def simple_interaction_kronecker_deprecated(snps,phenos,covs=None,Acovs=None,Asnps1=None,Asnps0=None,K1r=None,K1c=None,K2r=None,K2c=None,covar_type='lowrank_diag',rank=1,searchDelta=False):
    """
    I-variate fixed effects interaction test for phenotype specific SNP effects.
    (Runs multiple likelihood ratio tests and computes the P-values in python from the likelihood ratios)

    Args:
        snps:   [N x S] SP.array of S SNPs for N individuals (test SNPs)
        phenos: [N x P] SP.array of P phenotypes for N individuals
        covs:           list of SP.arrays holding covariates. Each covs[i] has one corresponding Acovs[i]
        Acovs:          list of SP.arrays holding the phenotype design matrices for covariates.
                        Each covs[i] has one corresponding Acovs[i].
        Asnps1:         list of SP.arrays of I interaction variables to be tested for N
                        individuals. Note that it is assumed that Asnps0 is already included.
                        If not provided, the alternative model will be the independent model
        Asnps0:         single SP.array of I0 interaction variables to be included in the
                        background model when testing for interaction with Inters
        K1r:    [N x N] SP.array of LMM-covariance/kinship koefficients (optional)
                        If not provided, then linear regression analysis is performed
        K1c:    [P x P] SP.array of LMM-covariance/kinship koefficients (optional)
                        If not provided, then linear regression analysis is performed
        K2r:    [N x N] SP.array of LMM-covariance/kinship koefficients (optional)
                        If not provided, then linear regression analysis is performed
        K2c:    [P x P] SP.array of LMM-covariance/kinship koefficients (optional)
                        If not provided, then linear regression analysis is performed
        covar_type:     type of covaraince to use. Default 'freeform'. possible values are
                        'freeform': free form optimization,
                        'fixed': use a fixed matrix specified in covar_K0,
                        'diag': optimize a diagonal matrix,
                        'lowrank': optimize a low rank matrix. The rank of the lowrank part is specified in the variable rank,
                        'lowrank_id': optimize a low rank matrix plus the weight of a constant diagonal matrix. The rank of the lowrank part is specified in the variable rank,
                        'lowrank_diag': optimize a low rank matrix plus a free diagonal matrix. The rank of the lowrank part is specified in the variable rank,
                        'block': optimize the weight of a constant P x P block matrix of ones,
                        'block_id': optimize the weight of a constant P x P block matrix of ones plus the weight of a constant diagonal matrix,
                        'block_diag': optimize the weight of a constant P x P block matrix of ones plus a free diagonal matrix,
        rank:           rank of a possible lowrank component (default 1)
        searchDelta:    Boolean indicator if delta is optimized during SNP testing (default False)

    Returns:
        pv:     P-values of the interaction test
        lrt0:   log likelihood ratio statistics of the null model
        pv0:    P-values of the null model
        lrt:    log likelihood ratio statistics of the interaction test
        lrtAlt: log likelihood ratio statistics of the alternative model
        pvAlt:  P-values of the alternative model
    """
    S=snps.shape[1]
    #0. checks
    N  = phenos.shape[0]
    P  = phenos.shape[1]

    if K1r==None:
        K1r = SP.dot(snps,snps.T)
    else:
        assert K1r.shape[0]==N, 'K1r: dimensions dismatch'
        assert K1r.shape[1]==N, 'K1r: dimensions dismatch'

    if K2r==None:
        K2r = SP.eye(N)
    else:
        assert K2r.shape[0]==N, 'K2r: dimensions dismatch'
        assert K2r.shape[1]==N, 'K2r: dimensions dismatch'

    covs,Acovs = updateKronCovs(covs,Acovs,N,P)

    #Asnps can be several designs
    if (Asnps0 is None):
        Asnps0 = [SP.ones([1,P])]
    if Asnps1 is None:
        Asnps1 = [SP.eye([P])]
    if (type(Asnps0)!=list):
        Asnps0 = [Asnps0]
    if (type(Asnps1)!=list):
        Asnps1 = [Asnps1]
    assert (len(Asnps0)==1) and (len(Asnps1)>0), "need at least one Snp design matrix for null and alt model"

    #one row per column design matrix
    pv = SP.zeros((len(Asnps1),snps.shape[1]))
    lrt = SP.zeros((len(Asnps1),snps.shape[1]))
    pvAlt = SP.zeros((len(Asnps1),snps.shape[1]))
    lrtAlt = SP.zeros((len(Asnps1),snps.shape[1]))

    #1. run GP model to infer suitable covariance structure
    if K1c==None or K2c==None:
        vc = estimateKronCovariances(phenos=phenos, K1r=K1r, K2r=K2r, K1c=K1c, K2c=K2c, covs=covs, Acovs=Acovs, covar_type=covar_type, rank=rank)
        K1c = vc.getEstTraitCovar(0)
        K2c = vc.getEstTraitCovar(1)
    else:
        assert K1c.shape[0]==P, 'K1c: dimensions dismatch'
        assert K1c.shape[1]==P, 'K1c: dimensions dismatch'
        assert K2c.shape[0]==P, 'K2c: dimensions dismatch'
        assert K2c.shape[1]==P, 'K2c: dimensions dismatch'

    #2. run kroneckerLMM for null model
    lmm = limix.CKroneckerLMM()
    lmm.setK1r(K1r)
    lmm.setK1c(K1c)
    lmm.setK2r(K2r)
    lmm.setK2c(K2c)
    lmm.setSNPs(snps)
    #add covariates
    for ic  in range(len(Acovs)):
        lmm.addCovariates(covs[ic],Acovs[ic])
    lmm.setPheno(phenos)
    if searchDelta:      lmm.setNumIntervalsAlt(100)
    else:                   lmm.setNumIntervalsAlt(0)
    lmm.setNumIntervals0(100)
    #add SNP design
    lmm.setSNPcoldesign(Asnps0[0])
    lmm.process()
    dof0 = Asnps0[0].shape[0]
    pv0 = lmm.getPv()
    lrt0 = ST.chi2.isf(pv0,dof0)
    for iA in range(len(Asnps1)):
        dof1 = Asnps1[iA].shape[0]
        dof = dof1-dof0
        lmm.setSNPcoldesign(Asnps1[iA])
        lmm.process()
        pvAlt[iA,:] = lmm.getPv()[0]
        lrtAlt[iA,:] = ST.chi2.isf(pvAlt[iA,:],dof1)
        lrt[iA,:] = lrtAlt[iA,:] - lrt0[0] # Don't need the likelihood ratios, as null model is the same between the two models
        pv[iA,:] = ST.chi2.sf(lrt[iA,:],dof)
    return pv,lrt0,pv0,lrt,lrtAlt,pvAlt