Selection indexes based on linear‐bilinear models applied to soybean breeding

2020 ◽  
Vol 112 (1) ◽  
pp. 175-182 ◽  
Author(s):  
Leomar Guilherme Woyann ◽  
Daniela Meira ◽  
Gilvani Matei ◽  
Andrei Daniel Zdziarski ◽  
Lucas Vinicius Dallacorte ◽  
...  
Keyword(s):  
Soybean Breeding ◽  
Bilinear Models ◽  
Selection Indexes

Identification of novel haplotype of a cyst nematode resistance gene, GmSNAP18 in soybean [Glycine max (L.) Merr.]

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Ik-Young Choi ◽  
Prakash Basnet ◽  
Hana Yoo ◽  
Neha Samir Roy ◽  
Rahul Vasudeo Ramekar ◽  
...  
Keyword(s):  
Soybean Cyst Nematode ◽  
Gene Families ◽  
Nematode Resistance ◽  
Cyst Nematode ◽  
Soybean Breeding ◽  
Resistant Varieties ◽  
Soybean Genotypes ◽  
Glycine Max L ◽  
Scn Resistance

Soybean cyst nematode (SCN) is one of the most damaging pest of soybean. Discovery and characterization of the genes involved in SCN resistance are important in soybean breeding. Soluble NSF attachment protein (SNAP) genes are related to SCN resistance in soybean. SNAP genes include five gene families, and 2 haplotypes of exons 6 and 9 of SNAP18 are considered resistant to the SCN. In present study the haplotypes of GmSNAP18 were surveyed and chacterized in a total of 60 diverse soybean genotypes including Korean cultivars, landraces, and wild-types. The target region of exons 6 and 9 in GmSNAP18 region was amplified and sequenced to examine nucleotide variation. Characterization of 5 haplotypes identified in present study for the GmSNAP18 gene revealed two haplotypes as resistant, 1 as susceptible and two as novel. A total of twelve genotypes showed resistant haplotypes, and 45 cultivars were found susceptible. Interestingly, the two novel haplotypes were present in 3 soybean lines. The information provided here about the haplotypic variation of GmSNAP18 gene can be further explored for soybean breeding to develop resistant varieties.

Genetic Gain of Soybean Breeding for Yield in Henan Summer Soybean Zone over the Last 30 Years

2016 ◽  
Vol 42 (7) ◽  
pp. 1009
Author(s):  
Zhong-Wen HUANG ◽  
Xin-Juan XU ◽  
Wei WANG ◽  
Pei-Pei MEI
Keyword(s):  
Genetic Gain ◽  
Soybean Breeding

scholarly journals Selection Response Due to Different Combination of Antagonistic Milk, Beef, and Morphological Traits in the Alpine Grey Cattle Breed

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1340
Author(s):  
Enrico Mancin ◽  
Cristina Sartori ◽  
Nadia Guzzo ◽  
Beniamino Tuliozi ◽  
Roberto Mantovani
Keyword(s):  
Genetic Correlation ◽  
Functional Traits ◽  
Performance Test ◽  
Genetic Relationships ◽  
Selection Index ◽  
Milk Traits ◽  
Linear Type ◽  
Dual Purpose ◽  
Type Traits ◽  
Selection Indexes

Selection in local dual-purpose breeds requires great carefulness because of the need to preserve peculiar traits and also guarantee the positive genetic progress for milk and beef production to maintain economic competitiveness. A specific breeding plan accounting for milk, beef, and functional traits is required by breeders of the Alpine Grey cattle (AG), a local dual-purpose breed of the Italian Alps. Hereditability and genetic correlations among all traits have been analyzed for this purpose. After that, different selection indexes were proposed to identify the most suitable for this breed. Firstly, a genetic parameters analysis was carried out with different datasets. The milk dataset contained 406,918 test day records of milk, protein, and fat yields and somatic cells (expressed as SCS). The beef dataset included performance test data conducted on 749 young bulls. Average daily gain, in vivo estimated carcass yields, and carcass conformation (SEUROP) were the phenotypes obtained from the performance tests. The morphological dataset included 21 linear type evaluations of 11,320 first party cows. Linear type traits were aggregated through factor analysis and three factors were retained, while head typicality (HT) and rear muscularity (RM) were analyzed as single traits. Heritability estimates (h2) for milk traits ranged from 0.125 to 0.219. Analysis of beef traits showed h2 greater than milk traits, ranging from 0.282 to 0.501. Type traits showed a medium value of h2 ranging from 0.238 to 0.374. Regarding genetic correlation, SCS and milk traits were strongly positively correlated. Milk traits had a negative genetic correlation with the factor accounting for udder conformations (−0.40) and with all performance test traits and RM. These latter traits showed also a negative genetic correlation with udder volume (−0.28). The HT and the factor accounting for rear legs traits were not correlated with milk traits, but negatively correlated with beef traits (−0.32 with RM). We argue that the consequence of these results is that the use of the current selection index, which is mainly focused on milk attitude, will lead to a deterioration of all other traits. In this study, we propose more appropriate selection indexes that account for genetic relationships among traits, including functional traits.

Linearized Robust Counterparts of Two-Stage Robust Optimization Problems with Applications in Operations Management

2020 ◽  
Author(s):  
Amir Ardestani-Jaafari ◽  
Erick Delage
Keyword(s):  
Robust Optimization ◽  
Operations Management ◽  
Optimization Problems ◽  
Close Relation ◽  
Bilinear Programming ◽  
Two Stage ◽  
Bilinear Models ◽  
Robust Counterpart ◽  
Adjustable Robust Counterpart ◽  
Affinely Adjustable Robust Counterpart

In this article, we discuss an alternative method for deriving conservative approximation models for two-stage robust optimization problems. The method mainly relies on a linearization scheme employed in bilinear programming; therefore, we will say that it gives rise to the linearized robust counterpart models. We identify a close relation between this linearized robust counterpart model and the popular affinely adjustable robust counterpart model. We also describe methods of modifying both types of models to make these approximations less conservative. These methods are heavily inspired by the use of valid linear and conic inequalities in the linearization process for bilinear models. We finally demonstrate how to employ this new scheme in location-transportation and multi-item newsvendor problems to improve the numerical efficiency and performance guarantees of robust optimization.

The Relative Efficiencies of Selection Indexes

1969 ◽  
Vol 19 (1) ◽  
pp. 45-48 ◽  
Author(s):  
E. P. Cunningham
Keyword(s):  
Selection Indexes
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