Beyond the traditional simulation design for evaluating type 1 error control: From the “theoretical” null to “empirical” null
AbstractWhen evaluating a newly developed statistical test, an important step is to check its type 1 error (T1E) control using simulations. This is often achieved by the standard simulation design S0 under the so ‐called “theoretical” null of no association. In practice, the whole‐genome association analyses scan through a large number of genetic markers (s) for the ones associated with an outcome of interest (), where comes from an alternative while the majority of s are not associated with ; the relationships are under the “empirical” null. This reality can be better represented by two other simulation design...
Source: Genetic Epidemiology - November 26, 2018 Category: Epidemiology Authors: Ting Zhang,
Lei Sun Tags: RESEARCH ARTICLE Source Type: research
Assessing potential shared genetic aetiology between body mass index and sleep duration in 142,209 individuals
AbstractObservational studies find an association between increased body mass index (BMI) and short self ‐reported sleep duration in adults. However, the underlying biological mechanisms that underpin these associations are unclear. Recent findings from the UK Biobank suggest a weak genetic correlation between BMI and self‐reported sleep duration. However, the potential shared genetic aetiology bet ween these traits has not been examined using a comprehensive approach. To investigate this, we created a polygenic risk score (PRS) of BMI and examined its association with self‐reported sleep duration in a combination of...
Source: Genetic Epidemiology - November 26, 2018 Category: Epidemiology Authors: Victoria Garfield,
Ghazaleh Fatemifar,
Caroline Dale,
Melissa Smart,
Yanchun Bao,
Clare H. Llewellyn,
Andrew Steptoe,
Delilah Zabaneh,
Meena Kumari Tags: RESEARCH ARTICLE Source Type: research
Estimating cross ‐population genetic correlations of causal effect sizes
AbstractRecent studies have examined the genetic correlations of single ‐nucleotide polymorphism (SNP) effect sizes across pairs of populations to better understand the genetic architectures of complex traits. These studies have estimated , the cross‐population correlation of joint‐fit effect sizes at genotyped SNPs. However, the value of depends both on the cros s‐population correlation of true causal effect sizes () and on the similarity in linkage disequilibrium (LD) patterns in the two populations, which drive tagging effects. Here, we derive the value of the ratio as a function of LD in each population. By app...
Source: Genetic Epidemiology - November 25, 2018 Category: Epidemiology Authors: Kevin J. Galinsky,
Yakir A. Reshef,
Hilary K. Finucane,
Po ‐Ru Loh,
Noah Zaitlen,
Nick J. Patterson,
Brielin C. Brown,
Alkes L. Price Tags: RESEARCH ARTICLE Source Type: research
Relative impact of indels versus SNPs on complex disease
AbstractIt is unclear whether insertions and deletions (indels) are more likely to influence complex traits than abundant single ‐nucleotide polymorphisms (SNPs). We sought to understand which category of variation is more likely to impact health. Using the SardiNIA study as an exemplar, we characterized 478,876 common indels and 8,246,244 common SNPs in up to 5,949 well‐phenotyped individuals from an isolated valley in S ardinia. We assessed association between 120 traits, resulting in 89 nonoverlapping‐associated loci.We evaluated whether indels were enriched among credible sets of potential causal variants. These ...
Source: Genetic Epidemiology - November 22, 2018 Category: Epidemiology Authors: Sarah A. Gagliano,
Sebanti Sengupta,
Carlo Sidore,
Andrea Maschio,
Francesco Cucca,
David Schlessinger,
Gon çalo R. Abecasis Tags: BRIEF REPORT Source Type: research
Using Bayes model averaging to leverage both gene main effects and G ×
E interactions to identify genomic regions in genome‐wide association studies
We present a framework that (a) balances the robustness of a CC approach with the power of the case ‐only (CO) approach; (b) incorporates main SNP effects; (c) allows for incorporation of prior information; and (d) allows the data to determine the most appropriate model. Our framework is based on Bayes model averaging, which provides a principled statistical method for incorporating model uncert ainty. We average over inclusion of parameters corresponding to the main andG × E interaction effects and theG –E association in controls. The resulting method exploits the joint evidence for main and interaction effects w...
