Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.05.13.540664

Models to Estimate Genetic Gain of Soybean Seed Yield from Annual Multi-Environment Field Trials

Dalsente Krause, M.·Piepho, H.-P.·Dias Olimpio das Gracas, K.·Singh, A. K.·Beavis, W. D.
View original source ↗Export metadata ↓

Abstract

1Genetic improvements of discrete characteristics such as flower color, the genetic improvements are obvious and easy to demonstrate; however, for characteristics that are measured on continuous scales, the genetic contributions are incremental and less obvious. Reliable and accurate methods are required to disentangle the confounding genetic and non-genetic components of quantitative traits. Stochastic simulations of soybean (Glycine max (L.) Merr.) breeding programs were performed to evaluate models to estimate the realized genetic gain (RGG) from 30 years of multi-environment trials (MET). True breeding values were simulated under an infinitesimal model to represent the genetic contributions to soybean seed yield under various MET conditions. Estimators were evaluated using objective criteria of bias and linearity. Results indicated all estimation models were biased. Covariance modeling as well as direct versus indirect estimation resulted in substantial differences in RGG estimation. Although there were no unbiased models, the three best-performing models resulted in an average bias of {+/-}7.41 kg/ha-1/yr-1 ({+/-}0.11 bu/ac-1/yr-1). Rather than relying on a single model to estimate RGG, we recommend the application of multiple models and consider the range of the estimated values. Further, based on our simulations parameters, we do not think it is appropriate to use any single models to compare breeding programs or quantify the efficiency of proposed new breeding strategies. Lastly, for public soybean programs breeding for maturity groups II and III in North America from 1989 to 2019, the range of estimated RGG values was from 18.16 to 39.68 kg/ha-1/yr-1 (0.27 to 0.59 bu/ac-1/yr-1).

Source connections

Explore related subjects

geneticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dalsente Krause, M., Piepho, H.-P., Dias Olimpio das Gracas, K., Singh, A. K., Beavis, W. D.. 2023-05-14. Models to Estimate Genetic Gain of Soybean Seed Yield from Annual Multi-Environment Field Trials. https://doi.org/10.1101/2023.05.13.540664

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

A genetic screen to identify host factors required for commensal bacterial adherence to the gut epithelia

Bacterial adherence to gut epithelial cells likely involves host genetic components. We isolated a gut commensal bacterium, Lelliottia jeotgali, that can robustly colonize C. elegans through attachment to intestinal epithelial cells. Here, we developed a novel screen in C. elegans to identify host genes required for microbiome bacterial colonization. Using transgenic L. jeotgali strain expressing tdTomato, we conducted forward genetic screens in C. elegans to select for mutants that completely lost colonization. We identified multiple allelic mutations in five different C. elegans genes, termed knockout of Lelliottia adherence (kola) mutants, with a near complete loss of adherence. Each of kola-1-5 is uncharacterized and predicted to encode for a secreted or membrane-bound protein. We also identified gly-5, a GalNAc transferase that initiates O-linked glycosylation. Overall, we describe the first C. elegans screen to identify host factors that control microbiome bacterial colonization in the gut.

genetics↗
bioRxiv2026

Deciphering the Dynamics of Leukemogenic Transformation from Clonal Hematopoiesis in Congenital Neutropenia

Inherited neutropenia syndromes, severe congenital neutropenia (CN) and Shwachman-Diamond syndrome (SDS), confer a high cumulative risk of MDS and AML. It is not yet clear which somatic mutations, in which combination, allele frequency and sequence identify patients at high risk. We analyzed clonal hematopoiesis (CH) in the largest representative cohort of inherited neutropenia patients to date - 658 samples from 309 patients - using error-corrected panel sequencing with longitudinal sampling. This was complemented in selected cases by single-cell DNA sequencing and genotyping of single-cell derived colonies. CH was detected in early childhood, affecting 37.7% of CN and 60% of SDS patients. The somatic mutation burden remained essentially unchanged over decades. In CN, CSF3R mutations were dominant across genetic subtypes, while additional mutations were rare and included ASXL1, TET2, and DNMT3A. ASXL1 mutations most frequently co-occurred with CSF3R mutations. Co-occurring CSF3R and TP53 mutations were only found in SRP54-CN and CLPB-CN patients. In SDS, TP53 was the most frequently mutated gene, followed by low-prevalence somatic mutations in DNMT3A, IDH1, PTPN11, and RUNX1. In CN-MDS/AML, RUNX1 and SETBP1 mutations emerged shortly before progression to MDS/AML, and membrane-proximal CSF3R mutations only at the leukemic stage. In SDS, biallelic TP53-mutant clones expanded prior to transformation, and a variant allele frequency (VAF) of dominant TP53 clone above 0.074 discriminated between patients who progressed and those who did not. Peripheral blood reflected bone marrow genotype for most key drivers. Leukemic transformation in inherited neutropenia is therefore not stochastic, but rather follows a defined mutational sequence, initiated in childhood.

genetics↗
bioRxiv2026

A largely shared genetic architecture underlies Saccharomyces cerevisiae persistence across different mouse host genotypes

The extent to which interactions between microbial and host genotypes shape opportunistic pathogen persistence within hosts is an important open question. To assess the contribution of these interactions to persistence, we used Saccharomyces cerevisiae , a genetically tractable model for studying opportunistic fungal persistence in mammalian hosts. A pooled panel of barcoded haploid segregants from a cross between a laboratory strain and a clinical isolate, together with both parents, was introduced intravenously into seven genetically diverse mouse strains. We measured the relative persistence of segregants in mouse brains and livers using barcode sequencing. Persistence showed transgressive segregation in both organs. On average across the two organs, yeast genotype explained 45.2% of the variance in segregant persistence, whereas yeast genotype-by-host genotype interactions explained only 2.9%. Linkage mapping showed that loci detected in more host genotypes generally explained more phenotypic variance than those detected in fewer host genotypes. At some loci, genetic variation correlated with increased yeast persistence across multiple organs (general loci) while at others increased persistence in one organ came at the cost of decreased persistence in another (antagonistic loci). General loci contributed disproportionately to brain persistence, whereas liver persistence reflected contributions from both classes. The clinical parent showed greater persistence than the laboratory parent in every host-organ combination, and the additive effects of detected loci predicted the direction of this advantage. Overall, this work shows that in this system, relative microbial persistence is polygenic, with a strong additive component and only a very small contribution from microbial genotype-by-host genotype interactions.

genetics↗