Ó³»­´«Ã½

An analysis of genetic data from more than 20,000 people with schizophrenia and 21,000 controls has revealed strong evidence linking copy number variations (CNVs; duplications or deletions of relatively large swaths of the genome) in eight regions of the genome to increased risk of schizophrenia. The analysis — by Dan Howrigan and Benjamin Neale of the Stanley Center for Psychiatric Research at Ó³»­´«Ã½ and more than 270 other members of the — also uncovered an additional nine genomic regions where CNVs may have either a deleterious or a protective role.

The study represents the largest integrated analysis of CNVs using data from people with schizophrenia to date, and reinforces what is possible when researchers come together around the principle of open data sharing.

To achieve their results — which the PGC has made available in granular detail — Neale and his collaborators developed a centralized CNV detection pipeline that brought together data from 43 datasets generated using a variety of genotyping technologies and algorithms, creating a unified dataset and allowing for a robust search for CNV events.

The eight CNVs the PGC researchers linked to schizophrenia risk (some of which are quite large and span several genes) were collectively present in only 1.4 percent of the study’s cases, stressing the view that only by sharing data collaboratively can researchers gather the sample sizes needed to discover very rare but sometimes very powerful genetic variations.

The results complement , which reported associations between schizophrenia and single nucleotide polymorphisms (SNPs, common variations in the genome composed of individual base pair changes) in 108 regions of the genome using the same as well as additional data. Together, the two analyses emphasize that lesions in many genes scattered across the genome may contribute to schizophrenia.