title: Genotype imputation of Metabochip SNPs using a study-specific reference panel of ~4,000 haplotypes in African Americans from the Women's Health Initiative.
abstract: Genetic imputation has become standard practice in modern genetic studies. However, several important issues have not been adequately addressed including the utility of study-specific reference, performance in admixed populations, and quality for less common (minor allele frequency [MAF] 0.005-0.05) and rare (MAF < 0.005) variants. These issues only recently became addressable with genome-wide association studies (GWAS) follow-up studies using dense genotyping or sequencing in large samples of non-European individuals. In this work, we constructed a study-specific reference panel of 3,924 haplotypes using African Americans in the Women's Health Initiative (WHI) genotyped on both the Metabochip and the Affymetrix 6.0 GWAS platform. We used this reference panel to impute into 6,459 WHI SNP Health Association Resource (SHARe) study subjects with only GWAS genotypes. Our analysis confirmed the imputation quality metric Rsq (estimated r(2) , specific to each SNP) as an effective post-imputation filter. We recommend different Rsq thresholds for different MAF categories such that the average (across SNPs) Rsq is above the desired dosage r(2)  (squared Pearson correlation between imputed and experimental genotypes). With a desired dosage r(2)  of 80%, 99.9% (97.5%, 83.6%, 52.0%, 20.5%) of SNPs with MAF > 0.05 (0.03-0.05, 0.01-0.03, 0.005-0.01, and 0.001-0.005) passed the post-imputation filter. The average dosage r(2)  for these SNPs is 94.7%, 92.1%, 89.0%, 83.1%, and 79.7%, respectively. These results suggest that for African Americans imputation of Metabochip SNPs from GWAS data, including low frequency SNPs with MAF 0.005-0.05, is feasible and worthwhile for power increase in downstream association analysis provided a sizable reference panel is available.
authors.affiliation: Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA.
authors.forename: Eric Yi
authors.initials: EY
authors.lastname: Liu
authors.forename: Steven
authors.initials: S
authors.lastname: Buyske
authors.forename: Aaron K
authors.initials: AK
authors.lastname: Aragaki
authors.forename: Ulrike
authors.initials: U
authors.lastname: Peters
authors.forename: Eric
authors.initials: E
authors.lastname: Boerwinkle
authors.forename: Chris
authors.initials: C
authors.lastname: Carlson
authors.forename: Cara
authors.initials: C
authors.lastname: Carty
authors.forename: Dana C
authors.initials: DC
authors.lastname: Crawford
authors.forename: Jeff
authors.initials: J
authors.lastname: Haessler
authors.forename: Lucia A
authors.initials: LA
authors.lastname: Hindorff
authors.forename: Loic Le
authors.initials: LL
authors.lastname: Marchand
authors.forename: Teri A
authors.initials: TA
authors.lastname: Manolio
authors.forename: Tara
authors.initials: T
authors.lastname: Matise
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.forename: Charles
authors.initials: C
authors.lastname: Kooperberg
authors.forename: Kari E
authors.initials: KE
authors.lastname: North
authors.forename: Yun
authors.initials: Y
authors.lastname: Li
mesh_terms: D001741:Black or African American; D000483:Alleles; D005260:Female; D005787:Gene Frequency; D015894:Genome, Human; D055106:Genome-Wide Association Study; D005838:Genotype; D006239:Haplotypes; D006801:Humans; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D012984:Software; D014481:United States; D016387:Women's Health