Association mapping reveals candidate loci for resistance and anaemic response to an emerging temperature-driven parasitic disease in a wild salmonid fish

2018 ◽  
Vol 27 (6) ◽  
pp. 1385-1401 ◽  
Author(s):  
F. Ahmad ◽  
P. V. Debes ◽  
G. Palomar ◽  
A. Vasemägi
Keyword(s):  
Association Mapping ◽  
Salmonid Fish ◽  
Parasitic Disease ◽  
Candidate Loci

scholarly journals Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Jordan Ubbens ◽  
Mikolaj Cieslak ◽  
Przemyslaw Prusinkiewicz ◽  
Isobel Parkin ◽  
Jana Ebersbach ◽  
...  
Keyword(s):  
Image Processing ◽  
Association Mapping ◽  
Stress Responses ◽  
Interspecific Cross ◽  
Response To Treatment ◽  
Brassica Napus L ◽  
Nested Association Mapping ◽  
Latent Space ◽  
Using Data ◽  
Candidate Loci

Association mapping studies have enabled researchers to identify candidate loci for many important environmental tolerance factors, including agronomically relevant tolerance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images. We demonstrate example applications using data from an interspecific cross of the model C4 grass Setaria, a diversity panel of sorghum (S. bicolor), and the founder panel for a nested association mapping population of canola (Brassica napus L.). Using two synthetically generated image datasets, we then show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling the phenotyping of arbitrary and potentially complex response traits without the need for engineering-complicated image-processing pipelines.

scholarly journals Combined linkage and association mapping reveal candidate loci for kernel size and weight in maize

2019 ◽  
Vol 69 (3) ◽  
pp. 420-428 ◽  
Author(s):  
Derong Hao ◽  
Lin Xue ◽  
Zhenliang Zhang ◽  
Yujing Cheng ◽  
Guoqing Chen ◽  
...  
Keyword(s):  
Association Mapping ◽  
Kernel Size ◽  
Candidate Loci

scholarly journals Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies

2019 ◽  
Author(s):  
Jordan Ubbens ◽  
Mikolaj Cieslak ◽  
Przemyslaw Prusinkiewicz ◽  
Ian Stavness
Keyword(s):  
Image Processing ◽  
Association Mapping ◽  
Stress Responses ◽  
Interspecific Cross ◽  
Response To Treatment ◽  
Resistance Factors ◽  
Complex Image ◽  
Latent Space ◽  
Model C ◽  
Candidate Loci

AbstractAssociation mapping studies have enabled researchers to identify candidate loci for many important environmental resistance factors, including agronomically relevant resistance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately and consistently measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response to treatment directly from images. Using two synthetically generated image datasets, we first show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery. We then demonstrate an example application of an interspecific cross of the model C4 grass Setaria. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling association mapping studies without the need for engineering complex image processing pipelines.

Reduce Disease Burden of Human Schistosomiasis in Asia Through Biological Control

2020 ◽  
Vol 20 (12) ◽  
pp. 1118-1132
Author(s):  
Sidra Saleem ◽  
Haroon Ahmed ◽  
Tooba Siddiqui ◽  
Seyma Gunyakti Kilinc ◽  
Aisha Khan ◽  
...  
Keyword(s):  
Biological Control ◽  
Geographical Distribution ◽  
Disease Burden ◽  
Management Practices ◽  
Control Strategies ◽  
Parasitic Disease ◽  
Asian Countries ◽  
Neglected Disease ◽  
Current Review ◽  
Near Future

Schistosomiasis is a chronic parasitic disease caused by a trematode blood fluke of the genus Schistosoma that belongs to the Schistosomatidae family. It is a neglected disease in different regions of Asia. In this review, 218 articles (between 2000 and 2017) related to the topic were collected from PubMed and Google scholar and reviewed. After thoroughly reading collected articles, due to irrelevant topic requirements, 94 articles were excluded. Articles that have data associated with Asian regions are considered. In Asia, the disease is prevalent in China, Philippines, Indonesia, Yemen, Nepal and Laos, etc. While in Pakistan, India and Bangladesh, the disease is not endemic and very few cases were reported. The disease was eliminated from Japan and Iran. The current review highlights the geographical distribution among Asian countries, transmission patterns, diagnosis, control strategies based on the use of anthelmintic plants and management practices implemented in Asia for the control of schistosomiasis. However, new implementations to treat schistosomiasis in humans should be proved to eliminate the disease finally in the future. This review emphasizes the biological control of schistosomiasis for the eradication of the disease from Asia in the near future.

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