Enhanced Identification of Potential Pleiotropic Genetic Variants for Bone Mineral Density and Breast Cancer

From function to translation: Decoding genetic susceptibility to human diseases via artificial intelligence

While genome-wide association studies (GWAS) have discovered thousands of disease-associated loci, molecular mechanisms for a considerable fraction of the loci remain to be explored. The logical next steps for post-GWAS are interpreting these genetic associations to understand disease etiology (GWAS functional studies) and translating this knowledge into clinical benefits for the patients (GWAS translational studies). Although various datasets and approaches using functional genomics have been developed to facilitate these studies, significant challenges remain due to data heterogeneity, multiplicity, and high dimensionality. To address these challenges, artificial intelligence (AI) technology has demonstrated considerable promise in decoding complex functional datasets and providing novel biological insights into GWAS findings. This perspective first describes the landmark progress driven by AI in interpreting and translating GWAS findings and then outlines specific challenges followed by actionable recommendations related to data availability, model optimization, and interpretation, as well as ethical concerns.

Bone mineral density and risk of breast cancer: A cohort study and Mendelian randomization analysis

BACKGROUND

Estrogen is involved in both bone metabolism and breast cancer proliferation. However, evidence about the risk of breast cancer according to women's bone mineral density (BMD) is scarce, and little is known about their causal associations.

METHODS

Women participating in the UK Biobank cohort were used to investigate the association between BMD and the risk of breast cancer using Cox regression models. Instrumental variants associated with estimated BMD (eBMD) were extracted from genome-wide association studies with European ancestry. Logistic regression was used to calculate the genetic association with breast cancer in the UK Biobank and 2-sample Mendelian randomization (MR) analyses to assess their causal associations with breast cancer. Finally, the pleiotropic conditional false discovery rate (cFDR) method was conducted to further detect common genetic variants between BMD and breast cancer.

RESULTS

Compared with the general population, postmenopausal women with BMD T scores <-2.5 had a lower risk of breast cancer (hazard ratio [HR], 0.77; 95% CI, 0.59-1.00), and this effect was stronger in women with fracture (HR, 0.31; 95% CI, 0.12-0.82). In MR analysis, no causal associations between eBMD and breast cancer were observed. The cFDR method identified 63 pleiotropic loci associated with both BMD and breast cancer, of which CCDC170, ESR1, and FTO might play crucial roles in their pleiotropy.

CONCLUSIONS

An association between BMD and the risk of postmenopausal breast cancer in the UK Biobank was observed, whereas no evidence supported their causal association. Instead, their association could be explained by pleiotropic genetic variants leading to the pathology of osteoporosis and breast cancer.

Relationship between rs7586085, GALNT3 and CCDC170 gene polymorphisms and the risk of osteoporosis among the Chinese Han population

Osteoporosis (OP) has plagued many women for years, and bone density loss is an indicator of OP. The purpose of this study was to evaluate the relationship between the polymorphism of the rs7586085, CCDC170 and GALNT3 gene polymorphisms and the risk of OP in the Chinese Han population. Using the Agena MassArray method, we identified six candidate SNPs on chromosomes 2 and 6 in 515 patients with OP and 511 healthy controls. Genetic model analysis was performed to evaluate the significant association between variation and OP risk, and meanwhile, the multiple tests were corrected by false discovery rate (FDR). Haploview 4.2 was used for haplotype analysis. In stratified analysis of BMI ˃ 24, rs7586085, rs6726821, rs6710518, rs1346004, and rs1038304 were associated with the risk of OP based on the results of genetic models among females even after the correction of FDR (qd < 0.05). In people at age ≤ 60 years, rs1038304 was associated with an increased risk of OP under genetic models after the correction of FDR (qd < 0.05). Our study reported that GALNT3 and CCDC170 gene polymorphisms and rs7586085 are the effective risk factors for OP in the Chinese Han population.

Twelve years of GWAS discoveries for osteoporosis and related traits: advances, challenges and applications

Osteoporosis is a common skeletal disease, affecting ~200 million people around the world. As a complex disease, osteoporosis is influenced by many factors, including diet (e.g. calcium and protein intake), physical activity, endocrine status, coexisting diseases and genetic factors. In this review, we first summarize the discovery from genome-wide association studies (GWASs) in the bone field in the last 12 years. To date, GWASs and meta-analyses have discovered hundreds of loci that are associated with bone mineral density (BMD), osteoporosis, and osteoporotic fractures. However, the GWAS approach has sometimes been criticized because of the small effect size of the discovered variants and the mystery of missing heritability, these two questions could be partially explained by the newly raised conceptual models, such as omnigenic model and natural selection. Finally, we introduce the clinical use of GWAS findings in the bone field, such as the identification of causal clinical risk factors, the development of drug targets and disease prediction. Despite the fruitful GWAS discoveries in the bone field, most of these GWAS participants were of European descent, and more genetic studies should be carried out in other ethnic populations to benefit disease prediction in the corresponding population.

Genetic Pleiotropy of Bone-Related Phenotypes: Insights from Osteoporosis

PURPOSE OF REVIEW

We summarize recent evidence on the shared genetics within and outside the musculoskeletal system (mostly related to bone density and osteoporosis).

RECENT FINDINGS

Osteoporosis is determined by an interplay between multiple genetic and environmental factors. Significant progress has been made regarding its genetic background revealing a number of robustly validated loci and respective pathways. However, pleiotropic factors affecting bone and other tissues are not well understood. The analytical methods proposed to test for potential associations between genetic variants and multiple phenotypes can be applied to bone-related data. A number of recent genetic studies have shown evidence of pleiotropy between bone density and other different phenotypes (traits, conditions, or diseases), within and outside the musculoskeletal system. Power benefits of combining correlated phenotypes, as well as unbiased discovery, make these studies promising. Studies in humans are supported by evidence from animal models. Drug development and repurposing should benefit from the pleiotropic approach. We believe that future studies should take into account shared genetics between the bone and related traits.

