Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064203%3A_____%2F15%3A10337230" target="_blank" >RIV/00064203:_____/15:10337230 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11130/15:10337230

Výsledek na webu

<a href="http://dx.doi.org/10.1038/ng.3431" target="_blank" >http://dx.doi.org/10.1038/ng.3431</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/ng.3431" target="_blank" >10.1038/ng.3431</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis

Popis výsledku v původním jazyce

Heritability analyses of genome-wide association study (GWAS) cohorts have yielded important insights into complex disease architecture, and increasing sample sizes hold the promise of further discoveries. Here we analyze the genetic architectures of schizophrenia in 49,806 samples from the PGC and nine complex diseases in 54,734 samples from the GERA cohort. For schizophrenia, we infer an overwhelmingly polygenic disease architecture in which >= 71% of 1-Mb genomic regions harbor >= 1 variant influencing schizophrenia risk. We also observe significant enrichment of heritability in GC-rich regions and in higher-frequency SNPs for both schizophrenia and GERA diseases. In bivariate analyses, we observe significant genetic correlations (ranging from 0.18 to 0.85) for several pairs of GERA diseases; genetic correlations were on average 1.3 tunes stronger than the correlations of overall disease liabilities. To accomplish these analyses, we developed a fast algorithm for multicomponent, multi-trait variance-components analysis that overcomes prior computational barriers that made such analyses intractable at this scale.

Název v anglickém jazyce

Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis

Popis výsledku anglicky

Heritability analyses of genome-wide association study (GWAS) cohorts have yielded important insights into complex disease architecture, and increasing sample sizes hold the promise of further discoveries. Here we analyze the genetic architectures of schizophrenia in 49,806 samples from the PGC and nine complex diseases in 54,734 samples from the GERA cohort. For schizophrenia, we infer an overwhelmingly polygenic disease architecture in which >= 71% of 1-Mb genomic regions harbor >= 1 variant influencing schizophrenia risk. We also observe significant enrichment of heritability in GC-rich regions and in higher-frequency SNPs for both schizophrenia and GERA diseases. In bivariate analyses, we observe significant genetic correlations (ranging from 0.18 to 0.85) for several pairs of GERA diseases; genetic correlations were on average 1.3 tunes stronger than the correlations of overall disease liabilities. To accomplish these analyses, we developed a fast algorithm for multicomponent, multi-trait variance-components analysis that overcomes prior computational barriers that made such analyses intractable at this scale.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

EB - Genetika a molekulární biologie

OECD FORD obor

—

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Nature Genetics

ISSN

1061-4036

e-ISSN

—

Svazek periodika

47

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

1385-1392

Kód UT WoS článku

000365813200007

EID výsledku v databázi Scopus

2-s2.0-85000643907