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Shelina Ramnarine Juan Zhang Li-Shiun Chen Robert Culverhouse Weimin Duan Dana B. Hancock Sarah M. Hartz Eric O. Johnson Emily Olfson Tae-Hwi Schwantes-An Nancy L. Saccone 《PloS one》2015,10(10)
Imputation, the process of inferring genotypes for untyped variants, is used to identify and refine genetic association findings. Inaccuracies in imputed data can distort the observed association between variants and a disease. Many statistics are used to assess accuracy; some compare imputed to genotyped data and others are calculated without reference to true genotypes. Prior work has shown that the Imputation Quality Score (IQS), which is based on Cohen’s kappa statistic and compares imputed genotype probabilities to true genotypes, appropriately adjusts for chance agreement; however, it is not commonly used. To identify differences in accuracy assessment, we compared IQS with concordance rate, squared correlation, and accuracy measures built into imputation programs. Genotypes from the 1000 Genomes reference populations (AFR N = 246 and EUR N = 379) were masked to match the typed single nucleotide polymorphism (SNP) coverage of several SNP arrays and were imputed with BEAGLE 3.3.2 and IMPUTE2 in regions associated with smoking behaviors. Additional masking and imputation was conducted for sequenced subjects from the Collaborative Genetic Study of Nicotine Dependence and the Genetic Study of Nicotine Dependence in African Americans (N = 1,481 African Americans and N = 1,480 European Americans). Our results offer further evidence that concordance rate inflates accuracy estimates, particularly for rare and low frequency variants. For common variants, squared correlation, BEAGLE R2, IMPUTE2 INFO, and IQS produce similar assessments of imputation accuracy. However, for rare and low frequency variants, compared to IQS, the other statistics tend to be more liberal in their assessment of accuracy. IQS is important to consider when evaluating imputation accuracy, particularly for rare and low frequency variants. 相似文献
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Culverhouse RC Saccone NL Stitzel JA Wang JC Steinbach JH Goate AM Schwantes-An TH Grucza RA Stevens VL Bierut LJ 《Human genetics》2011,129(2):177-188
Results from genome-wide association studies of complex traits account for only a modest proportion of the trait variance
predicted to be due to genetics. We hypothesize that joint analysis of polymorphisms may account for more variance. We evaluated
this hypothesis on a case–control smoking phenotype by examining pairs of nicotinic receptor single-nucleotide polymorphisms
(SNPs) using the Restricted Partition Method (RPM) on data from the Collaborative Genetic Study of Nicotine Dependence (COGEND).
We found evidence of joint effects that increase explained variance. Four signals identified in COGEND were testable in independent
American Cancer Society (ACS) data, and three of the four signals replicated. Our results highlight two important lessons:
joint effects that increase the explained variance are not limited to loci displaying substantial main effects, and joint
effects need not display a significant interaction term in a logistic regression model. These results suggest that the joint
analyses of variants may indeed account for part of the genetic variance left unexplained by single SNP analyses. Methodologies
that limit analyses of joint effects to variants that demonstrate association in single SNP analyses, or require a significant
interaction term, will likely miss important joint effects. 相似文献
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