Association Mapping In Plants Research Articles

TWAS results are complementary to and less affected by linkage disequilibrium than GWAS

A genome-wide association study (GWAS) is used to identify genetic markers associated with phenotypic variation. In contrast, a transcriptome-wide association study (TWAS) detects associations between gene expression levels and phenotypic variation. It has previously been shown that in the cross-pollinated species, maize (Zea mays), GWAS, and TWAS identify complementary sets of trait-associated genes, many of which exhibit characteristics of true positives. Here, we extend this conclusion to the self-pollinated species, Arabidopsis thaliana and soybean (Glycine max). Linkage disequilibrium (LD) can result in the identification, via GWAS, of false-positive associations. In all three analyzed plant species, most trait-associated genes identified via TWAS are well separated physically from other candidate genes. Hence, TWAS is less affected by LD than is GWAS, demonstrating that TWAS is particularly well suited for association studies in genomes with slow rates of LD decay, such as soybean. TWAS is reasonably robust to the plant organs/tissues used to determine expression levels. In summary, this study confirms that TWAS is a promising approach for accurate gene-level association mapping in plants that is complementary to GWAS, and established that TWAS can exhibit substantial advantages relative to GWAS in species with slow rates of LD decay.

A Guide to Genome-Wide Association Mapping in Plants.

Genome-wide association studies (GWAS) have developed into a valuable approach for identifying the genetic basis of phenotypic variation. In this article, we provide an overview of the design, analysis, and interpretation of GWAS. First, we present results from simulations that explore key elements of experimental design as well as considerations for collecting the relevant genomic and phenotypic data. Next, we outline current statistical methods and tools used for GWA analyses and discuss the inclusion of covariates to account for population structure and the interpretation of results. Given that many false positive associations will occur in any GWA analysis, we highlight strategies for prioritizing GWA candidates for further statistical and empirical validation. While focused on plants, the material we cover is also applicable to other systems. © 2017 by John Wiley & Sons, Inc.

Genome-wide association mapping in plants.

We present new association mapping methods which address the unique challenges of analyzing genome-wide data from multi-environment plant studies. Association studies on a genome-wide scale are being performed in plants. Unlike human studies, plant studies contain replicates whose data may be recorded across different environments. Plant studies also often employ elaborate experimental designs for controlling extraneous phenotypic variation. As a result, the genome-wide analysis of data from plant studies can be challenging. In this paper, we present QK-based association mapping for the analysis of data from plant association studies. In doing so, we have developed: (a) a general multivariate QK framework for association mapping in plant studies of arbitrary complexity; (b) a new weighted two-stage analysis approach for QK-based association mapping; (c) a heuristic procedure for determining when two-stage analysis is appropriate; and (d) a Monte Carlo sampling procedure for controlling the genome-wide type I error rate. We conduct a simulation study to evaluate the performance of our genome-wide mapping technique. We also analyze data from a multi-environment association study in wheat.

Genomewide Markers for Controlling Background Variation in Association Mapping

Current procedures for association mapping in plants account for population structure (Q) and kinship (K). Here I propose an association mapping procedure that uses genomewide markers (G) to account for quantitative trait loci (QTL) on background chromosomes. My objective was to determine if the G and QG models are superior to the K and QK models. I simulated mapping population sizes ofN= 384, 768, and 1536 inbreds that belonged to three known subpopulations. The G and QG models showed the best adherence to the significance level (P) specified by the investigator for declaring QTL. Across different genetic models (15 or 30 QTL), population sizes, andPlevels, the Q model suffered from a high number of false positives (NFP). With the K and QK models, a relaxedPlevel led to a reasonable number of true QTL detected (NTQ) withN= 384 or 768 but it led to highNFPwithN= 1536. Compared with the K and QK models, the G and QG models had a better balance between highNTQand lowNFP. The results strongly indicated that the G and QG models are superior to the K and QK models.

