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Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of aggregate genetic variation influencing BMI from midlife and beyond is unknown. By analysing 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 72 years. We then applied genomic structural equation modeling to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that molecular genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin genome-wide variation in BMI from middle to early old age in men and women alike.

Genes influencing opioid use disorder (OUD) biology have been identified via genome-wide association studies (GWAS), gene expression, and network analyses. These discoveries provide opportunities to identifying existing compounds targeting these genes for drug repurposing studies. However, systematically integrating discovery results and identifying relevant available pharmacotherapies for OUD repurposing studies is challenging. To address this, we’ve constructed a framework that uses existing results and drug databases to identify candidate pharmacotherapies. For this study, two independent OUD related meta-analyses were used including a GWAS and a differential gene expression (DGE) study of post-mortem human brain. Protein-Protein Interaction (PPI) sub-networks enriched for GWAS risk loci were identified via network analyses. Drug databases Pharos, Open Targets, Therapeutic Target Database (TTD), and DrugBank were queried for clinical status and target selectivity. Cross-omic and drug query results were then integrated to identify candidate compounds. GWAS and DGE analyses revealed 3 and 335 target genes (FDR q < 0.05), respectively, while network analysis detected 70 genes in 22 enriched PPI networks. Four selection strategies were implemented, which yielded between 72 and 676 genes with statistically significant support and 110 to 683 drugs targeting these genes, respectively. After filtering out less specific compounds or those targeting well-established psychiatric-related receptors ( OPRM1 and DRD2 ), between 2 and 329 approved drugs remained across the four strategies. By leveraging multiple lines of biological evidence and resources, we identified many FDA approved drugs that target genes associated with OUD. This approach a) allows high-throughput querying of OUD-related genes, b) detects OUD-related genes and compounds not identified using a single domain or resource, and c) produces a succinct summary of FDA approved compounds eligible for efficient expert review. Identifying larger pools of candidate pharmacotherapies and summarizing the supporting evidence bridges the gap between discovery and drug repurposing studies.

Background Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease which involves multiple body systems (e.g., immune, nervous, digestive, circulatory) and research domains (e.g., immunology, metabolomics, the gut microbiome, genomics, neurology). Despite several decades of research, there are no established ME/CFS biomarkers available to diagnose and treat ME/CFS. Sharing data and integrating findings across these domains is essential to advance understanding of this complex disease by revealing diagnostic biomarkers and facilitating discovery of novel effective therapies. Methods The National Institutes of Health funded the development of a data sharing portal to support collaborative efforts among an initial group of three funded research centers. This was subsequently expanded to include the global ME/CFS research community. Using the open-source comprehensive knowledge archive network (CKAN) framework as the base, the ME/CFS Data Management and Coordinating Center developed an online portal with metadata collection, smart search capabilities, and domain-agnostic data integration to support data findability and reusability while reducing the barriers to sustainable data sharing. Results We designed the mapMECFS data portal to facilitate data sharing and integration by allowing ME/CFS researchers to browse, share, compare, and download molecular datasets from within one data repository. At the time of publication, mapMECFS contains data curated from public data repositories, peer-reviewed publications, and current ME/CFS Research Network members. Conclusions mapMECFS is a disease-specific data portal to improve data sharing and collaboration among ME/CFS researchers around the world. mapMECFS is accessible to the broader research community with registration. Further development is ongoing to include novel systems biology and data integration methods.

Background Gene set analysis is a valuable tool to summarize high-dimensional gene expression data in terms of biologically relevant sets. This is an active area of research and numerous gene set analysis methods have been developed. Despite this popularity, systematic comparative studies have been limited in scope. Methods In this study we present a semi-synthetic simulation study using real datasets in order to test and compare commonly used methods. Results A software pipeline, Flexible Algorithm for Novel Gene set Simulation (FANGS) develops simulated data based on a prostate cancer dataset where the KRAS and TGF-β pathways were differentially expressed. The FANGS software is compatible with other datasets and pathways. Comparisons of gene set analysis methods are presented for Gene Set Enrichment Analysis (GSEA), Significance Analysis of Function and Expression (SAFE), sigPathway, and Correlation Adjusted Mean RAnk (CAMERA) methods. All gene set analysis methods are tested using gene sets from the MSigDB knowledge base. The false positive rate and power are estimated and presented for comparison. Recommendations are made for the utility of the default settings of methods and each method’s sensitivity towards various effect sizes. Conclusions The results of this study provide empirical guidance to users of gene set analysis methods. The FANGS software is available for researchers for continued methods comparisons.

Genome-wide association studies (GWAS) have made impactful discoveries for complex diseases, often by amassing very large sample sizes. Yet, GWAS of many diseases remain underpowered, especially for non-European ancestries. One cost-effective approach to increase sample size is to combine existing cohorts, which may have limited sample size or be case-only, with public controls, but this approach is limited by the need for a large overlap in variants across genotyping arrays and the scarcity of non-European controls. We developed and validated a protocol, Genotyping Array-WGS Merge (GAWMerge), for combining genotypes from arrays and whole-genome sequencing, ensuring complete variant overlap, and allowing for diverse samples like Trans-Omics for Precision Medicine to be used. Our protocol involves phasing, imputation, and filtering. We illustrated its ability to control technology driven artifacts and type-I error, as well as recover known disease-associated signals across technologies, independent datasets, and ancestries in smoking-related cohorts. GAWMerge enables genetic studies to leverage existing cohorts to validly increase sample size and enhance discovery for understudied traits and ancestries. GAWMerge is a computational tool that allows users to integrate SNP genotyping data from array techniques or whole-genome sequencing, providing a feasible method to leverage existing cohorts to increase sample size in genetic studies.

