Comments on ‘Whole‐genome sequencing identifies missense mutation in GRM6 as the likely cause of congenital stationary night blindness in a Tennessee Walking Horse’

2021 ◽  
Vol 53 (6) ◽  
pp. 1296-1296
Author(s):  
Richard J. McMullen
2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Rueben G. Das ◽  
Doreen Becker ◽  
Vidhya Jagannathan ◽  
Orly Goldstein ◽  
Evelyn Santana ◽  
...  

Abstract Congenital stationary night blindness (CSNB), in the complete form, is caused by dysfunctions in ON-bipolar cells (ON-BCs) which are secondary neurons of the retina. We describe the first disease causative variant associated with CSNB in the dog. A genome-wide association study using 12 cases and 11 controls from a research colony determined a 4.6 Mb locus on canine chromosome 32. Subsequent whole-genome sequencing identified a 1 bp deletion in LRIT3 segregating with CSNB. The canine mutant LRIT3 gives rise to a truncated protein with unaltered subcellular expression in vitro. Genetic variants in LRIT3 have been associated with CSNB in patients although there is limited evidence regarding its apparently critical function in the mGluR6 pathway in ON-BCs. We determine that in the canine CSNB retina, the mutant LRIT3 is correctly localized to the region correlating with the ON-BC dendritic tips, albeit with reduced immunolabelling. The LRIT3-CSNB canine model has direct translational potential enabling studies to help understand the CSNB pathogenesis as well as to develop new therapies targeting the secondary neurons of the retina.


Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 1059-1059
Author(s):  
Philipp A Greif ◽  
Sebastian H Eck ◽  
Nikola Konstandin ◽  
Anna Benet-Pages ◽  
Annika Dufour ◽  
...  

Abstract Abstract 1059 Aims: Genetic lesions are crucial for cancer initiation. Recently, whole genome sequencing using next generation technology was used as a systematic approach to identify mutations in genomes of various types of tumors including melanoma, lung and breast cancer as well as cytognetically normal acute myeloid leukaemia (CN-AML). Despite its technical feasibility, whole genome sequencing is still time consuming and cost intensive. As an alternative approach, here we identify tumor-specific somatic mutations by sequencing transcriptionally active genes. Methods: Mutations were detected by comparing the transcriptome sequence of a CN-AML with the corresponding remission sample. In a single Genome Analyzer II run, we generated 4.35 Gbp of CN-AML and 5.54 of remission transcriptome sequence from the same patient. 63% of AML reads and 74% of remission reads mapped to exon regions. 10,152 genes had an average read depth of at least 7-fold and 6,989 genes an average read depth of 20 or greater in both samples. By comparing the 8,978 coding Single Nucleotide Variants (SNVs) discovered in the CN-AML sample with the remission sample, we identified 5 non-synonymous mutations specific to the tumor sample. Results: We found 5 tumor-specific somatic mutations. Among them is a nonsense mutation affecting the RUNX1 gene, which is a frequent mutational target in AML, and a missense mutation in the putative tumor suppressor gene TLE4, which encodes a RUNX1 interacting protein. A second missense mutation was identified in SHKBP1, which acts downstream of FLT3, a receptor tyrosine kinase mutated in about 30% of AML cases. The frequency of mutations in TLE4 and SHKBP1 in a cohort of 95 CN-AML patients was 2%. Conclusion: Our study demonstrates that whole transcriptome sequencing leads to the rapid detection of recurring point mutations in the coding regions of genes relevant to malignant transformation. Disclosures: No relevant conflicts of interest to declare.


BMC Genomics ◽  
2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Julia Metzger ◽  
Anna Nolte ◽  
Ann-Kathrin Uhde ◽  
Marion Hewicker-Trautwein ◽  
Ottmar Distl

Eye ◽  
2018 ◽  
Vol 32 (10) ◽  
pp. 1661-1668 ◽  
Author(s):  
Vanita Berry ◽  
Alexander C. W. Ionides ◽  
Nikolas Pontikos ◽  
Ismail Moghul ◽  
Anthony T. Moore ◽  
...  

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Xiao Xu ◽  
Xin Sun ◽  
Xue-Song Hu ◽  
Yan Zhuang ◽  
Yue-Chen Liu ◽  
...  

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