April 1:
We Start !!
April 8:
Fred Boehm:
Churchill, G.A. and R.W. Doerge, Empirical Threshold Values for Quantitative Trait Mapping. Genetics, 1994. 138(3): p. 963-971. link.
April 15:
Charles Cheung:
Doerge, R.W. and G.A. Churchill, Permutation tests for multiple loci affecting a quantitative character. Genetics, 1996. 142(1): p. 285-294. link.
April 22:
Yoonha Choi and Audrey Fu:
Churchill, G.A. and R.W. Doerge, Naive application of permutation testing leads to inflated type I error rates. Genetics, 2008. 178(1): p. 609-610 link, and
Shi, J.X., D. Siegmund, and B. Yakir, Importance sampling for estimating p values in linkage analysis. Journal of the American Statistical Association, 2007. 102(479): p. 929-937.
link.
April 29:
Sara Ng and Sangsoon Woo:
Lin, D.Y. and F. Zou, Assessing genomewide statistical significance in linkage studies. Genetic Epidemiology, 2004. 27(3): p. 202-214.
link
May 6:
Quenna Wong:
Visscher, P.M., R. Thompson, and C.S. Haley, Confidence intervals in QTL mapping by bootstrapping. Genetics, 1996. 143(2): p. 1013-1020. link.
May 13:
Yanming Di and Ming Su:
Papachristou, C. and S.L. Lin, Microsatellites versus single-nucleotide polymorphisms in confidence interval estimation of disease loci. Genetic Epidemiology, 2006. 30(1): p. 3-17.
link.
May 20:
Qunhua Li:
Benjamini, Y. and Y. Hochberg, Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B-Methodological, 1995. 57(1): p. 289-300.
link
May 27:
Indika Rajapakse and Tushar Bhangale:
Storey, J.D. and R. Tibshirani, Statistical significance for genomewide studies. Proceedings of the National Academy of Sciences of the United States of America, 2003. 100(16): p. 9440-9445.
link
June 3:
Jon Wakefield:
Wakefield, J., A Bayesian measure of the probability of false discovery in genetic epidemiology studies. American Journal of Human Genetics, 2007. 81(2): p. 208-227.
link
or Jon suggests instead (or in addition) that we read his just-accepted paper:
"Bayes factors for genome-wide association studies: comparison with p-values",
which is here, and he says is clearer.