Characterizing Race/Ethnicity and Genetic Ancestry for 100,000 Subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) Cohort

Banda, Yambazi; Kvale, Mark N.; Hoffmann, Thomas J.; Hesselson, Stephanie; Ranatunga, Dilrini K.; Tang, Hua; Sabatti, Chiara; Croen, Lisa; Dispensa, Brad; Henderson, Mary; Iribarren, Carlos; Jorgenson, Eric; Kushi, Lawrence H.; Ludwig, Dana; Olberg, Diane; Quesenberry, Charles P.; Rowell, Sarah; Sadler, Marianne; Sakoda, Lori C.; Sciortino, Stanley; Shen, Lei; Smethurst, David; Somkin, Carol P.; Eeden, Stephen K. Van Den; Walter, Lawrence; Whitmer, Rachel A.; Kwok, Pui‐Yan; Schaefer, Catherine; Risch, Neil

doi:10.1534/genetics.115.178616

Cited by 291 publications

(316 citation statements)

References 41 publications

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“…These benchmarks allowed us to explore a greater range of reference panel sizes and genetic ancestries than currently available in sequenced reference panels (as the N =32,470 samples currently in the HRC are predominantly European), understanding that genotyped reference panels containing a limited set of markers are not broadly useful for reference-based phasing. We performed benchmarks using a total of five data sets: the UK Biobank cohort 22 and the four GERA sub-cohorts, which were genotyped on four distinct European, African, East Asian, and Latino genotyping arrays 23,24 . All five data sets were typed on arrays containing 650K–850K autosomal markers with typical heterozygosity and missingness rates, and each data set contained a small subset of mother-father-child trios (Online Methods and Supplementary Table 1).…”

Section: Resultsmentioning

confidence: 99%

Reference-based phasing using the Haplotype Reference Consortium panel

et al. 2016

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Haplotype phasing is a fundamental problem in medical and population genetics. Phasing is generally performed via statistical phasing within a genotyped cohort, an approach that can attain high accuracy in very large cohorts but attains lower accuracy in smaller cohorts. Here, we instead explore the paradigm of reference-based phasing. We introduce a new phasing algorithm, Eagle2, that attains high accuracy across a broad range of cohort sizes by efficiently leveraging information from large external reference panels (such as the Haplotype Reference Consortium, HRC) using a new data structure based on the positional Burrows-Wheeler transform. We demonstrate that Eagle2 attains a ≈20x speedup and ≈10% increase in accuracy compared to reference-based phasing using SHAPEIT2. On European-ancestry samples, Eagle2 with the HRC panel achieves >2x the accuracy of 1000 Genomes-based phasing. Eagle2 is open source and freely available for HRC-based phasing via the Sanger Imputation Service and the Michigan Imputation Server.

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Section: Resultsmentioning

confidence: 99%

Reference-based phasing using the Haplotype Reference Consortium panel

et al. 2016

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“…These cohorts were used extensively in our multi-ethnic analysis for replication purposes. Participants from the largest PMT cohort, PMT2, were selected from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort, a subsample of the Kaiser Permanente Research Program on Genes, Environment, and Health (RPEGH) 37. Three MetGen cohorts, GoDARTS, UKPDS and DCS also provided data on response to sulfonylureas.…”

Section: Methodsmentioning

confidence: 99%

Variation in the glucose transporter gene SLC2A2 is associated with glycemic response to metformin

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et al. 2016

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Metformin is the first-line antidiabetic drug with over 100 million users worldwide, yet its mechanism of action remains unclear1. Here the Metformin Genetics (MetGen) Consortium reports a three-stage genome-wide association study (GWAS), consisting of 13,123 participants of different ancestries. The C allele of rs8192675 in the intron of SLC2A2, which encodes the facilitated glucose transporter GLUT2, was associated with a 0.17% (p=6.6×10−14) greater metformin-induced in haemoglobin A1c (HbA1c) in 10,577 participants of European ancestry. rs8192675 is the top cis expression quantitative trait locus (cis-eQTL) for SLC2A2 in 1,226 human liver samples, suggesting a key role for hepatic GLUT2 in regulation of metformin action. Among obese individuals, C-allele homozygotes at rs8192675 had a 0.33% (3.6 mmol/mol) greater absolute HbA1c reduction than T-allele homozygotes. This was about half the effect seen with the addition of a DPP-4 inhibitor, and equated to a dose difference of 550mg of metformin, suggesting rs8192675 as a potential biomarker for stratified medicine.

