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Polushina, Tatiana [VerfasserIn]; Giddaluru, Sudheer [VerfasserIn]; Andreassen, Ole A. [VerfasserIn]; Le Hellard, Stéphanie [VerfasserIn]; Bettella, Francesco [VerfasserIn]; Espeseth, Thomas [VerfasserIn]; Lundervold, Astri J. [VerfasserIn]; Djurovic, Srdjan [VerfasserIn]; Cichon, Sven [VerfasserIn]; Hoffmann, Per [VerfasserIn]; Nöthen, Markus M. [VerfasserIn]; Steen, Vidar M. [VerfasserIn]

Analysis of the joint effect of SNPs to identify independent loci and allelic heterogeneity in schizophrenia GWAS data

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  • Medientyp: E-Artikel
  • Titel: Analysis of the joint effect of SNPs to identify independent loci and allelic heterogeneity in schizophrenia GWAS data
  • Beteiligte: Polushina, Tatiana [VerfasserIn]; Giddaluru, Sudheer [VerfasserIn]; Andreassen, Ole A. [VerfasserIn]; Le Hellard, Stéphanie [VerfasserIn]; Bettella, Francesco [VerfasserIn]; Espeseth, Thomas [VerfasserIn]; Lundervold, Astri J. [VerfasserIn]; Djurovic, Srdjan [VerfasserIn]; Cichon, Sven [VerfasserIn]; Hoffmann, Per [VerfasserIn]; Nöthen, Markus M. [VerfasserIn]; Steen, Vidar M. [VerfasserIn]
  • Erschienen: Nature Publishing Group, 2017
  • Erschienen in: Translational Psychiatry 7(12), 1289 (2017). doi:10.1038/s41398-017-0033-2
  • Sprache: Englisch
  • DOI: https://doi.org/10.1038/s41398-017-0033-2
  • ISSN: 2158-3188
  • Entstehung:
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  • Beschreibung: We have tested published methods for capturing allelic heterogeneity and identifying loci of joint effects to uncover more of the “hidden heritability” of schizophrenia (SCZ). We used two tools, cojo-GCTA and multi-SNP, to analyze meta-statistics from the latest genome-wide association study (GWAS) on SCZ by the Psychiatric Genomics Consortium (PGC). Stepwise regression on markers with p values <10−7 in cojo-GCTA identified 96 independent signals. Eighty-five passed the genome-wide significance threshold. Cross-validation of cojo-GCTA by CLUMP was 76%, i.e., 26 of the loci identified by the PGC using CLUMP were found to be dependent on another locus by cojo-GCTA. The overlap between cojo-GCTA and multi-SNP was better (up to 92%). Three markers reached genome-wide significance (5 × 10−8) in a joint effect model. In addition, two loci showed possible allelic heterogeneity within 1-Mb genomic regions, while CLUMP analysis had identified 16 such regions. Cojo-GCTA identified fewer independent loci than CLUMP and seems to be more conservative, probably because it accounts for long-range LD and interaction effects between markers. These findings also explain why fewer loci with possible allelic heterogeneity remained significant after cojo-GCTA analysis. With multi-SNP, 86 markers were selected at the threshold 10−7. Multi-SNP identifies fewer independent signals, due to splitting of the data and use of smaller samples. We recommend that cojo-GCTA and multi-SNP are used for post-GWAS analysis of all traits to call independent loci. We conclude that only a few loci in SCZ show joint effects or allelic heterogeneity, but this could be due to lack of power for that data set.
  • Zugangsstatus: Freier Zugang