Using association mapping to dissect the genetic basis of complex traits in plants

Association or linkage disequilibrium mapping has become a very popular method for dissecting the genetic basis of complex traits in plants. The benefits of association mapping, compared with traditional quantitative trait locus mapping, is, for example, a relatively detailed mapping resolution and that it is far less time consuming since no mapping populations need to be generated. The surge of interest in association mapping has been fueled by recent developments in genomics that allows for rapid identification and scoring of genetic markers which has traditionally limited mapping experiments. With the decreasing cost of genotyping future emphasis will likely focus on phenotyping, which can be both costly and time consuming but which is crucial for obtaining reliable results in association mapping studies. In addition, association mapping studies are prone to the identification of false positives, especially if the experimental design is not rigorously controlled. For example, population structure has long been known to induce many false positives and accounting for population structure has become one of the main issues when implementing association mapping in plants. Also, with increasing numbers of genetic markers used, the problem becomes separating true from false positive and this highlights the need for independent validation of identified association. With these caveats in mind, association mapping nevertheless shows great promise for helping us understand the genetic basis of complex traits of both economic and ecological importance.

An introduction to association mapping in plants.

Abstract Most traits that are related to fitness and agricultural value of plants are quantitatively inherited. Currently, there is tremendous interest in using association mapping approaches to dissect such traits. This is enabled by advances in genomic technology as well as the development of statistical models for association mapping that take into account population structure and familial relatedness. In this introductory review, we describe the relation of linkage disequilibrium to association mapping, give an overview of the steps required for association mapping as well as point out the possible limitations of such approaches.

Association mapping in plants

Association mapping in plants

Status and Prospects of Association Mapping in Plants

There is tremendous interest in using association mapping to identify genes responsible for quantitative variation of complex traits with agricultural and evolutionary importance. Recent advances in genomic technology, impetus to exploit natural diversity, and development of robust statistical analysis methods make association mapping appealing and affordable to plant research programs. Association mapping identifies quantitative trait loci (QTLs) by examining the marker‐trait associations that can be attributed to the strength of linkage disequilibrium between markers and functional polymorphisms across a set of diverse germplasm. General understanding of association mapping has increased significantly since its debut in plants. We have seen a more concerted effort in assembling various association‐mapping populations and initiating experiments through either candidate‐gene or genome‐wide approaches in different plant species. In this review, we describe the current status of association mapping in plants and outline opportunities and challenges in complex trait dissection and genomics‐assisted crop improvement.

Association mapping of partitioning loci in barley

BackgroundAssociation mapping, initially developed in human disease genetics, is now being applied to plant species. The model species Arabidopsis provided some of the first examples of association mapping in plants, identifying previously cloned flowering time genes, despite high population sub-structure. More recently, association genetics has been applied to barley, where breeding activity has resulted in a high degree of population sub-structure. A major genotypic division within barley is that between winter- and spring-sown varieties, which differ in their requirement for vernalization to promote subsequent flowering. To date, all attempts to validate association genetics in barley by identifying major flowering time loci that control vernalization requirement (VRN-H1 and VRN-H2) have failed. Here, we validate the use of association genetics in barley by identifying VRN-H1 and VRN-H2, despite their prominent role in determining population sub-structure.ResultsBy taking barley as a typical inbreeding crop, and seasonal growth habit as a major partitioning phenotype, we develop an association mapping approach which successfully identifies VRN-H1 and VRN-H2, the underlying loci largely responsible for this agronomic division. We find a combination of Structured Association followed by Genomic Control to correct for population structure and inflation of the test statistic, resolved significant associations only with VRN-H1 and the VRN-H2 candidate genes, as well as two genes closely linked to VRN-H1 (HvCSFs1 and HvPHYC).ConclusionWe show that, after employing appropriate statistical methods to correct for population sub-structure, the genome-wide partitioning effect of allelic status at VRN-H1 and VRN-H2 does not result in the high levels of spurious association expected to occur in highly structured samples. Furthermore, we demonstrate that both VRN-H1 and the candidate VRN-H2 genes can be identified using association mapping. Discrimination between intragenic VRN-H1 markers was achieved, indicating that candidate causative polymorphisms may be discerned and prioritised within a larger set of positive associations. This proof of concept study demonstrates the feasibility of association mapping in barley, even within highly structured populations. A major advantage of this method is that it does not require large numbers of genome-wide markers, and is therefore suitable for fine mapping and candidate gene evaluation, especially in species for which large numbers of genetic markers are either unavailable or too costly.

Haplotype diversity: the link between statistical and biological association

In the rapidly growing field of association mapping in plants, the use of (marker) haplotypes rather than single markers can be an effective way of improving detection power. Here, we highlight the information that can be obtained from deducing the historical relationships between haplotypes. The ordering of haplotype classes according to deduced historical relationships should further enhance association detection power, but can also be used to predict the genotypic and phenotypic values of unobserved germplasm.