Source: Genetic Epidemiology - November 19, 2018 Category: Epidemiology Authors: Lilit C. Moss,
William J. Gauderman,
Juan Pablo Lewinger,
David V. Conti Tags: RESEARCH ARTICLE Source Type: research
An optimal kernel ‐based U‐statistic method for quantitative gene‐set association analysis
In this study, we proposed an efficient testing procedure that cannot only control Type 1 error rate but also have power close to the one obtained under the optimal kernel in the candidate kernel set, for quantitative trait association studies. Our method, a maximum kernel‐basedU‐statistic method, is built upon the KBT framework and is based on asymptotic results under a high‐dimensional setting. Hence it can efficiently deal with the case where the number of variants in a set is much larger than the sample size. Both simulation and real data analysis demonstrate the ad vantages of the method compared with its counte...
Source: Genetic Epidemiology - November 19, 2018 Category: Epidemiology Authors: Tao He,
Shaoyu Li,
Ping ‐Shou Zhong,
Yuehua Cui Tags: RESEARCH ARTICLE Source Type: research
Issue Information
Genetic Epidemiology, Volume 42, Issue 8, Page 751-753, December 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - November 16, 2018 Category: Epidemiology Tags: ISSUE INFORMATION Source Type: research
Generalized multifactor dimensionality reduction approaches to identification of genetic interactions underlying ordinal traits
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - November 2, 2018 Category: Epidemiology Authors: Ting ‐Ting Hou,
Feng Lin,
Shasha Bai,
Mario A. Cleves,
Hai‐Ming Xu,
Xiang‐Yang Lou Source Type: research
Variance components genetic association test for zero ‐inflated count outcomes
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - October 24, 2018 Category: Epidemiology Authors: Matthew O. Goodman,
Lori Chibnik,
Tianxi Cai Source Type: research
ComPaSS ‐GWAS: A method to reduce type I error in genome‐wide association studies when replication data are not available
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - October 18, 2018 Category: Epidemiology Authors: Jeremy A. Sabourin,
Cheryl D. Cropp,
Heejong Sung,
Lawrence C. Brody,
Joan E. Bailey ‐Wilson,
Alexander F. Wilson Source Type: research
Generalizing polygenic risk scores from Europeans to Hispanics/Latinos
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - October 15, 2018 Category: Epidemiology Authors: Kelsey E. Grinde,
Qibin Qi,
Timothy A. Thornton,
Simin Liu,
Aladdin H. Shadyab,
Kei Hang K. Chan,
Alexander P. Reiner,
Tamar Sofer Source Type: research
Issue Information
Genetic Epidemiology,Volume 42, Issue 7, Page 587-589, October 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - October 12, 2018 Category: Epidemiology Source Type: research
Prediction of treatment response in rheumatoid arthritis patients using genome ‐wide SNP data
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - October 12, 2018 Category: Epidemiology Authors: Svetlana Cherlin,
Darren Plant,
John C. Taylor,
Marco Colombo,
Athina Spiliopoulou,
Evan Tzanis,
Ann W. Morgan,
Michael R. Barnes,
Paul McKeigue,
Jennifer H. Barrett,
Costantino Pitzalis,
Anne Barton,
MATURA Consortium,
Heather J. Cordell Source Type: research
Bias in parameter estimates due to omitting gene –environment interaction terms in case‐control studies
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - October 10, 2018 Category: Epidemiology Authors: Iryna Lobach Source Type: research
Overlapping clustering of gene expression data using penalized weighted normalized cut
Genetic Epidemiology, EarlyView. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - October 10, 2018 Category: Epidemiology Authors: Sebastian J. Teran Hidalgo,
Tingyu Zhu,
Mengyun Wu,
Shuangge Ma Source Type: research
