Genome-Wide Association Studies Identifying Multiple Loci Associated With Alfalfa Forage Quality

Abstract

Autotetraploid alfalfa is a major hay crop planted all over the world due to its adaptation in different environments and high quality for animal feed. However, the genetic basis of alfalfa quality is not fully understood. In this study, a diverse panel of 200 alfalfa accessions were planted in field trials using augmented experimental design at three locations in 2018 and 2019. Thirty-four quality traits were evaluated by Near Infrared Reflectance Spectroscopy (NIRS). The plants were genotyped using a genotyping by sequencing (GBS) approach and over 46,000 single nucleotide polymorphisms (SNPs) were obtained after variant calling and filtering. Genome-wide association studies (GWAS) identified 28 SNP markers associated with 16 quality traits. Among them, most of the markers were associated with fiber digestibility and protein content. Phenotypic variations were analyzed from three locations and different sets of markers were identified by GWAS when using phenotypic data from different locations, indicating that alfalfa quality traits were also affected by environmental factors. Among different sets of markers identified by location, two markers were associated with nine traits of fiber digestibility. One marker associated with lignin content was identified consistently in multiple environments. Putative candidate genes underlying fiber-related loci were identified and they are involved in the lignin and cell wall biosynthesis. The DNA markers and associated genes identified in this study will be useful for the genetic improvement of forage quality in alfalfa after the validation of the markers.

Effects of SNP marker density and training population size on prediction accuracy in alfalfa (Medicago sativa L.) genomic selection

“…This difference may be caused by variations in the assumptions of SNP effects and variance of Bayesian methods (de los Campos et al., 2009; Erbe et al., 2012; Habier et al., 2011; Meuwissen et al., 2001) and renders them less suitable for complex quantitative traits when compared to rrBLUP (Beaulieu et al., 2014; Meuwissen et al., 2001). All the agronomic traits examined in our research are quantitative traits and are governed by a combination of multiple large‐effect and numerous small‐effect genes (Adhikari et al., 2019; He et al., 2020; Jiang et al., 2022; Li, Alarcón‐Zúñiga, et al., 2015; Lin et al., 2021; Mackay, 2001; McCord et al., 2014; Wang et al., 2020; Zhang et al., 2022), and the rrBLUP method may offer better predictions for these traits compared to the Bayesian methods. Although rrBLUP method may seem superior, its prediction accuracy is not consistently better than that of Bayesian methods.…”

Identification of genetic loci associated with five agronomic traits in alfalfa using multi-environment trials

“…Plant materials were described previously (Lin et al 2021). In brief, a diverse panel of 200 genotypes consisted of 148 accessions from USDA-ARS National Plant Germplasm System, and 52 cultivars from S&W Seed Co., Alforex Seeds™, Legacy Seeds, and Blue River Hybrids.…”

“…DNA isolation and library construction were completed as previously described (Lin et al 2021). In brief, DNA was extracted from young leaves using Qiagen DNeasy 96 Plant Kit (Qiagen, CA) according to the manufacturer's protocol.…”

“…GWAS allows modeling of the genetic associations between quantitative traits and molecular markers using diverse populations, the information needed for MAS. Our previous studies on GWAS in alfalfa have identified useful genetic markers associated with drought tolerance (Yu 2017), salt tolerance (Medina et al 2020), and forage quality (Lin et al 2021).…”

Genome‐wide association study identified the candidate genes associated with angel wing trait in Pekin duck

“…Owing to the distal regulation of the SNPs and referring to other related studies (Lin et al, 2021), we considered that significant SNPs may affect the genes in the nearby 50 kb range, and defined the genes in the 50 kb extended region surrounding significant SNPs as candidate genes, including 17 annotated protein coding genes and four anonymous genes, namely uncharacterized LOC106017654 , SOX17 , OPCML , ENSAPLG00020016661 , MLN , ENSAPLG00020016665 , LEMD2 , IP6K3 , UQCC2 , ENSAPLG00020016921 , LZTS1 , ATP6V1B2 , SLC18A1 , MAK16 , TTI2 , RNF122 , DUSP26 , UHRF1 , ENSAPLG00020008207 , FEM1A and DPP9 .…”