Bias in heritability estimates from genomic restricted maximum likelihood methods under different genotyping strategies

2018 ◽  
Vol 136 (1) ◽  
pp. 40-50 ◽  
Author(s):  
Alberto Cesarani ◽  
Ivan Pocrnic ◽  
Nicolò P. P. Macciotta ◽  
Breno O. Fragomeni ◽  
Ignacy Misztal ◽  
...  
1992 ◽  
Vol 55 (1) ◽  
pp. 11-21 ◽  
Author(s):  
B. L. Pander ◽  
W. G. Hill ◽  
R. Thompson

AbstractEstimates of genetic parameters for test day records of yields of milk, fat and protein and concentrations of fat and protein were obtained on 47 736 British Holstein-Friesian heifers in 7973 herds, progeny of 40 proven (to improve connectedness) and 707 young sires (comprising about one-fifth of the progeny), using multivariate restricted maximum likelihood methods with a sire model.Heritability estimates for lactation yields of milk, fat and protein and concentrations of fat and protein were 0·49, 0·39, 0·43, 0·63 and 0·47, respectively. Estimates for individual test day records of these traits ranged from 0·27 to 0·43, 0·16 to 0·34, 0·22 to 0·33, 0·11 to 0·48 and 0·21 to 0·43, respectively. Generally, heritability estimates for test day records were lowest at start and highest in mid lactation.Estimates of genetic correlations among yields of a trait on different test days ranged from 0·57 to 0·99, and for fat and protein concentrations from 0·34 to 0·99, the correlations being highest for adjacent tests. Phenotypic correlations were lower than genetic correlations. Genetic correlations of test day records with corresponding lactation traits were high (0·76 to 0·99), being highest in mid lactation.Genetic correlations of test day milk yield with test day yields and concentrations of fat and protein throughout the lactation were similar to those for complete lactation.The high heritabilities of test day yields and their high genetic correlations with complete lactation, except for the first 1 or 2 test days, suggest that lactation performance may be predicted from test days in early and mid lactation.


2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Alain Hecq ◽  
Li Sun

AbstractWe propose a model selection criterion to detect purely causal from purely noncausal models in the framework of quantile autoregressions (QAR). We also present asymptotics for the i.i.d. case with regularly varying distributed innovations in QAR. This new modelling perspective is appealing for investigating the presence of bubbles in economic and financial time series, and is an alternative to approximate maximum likelihood methods. We illustrate our analysis using hyperinflation episodes of Latin American countries.


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