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

Authors: MURAL, RAVI - University Of Nebraska; SUN, GUANGCHAO - University Of Nebraska; GRZYBOWSKI, MARCIN - University Of Nebraska; TROSS, MICHAEL - University Of Nebraska; JIN, HONGYU - University Of Nebraska; SMITH, CHRISTIE - University Of Nebraska; NEWTON, LINSEY - Michigan State University; Andorf, Carson; Woodhouse, Margaret; THOMPSON, ADDIE - Michigan State University; SIGMON, BRANDI - University Of Nebraska; SCHNABLE, JAMES - University Of Nebraska

Submitted to: Gigascience
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/25/2022
Publication Date: 8/23/2022
Citation: Mural, R.V., Sun, G., Grzybowski, M., Tross, M.C., Jin, H., Smith, C., Newton, L., Andorf, C.M., Woodhouse, M.H., Thompson, A.M., Sigmon, B., Schnable, J.C. 2022. Association mapping across a multitude of traits collected in diverse environments in maize. Gigascience. 11.Article giac080. https://doi.org/10.1093/gigascience/giac080.
DOI: https://doi.org/10.1093/gigascience/giac080

Interpretive Summary: Genetic studies have identified many cases where mutations in individual genes can alter many different traits. Studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify the gene types that affect multiple traits. Here we assemble maize markers associated with various traits from two community studies grown in field trials across seven US states. Examples were observed of genes associated with variation in diverse traits (e.g. above ground and below ground traits), as well as genes associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown. This study demonstrates the potential of mining community data to develop testable hypotheses about gene functions, identify potential multi-trait effects of natural genetic variants, and study gene-environment interactions.

Technical Abstract: 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 a number of plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. Here we assemble high density genetic marker data – 18M markers – from two partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across seven US states and scored for 162 distinct trait datasets. A set 2,154 suggestive marker trait associations (RMIP=5) and 697 confident associations (RMIP=10) were identified across the maize genome using a resampling-based genome wide association strategy. The precision of individual marker trait associations was estimated to be three genes based on the point where enrichment of genes with known phenotypic consequences decays to the genome-wide background rate. Examples were observed of both genetic loci association 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.