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

Mahajan, Anubha; Wessel, Jennifer; Willems, Sara M; Zhao, Wei ; Robertson, Neil R; Chu, Audrey Y.; Kravic, Jasmina LU ; Ahlqvist, Emma LU ; Rosengren, Anders LU and Groop, Leif LU , et al. (2018) In Nature Genetics 50(4). p.559-571
Abstract
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 × 10-7); 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 ≤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... (More)
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 × 10-7); 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 ≤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. © 2018 The Author(s). (Less)
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ancestry group, Article, disease predisposition, gene locus, gene mapping, gene sequence, genetic association, genetic code, genetic variability, genome, human, major clinical study, non insulin dependent diabetes mellitus, odds ratio, priority journal
in
Nature Genetics
volume
50
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4
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13 pages
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Nature Publishing Group
external identifiers
  • scopus:85042368356
ISSN
1546-1718
DOI
10.1038/s41588-018-0084-1
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English
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yes
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ae87bbf0-3e1f-4d3a-b208-36042660b502
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2018-05-23 13:05:07
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2018-08-12 04:44:11
@article{ae87bbf0-3e1f-4d3a-b208-36042660b502,
  abstract     = {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 &lt; 2.2 × 10-7); 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 ≤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. © 2018 The Author(s).},
  author       = {Mahajan, Anubha and Wessel, Jennifer and Willems, Sara M and Zhao, Wei  and Robertson, Neil R and Chu, Audrey Y. and Kravic, Jasmina and Ahlqvist, Emma and Rosengren, Anders and Groop, Leif and V Varga, Tibor and Franks, Paul and Almgren, Peter and Melander, Olle and Orho-Melander, Marju and McCarthy, Mark I.},
  issn         = {1546-1718},
  keyword      = {ancestry group,Article,disease predisposition,gene locus,gene mapping,gene sequence,genetic association,genetic code,genetic variability,genome,human,major clinical study,non insulin dependent diabetes mellitus,odds ratio,priority journal},
  language     = {eng},
  number       = {4},
  pages        = {559--571},
  publisher    = {Nature Publishing Group},
  series       = {Nature Genetics},
  title        = {Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes article},
  url          = {http://dx.doi.org/10.1038/s41588-018-0084-1},
  volume       = {50},
  year         = {2018},
}