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      Identification of NID1 as a novel candidate susceptibility gene for familial non-medullary thyroid carcinoma using whole-exome sequencing

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          Abstract

          The genetics underlying non-syndromic familial non-medullary thyroid carcinoma (FNMTC) is still poorly understood. To identify susceptibility genes for FNMTC, we performed whole-exome sequencing (WES) in a Brazilian family affected by papillary thyroid carcinoma (PTC) in three consecutive generations. WES was performed in four affected and two unaffected family members. Manual inspection in over 100 previously reported susceptibility genes for FNMTC showed that no variants in known genes co-segregated with disease phenotype in this family. Novel candidate genes were investigated using PhenoDB and filtered using Genome Aggregation (gnomAD) and Online Archive of Brazilian Mutations (ABraOM) population databases. The missense variant p.Ile657Met in the NID1 gene was the only variant that co-segregated with the disease, while absent in unaffected family members and controls. The allele frequency for this variant was <0.0001 in the gnomAD and ABbraOM databases. In silico analysis predicted the variant to be deleterious or likely damaging to the protein function. Somatic mutations in NID1 gene were found in nearly 500 cases of different cancer subtypes in the intOGen platform. Immunohistochemistry analysis showed NID1 expression in PTC cells, while it was absent in normal thyroid tissue. Our findings were corroborated using data from the TCGA cohort. Moreover, higher expression of NID1 was associated with higher likelihood of relapse after treatment and N1b disease in PTCs from the TCGA cohort. Although replication studies are needed to better understand the role of this variant in the FNMTC susceptibility, the NID1 variant (c.1971T>G) identified in this study fulfills several criteria that suggest it as a new FNMTC predisposing gene.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            A map of human genome variation from population-scale sequencing.

            The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation as a foundation for investigating the relationship between genotype and phenotype. Here we present results of the pilot phase of the project, designed to develop and compare different strategies for genome-wide sequencing with high-throughput platforms. We undertook three projects: low-coverage whole-genome sequencing of 179 individuals from four populations; high-coverage sequencing of two mother-father-child trios; and exon-targeted sequencing of 697 individuals from seven populations. We describe the location, allele frequency and local haplotype structure of approximately 15 million single nucleotide polymorphisms, 1 million short insertions and deletions, and 20,000 structural variants, most of which were previously undescribed. We show that, because we have catalogued the vast majority of common variation, over 95% of the currently accessible variants found in any individual are present in this data set. On average, each person is found to carry approximately 250 to 300 loss-of-function variants in annotated genes and 50 to 100 variants previously implicated in inherited disorders. We demonstrate how these results can be used to inform association and functional studies. From the two trios, we directly estimate the rate of de novo germline base substitution mutations to be approximately 10(-8) per base pair per generation. We explore the data with regard to signatures of natural selection, and identify a marked reduction of genetic variation in the neighbourhood of genes, due to selection at linked sites. These methods and public data will support the next phase of human genetic research.
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              A compendium of mutational cancer driver genes

              A fundamental goal in cancer research is to understand the mechanisms of cell transformation. This is key to developing more efficient cancer detection methods and therapeutic approaches. One milestone towards this objective is the identification of all the genes with mutations capable of driving tumours. Since the 1970s, the list of cancer genes has been growing steadily. Because cancer driver genes are under positive selection in tumorigenesis, their observed patterns of somatic mutations across tumours in a cohort deviate from those expected from neutral mutagenesis. These deviations, which constitute signals of positive selection, may be detected by carefully designed bioinformatics methods, which have become the state of the art in the identification of driver genes. A systematic approach combining several of these signals could lead to a compendium of mutational cancer genes. In this Review, we present the Integrative OncoGenomics (IntOGen) pipeline, an implementation of such an approach to obtain the compendium of mutational cancer drivers. Its application to somatic mutations of more than 28,000 tumours of 66 cancer types reveals 568 cancer genes and points towards their mechanisms of tumorigenesis. The application of this approach to the ever-growing datasets of somatic tumour mutations will support the continuous refinement of our knowledge of the genetic basis of cancer.

                Author and article information

                Journal
                Endocr Connect
                Endocr Connect
                EC
                Endocrine Connections
                Bioscientifica Ltd (Bristol )
                2049-3614
                22 December 2021
                01 January 2022
                : 11
                : 1
                : e210406
                Affiliations
                [1 ]Genetic Bases of Thyroid Tumors Laboratory, Division of Genetics, Department of Morphology and Genetics, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo , Brazil
                [2 ]Postgraduate Program in Health Sciences , Universidade Federal do Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
                [3 ]Centro de Oncologia Molecular , Hospital Sírio-libanês, São Paulo, São Paulo, Brazil
                [4 ]Department of Clinical and Toxicological Analyses , Natal, Rio Grande do Norte, Brazil
                [5 ]Department of Nutrition , Universidade Federal do Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
                Author notes
                Correspondence should be addressed to J M Cerutti: j.cerutti@ 123456unifesp.br

                *(L E B de Mello and T N R Carneiro contributed equally to this work)

                Article
                EC-21-0406
                10.1530/EC-21-0406
                8859953
                34941562
                e7672183-82fd-4d15-a01d-dddaf189e57e
                © The authors

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

                History
                : 24 November 2021
                : 22 December 2021
                Categories
                Research

                nid1,fnmtc,papillary thyroid carcinomas,whole-exome sequencing

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