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“…27 These principal components (PCs) were used in the analysis to adjust for genetic ancestry (Supplementary Figure 1).…”

Section: Genotyping Qc Imputation and Genetic Ancestry Of The Geramentioning

confidence: 99%

Common coding variants in the HLA-DQB1 region confer susceptibility to age-related macular degeneration

et al. 2016

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Age-related macular degeneration (AMD) risk variants in the complement system point to the important role of immune response and inflammation in the pathogenesis of AMD. Although the human leukocyte antigen (HLA) region has a central role in regulating immune response, previous studies of genetic variation in HLA genes and AMD have been limited by sample size or incomplete coverage of the HLA region by first-generation genotyping arrays and imputation panels. Here, we conducted a large-scale HLA fine-mapping study with 4841 AMD cases and 23 790 controls of non-Hispanic white ancestry from the Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging cohort. Genotyping was conducted using custom Affymetrix Axiom arrays, with dense coverage of the HLA region. Classic HLA polymorphisms were imputed using SNP2HLA, which utilizes a large reference panel to provide improved imputation accuracy of variants in this region. We examined a total of 6937 SNPs and 172 classical HLA alleles, conditioning on established AMD risk variants, which revealed novel associations with two non-synonymous SNPs in perfect linkage disequilibrium, rs9274390 and rs41563814 (odds ratio (OR) = 1.21; P = 1.4 × 10 − 11 ) corresponding to amino-acid changes at position 66 and 67 in HLA-DQB1, respectively, and the DQB1*02 classical HLA allele (OR = 1.22; P = 3.9 × 10 − 10 ) with the risk of AMD. We confirmed these association signals, again conditioning on established risk variants, in the MMAP data set of subjects with advanced AMD (rs9274390/rs41563814: OR = 1.28; P = 1.30 × 10 − 3 , DQB1*02: OR = 1.32; P = 9.00 × 10 − 4 ). These findings support a role of HLA class II alleles in the risk of AMD. European Journal of Human Genetics (2016) 24, 1049-1055; doi:10.1038/ejhg.2015.247; published online 6 January 2016 INTRODUCTION Age-related macular degeneration (AMD) is a complex, late-onset vision disorder, which is the leading cause of blindness among the elderly in developed countries. [1][2][3][4] Immune response and inflammation play a causal role in the pathogenesis and progression of AMD. [5][6][7][8][9][10][11] The human leukocyte antigen (HLA) region on chromosome 6p21.31 encodes gene products that regulate immune response. Drusen, a pathologic hallmark of AMD, contain HLA class II antigens, 12 and increased HLA class II immunoreactivity has been associated with drusen formation, 13 suggesting that HLA may influence the development of AMD. Although a few small candidate gene studies found that certain HLA class I and class II polymorphisms were associated with a predisposition to AMD, 14,15 the largest genome-wide association study (GWAS) meta-analysis conducted by the AMD Gene Consortium, which included more than 77 000 subjects, did not identify any HLA polymorphisms that were independently associated with the risk of AMD. 16 The AMD Consortium meta-analysis, although well powered to detect common variants of small effect, included a number of studies that utilized genotyping arrays, with array density ranging from 165 770 ...

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Characterizing Race/Ethnicity and Genetic Ancestry for 100,000 Subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) Cohort

Cited by 291 publications

References 41 publications

Reference-based phasing using the Haplotype Reference Consortium panel

Reference-based phasing using the Haplotype Reference Consortium panel

Variation in the glucose transporter gene SLC2A2 is associated with glycemic response to metformin

Common coding variants in the HLA-DQB1 region confer susceptibility to age-related macular degeneration

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