-associated reproductive advantage increases the fitness of alleles that negatively affect male body condition, causing genome-wide positive selection of these alleles. To investigate this we genotyped 459 buffalo using 17 autosomal microsatellites. By correlating heterozygosity with body condition (heterozygosity-fitness correlations), we found that most microsatellites were associated with one of two gene types: one with elevated frequencies of deleterious alleles that have a negative effect on body condition, irrespective of sex; the other with elevated frequencies of sexually antagonistic alleles that are negative for male body condition but positive for female body condition. Positive selection and a direct association with a Y-chromosomal sex-ratio suppressor are indicated, respectively, by allele clines and by relatively high numbers of homozygous deleterious alleles among sex-ratio suppressor carriers. This study, which employs novel statistical techniques to analyse heterozygosity-fitness correlations, is the first to demonstrate the abundance of sexually-antagonistic genes in a natural mammal population. It also has important implications for our understanding not only of the evolutionary and ecological dynamics of sex-ratio distorters and suppressors, but also of the functioning of deleterious and sexually-antagonistic alleles, and their impact on population viability
Prioritization of candidate genes in QTL regions based on associations between traits and biological processes
Bargsten, J.W. ; Nap, J.P.H. ; Sanchez Perez, G.F. ; Dijk, A.D.J. van - \ 2014
BMC Plant Biology 14 (2014). - ISSN 1471-2229
genome-wide association - protein function prediction - arabidopsis-thaliana - nucleotide polymorphisms - enrichment analysis - flowering time - complex traits - oryza-sativa - rice - architecture
Background Elucidation of genotype-to-phenotype relationships is a major challenge in biology. In plants, it is the basis for molecular breeding. Quantitative Trait Locus (QTL) mapping enables to link variation at the trait level to variation at the genomic level. However, QTL regions typically contain tens to hundreds of genes. In order to prioritize such candidate genes, we show that we can identify potentially causal genes for a trait based on overrepresentation of biological processes (gene functions) for the candidate genes in the QTL regions of that trait. Results The prioritization method was applied to rice QTL data, using gene functions predicted on the basis of sequence- and expression-information. The average reduction of the number of genes was over ten-fold. Comparison with various types of experimental datasets (including QTL fine-mapping and Genome Wide Association Study results) indicated both statistical significance and biological relevance of the obtained connections between genes and traits. A detailed analysis of flowering time QTLs illustrates that genes with completely unknown function are likely to play a role in this important trait. Conclusions Our approach can guide further experimentation and validation of causal genes for quantitative traits. This way it capitalizes on QTL data to uncover how individual genes influence trait variation.
Genomic prediction of breeding values using previously estimated SNP variances
Calus, M.P.L. ; Schrooten, C. ; Veerkamp, R.F. - \ 2014
Genetics, Selection, Evolution 46 (2014). - ISSN 0999-193X - 13 p.
preconditioned conjugate-gradient - dairy-cattle - genotyping strategies - genetic evaluations - complex traits - selection - accuracy - information - preselection - population
Background Genomic prediction requires estimation of variances of effects of single nucleotide polymorphisms (SNPs), which is computationally demanding, and uses these variances for prediction. We have developed models with separate estimation of SNP variances, which can be applied infrequently, and genomic prediction, which can be applied routinely. Methods SNP variances were estimated with Bayes Stochastic Search Variable Selection (BSSVS) and BayesC. Genome-enhanced breeding values (GEBV) were estimated with RR-BLUP (ridge regression best linear unbiased prediction), using either variances obtained from BSSVS (BLUP-SSVS) or BayesC (BLUP-C), or assuming equal variances for each SNP. Datasets used to estimate SNP variances comprised (1) all animals, (2) 50% random animals (RAN50), (3) 50% best animals (TOP50), or (4) 50% worst animals (BOT50). Traits analysed were protein yield, udder depth, somatic cell score, interval between first and last insemination, direct longevity, and longevity including information from predictors. Results BLUP-SSVS and BLUP-C yielded similar GEBV as the equivalent Bayesian models that simultaneously estimated SNP variances. Reliabilities of these GEBV were consistently higher than from RR-BLUP, although only significantly for direct longevity. Across scenarios that used data subsets to estimate GEBV, observed reliabilities were generally higher for TOP50 than for RAN50, and much higher than for BOT50. Reliabilities of TOP50 were higher because the training data contained more ancestors of selection candidates. Using estimated SNP variances based on random or non-random subsets of the data, while using all data to estimate GEBV, did not affect reliabilities of the BLUP models. A convergence criterion of 10-8 instead of 10-10 for BLUP models yielded similar GEBV, while the required number of iterations decreased by 71 to 90%. Including a separate polygenic effect consistently improved reliabilities of the GEBV, but also substantially increased the required number of iterations to reach convergence with RR-BLUP. SNP variances converged faster for BayesC than for BSSVS. Conclusions Combining Bayesian variable selection models to re-estimate SNP variances and BLUP models that use those SNP variances, yields GEBV that are similar to those from full Bayesian models. Moreover, these combined models yield predictions with higher reliability and less bias than the commonly used RR-BLUP model.
