scholarly journals Simultaneous confidence intervals for high-dimensional linear models with many endogenous variables

2017 ◽  
Author(s):  
Victor Chernozhukov ◽  
Whitney K. Newey ◽  
Alexandre Belloni ◽  
Christian Hansen
Author(s):  
Alexandre Belloni ◽  
Christian Hansen ◽  
Whitney Newey

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
José Quero-García ◽  
Philippe Letourmy ◽  
José Antonio Campoy ◽  
Camille Branchereau ◽  
Svetoslav Malchev ◽  
...  

AbstractRain-induced fruit cracking is a major problem in sweet cherry cultivation. Basic research has been conducted to disentangle the physiological and mechanistic bases of this complex phenomenon, whereas genetic studies have lagged behind. The objective of this work was to disentangle the genetic determinism of rain-induced fruit cracking. We hypothesized that a large genetic variation would be revealed, by visual field observations conducted on mapping populations derived from well-contrasted cultivars for cracking tolerance. Three populations were evaluated over 7–8 years by estimating the proportion of cracked fruits for each genotype at maturity, at three different areas of the sweet cherry fruit: pistillar end, stem end, and fruit side. An original approach was adopted to integrate, within simple linear models, covariates potentially related to cracking, such as rainfall accumulation before harvest, fruit weight, and firmness. We found the first stable quantitative trait loci (QTLs) for cherry fruit cracking, explaining percentages of phenotypic variance above 20%, for each of these three types of cracking tolerance, in different linkage groups, confirming the high complexity of this trait. For these and other QTLs, further analyses suggested the existence of at least two-linked QTLs in each linkage group, some of which showed confidence intervals close to 5 cM. These promising results open the possibility of developing marker-assisted selection strategies to select cracking-tolerant sweet cherry cultivars. Further studies are needed to confirm the stability of the reported QTLs over different genetic backgrounds and environments and to narrow down the QTL confidence intervals, allowing the exploration of underlying candidate genes.


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