Dataset Information

Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome-wide associations.

ABSTRACT: Genome-wide association studies (GWAS) using high-density genotyping platforms offer an unbiased strategy to identify new candidate genes for osteoporosis. It is imperative to be able to clearly distinguish signal from noise by focusing on the best phenotype in a genetic study. We performed GWAS of multiple phenotypes associated with fractures [bone mineral density (BMD), bone quantitative ultrasound (QUS), bone geometry, and muscle mass] with approximately 433,000 single-nucleotide polymorphisms (SNPs) and created a database of resulting associations. We performed analysis of GWAS data from 23 phenotypes by a novel modification of a block clustering algorithm followed by gene-set enrichment analysis. A data matrix of standardized regression coefficients was partitioned along both axes--SNPs and phenotypes. Each partition represents a distinct cluster of SNPs that have similar effects over a particular set of phenotypes. Application of this method to our data shows several SNP-phenotype connections. We found a strong cluster of association coefficients of high magnitude for 10 traits (BMD at several skeletal sites, ultrasound measures, cross-sectional bone area, and section modulus of femoral neck and shaft). These clustered traits were highly genetically correlated. Gene-set enrichment analyses indicated the augmentation of genes that cluster with the 10 osteoporosis-related traits in pathways such as aldosterone signaling in epithelial cells, role of osteoblasts, osteoclasts, and chondrocytes in rheumatoid arthritis, and Parkinson signaling. In addition to several known candidate genes, we also identified PRKCH and SCNN1B as potential candidate genes for multiple bone traits. In conclusion, our mining of GWAS results revealed the similarity of association results between bone strength phenotypes that may be attributed to pleiotropic effects of genes. This knowledge may prove helpful in identifying novel genes and pathways that underlie several correlated phenotypes, as well as in deciphering genetic and phenotypic modularity underlying osteoporosis risk.

SUBMITTER: Gupta M

PROVIDER: S-EPMC3312758 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome-wide associations.

Gupta Mayetri M Cheung Ching-Lung CL Hsu Yi-Hsiang YH Demissie Serkalem S Cupples L Adrienne LA Kiel Douglas P DP Karasik David D

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research 20110601 6

Genome-wide association studies (GWAS) using high-density genotyping platforms offer an unbiased strategy to identify new candidate genes for osteoporosis. It is imperative to be able to clearly distinguish signal from noise by focusing on the best phenotype in a genetic study. We performed GWAS of multiple phenotypes associated with fractures [bone mineral density (BMD), bone quantitative ultrasound (QUS), bone geometry, and muscle mass] with approximately 433,000 single-nucleotide polymorphism ...[more]

PMID: 21611967

Similar Datasets

Project description:BackgroundDepression and anxiety are commonly observed in people with multiple sclerosis (pwMS). There is a growing body of literature supporting the hypothesis that personality traits can influence the mood disorders. This study aimed to investigate the personality traits and their relationships with depression and anxiety among pwMS.Methods234 pwMS were involved in this cross-sectional study. Personality traits, depression, and anxiety were assessed using the NEO Five-Factor Inventory (NEO-FFI) and Hospital Anxiety and Depression Scale (HADS), respectively. Pearson's correlation coefficient and generalized linear model were employed to evaluate the relationships between demographic and clinical characteristics, NEO-FFI, and HADS subscales.ResultsIn pwMS, longer disease duration was significantly associated with lower level of conscientiousness (β = - 0.23, p = 0.008) and agreeableness (β = - 0.2, p = 0.01). Moreover, higher expanded disability status scale (EDSS) of pwMS had a significant relationship with higher level of neuroticism (β = 0.89, p = 0.01). Increased level of neuroticism was significantly correlated with lower level of extraversion (r = - 0.28, p < 0.001), openness (r = - 0.37, p < 0.001), agreeableness (r = - 0.31, p < 0.001), and conscientiousness (r = - 0.45, p < 0.001). PwMS with higher level of conscientiousness showed more extraversion (r = 0.23, p < 0.001), openness (r = 0.61, p < 0.001), and agreeableness (r = 0.41, p < 0.001). Elevated level of neuroticism was significantly associated with higher level of anxiety (β = 0.47, p < 0.001) and depression (β = 0.11, p < 0.001) among pwMS.ConclusionThe co-occurrence of depression and anxiety is probably associated with neuroticism among pwMS. Additionally, the impact of personality traits extends to influencing key disease aspects such as physical disability and disease duration in MS.

Dataset Information

Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome-wide associations.

Publications

Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome-wide associations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets