9 research outputs found

    Assessment of Genotype Imputation Performance Using 1000 Genomes in African American Studies

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    <div><p>Genotype imputation, used in genome-wide association studies to expand coverage of single nucleotide polymorphisms (SNPs), has performed poorly in African Americans compared to less admixed populations. Overall, imputation has typically relied on HapMap reference haplotype panels from Africans (YRI), European Americans (CEU), and Asians (CHB/JPT). The 1000 Genomes project offers a wider range of reference populations, such as African Americans (ASW), but their imputation performance has had limited evaluation. Using 595 African Americans genotyped on Illumina’s HumanHap550v3 BeadChip, we compared imputation results from four software programs (IMPUTE2, BEAGLE, MaCH, and MaCH-Admix) and three reference panels consisting of different combinations of 1000 Genomes populations (February 2012 release): (1) 3 specifically selected populations (YRI, CEU, and ASW); (2) 8 populations of diverse African (AFR) or European (AFR) descent; and (3) all 14 available populations (ALL). Based on chromosome 22, we calculated three performance metrics: (1) concordance (percentage of masked genotyped SNPs with imputed and true genotype agreement); (2) imputation quality score (IQS; concordance adjusted for chance agreement, which is particularly informative for low minor allele frequency [MAF] SNPs); and (3) average r2hat (estimated correlation between the imputed and true genotypes, for all imputed SNPs). Across the reference panels, IMPUTE2 and MaCH had the highest concordance (91%–93%), but IMPUTE2 had the highest IQS (81%–83%) and average r2hat (0.68 using YRI+ASW+CEU, 0.62 using AFR+EUR, and 0.55 using ALL). Imputation quality for most programs was reduced by the addition of more distantly related reference populations, due entirely to the introduction of low frequency SNPs (MAF≤2%) that are monomorphic in the more closely related panels. While imputation was optimized by using IMPUTE2 with reference to the ALL panel (average r2hat = 0.86 for SNPs with MAF>2%), use of the ALL panel for African American studies requires careful interpretation of the population specificity and imputation quality of low frequency SNPs.</p> </div

    Concordance resulting from four different imputation programs and three different 1000 Genomes (February 2012 release) reference panels.

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    <p>Concordance rates were based on masking 2% of the genotyped SNPs on chromosome 22 and comparing imputed and true genotypes. The number of subjects corresponding to each reference panel is shown in parentheses.</p

    Average r2hat, based on imputation using IMPUTE2, across the minor allele frequency (MAF) spectrum.

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    <p>Imputation was conducted for all SNPs available on the YRI+CEU+ASW (N = 234, in red), AFR+EUR (N = 625, in green), or the ALL (N = 1,092, in blue) reference panel from 1000 Genomes. Imputed polymorphic SNPs were divided into MAF intervals of 1%, and their average r2hat values were calculated within each interval.</p

    Imputation quality score (IQS) resulting from four different imputation programs and three different 1000 Genomes (February 2012) reference panels.

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    <p>IQS results were based on masking 2% of the genotyped SNPs and adjusting the concordance rate chance agreement between imputed and true genotypes. The number of subjects corresponding to each reference panel is shown in parentheses.</p

    Average r2hat values resulting from four different imputation programs and three different 1000 Genomes (February 2012) reference panels.

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    <p>r2hat values were averaged across all imputed SNPs on chromosome 22. The number of subjects corresponding to each reference panel is shown in parentheses.</p

    <i>In situ</i> expression analysis of <i>CO1</i> and <i>CO2</i> in leaf, reproductive bud, and shoot apex collected during the growing season from mature <i>P. deltoides</i>.

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    <p>Panels in the first two columns were results from the bright-field image of <i>in situ</i> hybridization and colorimetric detection of <i>CO1</i> or <i>CO2</i> transcripts. The antisense probe generated positive signals (dark blue) if present, while the sense probe served as negative control. The third column (schematic drawing) illustrates leaf cross-sections and longitudinal reproductive bud and shoot apex sections where <i>CO1</i> and <i>CO2</i> transcripts (pink color) were located, based on visual observations, as well as captured images. Scale bar, 100 µm.</p

    Transcripts downstream of <i>CO1</i> and <i>CO2</i> and their year-round transcript levels were identified in mature <i>P. deltoides</i> via microarray.