Harvesting is a serious issue in oil palm plantations after 15–20 years owing to the increased height of the trees (>9 m). The slow vertical growth of the oil palm dura genotypes is desired for increasing the D × P progenies’ productivity and economic life span upto ten years. A reduced height increment has a long-term impact on harvesting costs. The current study assessed 308 genotypes generated from African germplasm. Over a three year period, the biometric properties of eleven D × D crosses were evaluated in order to quantify genetic parameters and phenotypic correlations, and principal component analysis was performed for genetic attributes of the better-performing dwarf progenies in terms of yield. The evaluated genotypes have a highly significant influence (p < 0.01) on the majority of characteristics. The progenies yielded between 165 and 208 kg of fresh fruit bunches (FFBs) per palm every year. The height increment (HI) varied between 17% and 19%, with an overall average of 18%. Genotypes G8, G300, and G221 had the lowest yearly height increments, measuring 28.98, 29.19, and 30.87 cm, respectively. The outcome of the present study shows that they are slow-height-increment genotypes with a high FFB yield (>25 T/Ha). The creation of dura parents with a slow height increment in combination with a high bunch weight helps for prolonging the productive life of the palm to more than 35 years, adding value to obtain distinct oil palm varieties. Overall, this targeted breeding effort towards developing dwarf oil palm hybrids reflects a strategic approach to addressing specific challenges in oil palm cultivation, ultimately helping to promote the oil palm sector globally.

India is at risk of Zika virus transmission due to high prevalence of its vector Aedes aegypti. Rajasthan, a state in the north-west region of India, has also high prevalence of Aedes mosquito. First laboratory confirmed case of Zika virus disease in Rajasthan was reported on 21 September 2018 in Jaipur. The Government of Rajasthan quickly implemented a containment strategy to contain the outbreak and prevent further spread of this disease. Strategy included active human and mosquito surveillance, laboratory testing and sequencing of the virus, integrated vector control measures, intersectoral coordination, risk communication and social mobilisation, all in a predefined geographic area around the epicentre. Timely action with appropriate coordination at all levels with multiple stakeholders contained the outbreak successfully. In all, 159 confirmed cases were reported from in and around the 3 km containment zone in Shastri Nagar area of Jaipur City and routine surveillance. Following this, a specially developed laboratory-based surveillance strategy was put in place to ensure that the disease does not spread beyond the containment zone. No fresh case was reported subsequently within or beyond the containment zone.

Mapping spatial variability of soil properties is the key to efficient soil resource management for sustainable crop yield in coastal areas. Therefore, the present study was conducted to assess the spatial variability of soil properties like-acidity (pH), salinity (Electrical Conductivity (EC)), organic carbon, available K, available P, exchangeable Ca.sup.2+, exchangeable Mg.sup.2+, available S and hot water soluble B in surface (0--20 cm) and subsurface (20--40 cm) soil layers of oil palm plantations in south Goa and north Goa districts of Goa situated in west coastal area of India. A total of 128 soil samples were collected from 64 oil palm plantations of Goa located at an approximate interval of 5--7 km and analyzed. Soil was acidic to neutral in reaction. Other soil properties varied widely in both the soil layers. Correlations between soil pH and exchangeable Ca.sup.2+, between soil EC and available K, between available P and available S and between exchangeable Ca.sup.2+ and exchangeable Mg.sup.2+ in both the soil layers were found to be positive and significant (P = 0.01). Geostatistical analysis revealed different spatial distribution pattern for the measured soil properties. Best fit models of measured soil properties were spherical, linear, exponential, circular and Gaussian with weak to strong spatial dependency. The results revealed that site-specific fertilizer management options needed to be adopted in the oil palm plantations of the study area owing to variability in soil properties.

Opioid addiction (OA) is moderately heritable, yet only rs1799971, the A118G variant in OPRM1 , has been identified as a genome-wide significant association with OA and independently replicated. We applied genomic structural equation modeling to conduct a GWAS of the new Genetics of Opioid Addiction Consortium (GENOA) data together with published studies (Psychiatric Genomics Consortium, Million Veteran Program, and Partners Health), comprising 23,367 cases and effective sample size of 88,114 individuals of European ancestry. Genetic correlations among the various OA phenotypes were uniformly high (r g  > 0.9). We observed the strongest evidence to date for OPRM1 : lead SNP rs9478500 ( p  = 2.56 × 10 –9 ). Gene-based analyses identified novel genome-wide significant associations with PPP6C and FURIN . Variants within these loci appear to be pleiotropic for addiction and related traits.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic, multisystem disease characterized by post-exertional malaise and persistent fatigue. The cause of ME/CFS is not well understood, and there are no established biomarkers or FDA-approved pharmacotherapies. The clinical heterogeneity of ME/CFS presents challenges to diagnosis and treatment and necessitates collaborative efforts to generate robust findings. This study leveraged gene and protein expression data from the mapMECFS data repository and the DecodeME Genome-Wide Association Study (GWAS) to assess consistent gene signatures across studies. The mitochondrial genes MT-RNR1 and MT-RNR2 exhibited lower expression in ME/CFS cases in two studies. Combining this with increased expression of mitochondrial genes in platelets from another study, this supports mitochondrial dysregulation as having a role in ME/CFS. Furthermore, ME/CFS-associated genes were mapped to compounds in drug databases as possible treatments for further investigation. In muscle gene expression data, 107 approved compounds target 26 genes with functions relevant to mitochondrial support and immunomodulators. From the DecodeME GWAS, 83 approved compounds target 24 genes with functions related to energy metabolism and mitochondrial function. Though little consistency in specific genes was observed across studies, which highlights the need for larger studies, mitochondrial dysfunction in ME/CFS cases was evident across studies.