Univariate fixed effects interaction test for phenotype specific SNP effects

    Args:
        snps:   [N x S] SP.array of S SNPs for N individuals (test SNPs)
        pheno1: [N x 1] SP.array of 1 phenotype for N individuals
        pheno2: [N x 1] SP.array of 1 phenotype for N individuals
        K:      [N x N] SP.array of LMM-covariance/kinship koefficients (optional)
                        If not provided, then linear regression analysis is performed
        covs:   [N x D] SP.array of D covariates for N individuals
        test:    'lrt' for likelihood ratio test (default) or 'f' for F-test

    Returns:
        limix LMM object

def phenSpecificEffects(snps,pheno1,pheno2,K=None,covs=None,test='lrt'):
    """
    Univariate fixed effects interaction test for phenotype specific SNP effects

    Args:
        snps:   [N x S] SP.array of S SNPs for N individuals (test SNPs)
        pheno1: [N x 1] SP.array of 1 phenotype for N individuals
        pheno2: [N x 1] SP.array of 1 phenotype for N individuals
        K:      [N x N] SP.array of LMM-covariance/kinship koefficients (optional)
                        If not provided, then linear regression analysis is performed
        covs:   [N x D] SP.array of D covariates for N individuals
        test:    'lrt' for likelihood ratio test (default) or 'f' for F-test

    Returns:
        limix LMM object
    """
    N=snps.shape[0]
    if K is None:
        K=SP.eye(N)
    assert (pheno1.shape[1]==pheno2.shape[1]), "Only consider equal number of phenotype dimensions"
    if covs is None:
        covs = SP.ones(N,1)
    assert (pheno1.shape[1]==1 and pheno2.shape[1]==1 and pheno1.shape[0]==N and pheno2.shape[0]==N and K.shape[0]==N and K.shape[1]==N and covs.shape[0]==N), "shapes missmatch"
    Inter = SP.zeros((N*2,1))
    Inter[0:N,0]=1
    Inter0 = SP.ones((N*2,1))
    Yinter=SP.concatenate((pheno1,pheno2),0)
    Xinter = SP.tile(snps,(2,1))
    Covitner= SP.tile(covs(2,1))
    lm = simple_interaction(snps=Xinter,pheno=Yinter,covs=Covinter,Inter=Inter,Inter0=Inter0,test=test)
    return lm