So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

Domestication provides an important model for the study of evolution, and information learned from domestication research aids in the continued improvement of crop species. Recent progress in de novo assembly and whole-genome resequencing of wild and cultivated soybean genomes, in addition to new archeological discoveries, sheds light on the origin of this important crop and provides a clearer view on the modes of artificial selection that drove soybean domestication and diversification. This novel genomic information enables the search for polymorphisms that underlie variation in agronomic traits and highlights genes that exhibit a signature of selection, leading to the identification of a number of candidate genes that may have played important roles in soybean domestication, diversification and improvement. These discoveries provide a novel point of comparison on the evolutionary bases of important agronomic traits among different crop species.

https://nph.onlinelibrary.wiley.com/doi/epdf/10.1111/nph.14418

Soybean domestication: the origin, genetic architecture and molecular bases

Advances in genotyping technology, such as genotyping by sequencing (GBS), are making genomic prediction more attractive to reduce breeding cycle times and costs associated with phenotyping. Genomic prediction and selection has been studied in several crop species, but no reports exist in soybean. The objectives of this study were (i) evaluate prospects for genomic selection using GBS in a typical soybean breeding program and (ii) evaluate the effect of GBS marker selection and imputation on genomic prediction accuracy. To achieve these objectives, a set of soybean lines sampled from the University of Nebraska Soybean Breeding Program were genotyped using GBS and evaluated for yield and other agronomic traits at multiple Nebraska locations. Genotyping by sequencing scored 16,502 single nucleotide polymorphisms (SNPs) with minor-allele frequency (MAF) > 0.05 and percentage of missing values ≤ 5% on 301 elite soybean breeding lines. When SNPs with up to 80% missing values were included, 52,349 SNPs were scored. Prediction accuracy for grain yield, assessed using cross validation, was estimated to be 0.64, indicating good potential for using genomic selection for grain yield in soybean. Filtering SNPs based on missing data percentage had little to no effect on prediction accuracy, especially when random forest imputation was used to impute missing values. The highest accuracies were observed when random forest imputation was used on all SNPs, but differences were not significant. A standard additive G-BLUP model was robust; modeling additive-by-additive epistasis did not provide any improvement in prediction accuracy. The effect of training population size on accuracy began to plateau around 100, but accuracy steadily climbed until the largest possible size was used in this analysis. Including only SNPs with MAF > 0.30 provided higher accuracies when training populations were smaller. Using GBS for genomic prediction in soybean holds good potential to expedite genetic gain. Our results suggest that standard additive G-BLUP models can be used on unfiltered, imputed GBS data without loss in accuracy.

/pdf/genotyping-by-sequencing-for-genomic-prediction-in-a-soybean-1k98jgshk1.pdf

Genotyping by sequencing for genomic prediction in a soybean breeding population.

Genome-wide association analysis is a powerful approach to identify the causal genetic polymorphisms underlying complex traits. In this study, we evaluated a population of 191 soybean landraces in five environments to detect molecular markers associated with soybean yield and its components using 1,536 single-nucleotide polymorphisms (SNPs) and 209 haplotypes. The analysis revealed that abundant phenotypic and genetic diversity existed in the studied population. This soybean population could be divided into two subpopulations and no or weak relatedness was detected between pair-wise landraces. The level of intra-chromosomal linkage disequilibrium was about 500 kb. Genome-wide association analysis based on the unified mixed model identified 19 SNPs and 5 haplotypes associated with soybean yield and yield components in three or more environments. Nine markers were found co-associated with two or more traits. Many markers were located in or close to previously reported quantitative trait loci mapped by linkage analysis. The SNPs and haplotypes identified in this study will help to further understand the genetic basis of soybean yield and its components, and may facilitate future high-yield breeding by marker-assisted selection in soybean.

Identification of single nucleotide polymorphisms and haplotypes associated with yield and yield components in soybean (Glycine max) landraces across multiple environments.

The objectives of this work were to investigate the genetic variation in 79 soybean (Glycine max) accessions from different regions of the world, to cluster the accessions based on their similarity, and to test the correlation between the two types of markers used. Simple sequence repeat markers present in genomic (SSR) and in expressed regions (EST-SSR) were used. Thirty SSR primer-pairs were selected (20 genomic and 10 EST-SSR) based on their distribution on the 20 genetic linkage groups of soybean, on their trinucleotide repetition unit and on their polymorphism information content. All analyzed loci were polymorphic, and 259 alleles were found. The number of alleles per locus varied from 2-21, with an average of 8.63. The accessions exhibit a significant number of rare alleles, with genotypes 19, 35, 63 and 65 carrying the greater number of exclusive alleles. Accessions 75 and 79 were the most similar and accessions 31 and 35, and 40 and 78, were the most divergent ones. A low correlation between SSR and EST-SSR data was observed, thus genomic and expressed microsatellite markers are required for an appropriate analysis of genetic diversity in soybean. The genetic diversity observed was high and allowed the formation of five groups and several subgroups. A moderate relationship between genetic divergence and geographic origin of accessions was observed.

/pdf/genetic-diversity-in-soybean-germplasm-identified-by-ssr-and-4xqqwbuckg.pdf

Genetic diversity in soybean germplasm identified by SSR and EST-SSR markers.

Plants and seeds of hybrid corn variety ch098773

Plants and seeds of hybrid corn variety ch945438

Plants and seeds of hybrid corn variety CH514730

Plants and seeds of hybrid corn variety ch336383

Plants and seeds of hybrid corn variety ch793010

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Peter Guzman

Author Tools

Chat about Author