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Pharmacogenomic variants have larger effect sizes than genetic variants associated with other dichotomous complex traits.

ABSTRACT: It has been suggested that pharmacogenomic phenotypes are influenced by genetic variants with larger effect sizes than other phenotypes, such as complex disease risk. This is presumed to reflect the fact that relevant environmental factors (drug exposure) are appropriately measured and taken into account. To test this hypothesis, we performed a systematic comparison of effect sizes between pharmacogenomic and non-pharmacogenomic phenotypes across all genome-wide association studies (GWAS) reported in the NHGRI GWAS catalog. We found significantly larger effect sizes for studies focused on pharmacogenomic phenotypes, as compared with complex disease risk, morphological phenotypes and endophenotypes. We found no significant differences in effect sizes between pharmacogenomic studies focused on adverse events versus those focused on drug efficacy. Furthermore, we found that this pattern persists among sample size-matched studies, suggesting that this pattern does not reflect overestimation of effect sizes due to smaller sample sizes in pharmacogenomic studies.The Pharmacogenomics Journal advance online publication, 7 July 2015; doi:10.1038/tpj.2015.47.

SUBMITTER: Maranville JC

PROVIDER: S-EPMC4704992 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Pharmacogenomic variants have larger effect sizes than genetic variants associated with other dichotomous complex traits.

Maranville J C JC Cox N J NJ

The pharmacogenomics journal 20150707 4

It has been suggested that pharmacogenomic phenotypes are influenced by genetic variants with larger effect sizes than other phenotypes, such as complex disease risk. This is presumed to reflect the fact that relevant environmental factors (drug exposure) are appropriately measured and taken into account. To test this hypothesis, we performed a systematic comparison of effect sizes between pharmacogenomic and non-pharmacogenomic phenotypes across all genome-wide association studies (GWAS) report ...[more]

PMID: 26149738

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Project description:In the present study, we systematically investigated population differentiation of drug-related (DR) genes in order to identify common genetic features underlying population-specific responses to drugs. To do so, we used the International HapMap project release 27 Data and Pharmacogenomics Knowledge Base (PharmGKB) database. First, we compared four measures for assessing population differentiation: the chi-square test, the analysis of variance (ANOVA) F-test, Fst, and Nearest Shrunken Centroid Method (NSCM). Fst showed high sensitivity with stable specificity among varying sample sizes; thus, we selected Fst for determining population differentiation. Second, we divided DR genes from PharmGKB into two groups based on the degree of population differentiation as assessed by Fst: genes with a high level of differentiation (HD gene group) and genes with a low level of differentiation (LD gene group). Last, we conducted a gene ontology (GO) analysis and pathway analysis. Using all genes in the human genome as the background, the GO analysis and pathway analysis of the HD genes identified terms related to cell communication. "Cell communication" and "cell-cell signaling" had the lowest Benjamini-Hochberg's q-values (0.0002 and 0.0006, respectively), and "drug binding" was highly enriched (16.51) despite its relatively high q-value (0.0142). Among the 17 genes related to cell communication identified in the HD gene group, five genes (STX4, PPARD, DCK, GRIK4, and DRD3) contained single nucleotide polymorphisms with Fst values greater than 0.5. Specifically, the Fst values for rs10871454, rs6922548, rs3775289, rs1954787, and rs167771 were 0.682, 0.620, 0.573, 0.531, and 0.510, respectively. In the analysis using DR genes as the background, the HD gene group contained six significant terms. Five were related to reproduction, and one was "Wnt signaling pathway," which has been implicated in cancer. Our analysis suggests that the HD gene group from PharmGKB is associated with cell communication and drug binding.

Project description:BackgroundPolymorphisms underlying complex traits often explain a small part (less than 1 %) of the phenotypic variance (σ2P). This makes identification of mutations underling complex traits difficult and usually only a subset of large-effect loci are identified. One approach to identify more loci is to increase sample size of experiments but here we propose an alternative. The aim of this paper is to use secondary phenotypes for genetically simple traits during the QTL discovery phase for complex traits. We demonstrate this approach in a dairy cattle data set where the complex traits were milk production phenotypes (fat, milk and protein yield; fat and protein percentage in milk) measured on thousands of individuals while secondary (potentially genetically simpler) traits are detailed milk composition traits (measurements of individual protein abundance, mineral and sugar concentrations; and gene expression).ResultsQuantitative trait loci (QTL) were identified using 11,527 Holstein cattle with milk production records and up to 444 cows with milk composition traits. There were eight regions that contained QTL for both milk production and a composition trait, including four novel regions. One region on BTAU1 affected both milk yield and phosphorous concentration in milk. The QTL interval included the gene SLC37A1, a phosphorous antiporter. The most significant imputed sequence variants in this region explained 0.001 σ2P for milk yield, and 0.11 σ2P for phosphorus concentration. Since the polymorphisms were non-coding, association mapping for SLC37A1 gene expression was performed using high depth mammary RNAseq data from a separate group of 371 lactating cows. This confirmed a strong eQTL for SLC37A1, with peak association at the same imputed sequence variants that were most significant for phosphorus concentration. Fitting any of these variants as covariables in the association analysis removed the QTL signal for milk production traits. Plausible causative mutations in the casein complex region were also identified using a similar strategy.ConclusionsMilk production traits in dairy cows are typical complex traits where polymorphisms explain only a small portion of the phenotypic variance. However, here we show that these mutations can have larger effects on secondary traits, such as concentrations of minerals, proteins and sugars in the milk, and expression levels of genes in mammary tissue. These larger effects were used to successfully map variants for milk production traits. Genetically simple traits also provide a direct biological link between possible causal mutations and the effect of these mutations on milk production.

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Pharmacogenomic variants have larger effect sizes than genetic variants associated with other dichotomous complex traits.

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Pharmacogenomic variants have larger effect sizes than genetic variants associated with other dichotomous complex traits.

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