Browsing by Subject "Gene Frequency"

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    Functional variants in the LRRK2 gene confer shared effects on risk for Crohn's disease and Parkinson's disease
    (American Association for the Advancement of Science, 2018-01-10) Hui, Ken Y.; Fernandez-Hernandez, Heriberto; Hu, Jianzhong; Schaffner, Adam; Pankratz, Nathan; Hsu, Nai-Yun; Chuang, Ling-Shiang; Carmi, Shai; Villaverde, Nicole; Li, Xianting; Rivas, Manual; Levine, Adam P.; Bao, Xiuliang; Labrias, Philippe R.; Haritunians, Talin; Ruane, Darren; Gettler, Kyle; Chen, Ernie; Li, Dalin; Schiff, Elena R.; Pontikos, Nikolas; Barzilai, Nir; Brant, Steven R.; Bressman, Susan; Cheifetz, Adam S.; Clark, Lorraine N.; Daly, Mark J.; Desnick, Robert J.; Duerr, Richard H.; Katz, Seymour; Lencz, Todd; Myers, Richard H.; Ostrer, Harry; Ozelius, Laurie; Payami, Haydeh; Peter, Yakov; Rioux, John D.; Segal, Anthony W.; Scott, William K.; Silverberg, Mark S.; Vance, Jeffery M.; Ubarretxena-Belandia, Iban; Foroud, Tatiana; Atzmon, Gil; Pe’er, Itsik; Ioannou, Yiannis; McGovern, Dermot P.B.; Yue, Zhenyu; Schadt, Eric E.; Cho, Judy H.; Peter, Inga; Medical and Molecular Genetics, School of Medicine
    Crohn's disease (CD), a form of inflammatory bowel disease, has a higher prevalence in Ashkenazi Jewish than in non-Jewish European populations. To define the role of nonsynonymous mutations, we performed exome sequencing of Ashkenazi Jewish patients with CD, followed by array-based genotyping and association analysis in 2066 CD cases and 3633 healthy controls. We detected association signals in the LRRK2 gene that conferred risk for CD (N2081D variant, P = 9.5 × 10-10) or protection from CD (N551K variant, tagging R1398H-associated haplotype, P = 3.3 × 10-8). These variants affected CD age of onset, disease location, LRRK2 activity, and autophagy. Bayesian network analysis of CD patient intestinal tissue further implicated LRRK2 in CD pathogenesis. Analysis of the extended LRRK2 locus in 24,570 CD cases, patients with Parkinson's disease (PD), and healthy controls revealed extensive pleiotropy, with shared genetic effects between CD and PD in both Ashkenazi Jewish and non-Jewish cohorts. The LRRK2 N2081D CD risk allele is located in the same kinase domain as G2019S, a mutation that is the major genetic cause of familial and sporadic PD. Like the G2019S mutation, the N2081D variant was associated with increased kinase activity, whereas neither N551K nor R1398H variants on the protective haplotype altered kinase activity. We also confirmed that R1398H, but not N551K, increased guanosine triphosphate binding and hydrolyzing enzyme (GTPase) activity, thereby deactivating LRRK2. The presence of shared LRRK2 alleles in CD and PD provides refined insight into disease mechanisms and may have major implications for the treatment of these two seemingly unrelated diseases.
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    RareVar: A Framework for Detecting Low-Frequency Single-Nucleotide Variants
    (Mary Ann Liebert, Inc., 2017-07) Hao, Yangyang; Xuei, Xiaoling; Li, Lang; Nakshatri, Harikrishna; Edenberg, Howard J.; Liu, Yunlong; Medical and Molecular Genetics, School of Medicine
    Accurate identification of low-frequency somatic point mutations in tumor samples has important clinical utilities. Although high-throughput sequencing technology enables capturing such variants while sequencing primary tumor samples, our ability for accurate detection is compromised when the variant frequency is close to the sequencer error rate. Most current experimental and bioinformatic strategies target mutations with ≥5% allele frequency, which limits our ability to understand the cancer etiology and tumor evolution. We present an experimental and computational modeling framework, RareVar, to reliably identify low-frequency single-nucleotide variants from high-throughput sequencing data under standard experimental protocols. RareVar protocol includes a benchmark design by pooling DNAs from already sequenced individuals at various concentrations to target variants at desired frequencies, 0.5%-3% in our case. By applying a generalized, linear model-based, position-specific error model, followed by machine-learning-based variant calibration, our approach outperforms existing methods. Our method can be applied on most capture and sequencing platforms without modifying the experimental protocol.