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Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P 2.2 #215; 10.sup.-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these s... Full description

Journal Title: Nature genetics April 2018, Vol.50(4), pp.559-571
Main Author: Mahajan, Anubha
Other Authors: Wessel, Jennifer , Willems, Sara M , Zhao, Wei , Robertson, Neil R , Chu, Audrey Y , Gan, Wei , Kitajima, Hidetoshi , Taliun, Daniel , Rayner, N William , Guo, Xiuqing , Lu, Yingchang , Li, Man , Jensen, Richard A , Hu, Yao , Huo, Shaofeng , Lohman, Kurt K , Zhang, Weihua , Cook, James P , Prins
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1546-1718 ; DOI: 10.1038/s41588-018-0084-1
Link: http://search.proquest.com/docview/2023728702/?pq-origsite=primo
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title: Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.
format: Article
creator:
  • Mahajan, Anubha
  • Wessel, Jennifer
  • Willems, Sara M
  • Zhao, Wei
  • Robertson, Neil R
  • Chu, Audrey Y
  • Gan, Wei
  • Kitajima, Hidetoshi
  • Taliun, Daniel
  • Rayner, N William
  • Guo, Xiuqing
  • Lu, Yingchang
  • Li, Man
  • Jensen, Richard A
  • Hu, Yao
  • Huo, Shaofeng
  • Lohman, Kurt K
  • Zhang, Weihua
  • Cook, James P
  • Prins
subjects:
  • Alleles–Statistics & Numerical Data
  • Chromosome Mapping–Classification
  • Diabetes Mellitus, Type 2–Genetics
  • European Continental Ancestry Group–Physiopathology
  • Female–Genetics
  • Genetic Predisposition to Disease–Statistics & Numerical Data
  • Genetic Variation–Statistics & Numerical Data
  • Genome-Wide Association Study–Statistics & Numerical Data
  • Humans–Statistics & Numerical Data
  • Male–Statistics & Numerical Data
  • Whole Exome Sequencing–Statistics & Numerical Data
ispartof: Nature genetics, April 2018, Vol.50(4), pp.559-571
description: We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P 2.2 #215; 10.sup.-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio [less than or equal to]1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition. Trans-ethnic analyses of exome array data identify new risk loci for type 2 diabetes. Fine-mapping analyses using genome-wide association data show that the index coding variants represent the likely causal variants at only a subset of these loci.
language: eng
source:
identifier: E-ISSN: 1546-1718 ; DOI: 10.1038/s41588-018-0084-1
fulltext: fulltext
issn:
  • 15461718
  • 1546-1718
url: Link


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titleRefining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.
creatorMahajan, Anubha ; Wessel, Jennifer ; Willems, Sara M ; Zhao, Wei ; Robertson, Neil R ; Chu, Audrey Y ; Gan, Wei ; Kitajima, Hidetoshi ; Taliun, Daniel ; Rayner, N William ; Guo, Xiuqing ; Lu, Yingchang ; Li, Man ; Jensen, Richard A ; Hu, Yao ; Huo, Shaofeng ; Lohman, Kurt K ; Zhang, Weihua ; Cook, James P ; Prins
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descriptionWe aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P 2.2 #215; 10.sup.-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio [less than or equal to]1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition. Trans-ethnic analyses of exome array data identify new risk loci for type 2 diabetes. Fine-mapping analyses using genome-wide association data show that the index coding variants represent the likely causal variants at only a subset of these loci.
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titleRefining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.
authorMahajan, Anubha ; Wessel, Jennifer ; Willems, Sara M ; Zhao, Wei ; Robertson, Neil R ; Chu, Audrey Y ; Gan, Wei ; Kitajima, Hidetoshi ; Taliun, Daniel ; Rayner, N William ; Guo, Xiuqing ; Lu, Yingchang ; Li, Man ; Jensen, Richard A ; Hu, Yao ; Huo, Shaofeng ; Lohman, Kurt K ; Zhang, Weihua ; Cook, James...
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5Genetic Predisposition to Disease–Statistics & Numerical Data
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atitleRefining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.
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