Boletim do resumo e programas (XIV EMR 2015)

XIV Escola de Modelos de Regressão
De 2 a 5 de Março de 2015 - Centro de Convenções - Unicamp
22
Sessão Temática 1 ( ST1.2): Big Data
Testing Association without Calling Genotypes Allows for Systematic
Differences in Read Depth and Sequencing Error Rate
Between Data from Case and Control Participants
Glen Satten
Centers for Disease Control and Prevention,
Atlanta, USA
Resumo: The quality of genotype calling for next-generation sequence data
depends on read depth. Loci with high coverage can typically be reliably called,
while those with low coverage may be difficult to call. In a case-control study, if data
from case participants is sequenced to a greater depth than data from controls, the
difference in genotype quality can introduce a systematic bias. This can easily occur
when historical controls (e.g., data from the 1000 Genomes Project) are used. This
imbalance may also occur by design, to reduce genotyping costs among controls.
For trios, bias can arise even when the coverage is the same in parents and offspring
since errors in parental genotype calls are considered non-transmissions while
errors in offspring genotype calls are detected as non-Mendelian transmissions.
Methods: We develop likelihood-based methods for analyzing data from casecontrol
and trio studies that directly uses data on reads without first making
intermediate genotype calls. When the location of polymorphic loci is known, we
show these likelihood approaches have appropriate size and good power compared
with methods that use called genotypes. When the locations of polymorphic loci are
not known in advance, we develop screening methods to screen out loci that are
estimated to be monomorphic, based on read data alone. We use a bootstrap
approach to estimate which of the loci that screen in are truly polymorphic. Using
these estimates, we then construct bootstrap tests for association that properly
account for screening and preserve size. We further show that restricting to loci with
estimated allele frequency ≥ 1/2N, so that the expected number of alleles seen is
greater than one, increases the power of our approach by excluding loci that have
negligible effect.
Results: We illustrate our approach using data from the UK10K project. We use data
from 784 cases from the Severe Childhood Onset Obesity Project, and are exome
sequenced at 60x. Data for 1702 controls are from the Avon Longitudinal Study of
Parents and Children and the TwinsUK study (only one twin used), and are whole
genome sequenced at 6x coverage.
: emrxiv@gmail.com
: http://www.ime.usp.br/~abe/emr2015