Defining the role of common variation in the genomic and biological architecture of adult human height
Wood, A.R. ; Esko, T. ; Yang, J. ; Dhonukshe-Rutten, R.A.M. ; Groot, C.P.G.M. de - \ 2014
Nature Genetics 46 (2014). - ISSN 1061-4036 - p. 1173 - 1186.
genetic-variation - complex traits - heritability - mutations - snps
Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ~2,000, ~3,700 and ~9,500 SNPs explained ~21%, ~24% and ~29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/ß-catenin and chondroitin sulfate–related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.
Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle
Binsbergen, R. van; Bink, M.C.A.M. ; Calus, M.P.L. ; Eeuwijk, F.A. van; Hayes, B.J. ; Hulsegge, B. ; Veerkamp, R.F. - \ 2014
Genetics, Selection, Evolution 46 (2014). - ISSN 0999-193X - 25 p.
haplotype-phase inference - genotype imputation - linkage disequilibrium - wide association - breeding programs - genetic-variation - complex traits - population - prediction - design
Background The use of whole-genome sequence data can lead to higher accuracy in genome-wide association studies and genomic predictions. However, to benefit from whole-genome sequence data, a large dataset of sequenced individuals is needed. Imputation from SNP panels, such as the Illumina BovineSNP50 BeadChip and Illumina BovineHD BeadChip, to whole-genome sequence data is an attractive and less expensive approach to obtain whole-genome sequence genotypes for a large number of individuals than sequencing all individuals. Our objective was to investigate accuracy of imputation from lower density SNP panels to whole-genome sequence data in a typical dataset for cattle. Methods Whole-genome sequence data of chromosome 1 (1737 471 SNPs) for 114 Holstein Friesian bulls were used. Beagle software was used for imputation from the BovineSNP50 (3132 SNPs) and BovineHD (40 492 SNPs) beadchips. Accuracy was calculated as the correlation between observed and imputed genotypes and assessed by five-fold cross-validation. Three scenarios S40, S60 and S80 with respectively 40%, 60%, and 80% of the individuals as reference individuals were investigated. Results Mean accuracies of imputation per SNP from the BovineHD panel to sequence data and from the BovineSNP50 panel to sequence data for scenarios S40 and S80 ranged from 0.77 to 0.83 and from 0.37 to 0.46, respectively. Stepwise imputation from the BovineSNP50 to BovineHD panel and then to sequence data for scenario S40 improved accuracy per SNP to 0.65 but it varied considerably between SNPs. Conclusions Accuracy of imputation to whole-genome sequence data was generally high for imputation from the BovineHD beadchip, but was low from the BovineSNP50 beadchip. Stepwise imputation from the BovineSNP50 to the BovineHD beadchip and then to sequence data substantially improved accuracy of imputation. SNPs with a low minor allele frequency were more difficult to impute correctly and the reliability of imputation varied more. Linkage disequilibrium between an imputed SNP and the SNP on the lower density panel, minor allele frequency of the imputed SNP and size of the reference group affected imputation reliability.
Genome-wide distribution of genetic diversity and linkage disequilibrium in a mass-selected population of maritime pine.
