Effects of insomnia and non-vasomotor menopausal symptoms on coronary heart disease risk: a mendelian randomization study.

Background: Previous studies suggested that vasomotor symptoms were associated with an increasing risk of coronary heart diseases (CHD) but not clear with menopausal symptoms other than vasomotor symptoms. Given the heterogeneity and interrelationship among menopausal symptoms, it is not easy to make causal inferences based on observational studies. We performed a Mendelian randomization (MR) to investigate the association of individual non-vasomotor menopausal symptoms and the risk of CHDs. Methods: A sample of 177,497 British women aged ≥51 years old (average age at menopause) without related cardiovascular diseases from the UK biobank is selected as our study population. Non-vasomotor menopausal symptoms, including anxiety, nervous, insomnia, urinary tract infection, fatigue, and vertigo, were selected as exposures based on the modified Kupperman index. Outcome variable is CHD. Results: In total, 54, 47, 24, 33, 22, and 81 instrumental variables were selected for anxiety, insomnia, fatigue, vertigo, urinary tract infection and nervous respectively. We conducted MR analyses of menopausal symptoms and CHD. Only insomnia symptoms increased the lifetime risk of CHD with OR 1.394 (p = 0.0003). There were no significant causal relationships with CHD and other menopausal symptoms. Insomnia near menopause age (45-50 years) does not increase the risk of CHD. However, postmenopausal (over 51) insomnia increases the risk of CHD. Conclusion: MR analyses support that among non-vasomotor menopausal symptoms, only insomnia symptoms may increase the lifetime risk of CHD. Insomnia at different ages near menopause has differential impacts on CHD risk.

Phenome-wide analysis of Taiwan Biobank reveals novel glycemia-related loci and genetic risks for diabetes.

To explore the complex genetic architecture of common diseases and traits, we conducted comprehensive PheWAS of ten diseases and 34 quantitative traits in the community-based Taiwan Biobank (TWB). We identified 995 significantly associated loci with 135 novel loci specific to Taiwanese population. Further analyses highlighted the genetic pleiotropy of loci related to complex disease and associated quantitative traits. Extensive analysis on glycaemic phenotypes (T2D, fasting glucose and HbA1c) was performed and identified 115 significant loci with four novel genetic variants (HACL1, RAD21, ASH1L and GAK). Transcriptomics data also strengthen the relevancy of the findings to metabolic disorders, thus contributing to better understanding of pathogenesis. In addition, genetic risk scores are constructed and validated for absolute risks prediction of T2D in Taiwanese population. In conclusion, our data-driven approach without a priori hypothesis is useful for novel gene discovery and validation on top of disease risk prediction for unique non-European population.

Maximal Segmental Score Method for Localizing Recessive Disease Variants Based on Sequence Data.

BACKGROUND: Due to the affordability of whole-genome sequencing, the genetic association design can now address rare diseases. However, some common statistical association methods only consider homozygosity mapping and need several criteria, such as sliding windows of a given size and statistical significance threshold setting, such as P-value < 0.05 to achieve good power in rare disease association detection. METHODS: Our region-specific method, called expanded maximal segmental score (eMSS), converts p-values into continuous scores based on the maximal segmental score (MSS) (Lin et al., 2014) for detecting disease-associated segments. Our eMSS considers the whole genome sequence data, not only regions of homozygosity in candidate genes. Unlike sliding window methods of a given size, eMSS does not need predetermined parameters, such as window size or minimum or maximum number of SNPs in a segment. The performance of eMSS was evaluated by simulations and real data analysis for autosomal recessive diseases multiple intestinal atresia (MIA) and osteogenesis imperfecta (OI), where the number of cases is extremely small. For the real data, the results by eMSS were compared with a state-of-the-art method, HDR-del (Imai et al., 2016). RESULTS: Our simulation results show that eMSS had higher power as the number of non-causal haplotype blocks decreased. The type I error for eMSS under different scenarios was well controlled, p < 0.05. For our observed data, the bone morphogenetic protein 1 (BMP1) gene on chromosome 8, the Violaxanthin de-epoxidase-related chloroplast (VDR) gene on chromosome 12 associated with OI, and the tetratricopeptide repeat domain 7A (TTC7A) gene on chromosome 2 associated with MIA have previously been identified as harboring the relevant pathogenic mutations. CONCLUSIONS: When compared to HDR-del, our eMSS is powerful in analyzing even small numbers of recessive cases, and the results show that the method can further reduce numbers of candidate variants to a very small set of susceptibility pathogenic variants underlying OI and MIA. When we conduct whole-genome sequence analysis, eMSS used 3/5 the computation time of HDR-del. Without additional parameters needing to be set in the segment detection, the computational burden for eMSS is lower compared with that in other region-specific approaches.