Serveur d'exploration sur les relations entre la France et l'Australie

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Genomic prediction of celiac disease targeting HLA-positive individuals.

Identifieur interne : 002B51 ( PubMed/Checkpoint ); précédent : 002B50; suivant : 002B52

Genomic prediction of celiac disease targeting HLA-positive individuals.

Auteurs : Gad Abraham [Australie] ; Alexia Rohmer [Australie] ; Jason A. Tye-Din [Australie] ; Michael Inouye [Australie]

Source :

RBID : pubmed:26244058

Abstract

Genomic prediction aims to leverage genome-wide genetic data towards better disease diagnostics and risk scores. We have previously published a genomic risk score (GRS) for celiac disease (CD), a common and highly heritable autoimmune disease, which differentiates between CD cases and population-based controls at a clinically-relevant predictive level, improving upon other gene-based approaches. HLA risk haplotypes, particularly HLA-DQ2.5, are necessary but not sufficient for CD, with at least one HLA risk haplotype present in up to half of most Caucasian populations. Here, we assess a genomic prediction strategy that specifically targets this common genetic susceptibility subtype, utilizing a supervised learning procedure for CD that leverages known HLA-DQ2.5 risk.

DOI: 10.1186/s13073-015-0196-5
PubMed: 26244058


Affiliations:


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pubmed:26244058

Le document en format XML

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<div type="abstract" xml:lang="en">Genomic prediction aims to leverage genome-wide genetic data towards better disease diagnostics and risk scores. We have previously published a genomic risk score (GRS) for celiac disease (CD), a common and highly heritable autoimmune disease, which differentiates between CD cases and population-based controls at a clinically-relevant predictive level, improving upon other gene-based approaches. HLA risk haplotypes, particularly HLA-DQ2.5, are necessary but not sufficient for CD, with at least one HLA risk haplotype present in up to half of most Caucasian populations. Here, we assess a genomic prediction strategy that specifically targets this common genetic susceptibility subtype, utilizing a supervised learning procedure for CD that leverages known HLA-DQ2.5 risk.</div>
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<AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Genomic prediction aims to leverage genome-wide genetic data towards better disease diagnostics and risk scores. We have previously published a genomic risk score (GRS) for celiac disease (CD), a common and highly heritable autoimmune disease, which differentiates between CD cases and population-based controls at a clinically-relevant predictive level, improving upon other gene-based approaches. HLA risk haplotypes, particularly HLA-DQ2.5, are necessary but not sufficient for CD, with at least one HLA risk haplotype present in up to half of most Caucasian populations. Here, we assess a genomic prediction strategy that specifically targets this common genetic susceptibility subtype, utilizing a supervised learning procedure for CD that leverages known HLA-DQ2.5 risk.</AbstractText>
<AbstractText Label="METHODS" NlmCategory="METHODS">Using L1/L2-regularized support-vector machines trained on large European case-control datasets, we constructed novel CD GRSs specific to individuals with HLA-DQ2.5 risk haplotypes (GRS-DQ2.5) and compared them with the predictive power of the existing CD GRS (GRS14) as well as two haplotype-based approaches, externally validating the results in a North American case-control study.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">Consistent with previous observations, both the existing GRS14 and the GRS-DQ2.5 had better predictive performance than the HLA haplotype approaches. GRS-DQ2.5 models, based on directly genotyped or imputed markers, achieved similar levels of predictive performance (AUC = 0.718-0.73), which were substantially higher than those obtained from the DQ2.5 zygosity alone (AUC = 0.558), the HLA risk haplotype method (AUC = 0.634), or the generic GRS14 (AUC = 0.679). In a screening model of at-risk individuals, the GRS-DQ2.5 lowered the number of unnecessary follow-up tests for CD across most sensitivity levels. Relative to a baseline implicating all DQ2.5-positive individuals for follow-up, the GRS-DQ2.5 resulted in a net saving of 2.2 unnecessary follow-up tests for each justified test while still capturing 90 % of DQ2.5-positive CD cases.</AbstractText>
<AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Genomic risk scores for CD that target genetically at-risk sub-groups improve predictive performance beyond traditional approaches and may represent a useful strategy for prioritizing individuals at increased risk of disease, thus potentially reducing unnecessary follow-up diagnostic tests.</AbstractText>
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<name sortKey="Abraham, Gad" sort="Abraham, Gad" uniqKey="Abraham G" first="Gad" last="Abraham">Gad Abraham</name>
</region>
<name sortKey="Inouye, Michael" sort="Inouye, Michael" uniqKey="Inouye M" first="Michael" last="Inouye">Michael Inouye</name>
<name sortKey="Rohmer, Alexia" sort="Rohmer, Alexia" uniqKey="Rohmer A" first="Alexia" last="Rohmer">Alexia Rohmer</name>
<name sortKey="Tye Din, Jason A" sort="Tye Din, Jason A" uniqKey="Tye Din J" first="Jason A" last="Tye-Din">Jason A. Tye-Din</name>
</country>
</tree>
</affiliations>
</record>

Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Wicri/Asie/explor/AustralieFrV1/Data/PubMed/Checkpoint
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 002B51 | SxmlIndent | more

Ou

HfdSelect -h $EXPLOR_AREA/Data/PubMed/Checkpoint/biblio.hfd -nk 002B51 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Wicri/Asie
   |area=    AustralieFrV1
   |flux=    PubMed
   |étape=   Checkpoint
   |type=    RBID
   |clé=     pubmed:26244058
   |texte=   Genomic prediction of celiac disease targeting HLA-positive individuals.
}}

Pour générer des pages wiki

HfdIndexSelect -h $EXPLOR_AREA/Data/PubMed/Checkpoint/RBID.i   -Sk "pubmed:26244058" \
       | HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Checkpoint/biblio.hfd   \
       | NlmPubMed2Wicri -a AustralieFrV1 

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This area was generated with Dilib version V0.6.33.
Data generation: Tue Dec 5 10:43:12 2017. Site generation: Tue Mar 5 14:07:20 2024