Frequency and genetic effect of 1 qh+

1974 ◽  
Vol 21 (2) ◽  
pp. 193-196 ◽  
Author(s):  
Johannes Nielsen ◽  
Ursula Friedrich ◽  
�str�dur B. Hreidarsson
Keyword(s):  
2009 ◽  
Vol 14 (2) ◽  
pp. 160-167 ◽  
Author(s):  
Katariina Salmela-Aro ◽  
Sanna Read ◽  
Jari-Erik Nurmi ◽  
Markku Koskenvuo ◽  
Jaakko Kaprio ◽  
...  

This study examined genetic and environmental influences on older women’s personal goals by using data from the Finnish Twin Study on Aging. The interview for the personal goals was completed by 67 monozygotic (MZ) pairs and 75 dizygotic (DZ) pairs. The tetrachoric correlations for personal goals related to health and functioning, close relationships, and independent living were higher in MZ than DZ twins, indicating possible genetic influence. The pattern of tetrachoric correlations for personal goals related to cultural activities, care of others, and physical exercise indicated environmental influence. For goals concerning health and functioning, independent living, and close relationships, additive genetic effect accounted for about half of the individual variation. The rest was the result of a unique environmental effect. Goals concerning physical exercise and care of others showed moderate common environmental effect, while the rest of the variance was the result of a unique environmental effect. Personal goals concerning cultural activities showed unique environmental effects only.


2012 ◽  
Vol 34 (5) ◽  
pp. 584-590
Author(s):  
Peng-Yu WANG ◽  
Zha-Xi GUANQUE ◽  
Quan-Qing QI ◽  
Mao DE ◽  
Wen-Guang ZHANG ◽  
...  

2011 ◽  
Vol 33 (11) ◽  
pp. 1245-1250 ◽  
Author(s):  
Tian-Qi ZHANG ◽  
Xiao-Feng ZHANG ◽  
Zhao-Jun TAN ◽  
Zhu CAO ◽  
Xuan-Peng WANG ◽  
...  

2017 ◽  
Vol 43 (1) ◽  
pp. 63 ◽  
Author(s):  
Na BAI ◽  
Yong-Xiang LI ◽  
Fu-Chao JIAO ◽  
Lin CHEN ◽  
Chun-Hui LI ◽  
...  

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Akio Onogi ◽  
Toshio Watanabe ◽  
Atsushi Ogino ◽  
Kazuhito Kurogi ◽  
Kenji Togashi

Abstract Background Genomic prediction is now an essential technology for genetic improvement in animal and plant breeding. Whereas emphasis has been placed on predicting the breeding values, the prediction of non-additive genetic effects has also been of interest. In this study, we assessed the potential of genomic prediction using non-additive effects for phenotypic prediction in Japanese Black, a beef cattle breed. In addition, we examined the stability of variance component and genetic effect estimates against population size by subsampling with different sample sizes. Results Records of six carcass traits, namely, carcass weight, rib eye area, rib thickness, subcutaneous fat thickness, yield rate and beef marbling score, for 9850 animals were used for analyses. As the non-additive genetic effects, dominance, additive-by-additive, additive-by-dominance and dominance-by-dominance effects were considered. The covariance structures of these genetic effects were defined using genome-wide SNPs. Using single-trait animal models with different combinations of genetic effects, it was found that 12.6–19.5 % of phenotypic variance were occupied by the additive-by-additive variance, whereas little dominance variance was observed. In cross-validation, adding the additive-by-additive effects had little influence on predictive accuracy and bias. Subsampling analyses showed that estimation of the additive-by-additive effects was highly variable when phenotypes were not available. On the other hand, the estimates of the additive-by-additive variance components were less affected by reduction of the population size. Conclusions The six carcass traits of Japanese Black cattle showed moderate or relatively high levels of additive-by-additive variance components, although incorporating the additive-by-additive effects did not improve the predictive accuracy. Subsampling analysis suggested that estimation of the additive-by-additive effects was highly reliant on the phenotypic values of the animals to be estimated, as supported by low off-diagonal values of the relationship matrix. On the other hand, estimates of the additive-by-additive variance components were relatively stable against reduction of the population size compared with the estimates of the corresponding genetic effects.


BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Osval Antonio Montesinos-López ◽  
Abelardo Montesinos-López ◽  
Paulino Pérez-Rodríguez ◽  
José Alberto Barrón-López ◽  
Johannes W. R. Martini ◽  
...  

Abstract Background Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns. Main body We review the applications of deep learning (DL) methods in genomic selection (GS) to obtain a meta-picture of GS performance and highlight how these tools can help solve challenging plant breeding problems. We also provide general guidance for the effective use of DL methods including the fundamentals of DL and the requirements for its appropriate use. We discuss the pros and cons of this technique compared to traditional genomic prediction approaches as well as the current trends in DL applications. Conclusions The main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets.


Nitric Oxide ◽  
2017 ◽  
Vol 70 ◽  
pp. 25-30 ◽  
Author(s):  
Yingshui Yao ◽  
Zhengmei Fang ◽  
Song Yang ◽  
Hailong Zhao ◽  
Yanchun Chen ◽  
...  

2000 ◽  
Vol 23 (1) ◽  
pp. 1-10 ◽  
Author(s):  
A. Collins ◽  
S. Ennis ◽  
W. Tapper ◽  
N.E. Morton

Meta-analysis is presented for published studies on linkage or allelic association that have in common only reported significance levels. Reporting is biassed, and nonsignificance is seldom quantified. Therefore meta-analysis cannot identify oligogenes within a candidate region nor establish their significance, but it defines candidate regions well. Applied to a database on atopy and asthma, candidate regions are identified on chromosomes 6, 5, 16, 11, 12, 13, 14, 7, 20, and 10, in rank order from strongest to weakest evidence. On the other hand, there is little support for chromosomes 9, 8, 18, 1, and 15 in the same rank order. The evidence from 156 publications is reviewed for each region. With reasonable type I and II errors several thousand affected sib pairs would be required to detect a locus accounting for 1/10 of the genetic effect on asthma. Identification of regions by a genome scan for linkage and allelic association requires international collaborative studies to reach the necessary sample size, using lod-based methods that specify a weakly parametric alternative hypothesis and can be combined over studies that differ in ascertainment, phenotypes, and markers. This has become the central problem in complex inheritance.


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