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Motivation Transcriptome-wide association studies (TWAS) have successfully facilitated the discovery of novel genetic risk loci for many complex traits, including late-onset Alzheimer's disease (AD). However, most existing TWAS methods rely only on gene expression and ignore epigenetic modification (i.e. DNA methylation) and functional regulatory information (i.e. enhancer-promoter interactions), both of which contribute significantly to the genetic basis of AD. Results We develop a novel gene-level association testing method that integrates genetically regulated DNA methylation and enhancer–target gene pairs with genome-wide association study (GWAS) summary results. Through simulations, we show that our approach, referred to as the CMO (cross methylome omnibus) test, yielded well controlled type I error rates and achieved much higher statistical power than competing methods under a wide range of scenarios. Furthermore, compared with TWAS, CMO identified an average of 124% more associations when analyzing several brain imaging-related GWAS results. By analyzing to date the largest AD GWAS of 71 880 cases and 383 378 controls, CMO identified six novel loci for AD, which have been ignored by competing methods. Availabilityand implementation The data used in this work were obtained from the following publicly available datasets: IGAP1, GWAX, UK Biobank, a 2019 meta-analyzed AD GWAS results and a imaging-derived phenotype GWAS results. The data resources are summarized in Supplementary Table S7. We used the publicly available software and tools for competing methods. All codes used to generate results that are reported in this manuscript and software for our newly proposed method CMO are available at https://github.com/ChongWuLab/CMO. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Both bone mineral density (BMD) and lean body mass (LBM) are important physiological measures with strong genetic determination. Besides, BMD and LBM might have common genetic factors. Aiming to identify pleiotropic genomic loci underlying BMD and LBM, we performed bivariate genome-wide association study meta-analyses of femoral neck bone mineral density and LBM at arms and legs, and replicated in the large-scale UK Biobank cohort sample. Combining the results from discovery meta-analysis and replication sample, we identified three genomic loci at the genome-wide significance level (p < 5.0 × 10–8): 2p23.2 (lead SNP rs4477866, discovery p = 3.47 × 10–8, replication p = 1.03 × 10–4), 16q12.2 (rs1421085, discovery p = 2.04 × 10–9, replication p = 6.47 × 10–14) and 18q21.32 (rs11152213, discovery p = 3.47 × 10–8, replication p = 6.69 × 10–6). Our findings not only provide useful insights into lean mass and bone mass development, but also enhance our understanding of the potential genetic correlation between BMD and LBM. [ABSTRACT FROM AUTHOR]

Aiming to uncover a shared genetic basis of abdominal obesity and osteoporosis, we performed a bivariate GWAS meta-analysis of femoral neck BMD (FNK-BMD) and trunk fat mass adjusted by trunk lean mass (TFMadj) in 11,496 subjects from 6 samples, followed by in silico replication in the large-scale UK Biobank (UKB) cohort. A series of functional investigations were conducted on the identified variants. Bivariate GWAS meta-analysis identified two novel pleiotropic loci 12q15 (lead SNP rs73134637, p = 3.45 × 10–7) and 10p14 (lead SNP rs2892347, p = 2.63 × 10–7) that were suggestively associated and that were replicated in the analyses of related traits in the UKB sample (osteoporosis p = 0.06 and 0.02, BMI p = 0.03 and 4.61 × 10–3, N up to 499,520). Cis-eQTL analysis demonstrated that allele C at rs73134637 was positively associated with IFNG expression in whole blood (N = 369, p = 0.04), and allele A at rs11254759 (10p14, p = 9.49 × 10–7) was negatively associated with PRKCQ expression in visceral adipose tissue (N = 313, p = 0.04) and in lymphocytes (N = 117, p = 0.03). As a proof-of-principle experiment, the function of rs11254759, which is 235 kb 5′-upstream from PRKCQ gene, was investigated by the dual-luciferase reporter assay, which clearly showed that the haplotype carrying rs11254759 regulated PRKCQ expression by upregulating PRKCQ promoter activity (p = 4.60 × 10–7) in an allelic specific manner. Mouse model analysis showed that heterozygous PRKCQ deficient mice presented decreased fat mass compared to wild-type control mice (p = 3.30 × 10–3). Mendelian randomization analysis demonstrated that both FNK-BMD and TFMadj were causally associated with fracture risk (p = 1.26 × 10–23 and 1.18 × 10–11). Our findings may provide useful insights into the genetic association between osteoporosis and abdominal obesity. [ABSTRACT FROM AUTHOR]