Identification of novel functional CpG-SNPs associated with type 2 diabetes and coronary artery disease

Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D) and coronary artery disease (CAD), respectively. Nevertheless, these studies were generally performed for single-trait/disease and failed to assess the pleiotropic role of the identified variants. To identify novel functional loci and the pleiotropic relationship between CAD and T2D, the targeted cFDR analysis on CpG-SNPs was performed by integrating two independent large and multi-centered GWASs with summary statistics of T2D (26,676 cases and 132,532 controls) and CAD (60,801 cases and 123,504 controls). Applying the cFDR significance threshold of 0.05, we observed a pleiotropic enrichment between T2D and CAD by incorporating pleiotropic effects into a conditional analysis framework. We identified 79 novel CpG-SNPs for T2D, 61 novel CpG-SNPs for CAD, and 18 novel pleiotropic loci for both traits. Among these novel CpG-SNPs, 33 of them were annotated as methylation quantitative trait locus (meQTL) in whole blood, and ten of them showed expression QTL (eQTL), meQTL, and metabolic QTL (metaQTL) effects simultaneously. To the best of our knowledge, we performed the first targeted cFDR analysis on CpG-SNPs, and our findings provided novel insights into the shared biological mechanisms and overlapped genetic heritability between T2D and CAD.

Additional common variants associated with type 2 diabetes and coronary artery disease detected using a pleiotropic cFDR method

Genome-wide association studies (GWASs) have been performed extensively in diverse populations to identify single nucleotide polymorphisms (SNPs) associated with complex diseases or traits. However, to date, the SNPs identified fail to explain a large proportion of the variance of the traits/diseases. GWASs on type 2 diabetes (T2D) and coronary artery disease (CARD) are generally performed as single-trait studies, rather than analyzing the related traits simultaneously. Despite the extensive evidence suggesting that these two phenotypes share both genetic and environmental risk factors, the shared overlapping genetic biological mechanisms between these traits remain largely unexplored. Here, we adopted a recently developed genetic pleiotropic conditional false discovery rate (cFDR) approach to discover novel loci associated with T2D and CARD by incorporating the summary statistics from existing GWASs of these two traits. Applying the cFDR level of 0.05, 33 loci were identified for T2D and 34 loci for CARD, 9 of which for both. By incorporating pleiotropic effects into a conditional analysis framework, we observed that there is significant pleiotropic enrichment between T2D and CARD. These findings may provide novel insights into the etiology of T2D and CARD, as well as the processes that may influence disease development both individually and jointly.

Identification of Novel Potentially Pleiotropic Variants Associated With Osteoporosis and Obesity Using the cFDR Method

CONTEXT

Genome-wide association studies (GWASs) have been successful in identifying loci associated with osteoporosis and obesity. However, the findings explain only a small fraction of the total genetic variance.

OBJECTIVE

The aim of this study was to identify novel pleiotropic genes important in osteoporosis and obesity.

DESIGN AND SETTING

A pleiotropic conditional false discovery rate method was applied to three independent GWAS summary statistics of femoral neck bone mineral density, body mass index, and waist-to-hip ratio. Next, differential expression analysis was performed for the potentially pleiotropic genes, and weighted genes coexpression network analysis (WGCNA) was conducted to identify functional connections between the suggested pleiotropic genes and known osteoporosis/obesity genes using transcriptomic expression data sets in osteoporosis/obesity-related cells.

RESULTS

We identified seven potentially pleiotropic loci-rs3759579 (MARK3), rs2178950 (TRPS1), rs1473 (PUM1), rs9825174 (XXYLT1), rs2047937 (ZNF423), rs17277372 (DNM3), and rs335170 (PRDM6)-associated with osteoporosis and obesity. Of these loci, the PUM1 gene was differentially expressed in osteoporosis-related cells (B lymphocytes) and obesity-related cells (adipocytes). WGCNA showed that PUM1 positively interacted with several known osteoporosis genes (AKAP11, JAG1, and SPTBN1). ZNF423 was the highly connected intramodular hub gene and interconnected with 21 known osteoporosis-related genes, including JAG1, EN1, and FAM3C.

CONCLUSIONS

Our study identified seven potentially pleiotropic genes associated with osteoporosis and obesity. The findings may provide new insights into a potential genetic determination and codetermination mechanism of osteoporosis and obesity.

Novel Common Variants Associated with Obesity and Type 2 Diabetes Detected Using a cFDR Method

Genome-wide association studies (GWASs) have been performed extensively in diverse populations to identify single nucleotide polymorphisms (SNPs) associated with complex diseases or traits. However, to date, the SNPs identified fail to explain a large proportion of the variance of the traits/diseases. GWASs on type 2 diabetes (T2D) and obesity are generally focused on individual traits independently, and genetic intercommunity (common genetic contributions or the product of over correlated phenotypic world) between them are largely unknown, despite extensive data showing that these two phenotypes share both genetic and environmental risk factors. Here, we applied a recently developed genetic pleiotropic conditional false discovery rate (cFDR) approach to discover novel loci associated with BMI and T2D by incorporating the summary statistics from existing GWASs of these two traits. Conditional Q-Q and fold enrichment plots were used to visually demonstrate the strength of pleiotropic enrichment. Adopting a cFDR nominal significance level of 0.05, 287 loci were identified for BMI and 75 loci for T2D, 23 of which for both traits. By incorporating related traits into a conditional analysis framework, we observed significant pleiotropic enrichment between obesity and T2D. These findings may provide novel insights into the etiology of obesity and T2D, individually and jointly.