Conditional testing of multiple variants associated with bone mineral density in the FLNB gene region suggests that they represent a single association signal

Detecting PCOS susceptibility loci from genome-wide association studies via iterative trend correlation based feature screening

Background

Feature screening plays a critical role in handling ultrahigh dimensional data analyses when the number of features exponentially exceeds the number of observations. It is increasingly common in biomedical research to have case-control (binary) response and an extremely large-scale categorical features. However, the approach considering such data types is limited in extant literature. In this article, we propose a new feature screening approach based on the iterative trend correlation (ITC-SIS, for short) to detect important susceptibility loci that are associated with the polycystic ovary syndrome (PCOS) affection status by screening 731,442 SNP features that were collected from the genome-wide association studies.

Results

We prove that the trend correlation based screening approach satisfies the theoretical strong screening consistency property under a set of reasonable conditions, which provides an appealing theoretical support for its outperformance. We demonstrate that the finite sample performance of ITC-SIS is accurate and fast through various simulation designs.

Conclusion

ITC-SIS serves as a good alternative method to detect disease susceptibility loci for clinic genomic data.

Filamin B extensively regulates transcription and alternative splicing, and is associated with apoptosis in HeLa cells

Post-transcriptional mechanisms are an important approach in the treatment of cancer, and may also be hijacked by tumor cells to help adapt to the local microenvironment. Filamin B (FLNB), an actin-binding protein that provides crucial scaffolds for cell motility and signaling, has also been identified as an RNA-binding protein. Recent studies demonstrated that FLNB might play an important role, not only in skeletal development, but also in regulating tumorigenesis; however, the effects of dysregulated expression of FLNB at the molecular level are not clear. In the present study, RNA-sequencing was performed to analyze changes in overall transcriptional and alternative splicing between the knocked-down FLNB and the control in HeLa cells. Decreased FLNB levels resulted in significantly lower apoptosis compared with control cells. FLNB knockdown extensively regulated the expression of genes in cell apoptosis, tumorigenesis, metastases, transmembrane transport and cartilage development. Moreover, FLNB regulated alternative splicing of a large number of genes involved in ‘cell death’ and the ‘apoptotic process’. Some genes and alternative splicing related to skeletal development were enriched and regulated by FLNB. Reverse transcription-quantitative-PCR identified FLNB-regulated transcription and alternative splicing of genes, such as NLR family apoptosis inhibitory protein, interleukin 23 subunit α, metastasis associated lung adenocarcinoma transcript 1, phosphofurin acidic cluster sorting protein 2, bone morphogenetic protein 7, matrix metallopeptidase 13, collagen type II α 1 chain, fibroblast growth factor receptor 2 and vitamin D receptor. The present study is the first study, to the best of the authors’ knowledge, to provide transcriptome-wide analysis of differential gene expression and alternative splicing upon FLNB silencing. The present results suggested that FLNB may play an important regulatory role in cervical cancer cell apoptosis via regulation of transcription and alternative splicing, which provide insight for the current understanding of the mechanisms of FLNB-mediated gene regulation.

Analyzing Genome-Wide Association Study Dataset Highlights Immune Pathways in Lip Bone Mineral Density

Osteoporosis is a common complex human disease. Until now, large-scale genome-wide association studies (GWAS) using single genetic variant have reported some novel osteoporosis susceptibility variants. However, these risk variants only explain a small proportion of osteoporosis genetic risk, and most genetic risk is largely unknown. Interestingly, the pathway analysis method has been used in investigation of osteoporosis mechanisms and reported some novel pathways. Until now, it remains unclear whether there are other risk pathways involved in BMD. Here, we selected a lip BMD GWAS with 301,019 SNPs in 5,858 Europeans, and conducted a gene-based analysis (SET SCREEN TEST) and a pathway-based analysis (WebGestalt). On the gene level, BMD susceptibility genes reported by previous GWAS were identified to be the top 10 significant signals. On the pathway level, we identified 27 significant KEGG pathways. Three immune pathways including T cell receptor signaling pathway (hsa04660), complement and coagulation cascades (hsa04610), and intestinal immune network for IgA production (hsa04672) are ranked the top three significant signals. Evidence from the PubMed and Google Scholar databases further supports our findings. In summary, our findings provide complementary information to these nine risk pathways.

