Abstract:
BACKGROUND:In recent years, genome wide association studies have identified many genetic variants that are consistently associated with common complex diseases, but the amount of heritability explained by these risk alleles is still low. Part of the missing heritability may be due to genetic heterogeneity and small sample sizes, but non-optimal study designs in many genome wide association studies may also have contributed to the failure of identifying gene variants causing a predisposition to disease. The normally used odds ratio from a classical case-control study measures the association between genotype and being diseased. In comparison, under incidence density sampling, the incidence rate ratio measures the association between genotype and becoming diseased. We estimate the differences between the odds ratio and the incidence rate ratio under the presence of events precluding the disease of interest. Such events may arise due to pleiotropy and are known as competing events. In addition, we investigate how these differences impact the association test. METHODS:We simulate life spans of individuals whose gene variants are subject to competing events. To estimate the association between genotype and disease, we applied classical case-control studies and incidence density sampling. RESULTS:We find significant numerical differences between the odds ratio and the incidence rate ratio when the fact that gene variant may be associated with competing events, e.g. lifetime, is ignored. The only scenario showing little or no difference is an association with a rare disease and no other present associations. Furthermore, we find that p-values for association tests differed between the two study designs. CONCLUSIONS:If the interest is on the aetiology of the disease, a design based on incidence density sampling provides the preferred interpretation of the estimate. Under a classical case-control design and in the presence of competing events, the change in p-values in the association test may lead to false positive findings and, more importantly, false negative findings. The ranking of the SNPs according to p-values may differ between the two study designs.
journal_name
BMC Med Genetjournal_title
BMC medical geneticsauthors
Waltoft BL,Pedersen CB,Nyegaard M,Hobolth Adoi
10.1186/s12881-015-0210-1subject
Has Abstractpub_date
2015-08-30 00:00:00pages
71issn
1471-2350pii
10.1186/s12881-015-0210-1journal_volume
16pub_type
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journal_title:BMC medical genetics
pub_type: 杂志文章
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abstract:BACKGROUND:PMM2-CDG, is the most common N-linked glycosylation disorder and subtype among all CDG syndromes, which are a series of genetic disorders involving the synthesis and attachment of glycoproteins and glycolipid glycans. The mutations of PMM2-CDG might lead to the loss of PMM2, which is responsible for the conv...
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pub_type: 杂志文章
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更新日期:2018-12-12 00:00:00
abstract:BACKGROUND:Deep vein thrombosis (DVT) is associated with stroke. Here, we hypothesize that genes associated with DVT may also play roles in the development of stroke. METHODS:we firstly conducted large-scale literature based disease-gene relationship data analysis to explore the genes implicated with DVT and stroke. F...
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pub_type: 杂志文章
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pub_type: 杂志文章,meta分析
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pub_type: 杂志文章,meta分析
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journal_title:BMC medical genetics
pub_type: 杂志文章
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