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Träfflista för sökning "WFRF:(Srinivasan Chaitanya) "

Sökning: WFRF:(Srinivasan Chaitanya)

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1.
  • Ji, Xuemei, et al. (författare)
  • Protein-altering germline mutations implicate novel genes related to lung cancer development
  • 2020
  • Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Few germline mutations are known to affect lung cancer risk. We performed analyses of rare variants from 39,146 individuals of European ancestry and investigated gene expression levels in 7,773 samples. We find a large-effect association with an ATM L2307F (rs56009889) mutation in adenocarcinoma for discovery (adjusted Odds Ratio=8.82, P=1.18x10(-15)) and replication (adjusted OR=2.93, P=2.22x10(-3)) that is more pronounced in females (adjusted OR=6.81 and 3.19 and for discovery and replication). We observe an excess loss of heterozygosity in lung tumors among ATM L2307F allele carriers. L2307F is more frequent (4%) among Ashkenazi Jewish populations. We also observe an association in discovery (adjusted OR=2.61, P=7.98x10(-22)) and replication datasets (adjusted OR=1.55, P=0.06) with a loss-of-function mutation, Q4X (rs150665432) of an uncharacterized gene, KIAA0930. Our findings implicate germline genetic variants in ATM with lung cancer susceptibility and suggest KIAA0930 as a novel candidate gene for lung cancer risk. In lung cancer, relatively few germline mutations are known to impact risk. Here the authors looked at rare variants in 39,146 individuals and find novel germline mutations associated with risk, as well as implicating ATM and a new candidate gene for lung cancer risk.
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2.
  • Kaplow, Irene M., et al. (författare)
  • Relating enhancer genetic variation across mammals to complex phenotypes using machine learning
  • 2023
  • Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 380:6643
  • Tidskriftsartikel (refereegranskat)abstract
    • Protein-coding differences between species often fail to explain phenotypic diversity, suggesting the involvement of genomic elements that regulate gene expression such as enhancers. Identifying associations between enhancers and phenotypes is challenging because enhancer activity can be tissue-dependent and functionally conserved despite low sequence conservation. We developed the Tissue-Aware Conservation Inference Toolkit (TACIT) to associate candidate enhancers with species' phenotypes using predictions from machine learning models trained on specific tissues. Applying TACIT to associate motor cortex and parvalbumin-positive interneuron enhancers with neurological phenotypes revealed dozens of enhancer-phenotype associations, including brain size-associated enhancers that interact with genes implicated in microcephaly or macrocephaly. TACIT provides a foundation for identifying enhancers associated with the evolution of any convergently evolved phenotype in any large group of species with aligned genomes.
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