Plomion, C. ; Chancerel, E. ; Endelman, J. ; Lamy, J.B. ; Mandrou, E. ; Lesur, I. ; Ehrenmann, F. ; Isik, F. ; Bink, M.C.A.M. ; Heerwaarden, J. van; Bouffier, L. - \ 2014
BMC Genomics 15 (2014)1. - ISSN 1471-2164 - 17 p.
multilocus genotype data - loblolly-pine - forest trees - cryptomeria-japonica - density-estimation - cloned population - breeding values - complex traits - consensus map - white spruce
BACKGROUND: The accessibility of high-throughput genotyping technologies has contributed greatly to the development of genomic resources in non-model organisms. High-density genotyping arrays have only recently been developed for some economically important species such as conifers. The potential for using genomic technologies in association mapping and breeding depends largely on the genome wide patterns of diversity and linkage disequilibrium in current breeding populations. This study aims to deepen our knowledge regarding these issues in maritime pine, the first species used for reforestation in south western Europe. RESULTS: Using a new map merging algorithm, we first established a 1,712 cM composite linkage map (comprising 1,838 SNP markers in 12 linkage groups) by bringing together three already available genetic maps. Using rigorous statistical testing based on kernel density estimation and resampling we identified cold and hot spots of recombination. In parallel, 186 unrelated trees of a mass-selected population were genotyped using a 12k-SNP array. A total of 2,600 informative SNPs allowed to describe historical recombination, genetic diversity and genetic structure of this recently domesticated breeding pool that forms the basis of much of the current and future breeding of this species. We observe very low levels of population genetic structure and find no evidence that artificial selection has caused a reduction in genetic diversity. By combining these two pieces of information, we provided the map position of 1,671 SNPs corresponding to 1,192 different loci. This made it possible to analyze the spatial pattern of genetic diversity (He) and long distance linkage disequilibrium (LD) along the chromosomes. We found no particular pattern in the empirical variogram of He across the 12 linkage groups and, as expected for an outcrossing species with large effective population size, we observed an almost complete lack of long distance LD. CONCLUSIONS: These results are a stepping stone for the development of strategies for studies in population genomics, association mapping and genomic prediction in this economical and ecologically important forest tree species.
Genotype x environment interaction QTL mapping in plants: lessons from Arabidopsis
El-Soda, M. ; Malosetti, M. ; Zwaan, B.J. ; Koornneef, M. ; Aarts, M.G.M. - \ 2014
Trends in Plant Science 19 (2014)6. - ISSN 1360-1385 - p. 390 - 398.
quantitative trait loci - genome-wide association - adaptive phenotypic plasticity - flowering time - natural variation - mixed-model - missing heritability - drought tolerance - complex traits - life-history
Plant growth and development are influenced by the genetic composition of the plant (G), the environment (E), and the interaction between them (G × E). To produce suitable genotypes for multiple environments, G × E should be accounted for and assessed in plant-breeding programs. Here, we review the genetic basis of G × E and its consequence for quantitative trait loci (QTL) mapping in biparental and genome-wide association (GWA) mapping populations. We also consider the implications of G × E for understanding plant fitness trade-offs and evolutionary ecology
Signatures of Diversifying Selection in European Pig Breeds
Wilkinson, S. ; Lu, Z.H. ; Megens, H.J.W.C. ; Archibald, A.L. ; Haley, C. ; Jackson, I.J. ; Groenen, M.A.M. ; Crooijmans, R.P.M.A. ; Ogden, R. ; Wiener, P. - \ 2013
Plos Genetics 9 (2013)4. - ISSN 1553-7404
quantitative trait loci - fatty-acid-composition - coat color - artificial selection - kit-ligand - skin pigmentation - complex traits - teat number - wild boar - ear size
Following domestication, livestock breeds have experienced intense selection pressures for the development of desirable traits. This has resulted in a large diversity of breeds that display variation in many phenotypic traits, such as coat colour, muscle composition, early maturity, growth rate, body size, reproduction, and behaviour. To better understand the relationship between genomic composition and phenotypic diversity arising from breed development, the genomes of 13 traditional and commercial European pig breeds were scanned for signatures of diversifying selection using the Porcine60K SNP chip, applying a between-population (differentiation) approach. Signatures of diversifying selection between breeds were found in genomic regions associated with traits related to breed standard criteria, such as coat colour and ear morphology. Amino acid differences in the EDNRB gene appear to be associated with one of these signatures, and variation in the KITLG gene may be associated with another. Other selection signals were found in genomic regions including QTLs and genes associated with production traits such as reproduction, growth, and fat deposition. Some selection signatures were associated with regions showing evidence of introgression from Asian breeds. When the European breeds were compared with wild boar, genomic regions with high levels of differentiation harboured genes related to bone formation, growth, and fat deposition.
