Additional file 1: Table S1. of Immunoseq: the identification of functionally relevant variants through targeted capture and sequencing of active regulatory regions in human immune cells
Creators: name: AndrĂŠanne Morin
name: Kwan, Tony
name: Ge, Bing
name: Letourneau, Louis
name: Ban, Maria
name: Tandre, Karolina
name: Caron, Maxime
name: Sandling, Johanna
name: Carlsson, Jonas
name: Bourque, Guillaume
name: Laprise, Catherine
name: Montpetit, Alexandre
name: Ann-Christine Syvanen
name: Ronnblom, Lars
name: Sawcer, Stephen
name: Lathrop, Mark
name: Pastinen, Tomi
Publisher: name: Figshare
Year: 2016
Subjects: subject: Biochemistry
subject: Microbiology
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Genetics
subject: Molecular Biology
subject: Biotechnology
subject: Immunology
subject: FOS: Clinical medicine
subject: FOS: Clinical medicine
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: FOS: Mathematics
subject: Developmental Biology
subject: Cancer
subject: Hematology
subject: 110309 Infectious Diseases
subject: FOS: Health sciences
subject: FOS: Health sciences
subject: Computational Biology

description: Cell type selected to target regulatory regions in immune cells. Table S2. Cell types selected to target regulatory regions in other cell types not related to immune function. Table S3. Summary of shared common, rare and novel variants in selected DHS regions of different immune cells. Table S4. Sequencing statistics of the Cambridge Multiple sclerosis samples with Immunoseq. Figure S1. Variants quality control. Figure S2. Comparing sequencing data for NA18502 sample (Complete Genomics data and Immunoseq) considering only heterozygous SNVs identified by Complete Genomics that fall within Immunoseq custom capture panel regions. Figure S3. ImmunoChip hits that falls into Immunoseq custom capture panel. Figure S4. Discovery set distribution of allele specific expression (ASE). Figure S5. Average number of SNPs used to calculate allele specific expression (ASE) in discovery set samples. Figure S6. Adjusted proportion of transcripts with common (red), rare (blue) or novel (green) noncoding variants in the vicinity (+/-20kb) of a gene based on different allelic imbalance: 1.5 to 9, 2 to 9, 2.5 to 9, 3 to 9 and 3.5 to 9 fold difference in the discovery set. Figure S7. Discovery set distribution of Allelic imbalance (AI). Figure S8. Enrichment of proportion of AI transcripts with rare or novel variants in vicinity of a gene compared to AI transcripts with common variants in vicinity of a gene in the discovery set. Figure S9. Fold difference between proportions of AI transcripts with rare or novel variants in vicinity compared to AI transcripts with common variants in vicinity in the discovery set. Figure S10. Enrichment between proportions of AI transcripts with rare or novel variants in vicinity compared to AI transcripts with common variants in vicinity in the discovery set. Figure S11. Replication set distribution of allele specific expression (ASE). Figure S12. Average number of SNPs used to calculate allele specific expression (ASE) in the replication set. Figure S13. Distribution of allele specific expression of all transcripts and transcripts that did not carry the common allele in a heterozygous state. Figure S14. Replication set distribution of Allelic Imbalance (AI). Figure S15. Enrichment between proportions of AI transcripts with rare or novel variants in vicinity compared to AI transcripts with common variants in vicinity in the discovery and replication set. Figure S16. Enrichment between proportions of AI transcripts with rare or novel variants in vicinity compared to AI transcripts with common variants in vicinity in the replication set. (DOCX 1.61Â mb)