docs: 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