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.
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
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