Mapping complex disease traits with global gene expression

Cookson, William; Liang, Liming; Abecasis, Gonçalo R.; Moffatt, Miriam F.; Lathrop, Mark

doi:10.1038/nrg2537

Cited by 797 publications

(665 citation statements)

References 106 publications

(166 reference statements)

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“…Variants that change the amino acid sequence of proteins are more likely to affect phenotype than random sites in the genome, and this is used in the Bayes RC method described above. However, evidence is mounting that the majority of mutations that give rise to variation in complex traits reside in regulatory elements that alter gene expression [30][31][32] (reviewed by Pai et al [33]). …”

Section: Discussionmentioning

confidence: 99%

Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

Goddard

Kemper

MacLeod³

et al. 2016

Proc. R. Soc. B.

150

119

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Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

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Section: Discussionmentioning

confidence: 99%

Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

Goddard

Kemper

MacLeod³

et al. 2016

Proc. R. Soc. B.

150

119

View full text Add to dashboard Cite

show abstract

“…73 The association between nucleotide variants in a regulatory element of LCT gene and the lactase persistence phenotype in European population, identified about 10 years ago, 74 is one of the first -and perhaps better-knowndemonstration of this hypothesis. Since then, GWAS have unequivocally shown that SNPs affect gene expression.…”

Section: Rna-seq In Human Complex Diseases V Costa Et Almentioning

confidence: 99%

RNA-Seq and human complex diseases: recent accomplishments and future perspectives

et al. 2012

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“…Subtle effects of these SNPs on gene expression could be a functional mechanism by which they confer risk for development of schizophrenia. 10,11 Recently, it has been shown that true GWAS hits are enriched for expression QTLs (eQTLs). [11][12][13][14] Therefore, variations influencing gene expression are more likely to be contributing to the phenotype.…”

Section: Introductionmentioning

confidence: 99%

Expression QTL analysis of top loci from GWAS meta-analysis highlights additional schizophrenia candidate genes

et al. 2012

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and The PGC Schizophrenia (GWAS) Consortium 7There is genetic evidence that schizophrenia is a polygenic disorder with a large number of loci of small effect on disease susceptibility. Genome-wide association studies (GWASs) of schizophrenia have had limited success, with the best finding at the MHC locus at chromosome 6p. A recent effort of the Psychiatric GWAS consortium (PGC) yielded five novel loci for schizophrenia. In this study, we aim to highlight additional schizophrenia susceptibility loci from the PGC study by combining the top association findings from the discovery stage (9394 schizophrenia cases and 12 462 controls) with expression QTLs (eQTLs) and differential gene expression in whole blood of schizophrenia patients and controls. We examined the 6192 singlenucleotide polymorphisms (SNPs) with significance threshold at Po0.001. eQTLs were calculated for these SNPs in a sample of healthy controls (n¼437). The transcripts significantly regulated by the top SNPs from the GWAS meta-analysis were subsequently tested for differential expression in an independent set of schizophrenia cases and controls (n¼202). After correction for multiple testing, the eQTL analysis yielded 40 significant cis-acting effects of the SNPs. Seven of these transcripts show differential expression between cases and controls. Of these, the effect of three genes (RNF5, TRIM26 and HLA-DRB3) coincided with the direction expected from meta-analysis findings and were all located within the MHC region. Our results identify new genes of interest and highlight again the involvement of the MHC region in schizophrenia susceptibility.

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Mapping complex disease traits with global gene expression

Cited by 797 publications

References 106 publications

Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

RNA-Seq and human complex diseases: recent accomplishments and future perspectives

Expression QTL analysis of top loci from GWAS meta-analysis highlights additional schizophrenia candidate genes

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