Multi-trait and multi-environment QTL analyses of yield and a set of physiological traits in pepper
Alimi, N.A. ; Bink, M.C.A.M. ; Dieleman, J.A. ; Magán, J.J. ; Wubs, A.M. ; Palloix, A. ; Eeuwijk, F.A. van - \ 2013
Theoretical and Applied Genetics 126 (2013)10. - ISSN 0040-5752 - p. 2597 - 2625.
mixed-model approach - capsicum-annuum - complex traits - fruit size - loci - populations - barley - maize - covariables - regression
For many agronomic crops, yield is measured simultaneously with other traits across multiple environments. The study of yield can benefit from joint analysis with other traits and relations between yield and other traits can be exploited to develop indirect selection strategies. We compare the performance of three multi-response QTL approaches based on mixed models: a multi-trait approach (MT), a multi-environment approach (ME), and a multi-trait multi-environment approach (MTME). The data come from a multi-environment experiment in pepper, for which 15 traits were measured in four environments. The approaches were compared in terms of number of QTLs detected for each trait, the explained variance, and the accuracy of prediction for the final QTL model. For the four environments together, the superior MTME approach delivered a total of 47 regions containing putative QTLs. Many of these QTLs were pleiotropic and showed quantitative QTL by environment interaction. MTME was superior to ME and MT in the number of QTLs, the explained variance and accuracy of predictions. The large number of model parameters in the MTME approach was challenging and we propose several guidelines to help obtain a stable final QTL model. The results confirmed the feasibility and strengths of novel mixed model QTL methodology to study the architecture of complex traits.
Identifying genotype-by-environment interactions in the metabolism of germinating Arabidopsis seeds using Generalized Genetical Genomics
Joosen, R.V.L. ; Arends, D. ; Li, Y. ; Willems, L.A.J. ; Keurentjes, J.J.B. ; Ligterink, W. ; Jansen, R.C. ; Hilhorst, H.W.M. - \ 2013
Plant Physiology 162 (2013)2. - ISSN 0032-0889 - p. 553 - 566.
quantitative trait loci - chromatography-mass spectrometry - heterogeneous inbred family - natural allelic variation - controlling root-growth - plant development - line population - complex traits - potato-tuber - thaliana
A complex phenotype such as seed germination is the resultant of several genetic and environmental cues and requires the concerted action of many genes. The use of well-structured recombinant inbred lines in combination with omics analysis can help to disentangle the genetic basis of such quantitative traits. This so called genetical genomics approach can effectively capture both genetic (G) and epistatic interactions (G:G). However, to understand how the environment interacts with genomic encoded information (G:E) a better understanding of the perception and processing of environmental signals is needed. In a classical genetical genomics setup this requires replication of the whole experiment in different environmental conditions. A novel generalized setup overcomes this limitation and includes environmental perturbation within a single experimental design. We developed a dedicated QTL mapping procedure to implement this approach and used existing phenotypical data to demonstrate its power. Additionally, we studied the genetic regulation of primary metabolism in dry and imbibed Arabidopsis seeds. Many changes were observed in the metabolome which are both under environmental and genetic control and their interactions. This concept offers unique reduction of experimental load with minimal compromise of statistical power and is of great potential in the field of systems genetics which requires a broad understanding of both plasticity and dynamic regulation.
