Genome-Wide Association Mapping of Resistance to the Sugarcane Aphid in Sorghum bicolor

Punnuri, Somashekhar; Ayele, Addissu; Harris‐Shultz, Karen; Knoll, J. G.; Coffin, Alisa W.; Tadesse, Haile K.; Armstrong, Scott J.; Wiggins, Trahmad; Li, Hanxia; Sattler, Scott E.; Wallace, Jason

doi:10.21203/rs.3.rs-982658/v1

Cited by 1 publication

(1 citation statement)

References 45 publications

(83 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given the expected complexity of the genetic architectures controlling many quantitative traits in maize, we employed the Fixed and random model Circulating Probability Unification (FarmCPU) algorithm for genome-wide association [ 11 ]. While the FarmCPU algorithm provides greater power to detect true-positive signals, the set of positive associated signals identified by the algorithm can vary significantly based on moderately sized changes in the composition of the studied population [ 12 , 13 ]. The relative stability of GWAS signals identified via the FarmCPU algorithm can be assessed by evaluating the resample model inclusion probability (RMIP) of individual signals across multiple bootstraps [ 14 ], and this approach has been employed by a number of research groups working in different crop species [ 13 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Association mapping across a multitude of traits collected in diverse environments in maize

et al. 2022

View full text Add to dashboard Cite

Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data—18M markers—from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction.

show abstract