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Transancestral mapping and genetic load in systemic lupus erythematosus

Langefeld, Carl D.; Bengtsson, Anders A. LU ; Truedsson, Lennart LU ; Vyse, Timothy J. and , (2017) In Nature Communications 8.
Abstract

Systemic lupus erythematosus (SLE) is an autoimmune disease with marked gender and ethnic disparities. We report a large transancestral association study of SLE using Immunochip genotype data from 27,574 individuals of European (EA), African (AA) and Hispanic Amerindian (HA) ancestry. We identify 58 distinct non-HLA regions in EA, 9 in AA and 16 in HA (50% of these regions have multiple independent associations); these include 24 novel SLE regions (P<5×10 -8 ), refined association signals in established regions, extended associations to additional ancestries, and a disentangled complex HLA multigenic effect. The risk allele count (genetic... (More)

Systemic lupus erythematosus (SLE) is an autoimmune disease with marked gender and ethnic disparities. We report a large transancestral association study of SLE using Immunochip genotype data from 27,574 individuals of European (EA), African (AA) and Hispanic Amerindian (HA) ancestry. We identify 58 distinct non-HLA regions in EA, 9 in AA and 16 in HA (50% of these regions have multiple independent associations); these include 24 novel SLE regions (P<5×10 -8 ), refined association signals in established regions, extended associations to additional ancestries, and a disentangled complex HLA multigenic effect. The risk allele count (genetic load) exhibits an accelerating pattern of SLE risk, leading us to posit a cumulative hit hypothesis for autoimmune disease. Comparing results across the three ancestries identifies both ancestry-dependent and ancestry-independent contributions to SLE risk. Our results are consistent with the unique and complex histories of the populations sampled, and collectively help clarify the genetic architecture and ethnic disparities in SLE.

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author
organization
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type
Contribution to journal
publication status
published
subject
in
Nature Communications
volume
8
publisher
Nature Publishing Group
external identifiers
  • scopus:85024906085
ISSN
2041-1723
DOI
10.1038/ncomms16021
language
English
LU publication?
yes
id
64882865-cee0-439e-bc27-dea1cdf29498
date added to LUP
2019-05-23 09:34:09
date last changed
2019-10-15 07:06:20
@article{64882865-cee0-439e-bc27-dea1cdf29498,
  abstract     = {<p>                            Systemic lupus erythematosus (SLE) is an autoimmune disease with marked gender and ethnic disparities. We report a large transancestral association study of SLE using Immunochip genotype data from 27,574 individuals of European (EA), African (AA) and Hispanic Amerindian (HA) ancestry. We identify 58 distinct non-HLA regions in EA, 9 in AA and 16 in HA (50% of these regions have multiple independent associations); these include 24 novel SLE regions (P&lt;5×10                            <sup>-8</sup>                            ), refined association signals in established regions, extended associations to additional ancestries, and a disentangled complex HLA multigenic effect. The risk allele count (genetic load) exhibits an accelerating pattern of SLE risk, leading us to posit a cumulative hit hypothesis for autoimmune disease. Comparing results across the three ancestries identifies both ancestry-dependent and ancestry-independent contributions to SLE risk. Our results are consistent with the unique and complex histories of the populations sampled, and collectively help clarify the genetic architecture and ethnic disparities in SLE.                        </p>},
  articleno    = {16021},
  author       = {Langefeld, Carl D. and Bengtsson, Anders A. and Truedsson, Lennart and Vyse, Timothy J. and , },
  issn         = {2041-1723},
  language     = {eng},
  month        = {07},
  publisher    = {Nature Publishing Group},
  series       = {Nature Communications},
  title        = {Transancestral mapping and genetic load in systemic lupus erythematosus},
  url          = {http://dx.doi.org/10.1038/ncomms16021},
  volume       = {8},
  year         = {2017},
}