Multiple analyses of large-scale genome-wide association study highlight new risk pathways in lumbar spine bone mineral density

Osteoporosis is a common human complex disease. It is mainly characterized by low bone mineral density (BMD) and low-trauma osteoporotic fractures (OF). Until now, a large proportion of heritability has yet to be explained. The existing large-scale genome-wide association studies (GWAS) provide strong support for the investigation of osteoporosis mechanisms using pathway analysis. Recent findings showed that different risk pathways may be involved in BMD in different tissues. Here, we conducted multiple pathway analyses of a large-scale lumbar spine BMD GWAS dataset (2,468,080 SNPs and 31,800 samples) using two published gene-based analysis software including ProxyGeneLD and the PLINK. Using BMD genes from ProxyGeneLD, we identified 51 significant KEGG pathways with adjusted P<0.01. Using BMD genes from PLINK, we identified 38 significant KEGG pathways with adjusted P<0.01. Interestingly, 33 pathways are shared in both methods. In summary, we not only identified the known risk pathway such as Wnt signaling, in which the top GWAS variants are significantly enriched, but also highlight some new risk pathways. Interestingly, evidence from further supports the involvement of these pathways in MBD.

Association of ACTN3 polymorphisms with BMD, and physical fitness of elderly women

[Purpose] Association of ACTN3 polymorphism with bone mineral density and the physical fitness of elderly women is still unclear. Therefore, this study investigated the association between ACTN3 genotype and bone mineral density, and the physical fitness of elderly women. [Subjects and Methods] Sixty-eight elderly women (67.38 ± 3.68 years) were recruited at a Seongbuk-Gu (Seoul, Korea) Medical Service Public Health Center. Measurements of physical fitness included muscle strength, muscle endurance, flexibility, agility, balance and VO₂max. Bone mineral density (BMD), upper limb muscle mass, lower limb muscle mass, percent body fat and body fat mass for the entire body were measured by dual-energy X-ray absorptiometry and an analyzer. Genotyping for the ACTN3 R577X (rs1815739) polymorphism was performed using the TaqMan approach. [Results] ACTN3 gene distribution of subjects were in the Hardy-Weinberg equilibrium (p=0.694). The relative bone mineral density trunk, pelvis and spine differed significantly among the ACTN3 genotypes. There were no significant differences among bone mineral densities of the head, arms, legs, ribs and total, but the RR genotype tended to be higher than other genotypes. Physical fitness was not significantly different among the ACTN3 genotypes. [Conclusion] These results suggest that ACTN3 gene polymorphisms could be used as one of the genetic determinants of bone mass in elderly women, and in particular, they indicate that individuals with the RR genotype have higher BMD and bone mineral composition.

Exploiting Linkage Disequilibrium for Ultrahigh-Dimensional Genome-Wide Data with an Integrated Statistical Approach

Genome-wide data with millions of single-nucleotide polymorphisms (SNPs) can be highly correlated due to linkage disequilibrium (LD). The ultrahigh dimensionality of big data brings unprecedented challenges to statistical modeling such as noise accumulation, the curse of dimensionality, computational burden, spurious correlations, and a processing and storing bottleneck. The traditional statistical approaches lose their power due to [Formula: see text] (n is the number of observations and p is the number of SNPs) and the complex correlation structure among SNPs. In this article, we propose an integrated distance correlation ridge regression (DCRR) approach to accommodate the ultrahigh dimensionality, joint polygenic effects of multiple loci, and the complex LD structures. Initially, a distance correlation (DC) screening approach is used to extensively remove noise, after which LD structure is addressed using a ridge penalized multiple logistic regression (LRR) model. The false discovery rate, true positive discovery rate, and computational cost were simultaneously assessed through a large number of simulations. A binary trait of Arabidopsis thaliana, the hypersensitive response to the bacterial elicitor AvrRpm1, was analyzed in 84 inbred lines (28 susceptibilities and 56 resistances) with 216,130 SNPs. Compared to previous SNP discovery methods implemented on the same data set, the DCRR approach successfully detected the causative SNP while dramatically reducing spurious associations and computational time.