Genome-wide association study of insect bite hypersensitivity in Dutch Shetland pony mares
Schurink, A. ; Ducro, B.J. ; Bastiaansen, J.W.M. ; Frankena, K. ; Arendonk, J.A.M. van - \ 2013
Animal Genetics 44 (2013)1. - ISSN 0268-9146 - p. 44 - 52.
icelandic horses - sweet itch - genetic association - complex traits - summer eczema - polymorphisms - netherlands - sequence - diseases - british
Insect bite hypersensitivity (IBH) is the most common allergic disease present in horses worldwide. It has been shown that IBH is under genetic control, but the knowledge of associated genes is limited. We conducted a genome-wide association study to identify and quantify genomic regions contributing to IBH in the Dutch Shetland pony population. A total of 97 cases and 91 controls were selected and matched on withers height, coat colour and pedigree to minimise the population stratification. A blood sample was collected from participating Shetland pony mares, their IBH phenotype was scored and the owner filled in a questionnaire. A total of 40 021 single-nucleotide polymorphisms (SNPs) were fitted in a univariable logistic model fitting an additive effect. Analysis revealed no effects of population stratification. Significant associations with IBH were detected for 24 SNPs on 12 chromosomes [log10(P-value) > 2.5]. Odds ratios of allele substitution effects of the unfavourable allele were between 1.94 and 5.95. The most significant SNP was found on chromosome 27, with an odds ratio of 2.31 and with an allele frequency of the unfavourable allele of 0.72 in cases and 0.53 in controls. Genome-wide association studies on additional horse populations are desired to validate the identified associations, to identify the genes involved in IBH and to develop genomic tools to o decrease IBH prevalence.
Genome-wide association studies for Agronomical Traits in a world wide Spring Barley Collection
Pasam, R.K. ; Sharma, R. ; Malosetti, M. ; Eeuwijk, F.A. van; Haseneyer, G. ; Kilian, B. ; Graner, A. - \ 2012
BMC Plant Biology 12 (2012). - ISSN 1471-2229
multilocus genotype data - hordeum-vulgare l. - linkage disequilibrium - population-structure - complex traits - flowering time - qtl analysis - missing heritability - haplotype structure - genetic diversity
Background Genome-wide association studies (GWAS) based on linkage disequilibrium (LD) provide a promising tool for the detection and fine mapping of quantitative trait loci (QTL) underlying complex agronomic traits. In this study we explored the genetic basis of variation for the traits heading date, plant height, thousand grain weight, starch content and crude protein content in a diverse collection of 224 spring barleys of worldwide origin. The whole panel was genotyped with a customized oligonucleotide pool assay containing 1536 SNPs using Illumina's GoldenGate technology resulting in 957 successful SNPs covering all chromosomes. The morphological trait "row type" (two-rowed spike vs. six-rowed spike) was used to confirm the high level of selectivity and sensitivity of the approach. This study describes the detection of QTL for the above mentioned agronomic traits by GWAS. Results Population structure in the panel was investigated by various methods and six subgroups that are mainly based on their spike morphology and region of origin. We explored the patterns of linkage disequilibrium (LD) among the whole panel for all seven barley chromosomes. Average LD was observed to decay below a critical level (r2-value 0.2) within a map distance of 5-10 cM. Phenotypic variation within the panel was reasonably large for all the traits. The heritabilities calculated for each trait over multi-environment experiments ranged between 0.90-0.95. Different statistical models were tested to control spurious LD caused by population structure and to calculate the P-value of marker-trait associations. Using a mixed linear model with kinship for controlling spurious LD effects, we found a total of 171 significant marker trait associations, which delineate into 107 QTL regions. Across all traits these can be grouped into 57 novel QTL and 50 QTL that are congruent with previously mapped QTL positions. Conclusions Our results demonstrate that the described diverse barley panel can be efficiently used for GWAS of various quantitative traits, provided that population structure is appropriately taken into account. The observed significant marker trait associations provide a refined insight into the genetic architecture of important agronomic traits in barley. However, individual QTL account only for a small portion of phenotypic variation, which may be due to insufficient marker coverage and/or the elimination of rare alleles prior to analysis. The fact that the combined SNP effects fall short of explaining the complete phenotypic variance may support the hypothesis that the expression of a quantitative trait is caused by a large number of very small effects that escape detection. Notwithstanding these limitations, the integration of GWAS with biparental linkage mapping and an ever increasing body of genomic sequence information will facilitate the systematic isolation of agronomically important genes and subsequent analysis of their allelic diversity
SPICY: towards automated phenotyping of large pepper plants in the greenhouse
Heijden, G.W.A.M. van der; Song, Y. ; Horgan, G. ; Polder, G. ; Dieleman, J.A. ; Bink, M.C.A.M. ; Palloix, A. ; Eeuwijk, F.A. van; Glasbey, C. - \ 2012
Functional Plant Biology 39 (2012)11. - ISSN 1445-4408 - p. 870 - 877.
