Example #1
0
    def fit_alternative_model(self):
        ibd_model = MixedModel(self.pedigrees,
                               outcome=self.outcome,
                               fixed_effects=self.fixed_effects)
        ibd_model.add_genetic_effect()

        ranef = RandomEffect(self.analysis_individuals,
                             'IBD',
                             incidence_matrix='eye',
                             covariance_matrix=self.ibd_matrix)
        ibd_model.add_random_effect(ranef)

        ibd_model.fit_model()
        ibd_model.maximize(verbose=self.verbose, method=self.maximization)
        return ibd_model
Example #2
0
 def fit_null_model(self):
     null_model = MixedModel(self.pedigrees,
                             outcome=self.outcome,
                             fixed_effects=self.fixed_effects)
     null_model.add_genetic_effect()
     null_model.fit_model()
     null_model.maximize(method=self.maximization, verbose=self.verbose)
     return null_model
    def fit_alternative_model(self):
        ibd_model = MixedModel(self.pedigrees,
                               outcome=self.outcome,
                               fixed_effects=self.fixed_effects)
        ibd_model.add_genetic_effect()

        ranef = RandomEffect(self.analysis_individuals,
                             'IBD',
                             incidence_matrix='eye',
                             covariance_matrix=self.ibd_matrix)
        ibd_model.add_random_effect(ranef)

        ibd_model.fit_model()
        ibd_model.maximize(verbose=self.verbose, method=self.maximization)
        return ibd_model
 def fit_null_model(self):
     null_model = MixedModel(self.pedigrees,
                             outcome=self.outcome,
                             fixed_effects=self.fixed_effects)
     null_model.add_genetic_effect()
     null_model.fit_model()
     null_model.maximize(method=self.maximization, verbose=self.verbose)
     return null_model
Example #5
0
def vc_linkage(locus):
    ibd_model = MixedModel(
        peds, outcome=args.outcome, fixed_effects=args.fixefs)
    add_relat_mat = null_model.covariance_matrices[0]
    additive = RandomEffect(analysis_individuals,
                            'additive',
                            incidence_matrix='eye',
                            covariance_matrix=add_relat_mat)
    ibdmat = sgs.ibd_matrix(analysis_individuals,
                            locus,
                            location_type='index',
                            onlywithin=args.onlywithin)

    ranef = RandomEffect(analysis_individuals,
                         'IBD',
                         incidence_matrix='eye',
                         covariance_matrix=ibdmat)
    ibd_model.add_random_effect(additive)
    ibd_model.add_random_effect(ranef)

    ibd_model.fit_model()
    ibd_model.maximize(
        verbose=args.verbose, method=args.maxmethod, starts=args.starts)
    return ibd_model
Example #6
0
pydigree.io.read_phenotypes(peds, args.phen)

# Get valid individuals from phenotypes
analysis_individuals = [x for x in peds.individuals
                        if args.outcome in x.phenotypes]

# Read SGS data
print 'Reading SGS data'
sgs = read_germline(args.sgs)

print 'Updating references'
sgs.update_segment_references(peds)


print 'Fitting polygenic model'
null_model = MixedModel(peds, outcome=args.outcome, fixed_effects=args.fixefs)
null_model.add_genetic_effect()
null_model.fit_model()
null_model.maximize(method=args.maxmethod, verbose=args.verbose)
null_model.summary()
llik_null = null_model.loglikelihood()
print 'Done'

analysis_individuals = null_model.observations()


def vc_linkage(locus):
    ibd_model = MixedModel(
        peds, outcome=args.outcome, fixed_effects=args.fixefs)
    add_relat_mat = null_model.covariance_matrices[0]
    additive = RandomEffect(analysis_individuals,