def measured_genotype_association(extrapredictor):
model = MixedModel(peds,
outcome=args.outcome,
fixed_effects=args.fixefs + [extrapredictor])
model.add_genetic_effect()
model.fit_model()
# Under the null (i.e. most loci in the genome) estimates of beta
# for alleles should be close to zero most of the time. If they're
# near zero, they're not explaining any of the variance in the
# response variable, so variance component estimates shouldn't be
# far from the null model. If we start with the null model's estimates
# we can probably save an iteration or two of scoring (or probably like
# a hundred iterations of expectation-maximization), and get to our
# null result sooner. If we're not there, we'll move out to real estimate
# anyway so it's essentially a free optimization.
model.maximize(method=args.maxmethod,
starts=null_model.variance_components,
verbose=args.verbose,
restricted=False)
return model
def measured_genotype_association(extrapredictor):
model = MixedModel(peds,
outcome=args.outcome,
fixed_effects=args.fixefs + [extrapredictor])
model.add_genetic_effect()
model.fit_model()
# Under the null (i.e. most loci in the genome) estimates of beta
# for alleles should be close to zero most of the time. If they're
# near zero, they're not explaining any of the variance in the
# response variable, so variance component estimates shouldn't be
# far from the null model. If we start with the null model's estimates
# we can probably save an iteration or two of scoring (or probably like
# a hundred iterations of expectation-maximization), and get to our
# null result sooner. If we're not there, we'll move out to real estimate
# anyway so it's essentially a free optimization.
model.maximize(method=args.maxmethod,
starts=null_model.variance_components,
verbose=args.verbose,
restricted=False)
return model
only = frozenset(args.only)
print('Reading pedigree')
peds = pyd.io.read_ped(args.ped)
print('Reading phenotypes')
pyd.io.read_phenotypes(peds, args.phen)
print('Reading genotypes')
genodata = pyd.io.plink.read_plink(pedfile=args.geno,
mapfile=args.map)
peds.update(genodata)
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,
restricted=False)
null_model.summary()
llik_null = null_model.loglikelihood()
def parse_range(rangestr):
chrom, span = rangestr.split(':')
chrom = chrom.replace('chr', '')
span = [int(x) for x in span.split('-')]
return chrom, span[0], span[1]
granges = [parse_range(x) for x in args.range]
args = parser.parse_args()
print('Reading files')
peds = pydigree.io.read_ped(args.pedf)
pydigree.io.read_phenotypes(peds, args.phenf)
m = MixedModel(peds, outcome=args.outcome, fixed_effects=args.fixefs)
print('Calculating Kinships')
m.add_genetic_effect()
if args.d7:
m.add_genetic_effect(kind='dominance')
print('Done')
m.fit_model()
if args.center:
m.y = m._centery()
if args.inflate:
m.y *= 100
if args.garbley:
m.y = np.matrix(np.random.normal(10, 5, len(m.y))).T
starts = args.starts
if starts is not None:
starts = [float(x) for x in starts]
m.maximize(method=args.maxmethod, verbose=True, starts=starts, restricted=args.reml)
m.summary()
only = frozenset(args.only)
print('Reading pedigree')
peds = pyd.io.read_ped(args.ped)
print('Reading phenotypes')
pyd.io.read_phenotypes(peds, args.phen)
print('Reading genotypes')
genodata = pyd.io.plink.read_plink(pedfile=args.geno,
mapfile=args.map)
peds.update(genodata)
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,
restricted=False)
null_model.summary()
llik_null = null_model.loglikelihood()
def parse_range(rangestr):
chrom, span = rangestr.split(':')
chrom = chrom.replace('chr', '')
span = [int(x) for x in span.split('-')]
return chrom, span[0], span[1]
granges = [parse_range(x) for x in args.range]
args = parser.parse_args()
print('Reading files')
peds = pydigree.io.read_ped(args.pedf)
pydigree.io.read_phenotypes(peds, args.phenf)
m = MixedModel(peds, outcome=args.outcome, fixed_effects=args.fixefs)
print('Calculating Kinships')
m.add_genetic_effect()
if args.d7:
m.add_genetic_effect(kind='dominance')
print('Done')
m.fit_model()
if args.inflate:
m.y *= 100
if args.garbley:
m.y = np.matrix(np.random.normal(10, 5, len(m.y))).T
starts = args.starts
if starts is not None:
starts = [float(x) for x in starts]
m.maximize(method=args.maxmethod, verbose=True,
starts=starts, restricted=args.reml)
m.summary()
if args.interact: