Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking.
The improved characterisation of risk factors for rheumatoid arthritis (RA) suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated. We aimed to develop an RA prediction model capable of generating clinically relevant predicti...
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2013
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oai:doaj.org-article:3993128f7c024764988b842cfe98dea42021-11-18T06:21:50ZPredicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking.1553-73901553-740410.1371/journal.pgen.1003808https://doaj.org/article/3993128f7c024764988b842cfe98dea42013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24068971/?tool=EBIhttps://doaj.org/toc/1553-7390https://doaj.org/toc/1553-7404The improved characterisation of risk factors for rheumatoid arthritis (RA) suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated. We aimed to develop an RA prediction model capable of generating clinically relevant predictive data and to determine if it better predicted younger onset RA (YORA). Our novel modelling approach combined odds ratios for 15 four-digit/10 two-digit HLA-DRB1 alleles, 31 single nucleotide polymorphisms (SNPs) and ever-smoking status in males to determine risk using computer simulation and confidence interval based risk categorisation. Only males were evaluated in our models incorporating smoking as ever-smoking is a significant risk factor for RA in men but not women. We developed multiple models to evaluate each risk factor's impact on prediction. Each model's ability to discriminate anti-citrullinated protein antibody (ACPA)-positive RA from controls was evaluated in two cohorts: Wellcome Trust Case Control Consortium (WTCCC: 1,516 cases; 1,647 controls); UK RA Genetics Group Consortium (UKRAGG: 2,623 cases; 1,500 controls). HLA and smoking provided strongest prediction with good discrimination evidenced by an HLA-smoking model area under the curve (AUC) value of 0.813 in both WTCCC and UKRAGG. SNPs provided minimal prediction (AUC 0.660 WTCCC/0.617 UKRAGG). Whilst high individual risks were identified, with some cases having estimated lifetime risks of 86%, only a minority overall had substantially increased odds for RA. High risks from the HLA model were associated with YORA (P<0.0001); ever-smoking associated with older onset disease. This latter finding suggests smoking's impact on RA risk manifests later in life. Our modelling demonstrates that combining risk factors provides clinically informative RA prediction; additionally HLA and smoking status can be used to predict the risk of younger and older onset RA, respectively.Ian C ScottSeth D SeegobinSophia SteerRachael TanPaola ForaboscoAnne HinksStephen EyreAnn W MorganAnthony G WilsonLynne J HockingPaul WordsworthAnne BartonJane WorthingtonAndrew P CopeCathryn M LewisPublic Library of Science (PLoS)articleGeneticsQH426-470ENPLoS Genetics, Vol 9, Iss 9, p e1003808 (2013) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Genetics QH426-470 |
| spellingShingle |
Genetics QH426-470 Ian C Scott Seth D Seegobin Sophia Steer Rachael Tan Paola Forabosco Anne Hinks Stephen Eyre Ann W Morgan Anthony G Wilson Lynne J Hocking Paul Wordsworth Anne Barton Jane Worthington Andrew P Cope Cathryn M Lewis Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking. |
| description |
The improved characterisation of risk factors for rheumatoid arthritis (RA) suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated. We aimed to develop an RA prediction model capable of generating clinically relevant predictive data and to determine if it better predicted younger onset RA (YORA). Our novel modelling approach combined odds ratios for 15 four-digit/10 two-digit HLA-DRB1 alleles, 31 single nucleotide polymorphisms (SNPs) and ever-smoking status in males to determine risk using computer simulation and confidence interval based risk categorisation. Only males were evaluated in our models incorporating smoking as ever-smoking is a significant risk factor for RA in men but not women. We developed multiple models to evaluate each risk factor's impact on prediction. Each model's ability to discriminate anti-citrullinated protein antibody (ACPA)-positive RA from controls was evaluated in two cohorts: Wellcome Trust Case Control Consortium (WTCCC: 1,516 cases; 1,647 controls); UK RA Genetics Group Consortium (UKRAGG: 2,623 cases; 1,500 controls). HLA and smoking provided strongest prediction with good discrimination evidenced by an HLA-smoking model area under the curve (AUC) value of 0.813 in both WTCCC and UKRAGG. SNPs provided minimal prediction (AUC 0.660 WTCCC/0.617 UKRAGG). Whilst high individual risks were identified, with some cases having estimated lifetime risks of 86%, only a minority overall had substantially increased odds for RA. High risks from the HLA model were associated with YORA (P<0.0001); ever-smoking associated with older onset disease. This latter finding suggests smoking's impact on RA risk manifests later in life. Our modelling demonstrates that combining risk factors provides clinically informative RA prediction; additionally HLA and smoking status can be used to predict the risk of younger and older onset RA, respectively. |
| format |
article |
| author |
Ian C Scott Seth D Seegobin Sophia Steer Rachael Tan Paola Forabosco Anne Hinks Stephen Eyre Ann W Morgan Anthony G Wilson Lynne J Hocking Paul Wordsworth Anne Barton Jane Worthington Andrew P Cope Cathryn M Lewis |
| author_facet |
Ian C Scott Seth D Seegobin Sophia Steer Rachael Tan Paola Forabosco Anne Hinks Stephen Eyre Ann W Morgan Anthony G Wilson Lynne J Hocking Paul Wordsworth Anne Barton Jane Worthington Andrew P Cope Cathryn M Lewis |
| author_sort |
Ian C Scott |
| title |
Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking. |
| title_short |
Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking. |
| title_full |
Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking. |
| title_fullStr |
Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking. |
| title_full_unstemmed |
Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking. |
| title_sort |
predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2013 |
| url |
https://doaj.org/article/3993128f7c024764988b842cfe98dea4 |
| work_keys_str_mv |
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