December 2013

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Join the symposium live from Michigan State University by registering at the link below.

Title: Genetics of Seed Quality, Germination and Evolution
Date: Friday, December 13, 2013
Time: 9:00 am - 4:00 pm Eastern Time

Schedule:  http://www.extension.org/pages/70127/michigan-state-university-plant-breeding-and-genetics-symposium

9:10 - 10:00 Dr. Hiro Nonogaki, Oregon State University
“Transcriptional and Post-transcriptional Regulation of Gene Expression Associated with Hormone Signaling in Seeds.”

10:40 - 11:30 Dr. Kristin Mercer, Ohio State University
“Evolutionary Ecology Of Seeds In Crop-Wild Hybrid Zones.”

1:35 - 2:25 pm Dr. Chris Richards, US National Center for Genetic Preservation
"Genetic Dynamics in Conservation Collections"

3:05 - 3:55 pm Dr. Oswald Crasta, Leader, Genomics Assisted Breeding, Dow AgroSciences
"Next-generation breeding"

Publications of Interest
Plant breeding can be broadly defined as alterations caused in plants as a result of their use by humans, ranging from unintentional changes resulting from the advent of agriculture to the application of molecular tools for precision breeding. The vast diversity of breeding methods can be simplified into three categories: (i) plant breeding based on observed variation by selection of plants based on natural variants appearing in nature or within traditional varieties; (ii) plant breeding based on controlled mating by selection of plants presenting recombination of desirable genes from different parents; and (iii) plant breeding based on monitored recombination by selection of specific genes or marker profiles, using molecular tools for tracking within-genome variation. The continuous application of traditional breeding methods in a given species could lead to the narrowing of the gene pool from which cultivars are drawn, rendering crops vulnerable to biotic and abiotic stresses and hampering future progress. Several methods have been devised for introducing exotic variation into elite germplasm without undesirable effects. Cases in rice are given to illustrate the potential and limitations of different breeding approaches.
Neglected and underutilized species (NUS) are those to which little attention is paid or which are entirely ignored by agricultural researchers, plant breeders and policymakers1. Typically, NUS are not traded as commodities. They are wild or semi-domesticated varieties and non-timber forest species adapted to particular, often quite local, environments. Many of these varieties and species, along with a wealth of traditional knowledge about their cultivation and use, are being lost at an alarming rate. Yet NUS present tremendous opportunities for fighting poverty, hunger and malnutrition. And they can help make agricultural production systems more resilient to climate change. Not least, acknowledgment of the value of NUS in traditional foods and cultures can empower indigenous communities (women in particular) and reaffirm their identity. The time for action on NUS is now. There is a growing realization that agriculture must diversify. NUS have an important role to play in advancing agricultural development beyond the Green Revolution model of improving and raising the yields of staple crops.
Most methods for next-generation sequencing (NGS) data analyses incorporate information regarding allele frequencies using the assumption of Hardy–Weinberg equilibrium (HWE) as a prior. However, many organisms including those that are domesticated, partially selfing, or with asexual life cycles show strong deviations from HWE. For such species, and specially for low-coverage data, it is necessary to obtain estimates of inbreeding coefficients (F) for each individual before calling genotypes. Here, we present two methods for estimating inbreeding coefficients from NGS data based on an expectation-maximization (EM) algorithm. We assess the impact of taking inbreeding into account when calling genotypes or estimating the site frequency spectrum (SFS), and demonstrate a marked increase in accuracy on low-coverage highly inbred samples. We demonstrate the applicability and efficacy of these methods in both simulated and real data sets.