Multi-trait genome-wide association study of opioid addiction: OPRM1 and beyond.

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 (rg > 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.

Determining the stability of genome-wide factors in BMI between ages 40 to 69 years.

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.

GAWMerge expands GWAS sample size and diversity by combining array-based genotyping and whole-genome sequencing.

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.

mapMECFS: a portal to enhance data discovery across biological disciplines and collaborative sites.

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.

Clinico-epidemiological and genomic profile of first Zika Virus outbreak in India at Jaipur city of Rajasthan state.

BACKGROUND: First Zika virus (ZIKV) positive case from North India was detected on routine surveillance of Dengue-Like Illness in an 85-year old female. Objective of the study was to conduct an investigation for epidemiological, clinical and genomic analysis of first ZIKV outbreak in Rajasthan, North India and enhance routine ZIKV surveillance. METHOD: Outbreak investigation was performed in 3 Km radius of the index case among patient contacts, febrile cases, and pregnant women. Routine surveillance was enhanced to include samples from various districts of Rajasthan. Presence of ZIKV in serum and urine samples was detected by real time PCR test and CDC trioplex kit. Few ZIKV positive samples were sequenced using the next-generation sequencing method for genomic analysis. RESULT: On outbreak investigation 153/2043 (7.48%) cases were found positive: 1/153 (0.65%) among contacts, 90/153 (58.8%) in fever cases, 62/153(40.5%) in pregnant females. In routine surveillance, 6/4722 (0.12%) serum samples were ZIKV positive.Majority of patients had mild signs and symptoms, no case of microcephaly and Guillain- Barre Syndrome was seen, 25 (40.3%) pregnant females delivered healthy babies, four (6.4%) reported abortion and three (4.8%) had intrauterine death, one (1.6%) child had colorectal malformation and died after few days of birth. ZIKV was found to belong to Asian lineage, mutation related to enhanced neuro-virulence and transmission in animal models was not found. CONCLUSION: ZIKV was endogenous to India belonging to Asian Lineage. Disease profile of the ZIKV was asymptomatic to mild. No major anomaly was observed in infants born to ZIKV positive mothers; however, long term follow up of these children is required. There is need to scale up surveillance in the virology lab network of India for early detection and control. SUMMARY LINE: Zika virus infection was endogenous due to Asian Lineage with mild disease, no case of microcephaly or Guillain- Barre Syndrome was seen but children need to be followed for anomalies and surveillance of ZIKV needs to be enhanced in the country.

Cluster containment strategy: addressing Zika virus outbreak in Rajasthan, India.

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.

Gene set analysis methods: a systematic comparison.

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-ß 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.

Perturbation and candidate analysis to combat overfitting of gene expression microarray data.

Analysis of gene expression microarray datasets presents the high risk of over-fitting (spurious patterns) because of their feature-rich but case-poor nature. This paper describes our ongoing efforts to develop a method to combat over-fitting and determine the strongest signal in the dataset. A GA-SVM hybrid along with Gaussian noise (manual noise gain) is used to discover feature sets of minimal size that accurately classifies the cases under cross-validation. Initial results on a colorectal cancer dataset shows that the strongest signal (modest number of candidates) can be found by a binary search.