Application of the Bayesian inference and mixed linear model method to maize breeding

2006 ◽  
Vol 33 (3) ◽  
pp. 185-190
Author(s):  
Freddy Mora ◽  
◽  
Emmanuel Arnhold ◽  
2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Daniel L. McCartney ◽  
Futao Zhang ◽  
Robert F. Hillary ◽  
Qian Zhang ◽  
Anna J. Stevenson ◽  
...  

Abstract Background Advanced age is associated with cognitive and physical decline and is a major risk factor for a multitude of disorders. There is also a gap in life expectancy between males and females. DNA methylation differences have been shown to be associated with both age and sex. Here, we investigate age-by-sex differences in blood-based DNA methylation in an unrelated cohort of 2586 individuals between the ages of 18 and 87 years, with replication in a further 4450 individuals between the ages of 18 and 93 years. Methods Linear regression models were applied, with stringent genome-wide significance thresholds (p < 3.6 × 10−8) used in both the discovery and replication data. A second, highly conservative mixed linear model method that better controls the false-positive rate was also applied, using the same genome-wide significance thresholds. Results Using the linear regression method, 52 autosomal and 597 X-linked CpG sites, mapping to 251 unique genes, replicated with concordant effect size directions in the age-by-sex interaction analysis. The site with the greatest difference mapped to GAGE10, an X-linked gene. Here, DNA methylation levels remained stable across the male adult age range (DNA methylation by age r = 0.02) but decreased across female adult age range (DNA methylation by age r = − 0.61). One site (cg23722529) with a significant age-by-sex interaction also had a quantitative trait locus (rs17321482) that is a genome-wide significant variant for prostate cancer. The mixed linear model method identified 11 CpG sites associated with the age-by-sex interaction. Conclusion The majority of differences in age-associated DNA methylation trajectories between sexes are present on the X chromosome. Several of these differences occur within genes that have been implicated in sexually dimorphic traits.


1999 ◽  
Vol 99 (7-8) ◽  
pp. 1255-1264 ◽  
Author(s):  
D. L. Wang ◽  
J. Zhu ◽  
Z. K. L. Li ◽  
A. H. Paterson

2009 ◽  
Vol 174 (6) ◽  
pp. 836-849 ◽  
Author(s):  
Thomas Bell ◽  
Andrew K. Lilley ◽  
Andy Hector ◽  
Bernhard Schmid ◽  
Lindsay King ◽  
...  

2017 ◽  
Vol 39 (1) ◽  
pp. 25
Author(s):  
Alcinei Mistico Azevedo ◽  
Valter Carvalho de Andrade júnior ◽  
Albertir Aparecido dos Santos ◽  
Aderbal Soares de Sousa Júnior ◽  
Altino Júnior Mendes Oliveira ◽  
...  

Author(s):  
Jan Bocianowski ◽  
Kamila Nowosad ◽  
Piotr Szulc ◽  
Anna Tratwal ◽  
Ewa Bakinowska ◽  
...  

2007 ◽  
Vol 2007 ◽  
pp. 2-2
Author(s):  
S. J. Rowe ◽  
R. Pong-Wong ◽  
C.S. Haley ◽  
S.A. Knott ◽  
D.J. de Koning

Methods that detect QTL within commercial populations circumvent the need for expensive experimental populations and facilitate direct application of results through marker assisted selection. Variance component analysis (VCA) uses phenotypic, pedigree and marker information within a mixed linear model to simultaneously detect QTL and estimate breeding values. The inclusion of non-additive effects has potential for greater accuracy of selection and understanding of underlying mechanisms. The linear model can be extended to include higher order effects such as dominance, however, there is little information on empirical power. Here VCA was applied to real and simulated commercial broiler data to detect additive and dominant QTL effects.


2010 ◽  
Vol 42 (4) ◽  
pp. 355-360 ◽  
Author(s):  
Zhiwu Zhang ◽  
Elhan Ersoz ◽  
Chao-Qiang Lai ◽  
Rory J Todhunter ◽  
Hemant K Tiwari ◽  
...  

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