em_hmm

Estimate the rLIS values accounting for the linkage disequilibrium across two genome-wide association studies via the four-state hidden Markov model. Apply a step-up procedure to control the FDR of replicability null.

A robust and powerful approach is developed for replicability analysis of two Genome-wide association studies (GWASs) accounting for the linkage disequilibrium (LD) among genetic variants. The LD structure in two GWASs is captured by a four-state hidden Markov model (HMM). The unknowns involved in the HMM are estimated by an efficient expectation-maximization (EM) algorithm in combination with a non-parametric estimation of functions. By incorporating information from adjacent locations via the HMM, this approach identifies the entire clusters of genotype-phenotype associated signals, improving the power of replicability analysis while effectively controlling the false discovery rate.

Yan Li 

ReAD

Powerful Replicability Analysis of Genome-Wide Association
Studies

Haochen lei 

Xiaoquan Wen 

Hongyuan Cao 

em_hmm function

<dl><dt>pa_in</dt>
<dd>A numeric vector of p-values from study 1.</dd>
<dt>pb_in</dt>
<dd>A numeric vector of p-values from study 2.</dd>
<dt>pi0a_in</dt>
<dd>An initial estimate of the null probability in study 1.</dd>
<dt>pi0b_in</dt>
<dd>An initial estimate of the null probability in study 2.</dd></dl>

Arguments

EM algorithm in combination with a non-parametric algorithm for estimation of the rLIS statistic. — em_hmm

<dl>

<dt>pa_in</dt>
<dd>A numeric vector of p-values from study 1.</dd>


<dt>pb_in</dt>
<dd>A numeric vector of p-values from study 2.</dd>


<dt>pi0a_in</dt>
<dd>An initial estimate of the null probability in study 1.</dd>


<dt>pi0b_in</dt>
<dd>An initial estimate of the null probability in study 2.</dd>

</dl>

em_hmm: EM algorithm in combination with a non-parametric algorithm for estimation of the rLIS statistic.

Description

Usage

Value

Arguments