Genome-wide searching of rare genetic variants in WTCCC data

Feng, Tao; Zhu, Xiaofeng

doi:10.1007/s00439-010-0849-9

Cited by 51 publications

(49 citation statements)

References 29 publications

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“…Ablim1 is a candidate gene for congenital heart disease (77) and has also been linked to cardiovascular disease, hypertension, and type 1 and type 2 diabetes by genome association studies (78).…”

Section: Ablim1mentioning

confidence: 99%

Reactivation of Fetal Splicing Programs in Diabetic Hearts Is Mediated by Protein Kinase C Signaling

Verma

Deshmukh

Liu

et al. 2013

Journal of Biological Chemistry

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Background: Chronic PKC activation is the leading pathogenic component of diabetes in the heart. Results: PKC␣/␤ promotes fetal splicing patterns in adult diabetic hearts via phosphorylation of the RNA-binding proteins CELF1 and Rbfox2. Conclusion: PKC␣/␤ contributes to diabetes pathogenesis by manipulating developmentally regulated alternative splicing. Significance: Identifying downstream effectors of PKC can provide novel therapeutics for cardiac pathogenesis of diabetes.

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Section: Ablim1mentioning

confidence: 99%

Reactivation of Fetal Splicing Programs in Diabetic Hearts Is Mediated by Protein Kinase C Signaling

Verma

Deshmukh

Liu

et al. 2013

Journal of Biological Chemistry

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“…However, statistical methods for detecting rare variant associations under family-based designs have not received as much attention as methods for unrelated individuals, although family-based designs have been shown to improve power to detect rare variants. 28,29 Motivated by the facts that rare disease variants will be enriched in family data 33 and a large number of affected sib-pairs for a variety of diseases has been collected by traditional linkage studies, SPWSS is based on 1000 unrelated controls, 600 unrelated cases, and 200 affected sib-pairs. TOW-sib is based on 1000 unrelated controls and 500 affected sib-pairs.…”

Section: Discussionmentioning

confidence: 99%

Test of rare variant association based on affected sib-pairs

Sha

Zhang

2014

Eur J Hum Genet

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With the development of sequencing techniques, there is increasing interest to detect associations between rare variants and complex traits. Quite a few statistical methods to detect associations between rare variants and complex traits have been developed for unrelated individuals. Statistical methods for detecting rare variant associations under family-based designs have not received as much attention as methods for unrelated individuals. Recent studies show that rare disease variants will be enriched in family data and thus family-based designs may improve power to detect rare variant associations. In this article, we propose a novel test to test association between the optimally weighted combination of variants and trait of interests for affected sib-pairs. The optimal weights are analytically derived and can be calculated from sampled genotypes and phenotypes. Based on the optimal weights, the proposed method is robust to the directions of the effects of causal variants and is less affected by neutral variants than existing methods are. Our simulation results show that, in all the cases, the proposed method is substantially more powerful than existing methods based on unrelated individuals and existing methods based on affected sib-pairs. INTRODUCTIONRecent studies show that the large number of disease-associated variants identified through genome-wide association studies account for only a small portion of the presumed phenotypic variation. 1 One of the potential sources of missing heritability is the contribution of rare variants. [2][3][4][5][6][7] The recent advances of sequencing technology have made directly testing rare variants possible. 8,9 Therefore, there is increasing interest to detect associations between rare variants and complex traits.Recently, several statistical methods to detect associations between rare variants and complex traits have been developed for unrelated individuals. These methods can be roughly divided into three groups: burden tests, quadratic tests, and combined tests. Burden tests include the cohort allelic sums test, 10 the combined multivariate and collapsing method, 11 the weighted sum statistic (WSS), 12 the variable minor allele frequency (MAF) threshold method, 13 and the cumulative minor-allele test 14 among others. Burden tests implicitly assume that all the rare variants are causal and the directions of the effects are all the same. If these assumptions are true, burden tests can be powerful tests; otherwise, burden tests can perform poorly. [15][16][17][18] Quadratic tests include C-alpha test, 19 sequence kernel association test, 15 and the test for Testing the effects of the Optimally Weighted combination of variants (TOW). 17 Quadratic tests also include adaptive weighting methods 20-24 since, as pointed out by Derkach et al, 18 adaptive weighting methods are operationally similar to quadratic tests. Quadratic tests are robust to the directions of the effects of causal variants and are less affected by neutral variants than burden tests are. If most of the ra...

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“…71 These three ORFs may provide further insight into the association of type 1 and type 2 diabetes with cardiovascular and inflammatory diseases.…”

Section: Discussionmentioning

confidence: 99%