Genome-wide associations with longevity and reproductive traits in U.S. rangeland ewes.

Introduction: Improving ewe longevity is an important breeding and management goal, as death loss and early culling of mature ewes are economic burdens in the sheep industry. Ewe longevity can be improved by selecting for positive reproductive outcomes. However, the breeding approaches for accomplishing this come with the challenge of recording a lifetime trait. Characterizing genetic factors underpinning ewe longevity and related traits could result in the development of genomic selection strategies to improve the stayability of sheep through early, informed selection of replacement ewes. Methods: Towards this aim, a genome-wide association study (GWAS) was performed to identify genetic markers associated with ewe longevity, reproductive, and production traits. Traits evaluated included longevity (i.e., length of time in the flock), parity and the lifetime number of lambs born, lambs born alive, lambs weaned, and weight of lambs weaned. Ewe records from previous studies were used. Specifically, Rambouillet (n = 480), Polypay (n = 404), Suffolk (n = 182), and Columbia (n = 64) breed ewes (N = 1,130) were analyzed against 503,617 SNPs in across-breed and within-breed GWAS conducted with the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) model in R. Results: The across-breed GWAS identified 25 significant SNPs and the within-breed GWAS for Rambouillet, Polypay, and Suffolk ewes identified an additional 19 significant SNPs. The most significant markers were rs411309094 (13:22,467,143) associated with longevity in across-breed GWAS (p-value = 8.3E-13) and rs429525276 (2:148,398,336) associated with both longevity (p-value = 6.4E-15) and parity (p-value = 4.8E-15) in Rambouillet GWAS. Significant SNPs were identified within or in proximity (±50 kb) of genes with known or proposed roles in reproduction, dentition, and the immune system. These genes include ALPL, ANOS1, ARHGEF26, ASIC2, ASTN2, ATP8A2, CAMK2D, CEP89, DISC1, ITGB6, KCNH8, MBNL3, MINDY4, MTSS1, PLEKHA7, PRIM2, RNF43, ROBO2, SLCO1A2, TMEM266, TNFRSF21, and ZNF804B. Discussion: This study proposes multiple SNPs as candidates for use in selection indices and suggests genes for further research towards improving understanding of the genetic factors contributing to longevity, reproductive, and production traits of ewes.

Dissecting the Genetic Architecture of Aphanomyces Root Rot Resistance in Lentil by QTL Mapping and Genome-Wide Association Study.

Lentil (Lens culinaris Medikus) is an important source of protein for people in developing countries. Aphanomyces root rot (ARR) has emerged as one of the most devastating diseases affecting lentil production. In this study, we applied two complementary quantitative trait loci (QTL) analysis approaches to unravel the genetic architecture underlying this complex trait. A recombinant inbred line (RIL) population and an association mapping population were genotyped using genotyping by sequencing (GBS) to discover novel single nucleotide polymorphisms (SNPs). QTL mapping identified 19 QTL associated with ARR resistance, while association mapping detected 38 QTL and highlighted accumulation of favorable haplotypes in most of the resistant accessions. Seven QTL clusters were discovered on six chromosomes, and 15 putative genes were identified within the QTL clusters. To validate QTL mapping and genome-wide association study (GWAS) results, expression analysis of five selected genes was conducted on partially resistant and susceptible accessions. Three of the genes were differentially expressed at early stages of infection, two of which may be associated with ARR resistance. Our findings provide valuable insight into the genetic control of ARR, and genetic and genomic resources developed here can be used to accelerate development of lentil cultivars with high levels of partial resistance to ARR.

Phenotyping of Plant Biomass and Performance Traits Using Remote Sensing Techniques in Pea (Pisum sativum, L.).

Field pea cultivars are constantly improved through breeding programs to enhance biotic and abiotic stress tolerance and increase seed yield potential. In pea breeding, the Above Ground Biomass (AGBM) is assessed due to its influence on seed yield, canopy closure, and weed suppression. It is also the primary yield component for peas used as a cover crop and/or grazing. Measuring AGBM is destructive and labor-intensive process. Sensor-based phenotyping of such traits can greatly enhance crop breeding efficiency. In this research, high resolution RGB and multispectral images acquired with unmanned aerial systems were used to assess phenotypes in spring and winter pea breeding plots. The Green Red Vegetation Index (GRVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), plot volume, canopy height, and canopy coverage were extracted from RGB and multispectral information at five imaging times (between 365 to 1948 accumulated degree days/ADD after 1 May) in four winter field pea experiments and at three imaging times (between 1231 to 1648 ADD) in one spring field pea experiment. The image features were compared to ground-truth data including AGBM, lodging, leaf type, days to 50% flowering, days to physiological maturity, number of the first reproductive node, and seed yield. In two of the winter pea experiments, a strong correlation between image features and seed yield was observed at 1268 ADD (flowering). An increase in correlation between image features with the phenological traits such as days to 50% flowering and days to physiological maturity was observed at about 1725 ADD in these winter pea experiments. In the spring pea experiment, the plot volume estimated from images was highly correlated with ground truth canopy height (r = 0.83) at 1231 ADD. In two other winter pea experiments and the spring pea experiment, the GRVI and NDVI features were significantly correlated with AGBM at flowering. When selected image features were used to develop a least absolute shrinkage and selection operator model for AGBM estimation, the correlation coefficient between the actual and predicted AGBM was 0.60 and 0.84 in the winter and spring pea experiments, respectively. A SPOT-6 satellite image (1.5 m resolution) was also evaluated for its applicability to assess biomass and seed yield. The image features extracted from satellite imagery showed significant correlation with seed yield in two winter field pea experiments, however, the trend was not consistent. In summary, the study supports the potential of using unmanned aerial system-based imaging techniques to estimate biomass and crop performance in pea breeding programs.