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Despite strong vetting for disease activity, only 10% of candidate new molecular entities in early stage clinical trials are eventually approved. Analyzing historical pipeline data, Nelson et al. 2015 (Nat. Genet.) concluded pipeline drug targets with human genetic evidence of disease association are twice as likely to lead to approved drugs. Taking advantage of recent clinical development advances and rapid growth in GWAS datasets, we extend the original work using updated data, test whether genetic evidence predicts future successes and introduce statistical models adjusting for target and indication-level properties. Our work confirms drugs with genetically supported targets were more likely to be successful in Phases II and III. When causal genes are clear (Mendelian traits and GWAS associations linked to coding variants), we find the use of human genetic evidence increases approval by greater than two-fold, and, for Mendelian associations, the positive association holds prospectively. Our findings suggest investments into genomics and genetics are likely to be beneficial to companies deploying this strategy.

Background The remarkable growth of genome-wide association studies (GWAS) has created a critical need to experimentally validate the disease-associated variants, 90% of which involve non-coding variants. Methods To determine how the field is addressing this urgent need, we performed a comprehensive literature review identifying 36,676 articles. These were reduced to 1454 articles through a set of filters using natural language processing and ontology-based text-mining. This was followed by manual curation and cross-referencing against the GWAS catalog, yielding a final set of 286 articles. Results We identified 309 experimentally validated non-coding GWAS variants, regulating 252 genes across 130 human disease traits. These variants covered a variety of regulatory mechanisms. Interestingly, 70% (215/309) acted through cis-regulatory elements, with the remaining through promoters (22%, 70/309) or non-coding RNAs (8%, 24/309). Several validation approaches were utilized in these studies, including gene expression (n = 272), transcription factor binding (n = 175), reporter assays (n = 171), in vivo models (n = 104), genome editing (n = 96) and chromatin interaction (n = 33). Conclusions This review of the literature is the first to systematically evaluate the status and the landscape of experimentation being used to validate non-coding GWAS-identified variants. Our results clearly underscore the multifaceted approach needed for experimental validation, have practical implications on variant prioritization and considerations of target gene nomination. While the field has a long way to go to validate the thousands of GWAS associations, we show that progress is being made and provide exemplars of validation studies covering a wide variety of mechanisms, target genes, and disease areas.

Background Colocalization is a statistical method used in genetics to determine whether the same variant is causal for multiple phenotypes, for example, complex traits and gene expression. It provides stronger mechanistic evidence than shared significance, which can be produced through separate causal variants in linkage disequilibrium. Current colocalization methods require full summary statistics for both traits, limiting their use with the majority of reported GWAS associations (e.g. GWAS Catalog). We propose a new approximation to the popular coloc method that can be applied when limited summary statistics are available. Our method (POint EstiMation of Colocalization, POEMColoc) imputes missing summary statistics for one or both traits using LD structure in a reference panel, and performs colocalization using the imputed summary statistics. Results We evaluate the performance of POEMColoc using real (UK Biobank phenotypes and GTEx eQTL) and simulated datasets. We show good correlation between posterior probabilities of colocalization computed from imputed and observed datasets and similar accuracy in simulation. We evaluate scenarios that might reduce performance and show that multiple independent causal variants in a region and imputation from a limited subset of typed variants have a larger effect while mismatched ancestry in the reference panel has a modest effect. Further, we find that POEMColoc is a better approximation of coloc when the imputed association statistics are from a well powered study ( e.g. , relatively larger sample size or effect size). Applying POEMColoc to estimate colocalization of GWAS Catalog entries and GTEx eQTL, we find evidence for colocalization of 150,000 trait-gene-tissue triplets. Conclusions We find that colocalization analysis performed with full summary statistics can be closely approximated when only the summary statistics of the top SNP are available for one or both traits. When applied to the full GWAS Catalog and GTEx eQTL, we find that colocalized trait-gene pairs are enriched in tissues relevant to disease etiology and for matches to approved drug mechanisms. POEMColoc R package is available at https://github.com/AbbVie-ComputationalGenomics/POEMColoc .