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    <p>Log<sub>2</sub> fold-change of each time point relative to the baseline time point (September or Sep) was calculated. Clusters to the left of the heatmaps represent modules and the columns to right of the heatmaps represent the up- (red) and down-regulation (blue) of downstream genes. Months relative to September are above the heatmaps. The pie charts to the right of each heatmap show the functional categorization of GO Biological Process terms. N = number of genes. The Venn diagram shows the number of genes that were common to both <i>CO1</i> and <i>CO2</i> (<i>CO1</i>/<i>CO2</i>) datasets, and the pie chart below the diagram shows the GO categorization of <i>CO1</i>/<i>CO2</i> transcripts. Up (↑) and down (↓) arrows represent partitioning of overall percentage in each pie. “**” denotes the GO term is significantly (<i>P</i>≤0.001, except “development” for genes downstream of <i>CO1 P</i>≤0.006) over-represented in the microarray data when a hypergeometric test was conducted.</p

    Ectopic expression of <i>CO1</i> and <i>CO2</i> individually (<i>Pro<sub>35S</sub></i>:<i>CO1</i> or <i>Pro<sub>35S</sub></i>:<i>CO2</i>) or together (<i>Pro<sub>35S</sub></i>:<i>CO1/CO2</i>) in poplar (<i>P. tremula</i> Ă— <i>P alba</i>).

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    <p>(A) When compared with controls at age 5, <i>Pro<sub>35S</sub></i>:<i>CO1</i> or <i>Pro<sub>35S</sub></i>:<i>CO2</i> trees did not differ in reproductive onset, spring reproductive and vegetative bud break, and fall bud set. <i>Pro<sub>35S</sub></i>:<i>FT2</i> trees showed year-round active growth. Red arrows denote the emerging inflorescence in the spring, whereas black arrows point the dormant terminal vegetative bud in the fall. Unlike wild-type and vector controls, <i>Pro<sub>35S</sub></i>:<i>CO1</i> or <i>Pro<sub>35S</sub></i>:<i>CO2</i> trees (1, 2, and 3) significantly overproduced <i>CO1</i> or <i>CO2</i> transcripts when analyzed via qRT-PCR in leaves sampled in April. While the expression of <i>FT1</i> was undetectable, that of <i>FT2</i> fluctuated with no clear trend in controls and <i>CO1</i>- or <i>CO2</i>-overexpressing trees. Letters above the bars showing the abundance of <i>CO1</i> or <i>CO2</i> transcripts indicate statistically significant (<i>P</i>≤0.001) differences. Error bars indicate SD about the mean. (B) When <i>Pro<sub>35S</sub></i>:<i>CO1</i> and <i>Pro<sub>35S</sub></i>:<i>CO2</i> were co-expressed in the same trees, no difference between the transformants and controls was observed in spring bud break and fall bud set in two years. However, <i>Pro<sub>35S</sub></i>:<i>FT2</i> trees showed a non-dormant phenotype. Black arrows indicate the terminal vegetative bud, whereas purple arrows point to the axillary vegetative bud. The axillary vegetative buds were opening and preformed leaves were emerging from the control and co-expressing transgenic trees on March 23. Unlike wild-type and vector-control plants, co-expressing transgenic trees (1, 2, 3, and 4) significantly overproduced <i>CO1</i> and <i>CO2</i> transcripts in leaves sampled in April. While the expression of <i>FT1</i> was undetectable, that of <i>FT2</i> fluctuated with no clear trend in controls and <i>CO1</i>/<i>CO2</i> overexpressing trees. Letters above the bars showing the abundance of <i>CO1</i> or <i>CO2</i> transcripts indicate statistically significant (<i>P</i>≤0.001) differences. Error bars indicate SD about the mean.</p
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