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

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 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.

A Common Resequencing-Based Genetic Marker Dataset for Global Maize Diversity

“…Community association panels are typically reused by many research groups working to study the genetic control of variation in different traits of interest. A recent literature study identified more than 160 distinct trait datasets scored across North American temperate maize association panels between 2010 and 2020 (Mural et al ., 2022). During the past seventeen years, the density of publicly available markers for maize association panels has grown from 94 microsatellite markers (Flint-Garcia et al ., 2005) to 1,536 microarray-based SNP markers (Hansey et al ., 2011) to hundreds of thousands of markers scored using genotyping by sequencing (Romay et al ., 2013) and approximately one million markers scored using RNA-seq (Leiboff et al ., 2015; Mazaheri et al ., 2019) and now typically include tens of millions of markers discovered and scored via whole genome resequencing (Bukowski et al ., 2018; Chen et al ., 2022; Li et al ., 2022; Qiu et al ., 2021; Wang et al ., 2020) or a combination of whole genome resequencing for a subset of lines and imputation from lower density markers for additional lines (Mural et al ., 2022; Sun et al ., 2022).…”

“…A published dataset of female flowering time (days to silking) for 752 inbreds drawn from the Wisconsin Diversity panel (Mazaheri et al ., 2019) and grown in a replicated field study in Lincoln, NE in 2020 was employed for genome wide association (Mural et al ., 2022). Three genetic marker sets for the same population of 752 maize inbreds were used to conduct GWAS.…”

“…This leads to a creation set containing 428,487 variants. The second was a set of ∼17.2 million markers called using a combination of resequencing (581 lines) (Bukowski et al ., 2018; Qiu et al ., 2021) and RNA-seq (361 lines) (Hirsch et al ., 2014; Mazaheri et al ., 2019) with extensive imputation to fill in non-exonic SNPs for the subset of samples genotyped only with RNA-seq (Mural et al ., 2022; Sun et al ., 2022). The third was a set of 16,634,049 markers obtained by subsetting the filtered and imputed SNP set assembled in this study to include only those markers with a minor allele frequency >5% among the 752 genotypes for which female flowering time phenotypes were available.…”

“…Identification of the candidate genes for flowering time (days to silking) via GWAS. a Association between days to silking, as reported in Mural et al (2022), and 428,487 segregating SNPs identified and genotyped using RNA-seq data in Mazaheri et al (2019).b Association test for days to silking using the marker set defined in this study (n = 16,634,049). The horizontal dashed line on each plot indicates an α = 1% significance threshold after applying Bonferroni correction assuming n number of variants in each dataset as independent tests.…”

“…This diversity includes not only small scale variation – single-nucleotide polymorphisms (SNPs) and insertions/deletions (InDels) – but also copy number and presence absence variation (Swanson-Wagner et al ., 2010). Scoring large maize populations for common sets of segregating DNA sequence polymorphisms (markers) is a key step in a range of research approaches to identify targets of selection (Hufford et al ., 2012; Wang et al ., 2020), inferring past demographic events, geographic diffusion (Da Fonseca et al ., 2015; Kistler et al ., 2018; Swarts et al ., 2017), and linking genotype to phenotype (Mural et al ., 2022). Early approaches to scoring common sets of genetic markers across large maize populations targeted thousands to hundreds of thousands of known markers, in the case of arrays (Ganal et al ., 2011; Unterseer et al ., 2014a).…”