IntroductionIn genetic association studies (GAS) as well as in genome-wide association studies (GWAS), the model of inheritance (dominant, additive and recessive) is usually not known a priori. Assuming an incorrect model of inheritance may lead to substantial loss of power, whereas on the other hand, testing all possible models may result in an increased type I error rate. The situation is even more complicated in meta-analysis of GAS or GWAS, in which individual studies are synthesized in order to derive an overall estimate. Meta-analysis is widely used in order to increase the power to detect weak genotype effects, but heterogeneity and incompatibility between the included studies complicate things further. For both practical and theoretical reasons meta-analysis of GWAS is usually performed using only summary estimates (effect sizes or Z-values) from the included studies. Along these lines, using the theoretical results described by (Zhou et al. 2011), we show that a simple approach for robust meta-analysis would be to perform separate analysis for each study using a robust method of choice (i.e. MAX, MERT or MIN2) and afterwards, combine the individual results by pooling the Z-values or the effect sizes in a fixed or random-effects method. This approach, is closer to the spirit of meta-analysis of GWAS described by (de Bakker et al. 2008) and it is very easily implemented, provided that the summary estimates of the individual studies are available. As a proof of concept, a Stata program that implements the meta-analysis using the MERT statistic is given below. A list of available software for robust analysis and meta-analysis of GAS and GWAS is also given. Moreover, STATA implementations for the MAX, MIN2 and GMS approach are given for the first time. This is the first complete effort to implement procedures for robust analysis and selection of the appropriate genetic model in GAS or GWAS using STATA. Since there are only a few available software implementations of the robust methods for meta-analysis of GAS or GWAS our future goal is to extend our software in the context of meta-analysis using STATA STATA codeclear set more off input study aa0 ab0 bb0 aa1 ab1 bb1 1 3 34 44 2 20 83 2 4 30 49 3 17 62 3 17 84 48 3 30 31 4 5 32 63 5 23 52 5 12 62 119 8 39 133 6 9 48 47 6 34 68 7 18 134 363 20 214 483 end replace aa1=aa1+0.5 if aa1==0 replace ab1=ab1+0.5 if ab1==0 replace bb1=bb1+0.5 if bb1==0 replace aa0=aa0+0.5 if aa0==0 replace ab0=ab0+0.5 if ab0==0 replace bb0=bb0+0.5 if bb0==0 gen R=aa1+ab1+bb1 gen S=aa0+ab0+bb0 gen n0=aa1+aa0 gen n1=ab1+ab0 gen n2=bb1+bb0 gen N=R+S * Numerator of the trend tests: 00 for REC, 05 for ADD, and 10 for DOM gen u00=1/N*( S*(0*aa1+0.0*ab1+1*bb1)-R*(0*aa0+0.0*ab0+1*bb0) ) gen u05=1/N*( S*(0*aa1+0.5*ab1+1*bb1)-R*(0*aa0+0.5*ab0+1*bb0) ) gen u10=1/N*( S*(0*aa1+1.0*ab1+1*bb1)-R*(0*aa0+1.0*ab0+1*bb0) ) * Denominator of the trend tests; gen vu00=(R*S/N)*( (0*n0+0.0^2*n1+1*n2)/N - ((0*n0+0.0*n1+1*n2)/N)^2 ) gen vu05=(R*S/N)*( (0*n0+0.5^2*n1+1*n2)/N - ((0*n0+0.5*n1+1*n2)/N)^2 ) gen vu10=(R*S/N)*( (0*n0+1.0^2*n1+1*n2)/N - ((0*n0+1.0*n1+1*n2)/N)^2 ) * Three CATT gen z1=u00/sqrt(vu00) gen z2=u05/sqrt(vu05) gen z3=u10/sqrt(vu10) gen r23=(n0*(n1+2*n2))/(sqrt(n0*(n1+n2))*sqrt(n0*(n1+2*n2)+n2*(n1+2*n0))) gen r12= (n2*(n1+2*n0))/(sqrt(n2*(n1+n0))*sqrt(n0*(n1+2*n2)+n2*(n1+2*n0))) gen r13=(n0*n2)/(sqrt(n0*(n2+n1))*sqrt(n2*(n0+n1))) gen z_mert=(z1+z3)/(sqrt(2*(1+r13))) gen Ib=(1/(1/S +1/R)) gen beta_mert=z_mert/sqrt(Ib) gen sebeta=sqrt(1/Ib) metan beta_mert sebeta,randomi Software for robust analysis of GAS/GWAS
Software for robust meta-analysis of GAS/GWAS
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