The 3′-UTR (CA)n microsatellite on CD40LG gene as a possible genetic marker for rheumatoid arthritis in Mexican population: impact on CD40LG mRNA expression

Román-Fernández, Ilce Valeria; Sánchez-Zuno, Gabriela Athziri; Padilla-Gutiérrez, Jorge Ramón; Cerpa-Cruz, Sergio; Hernández‐Bello, Jorge; Valle, Yeminia; Ramírez‐Dueñas, Maria Guadalupe; Carrillo, C.; Muñoz-Valle, J.F.

doi:10.1007/s10067-017-3853-9

Cited by 2 publications

(1 citation statement)

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“…Previous reports have shown that SSR variations located within the 3′ untranslated region (UTR), 5′ UTR, introns, or CDS of a gene affect gene function [36–40]. We are concerned about the unintended effects of the 62 differential SSRs on the traits of D62B and DXB.…”

Section: Discussionmentioning

confidence: 99%

Application of high-throughput amplicon sequencing-based SSR genotyping in genetic background screening

Fang

Peng

et al. 2019

BMC Genomics

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Background Host genetic backgrounds affect gene functions. The genetic backgrounds of genetically engineered organisms must be identified to confirm their genetic backgrounds identity with those of recipients. Marker-assisted backcrossing (MAB), transgenesis and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (CRISPR/Cas9) editing are three commonly used genetic engineering techniques. However, methods for genetic background screening between genetically engineered organisms and corresponding recipients suffer from low efficiency, low accuracy or high cost. Results Here, we improved our previously reported AmpSeq-SSR method, an amplicon sequencing-based simple sequence repeat (SSR) genotyping method, by selecting SSR loci with high polymorphism among varieties. Ultimately, a set of 396 SSRs was generated and applied to evaluate the genetic backgrounds identity between rice lines developed through MAB, transgenesis, and CRISPR/Cas9 editing and the respective recipient rice. We discovered that the percentage of different SSRs between the MAB-developed rice line and its recipient was as high as 23.5%. In contrast, only 0.8% of SSRs were different between the CRISPR/Cas9-system-mediated rice line and its recipient, while no SSRs showed different genotypes between the transgenic rice line and its recipient. Furthermore, most differential SSRs induced by MAB technology were located in non-coding regions (62.9%), followed by untranslated regions (21.0%) and coding regions (16.1%). Trinucleotide repeats were the most prevalent type of altered SSR. Most importantly, all altered SSRs located in coding regions were trinucleotide repeats. Conclusions This method is not only useful for the background evaluation of genetic resources but also expands our understanding of the unintended effects of different genetic engineering techniques. While the work we present focused on rice, this method can be readily extended to other organisms. Electronic supplementary material The online version of this article (10.1186/s12864-019-5800-4) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%