Combination of meta‐analysis of QTL and GWAS to uncover the genetic architecture of seed yield and seed yield components in common bean

Abstract

Increasing seed yield in common bean could help to improve food security and reduce malnutrition globally due to the high nutritional quality of this crop. However, the complex genetic architecture and prevalent genotype by environment interactions for seed yield makes increasing genetic gains challenging. The aim of this study was to identify the most consistent genomic regions related with seed yield components and phenology reported in the last 20 years in common bean. A meta‐analysis of quantitative trait locus (QTL) for seed yield components and phenology (MQTL‐YC) was performed for 394 QTL reported in 21 independent studies under sufficient water and drought conditions. In total, 58 MQTL‐YC over different genetic backgrounds and environments were identified, reducing threefold on average the confidence interval (CI) compared with the CI for the initial QTL. Furthermore, 40 MQTL‐YC identified were co‐located with 210 SNP peak positions reported via genome‐wide association (GWAS), guiding the identification of candidate genes. Comparative genomics among these MQTL‐YC with MQTL‐YC reported in soybean and pea allowed the identification of 14 orthologous MQTL‐YC shared across species. The integration of MQTL‐YC, GWAS, and comparative genomics used in this study is useful to uncover and refine the most consistent genomic regions related with seed yield components for their use in plant breeding.

Mapping yield and yield-related traits using diverse common bean germplasm

“…Two SNPs associated with yield and YGD were located close to a previously identified yield QTL on Pv01 ( Trapp et al, 2015 ; Diaz et al, 2020 ; Izquierdo et al, 2023 ). The SNP (ss715646889) on Pv04 was associated with over 40% of the variability in yield, and YGD is located close to the previously identified yield QTL on this chromosome ( Diaz et al, 2018 ).…”

“…QTL tags consist of QTL name (original, if available) and first initials of last names of the first two authors and the year of publication [AP20 ( Almeida et al, 2020 ), BG12a ( Blair et al, 2012a ), BI06 ( Blair et al, 2006 ), CB08 ( Checa and Blair, 2008 ), CB12 ( Checa and Blair, 2012 ), DAS20 ( Diaz et al, 2020 ), DC15 ( Diaz Castro, 2015 ), DR17 ( Diaz et al, 2017 ), DR18 ( Diaz et al, 2018 ), GC12 ( Galeano et al, 2012 ), GYL21 ( González et al, 2021 ), HVS16 ( Hoyos-Villegas et al, 2016 ), HVS17 ( Hoyos-Villegas et al, 2017 ), KC15 ( Kamfwa et al, 2015 ), MB14 ( Mukeshimana et al, 2014 ), MK06 ( Miklas et al, 2006 ), MS00b ( Miklas et al, 2000b ), MC21 ( Mir et al, 2021 ), OP19 ( Oladzad et al, 2019 ), SN11 ( Shi et al, 2011 ), SO21 ( Simons et al, 2021 ), TM01 ( Tar’an et al, 2001 ), TU15 ( Trapp et al, 2015 ), VC15 ( Viteri et al, 2015 ), XK17 ( Xie et al, 2017 ), YP00a ( Yu et al, 2000a ), YP00b ( Yu et al, 2000b ), and ZW16 ( Zhu et al, 2016 )]. QTLs from the meta-analysis ( Izquierdo et al, 2023 ) were labeled as META_name (trait). Additional markers on the map were downloaded from the Legume Information System (LIS; https://www.legumeinfo.org ), and background literature was searched and BLASTed against the current common bean genome sequence ( P. vulgaris v2.1) in Phytozome ( https://phytozome-next.jgi.doe.gov ).…”

“…Comparative studies have revealed extensive preservation of genome order, or synteny, among species ( McConnell et al, 2010 ). Recently, a meta-analysis that combined data from different QTL and/or GWASs ( Goffinet and Gerber, 2000 ) identified the genomic regions in the current P. vulgaris v2.1 sequence map that were most consistently associated with yield and yield components in bean ( Arriagada et al, 2023 ; Izquierdo et al, 2023 ). A similar approach was used to identify meta-QTL (MQTL) for major diseases in bean, including CBB ( Rahmanzadeh et al, 2022 ).…”

Genetic insights into agronomic and morphological traits of drug-type cannabis revealed by genome-wide association studies

“…As the identification of high-value markers for Cannabis is in its early stages, the practical implementation of these markers by breeding programs will nevertheless require preliminary cross-validation. This can be achieved through meta-GWAS 81 , QTL mapping with biparental population and BSA. Additionally, comprehensive functional analyses of the candidate genes will be crucial.…”

Genome-wide association and genomic prediction for iron and zinc concentration and iron bioavailability in a collection of yellow dry beans

“…A strong environment effect was observed for yield, and Fe and Zn concentrations; and no consistent associations were identified for these traits on the YBC. The complex architecture of these traits and the GxE interaction has yielded hundreds of marker-trait associations across locations and populations ( Izquierdo et al, 2018 ; Izquierdo et al, 2023 ), and the usage of strategies such as marker-assisted selection or GWAS-assisted genomic prediction does not appear promising for these traits ( Keller et al, 2020 ).…”