scholarly journals Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs

2015 ◽  
Vol 97 (1) ◽  
pp. 35-53 ◽  
Author(s):  
Zheng-Zheng Tang ◽  
Dan-Yu Lin
Author(s):  
Dankmar Böhning ◽  
Uwe Malzahn ◽  
Peter Schlattmannn ◽  
Uwe-Peter Dammann ◽  
Wolfgang Mehnert ◽  
...  

1995 ◽  
Vol 117 (1) ◽  
pp. 176-180
Author(s):  
Malcolm S. Taylor ◽  
Csaba K. Zoltani

Measurements of the resistance to flow through packed beds of inert spheres have been reported by a number of authors through relations expressing the coefficient of drag as a function of Reynolds number. A meta-analysis of the data using improved statistical methods is undertaken to aggregate the available experimental results. For Reynolds number in excess of 103 the relation log Fv = 0.49 + 0.90 log Re′ is shown to be a highly effective representation of all available data.


2019 ◽  
Vol 44 (1) ◽  
pp. 104-116
Author(s):  
Tianzhong Yang ◽  
Junghi Kim ◽  
Chong Wu ◽  
Yiding Ma ◽  
Peng Wei ◽  
...  

2009 ◽  
Vol 9 (5) ◽  
pp. 424-425 ◽  
Author(s):  
Dino Samartzis ◽  
Rafael Perera

2014 ◽  
Vol 34 (2) ◽  
pp. 343-360 ◽  
Author(s):  
Zhi-Chao Jin ◽  
Xiao-Hua Zhou ◽  
Jia He

1993 ◽  
Vol 23 (4) ◽  
pp. 871-889 ◽  
Author(s):  
G. Dunn ◽  
P. Sham ◽  
D. Hand

SynopsisA critical examination is made of the role that statistical methods have played in the understanding of depression. The development of instruments for measuring depression is illustrated by reference to the Beck Depression Inventory and the Hamilton Rating Scale. The controversy over the existence of one or two types of depression is examined from the perspective of the statistical tools used. Some of the problems in studies of the heritability of depression are outlined. The development of clinical trials of depression is examined, with particular reference to ECT and maintenance therapy, and the role of meta-analysis is discussed.


Biometrics ◽  
1986 ◽  
Vol 42 (2) ◽  
pp. 454 ◽  
Author(s):  
P. R. Freeman ◽  
L. V. Hedges ◽  
I. Olkin

2021 ◽  
Author(s):  
Guhan Ram Venkataraman ◽  
Yosuke Tanigawa ◽  
Matti Pirinen ◽  
Manuel A Rivas

Rare-variant aggregate analysis from exome and whole genome sequencing data typically summarizes with a single statistic the signal for a gene or the unit that is being aggre- gated. However, when doing so, the effect profile within the unit may not be easily characterized across one or multiple phenotypes. Here, we present an approach we call Multiple Rare-Variants and Phenotypes Mixture Model (MRPMM), which clusters rare variants into groups based on their effects on the multivariate phenotype and makes statistical inferences about the properties of the underlying mixture of genetic effects. Using summary statistic data from a meta-analysis of exome sequencing data of 184,698 individuals in the UK Biobank across 6 populations, we demonstrate that our mixture model can identify clusters of variants responsible for significantly disparate effects across a multivariate phenotype; we study three lipid and three renal traits separately. The method is able to estimate (1) the proportion of non-null variants, (2) whether variants with the same predicted consequence in one gene behave similarly, (3) whether variants across genes share effect profiles across the multivariate phenotype, and (4) whether different annotations differ in the magnitude of their effects. As rare-variant data and aggregation techniques become more common, this method can be used to ascribe further meaning to association results.


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