Validation of Genome-Wide Association Studies (GWAS) Results

Henshall, John

doi:10.1007/978-1-62703-447-0_18

Cited by 7 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, only a few QTLs detected in the VP colocalized with the QTLs detected in the RP, despite loosening of the interval surrounding each QTL, or lowering the significance threshold from 1e-05 to 1e-04. Replication of GWAS experiment requires similarity for several features between the two populations and sufficiently large validation population to ensure detection power [70]. Our validation experiment fulfilled all similarity conditions as VP had similar population structure to RP (composed of temperate and tropical japonica ), similar relationship between population structure and variability of the target trait (the temperate japonica having the highest As contents) and similar MAF distribution.…”

Section: Discussionmentioning

confidence: 99%

Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains

et al. 2019

View full text Add to dashboard Cite

The high concentration of arsenic ( As ) in rice grains, in a large proportion of the rice growing areas, is a critical issue. This study explores the feasibility of conventional (QTL-based) marker-assisted selection and genomic selection to improve the ability of rice to prevent As uptake and accumulation in the edible grains. A japonica diversity panel (RP) of 228 accessions phenotyped for As concentration in the flag leaf (FL- As ) and in the dehulled grain (CG- As ), and genotyped at 22,370 SNP loci, was used to map QTLs by association analysis (GWAS) and to train genomic prediction models. Similar phenotypic and genotypic data from 95 advanced breeding lines (VP) with japonica genetic backgrounds, was used to validate related QTLs mapped in the RP through GWAS and to evaluate the predictive ability of across populations (RP-VP) genomic estimate of breeding value (GEBV) for As exclusion. Several QTLs for FL- As and CG- As with a low-medium individual effect were detected in the RP, of which some colocalized with known QTLs and candidate genes. However, less than 10% of those QTLs could be validated in the VP without loosening colocalization parameters. Conversely, the average predictive ability of across populations GEBV was rather high, 0.43 for FL- As and 0.48 for CG- As , ensuring genetic gains per time unit close to phenotypic selection. The implications of the limited robustness of the GWAS results and the rather high predictive ability of genomic prediction are discussed for breeding rice for significantly low arsenic uptake and accumulation in the edible grains.

show abstract

Section: Discussionmentioning

confidence: 99%

Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Because a large amount of marker data is collected, a GWA study can identify numerous associations that are likely to be overestimates and unbiased effects can only be made in a data set not used in the discovery process. 73 In summary, validation of GWAS results is an important factor to ensure linkage between a marker and a trait of interest.…”

Section: Genome Wide Association Studiesmentioning

confidence: 99%

A Case of Need: Linking Traits to Genebank Accessions

Anglin

Amri²,

Kehel³

et al. 2018

Biopreservation and Biobanking

View full text Add to dashboard Cite

Genebanks are responsible for collecting, maintaining, characterizing, documenting, and distributing plant genetic resources for research, education, and breeding purposes. The rationale for requests of plant materials varies highly from areas of anthropology, social science, small-holder farmers, the commercial sector, rehabilitation of degraded systems, all the way to crop improvement and basic research. Matching “the right” accessions to a particular request is not always a straightforward process especially when genetic resource collections are large and the user does not already know which accession or even which species they want to study. Some requestors have limited knowledge of the crop; therefore, they do not know where to begin and thus, initiate the search by consultation with crop curators to help direct their request to the most suitable germplasm. One way to enhance the use of genebank material and aid in the selection of genetic resources is to have thoroughly cataloged agronomic, biochemical, genomic, and other traits linked to genebank accessions. In general, traits of importance to most users include genotypes that thrive under various biotic and abiotic stresses, morphological traits (color, shape, size of fruits), plant architecture, disease resistance, nutrient content, yield, and crop specific quality traits. In this review, we discuss methods for linking traits to genebank accessions, examples of linked traits, and some of the complexities involved, while reinforcing why it is critical to have well characterized accessions with clear trait data publicly available.

show abstract