CMC count data was ingested and processed similar to the AMP-AD transcriptome data. An iterative normalization model was deployed to identify significant covariates and regress them from the expression data before scaling the data (Additional file 2 : Table S2). Genotype data was profiled with Affymetrix GeneChip Mapping 5.0 K Array and a custom version of the Illumina Infinium CoreExome-24 v1.1 BeadChip (#WG-331-1111). Raw data was filtered to remove SNPs with: zero alternate alleles, MAF < 1%, genotyping rate < 0.95, Hardy-Weinberg p value < 1 × 10−6, and individuals with genotyping rate < 0.95. Imputation was performed using eagle, Minimac, and the HRC Reference Panel [18 ]. Imputed variant data was filtered for SNPs present in the LD reference panel using Plink (v1.9). CMC data was withheld from training gene weight models for the purpose of validating gene weights in an independent cohort, blinded from the training models. Expression values were imputed, and Kendall correlation values were calculated comparing imputed gene expression to the scaled, assayed expression values. Correlation test values were FDR corrected for the number of matched comparisons N = 6643.
Infinium coreexome 24 v1.1 beadchip
Manufactured by Illumina
The Infinium CoreExome-24 v1.1 BeadChip is a comprehensive genotyping array designed by Illumina for genome-wide association studies and genetic analysis. It provides coverage of over 540,000 genetic variants across the human genome.
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6 protocols using infinium coreexome 24 v1.1 beadchip
1
Robust Transcriptome and Genotype Imputation
2
Genome-wide linkage analysis of hereditary hearing loss
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DNA was extracted from peripheral blood samples using standard methods. DNA samples of 11 individuals (seven affected individuals from four generations, including the young proband with occasional “borderline” audiometry results, IV:3; Supplementary Table S1 ) were subjected to genome-wide linkage analysis using the Infinium CoreExome-24 v1.1 BeadChip (Illumina) according to the manufacturer’s protocol. Subsequent data handling was performed using the graphical user interface ALOHOMORA.21 (link) Relationship errors were identified by using the program Graphical Relationship Representation.22 (link) The program PedCheck was applied to find Mendelian errors,23 (link) and data for single-nucleotide polymorphisms (SNPs) with such errors were removed. Non-Mendelian errors were identified by using the program MERLIN24 (link) and unlikely genotypes for related samples were deleted. Linkage analysis was performed assuming autosomal-dominant inheritance, full penetrance, and a disease gene frequency of 0.0001. Multipoint logarithm of the odds (LOD) scores were calculated using the program ALLEGRO.25 (link) Haplotypes were reconstructed with ALLEGRO and presented graphically with HaploPainter.26 (link)
3
Genome-Wide Linkage Analysis of Autosomal-Recessive Disorder
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DNA was extracted from peripheral blood samples using standard methods. DNA samples of the parents and the four affected siblings (family as displayed in Fig. 1A ) were analyzed for genome wide linkage using the Infinium CoreExome-24 v1.1 BeadChip (Illumina Inc., San Diego, CA) according to the manufacturer’s protocol. Subsequent data handling was performed using the graphical user interface ALOHOMORA27 (link). Relationship errors were identified by using the program Graphical Relationship Representation28 (link). The program PedCheck was applied to find Mendelian errors29 (link) and data for SNPs with such errors were removed from the data set. Non-Mendelian errors were identified by using the program MERLIN30 (link) and unlikely genotypes for related samples were deleted. Linkage analysis was performed assuming autosomal-recessive inheritance, full penetrance, consanguinity, and a disease gene frequency of 0.0001. Multipoint LOD scores were calculated using the program Allegro31 (link). Haplotypes were reconstructed with Allegro and presented graphically with HaploPainter32 (link). Regions of homozygosity by descent (HBD) were annotated with their positions corresponding to NCBI Build 37.
4
Genome-wide Genotyping and Imputation
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A total of 551,839 genetic markers were genotyped using the Infinium® CoreExome-24 v1.1 BeadChip by Illumina. After SNP QC (MAF >10%, SNP call rate >95%, Hardy-Weinberg equilibrium (HWE) p value < 0.001) we retained 243,820 variants in our dataset. Samples with call rate <95% were removed from the analysis. After quality control per individual, the total genotyping call rate reached >99%. We performed imputation using BEAGLE 4.1 with a reference panel comprising the 1000 Genomes Phase 360 (link) and the UK10K73 (link) samples (modelscale parameter = 2). Following imputation we required allelic R-squared (AR2) ≥ 0.8, HWE p value < 0.001, and MAF >5% in both the analysed cohort and in the reference panel. We excluded 1,934 multiallelic polymorphisms from further analysis which resulted in 5,761,739 variants in our final dataset. Of those, 617,318 were insertion-deletions (INDELs). All genetic variant coordinates were lifted over to GRCh38.
Our samples clustered with the European populations included in the 1000 Genomes project (Figure S2 F). We removed 1 sample due to high relatedness (identity by state, pi_hat >0.2). We used VerifyBamID v1.0.061 (link) with the genotype information along with all the functional genomics sequencing assays (see below) to verify no sample swaps were present in the final dataset.
Our samples clustered with the European populations included in the 1000 Genomes project (
5
Chromosome Karyotyping and Copy Number Variation Analysis
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The number and shape of chromosomes of the samples were determined, i.e. karyotyped, using G-banding and KaryoLiteTM BoBsTM (Perkin Elmer) methods [9 (link)]. The karyotypes were validated with Illumina Infinium CoreExome-24 v1.1 BeadChip according to the manufacturer’s instructions.
The genotyping data were analyzed using Illumina’s GenomeStudio v2.0 software and its CNV Analysis Plugin was used to detect the CNVs for each sample separately. The software detects the CNVs based on the relative intensity shifts between breakpoints along the chromosomal segments, and the cnvPartition algorithm is used to calculate the copy numbers and their associated confidence scores [43 ]. The CNVs of ≥ 500 kb were included in a benchmarking dataset (Supplementary Table3 ).
The genotyping data were analyzed using Illumina’s GenomeStudio v2.0 software and its CNV Analysis Plugin was used to detect the CNVs for each sample separately. The software detects the CNVs based on the relative intensity shifts between breakpoints along the chromosomal segments, and the cnvPartition algorithm is used to calculate the copy numbers and their associated confidence scores [43 ]. The CNVs of ≥ 500 kb were included in a benchmarking dataset (Supplementary Table
6
RNA-Seq Data Normalization and Imputation
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CMC count data was ingested and processed similar to the AMP-AD transcriptome data. An iterative normalization model was deployed to identify significant covariates and regress them from the expression data before scaling the data (Table S2). Genotype data was profiled with Affymetrix GeneChip Mapping 5.0K Array and a custom version of the Illumina Infinium CoreExome-24 v1.1 BeadChip (#WG-331-1111). Raw data was filtered to remove SNPs with: zero alternate alleles, MAF <1%, genotyping rate < 0.95, Hardy-Weinberg p-value < 1 x 10-6, and individuals with genotyping rate < 0.95. Imputation was performed using eagle, Minimac and the HRC Reference Panel [16] (link). Imputed variant data was filtered for SNPs present in the LD reference panel using Plink (v1.9). Expression values were imputed, and kendall correlation values were calculated comparing imputed gene expression to the scaled, assayed expression values. Correlation test values were FDR corrected for the number of matched comparisons N=6643.
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