Sarcopenia is a complex polygenic disease, and its molecular mechanism is still unclear. Whole lean body mass (WLBM) is a heritable trait predicting sarcopenia. To identify genomic loci underlying, we performed a whole‐exome sequencing (WES) of WLBM variation with high sequencing depth (more than 40*) in 101 Chinese subjects. We then replicated in the major findings in the large‐scale UK Biobank (UKB) cohort (N = 217,822) for WLBM. The results of four single‐nucleotide polymorphisms (SNPs) were significant both in the discovery stage and replication stage: SNP rs740681 (discovery p = 1.66 × 10–6, replication p =.05), rs2272303 (discovery p = 3.20 × 10–4, replication p = 3.10 × 10–4), rs11170413 (discovery p = 3.99 × 10–4, replication p = 2.90 × 10–4), and rs2272302 (discovery p = 9.13 × 10–4, replication p = 3.10 × 10–4). We combined p values of the significant SNPs. Functional annotations highlighted two candidate genes, including FZR1 and SOAT2, that may exert pleiotropic effects to the development of body mass. Our findings provide useful insights that further enhance our understanding of genetic interplay in sarcopenia. [ABSTRACT FROM AUTHOR]

• The study the significant mixture effect between LPA, MPA, and VPA on the risk of dementia. • Taking into account genetic risk, physical activity patterns that are more adherent to MPA and VPA are associated with a reduced risk of dementia. • The findings reveal that the effectiveness of specific types of physical activity in reducing the risk of dementia varies based on the subtype of dementia. MPA is found to be more effective for Alzheimer's disease, while VPA is more powerful for vascular dementia. There is limited information on the mixture effect and weights of light physical activity (LPA), moderate physical activity (MPA), and vigorous physical activity (VPA) on dementia risk. A prospective cohort study was conducted based on the UK Biobank dataset. We included participants aged at least 45 years old without dementia at baseline between 2006-2010. The weighted quantile sum regression was used to explore the mixture effect and weights of three types of physical activity on dementia risk. This study includes 354,123 participants, with a mean baseline age of 58.0-year-old and 52.4 % of female participants. During a median follow-up time of 12.5 years, 5,136 cases of dementia were observed. The mixture effect of LPA, MPA, and VPA on dementia was statistically significant (β: -0.0924, 95 % Confidence Interval (CI): (-0.1402, -0.0446), P < 0.001), with VPA (weight: 0.7922) contributing most to a lower dementia risk, followed by MPA (0.1939). For Alzheimer's disease, MPA contributed the most (0.8555); for vascular dementia, VPA contributed the most (0.6271). For Alzheimer's disease, MPA was identified as the most influential factor, while VPA stood out as the most impactful for vascular dementia. [ABSTRACT FROM AUTHOR]

Background: Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations., Methods and Findings: We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5×10-6 and 5×10-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors., Conclusions: In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5×10-6 or 5×10-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Fat mass and lean mass are two biggest components of body mass. Both fat mass and lean mass are under strong genetic determinants and are correlated., Methods: We performed a bivariate genome-wide association meta-analysis of (lean adjusted) leg fat mass and (fat adjusted) leg lean mass in 12,517 subjects from 6 samples, and followed by in silico replication in large-scale UK biobank cohort sample (N = 370 097)., Results: We identified four loci that were significant at the genome-wide significance (GWS, α = 5.0 × 10 -8 ) level at the discovery meta-analysis, and successfully replicated in the replication sample: 2q36.3 (rs1024137, p discovery  = 3.32 × 10 -8 , p replication  = 4.07 × 10 -13 ), 5q13.1 (rs4976033, p discovery  = 1.93 × 10 -9 , p replication  = 6.35 × 10 -7 ), 12q24.31 (rs4765528, p discovery  = 7.19 × 10 -12 , p replication  = 1.88 × 10 -11 ) and 18q21.32 (rs371326986, p discovery  = 9.04 × 10 -9 , p replication  = 2.35 × 10 -95 ). The above four pleiotropic loci may play a pleiotropic role for fat mass and lean mass development., Conclusions: Our findings further enhance the understanding of the genetic association between fat mass and lean mass and provide a new theoretical basis for their understanding.