ObjectiveEven as genomic medicine is implemented globally, there remains a lack of rigorous, national assessments of physicians’ current genomic practice and continuing genomics education needs. The aim of this study was to address this gap.DesignA cross-sectional survey, informed by qualitative data and behaviour change theory, to assess the current landscape of Australian physicians’ genomic medicine practice, perceptions of proximity and individual preparedness, and preferred models of practice and continuing education. The survey was advertised nationally through 10 medical colleges, 24 societies, 62 hospitals, social media, professional networks and snowballing.Results409 medical specialists across Australia responded, representing 30 specialties (majority paediatricians, 20%), from mainly public hospitals (70%) in metropolitan areas (75%). Half (53%) had contacted their local genetics services and half (54%) had ordered or referred for a gene panel or exome/genome sequencing test in the last year. Two-thirds (67%) think genomics will soon impact their practice, with a significant preference for models that involved genetics services (p<0.0001). Currently, respondents mainly perform tasks associated with pretest family history taking and counselling, but more respondents expect to perform tasks at all stages of testing in the future, including tasks related to the test itself, and reporting results. While one-third (34%) recently completed education in genomics, only a quarter (25%) felt prepared to practise. Specialists would like (more) education, particularly on genomic technologies and clinical utility, and prefer this to be through varied educational strategies.ConclusionsThis survey provides data from a breadth of physician specialties that can inform models of genetic service delivery and genomics education. The findings support education providers designing and delivering education that best meet learner needs to build a competent, genomic-literate workforce. Further analyses are underway to characterise early adopters of genomic medicine to inform strategies to increase engagement.

Reproductive genetic carrier screening (RGCS) provides people with information about their chance of having children with autosomal recessive or X-linked genetic conditions, enabling informed reproductive decision-making. RGCS is recommended to be offered to all couples during preconception or in early pregnancy. However, cost and a lack of awareness may prevent access. To address this, the Australian Government funded Mackenzie’s Mission—the Australian Reproductive Genetic Carrier Screening Project. Mackenzie’s Mission aims to assess the acceptability and feasibility of an easily accessible RGCS program, provided free of charge to the participant. In study Phase 1, implementation needs were mapped, and key study elements were developed. In Phase 2, RGCS is being offered by healthcare providers educated by the study team. Reproductive couples who provide consent are screened for over 1200 genes associated with >750 serious, childhood-onset genetic conditions. Those with an increased chance result are provided comprehensive genetic counseling support. Reproductive couples, recruiting healthcare providers, and study team members are also invited to complete surveys and/or interviews. In Phase 3, a mixed-methods analysis will be undertaken to assess the program outcomes, psychosocial implications and implementation considerations alongside an ongoing bioethical analysis and a health economic evaluation. Findings will inform the implementation of an ethically robust RGCS program.

Despite strong vetting for disease activity, only 10% of candidate new molecular entities in early stage clinical trials are eventually approved. Analyzing historical pipeline data, Nelson et al. 2015 (Nat. Genet.) concluded pipeline drug targets with human genetic evidence of disease association are twice as likely to lead to approved drugs. Taking advantage of recent clinical development advances and rapid growth in GWAS datasets, we extend the original work using updated data, test whether genetic evidence predicts future successes and introduce statistical models adjusting for target and indication-level properties. Our work confirms drugs with genetically supported targets were more likely to be successful in Phases II and III. When causal genes are clear (Mendelian traits and GWAS associations linked to coding variants), we find the use of human genetic evidence increases approval from Phase I by greater than two-fold, and, for Mendelian associations, the positive association holds prospectively. Our findings suggest investments into genomics and genetics are likely to be beneficial to companies deploying this strategy.

A common approach to understanding the mechanisms of noncoding GWAS associations is to test the GWAS variant for association with lower level cellular phenotypes such as gene expression. However, significant association to gene expression will often arise from linkage disequilibrium to a separate causal variant and be unrelated to the mechanism underlying the GWAS association. Colocalization is a statistical genetic method used to determine whether the same variant is causal for multiple phenotypes and is stronger evidence for understanding mechanism than shared significance. Current colocalization methods require full summary statistics for both traits, limiting their use with the majority of reported GWAS associations (e.g. GWAS Catalog). We propose a new approximation to the popular coloc method that can be applied when limited summary statistics are available, as in the common scenario where a GWAS catalog hit would be tested for colocalization with a GTEx eQTL. Our method (POint EstiMation of Colocalization - POEMColoc) imputes missing summary statistics using LD structure in a reference panel, and performs colocalization between the imputed statistics and full summary statistics for a second trait. Competing Interest Statement All authors are employees of AbbVie. The design, study conduct, and financial support for this research were provided by AbbVie. AbbVie participated in the interpretation of data, review, and approval of the publication.

WITH the vast acreage devoted to its cultivation today, the grape is a strong contestant for first place among American fruits, along with the orange and the apple. In truth, the earlier name for the continent--Vineiand--is truly more descriptive now than it was at the time of its discovery.

WITH the vast acreage devoted to its cultivation today, the grape is a strong contestant for first place among American fruits, along with the orange and the apple. In truth, the earlier name for the continent Vineland is truly more descriptive now than it was t the time of its discovery.