Genome Wide Association Studies (GWAS) have successfully identified thousands of loci associated with human diseases. Bayesian genetic fine-mapping studies aim to identify the specific causal variants within GWAS loci responsible for each association, reporting credible sets of plausible causal variants, which are interpreted as containing the causal variant with some "coverage probability". Here, we use simulations to demonstrate that the coverage probabilities are over-conservative in most fine-mapping situations. We show that this is because fine-mapping data sets are not randomly selected from amongst all causal variants, but from amongst causal variants with larger effect sizes. We present a method to reestimate the coverage of credible sets using rapid simulations based on the observed, or estimated, SNP correlation structure, we call this the "adjusted coverage estimate". This is extended to find "adjusted credible sets", which are the smallest set of variants such that their adjusted coverage estimate meets the target coverage. We use our method to improve the resolution of a fine-mapping study of type 1 diabetes. We found that in 27 out of 39 associated genomic regions our method could reduce the number of potentially causal variants to consider for follow-up, and found that none of the 95% or 99% credible sets required the inclusion of more variants-a pattern matched in simulations of well powered GWAS. Crucially, our method requires only GWAS summary statistics and remains accurate when SNP correlations are estimated from a large reference panel. Using our method to improve the resolution of fine-mapping studies will enable more efficient expenditure of resources in the follow-up process of annotating the variants in the credible set to determine the implicated genes and pathways in human diseases.

AbstractGenome Wide Association Studies (GWAS) have successfully identified thousands of loci associated with human diseases. Bayesian genetic fine-mapping studies aim to identify the specific causal variants within GWAS loci responsible for each association, reporting credible sets of plausible causal variants, which are interpreted as containing the causal variant with some “coverage probability”.Here, we use simulations to demonstrate that the coverage probabilities are over-conservative in most fine-mapping situations. We show that this is because fine-mapping data sets are not randomly selected from amongst all causal variants, but from amongst causal variants with larger effect sizes. We present a method to re-estimate the coverage of credible sets using rapid simulations based on the observed, or estimated, SNP correlation structure, we call this the “corrected coverage estimate”. This is extended to find “corrected credible sets”, which are the smallest set of variants such that their corrected coverage estimate meets the target coverage.We use our method to improve the resolution of a fine-mapping study of type 1 diabetes. We found that in 27 out of 39 associated genomic regions our method could reduce the number of potentially causal variants to consider for follow-up, and found that none of the 95% or 99% credible sets required the inclusion of more variants – a pattern matched in simulations of well powered GWAS.Crucially, our correction method requires only GWAS summary statistics and remains accurate when SNP correlations are estimated from a large reference panel. Using our method to improve the resolution of fine-mapping studies will enable more efficient expenditure of resources in the follow-up process of annotating the variants in the credible set to determine the implicated genes and pathways in human diseases.Author summaryPinpointing specific genetic variants within the genome that are causal for human diseases is difficult due to complex correlation patterns existing between variants. Consequently, researchers typically prioritise a set of plausible causal variants for functional validation - these sets of putative causal variants are called “credible sets”. We find that the probabilistic interpretation that these credible sets do indeed contain the true causal variant is variable, in that the reported probabilities often underestimate the true coverage of the causal variant in the credible set. We have developed a method to provide researchers with a “corrected coverage estimate” that the true causal variant appears in the credible set, and this has been extended to find “corrected credible sets”, allowing for more efficient allocation of resources in the expensive follow-up laboratory experiments. We used our method to reduce the number of genetic variants to consider as causal candidates for follow-up in 27 genomic regions that are associated with type 1 diabetes.

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IntroductionIn January, the National Institutes of Health (NIH) implemented a Data Management and Sharing Policy aiming to leverage data collected during NIH-funded research. The COVID-19 pandemic illustrated that this practice is equally vital for augmenting patient research. In addition, data sharing acts as a necessary safeguard against the introduction of analytical biases. While the pandemic provided an opportunity to curtail critical research issues such as reproducibility and validity through data sharing, this did not materialise in practice and became an example of ‘Open Data in Appearance Only’ (ODIAO). Here, we define ODIAO as the intent of data sharing without the occurrence of actual data sharing (eg, material or digital data transfers).ObjectivePropose a framework that states the main risks associated with data sharing, systematically present risk mitigation strategies and provide examples through a healthcare lens.MethodsThis framework was informed by critical aspects of both the Open Data Institute and the NIH’s 2023 Data Management and Sharing Policy plan guidelines.ResultsThrough our examination of legal, technical, reputational and commercial categories, we find barriers to data sharing ranging from misinterpretation of General Data Privacy Rule to lack of technical personnel able to execute large data transfers. From this, we deduce that at numerous touchpoints, data sharing is presently too disincentivised to become the norm.ConclusionIn order to move towards Open Data, we propose the creation of mechanisms for incentivisation, beginning with recentring data sharing on patient benefits, additional clauses in grant requirements and committees to encourage adherence to data reporting practices.

The importance of vital sign monitoring to detect deterioration increases during healthcare at home. Continuous monitoring with wearables increases assessment frequency but may create information overload for clinicians. The goal of this work was to demonstrate the impact of vital sign observation frequency and alarm settings on alarms in a real-world dataset. Vital signs were collected from 76 patients admitted to healthcare at home programs using the Current Health (CH) platform; its wearable continuously measured respiratory rate (RR), pulse rate (PR), and oxygen saturation (SpO2). Total alarms, alarm rate, patient rate, and detection time were calculated for three alarm rulesets to detect changes in SpO2, PR, and RR under four vital sign observation frequencies and four window sizes for the alarm algorithms’ median filter. Total alarms ranged from 65 to 3113. The alarm rate and early detection increased with the observation frequency for all alarm rulesets. Median filter windows reduced alarms triggered by normal fluctuations in vital signs without compromising the granularity of time between assessments. Frequent assessments enabled with continuous monitoring support early intervention but need to pair with settings that balance sensitivity, specificity, clinical risk, and provider capacity to respond when a patient is home to minimize clinician burden.