complex traits - platform - cereals - qtl
Most high-throughput systems for automated plant phenotyping involve a fixed recording cabinet to which plants are transported. However, important greenhouse plants like pepper are too tall to be transported. In this research we developed a system to automatically measure plant characteristics of tall pepper plants in the greenhouse. With a device equipped with multiple cameras, images of plants are recorded at a 5 cm interval over a height of 3 m. Two types of features are extracted: (1) features from a 3D reconstruction of the plant canopy; and (2) statistical features derived directly from RGB images. The experiment comprised 151 genotypes of a recombinant inbred population of pepper, to examine the heritability and quantitative trait loci (QTL) of the features. Features extracted from the 3D reconstruction of the canopy were leaf size and leaf angle, with heritabilities of 0.70 and 0.56 respectively. Three QTL were found for leaf size, and one for leaf angle. From the statistical features, plant height showed a good correlation (0.93) with manual measurements, and QTL were in accordance with QTL of manual measurements. For total leaf area, the heritability was 0.55, and two of the three QTL found by manual measurement were found by image analysis
Association mapping of plant resistance to insects
Kloth, K.J. ; Thoen, H.P.M. ; Bouwmeester, H.J. ; Jongsma, M.A. ; Dicke, M. - \ 2012
Trends in Plant Science 17 (2012)5. - ISSN 1360-1385 - p. 311 - 319.
genome-wide association - arabidopsis-thaliana - specialist herbivores - complex traits - glucosinolate accumulation - generalist herbivores - signaling pathways - natural variation - systems biology - host-plant
Association mapping is rapidly becoming an important method to explore the genetic architecture of complex traits in plants and offers unique opportunities for studying resistance to insect herbivores. Recent studies indicate that there is a trade-off between resistance against generalist and specialist insects. Most studies, however, use a targeted approach that will easily miss important components of insect resistance. Genome-wide association mapping provides a comprehensive approach to explore the whole array of plant defense mechanisms in the context of the generalist–specialist paradigm. As association mapping involves the screening of large numbers of plant lines, specific and accurate high-throughput phenotyping (HTP) methods are needed. Here, we discuss the prospects of association mapping for insect resistance and HTP requirements.
Visualizing the genetic landscape of Arabidopsis seed performance
Joosen, R.V.L. ; Arends, D. ; Willems, L.A.J. ; Ligterink, J.W. ; Jansen, R.C. ; Hilhorst, H.W.M. - \ 2012
Plant Physiology 158 (2012)2. - ISSN 0032-0889 - p. 570 - 589.
quantitative trait loci - natural allelic variation - heterogeneous inbred family - controlling root-growth - abscisic-acid - environmental covariables - line population - complex traits - qtl analysis - thaliana
Perfect timing of germination is required to encounter optimal conditions for plant survival and is the result of a complex interaction between molecular processes, seed characteristics, and environmental cues. To detangle these processes, we made use of natural genetic variation present in an Arabidopsis (Arabidopsis thaliana) Bayreuth × Shahdara recombinant inbred line population. For a detailed analysis of the germination response, we characterized rate, uniformity, and maximum germination and discuss the added value of such precise measurements. The effects of after-ripening, stratification, and controlled deterioration as well as the effects of salt, mannitol, heat, cold, and abscisic acid (ABA) with and without cold stratification were analyzed for these germination characteristics. Seed morphology (size and length) of both dry and imbibed seeds was quantified by using image analysis. For the overwhelming amount of data produced in this study, we developed new approaches to perform and visualize high-throughput quantitative trait locus (QTL) analysis. We show correlation of trait data, (shared) QTL positions, and epistatic interactions. The detection of similar loci for different stresses indicates that, often, the molecular processes regulating environmental responses converge into similar pathways. Seven major QTL hotspots were confirmed using a heterogeneous inbred family approach. QTLs colocating with previously reported QTLs and well-characterized mutants are discussed. A new connection between dormancy, ABA, and a cripple mucilage formation due to a naturally occurring mutation in the MUCILAGE-MODIFIED2 gene is proposed, and this is an interesting lead for further research on the regulatory role of ABA in mucilage production and its multiple effects on germination parameters.