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Gene-based association tests aggregate multiple SNP-trait associations into sets defined by gene boundaries and are widely used in post-GWAS analysis. A common approach for gene-based tests is to combine SNPs associations by computing the sum of χ2 statistics. However, this strategy ignores the directions of SNP effects, which could result in a loss of power for SNPs with masking effects, e.g., when the product of two SNP effects and the linkage disequilibrium (LD) correlation is negative. Here, we introduce "mBAT-combo," a set-based test that is better powered than other methods to detect multi-SNP associations in the context of masking effects. We validate the method through simulations and applications to real data. We find that of 35 blood and urine biomarker traits in the UK Biobank, 34 traits show evidence for masking effects in a total of 4,273 gene-trait pairs, indicating that masking effects is common in complex traits. We further validate the improved power of our method in height, body mass index, and schizophrenia with different GWAS sample sizes and show that on average 95.7% of the genes detected only by mBAT-combo with smaller sample sizes can be identified by the single-SNP approach with a 1.7-fold increase in sample sizes. Eleven genes significant only in mBAT-combo for schizophrenia are confirmed by functionally informed fine-mapping or Mendelian randomization integrating gene expression data. The framework of mBAT-combo can be applied to any set of SNPs to refine trait-association signals hidden in genomic regions with complex LD structures. We introduce mBAT-combo, a set-based test that is better powered than other methods to detect multi-SNP associations when SNP effects are mutually masked by linkage disequilibrium and find that such a masking effect phenomenon is common, if not ubiquitous, in complex traits. [ABSTRACT FROM AUTHOR]

The article offers information on the bibliometric analysis of articles published in the journal of the History of Biology between the years 2001 and 2010. It informs that authors of the published articles mainly came from Europe and America and the scientists were mainly from Great Britain, U.S., Germany, and France. It informs that the results help us to understand the recent trends of research in world history of biology.

Recent large national and international cohorts describe the baseline characteristics and outcome of hospitalised patients with COVID-19, however there is limited granularity to these reports. We aimed to provide a detailed description of a UK COVID-19 cohort, focusing on management and outcome. We performed a retrospective single-centre analysis of clinical management and 28-day outcomes of consecutive adult inpatients with SARS-CoV-2 PCR-confirmed COVID-19 from 31 January to 16 April 2020 inclusive. In total, 316 cases were identified. Most patients were elderly (median age 75) with multiple comorbidities. One quarter were admitted from residential or nursing care. Mortality was 84 out of 316 (26.6%). Most deaths occurred in patients in whom a ceiling of inpatient treatment had been determined and for whom end of life care and specialist palliative care input was provided where appropriate. No deaths occurred in patients aged under 56 years. Decisions to initiate respiratory support were individualised after consideration of patient wishes, premorbid frailty and comorbidities. In total, 59 (18%) patients were admitted to intensive care, of which 31 (10% overall cohort) required intubation. Multiple logistic regression identified associations between death and age, frailty, and disease severity, with age as the most significant factor (odds ratio 1.07 [95% CI 1.03–1.10] per year increase, p < 0.001). These findings provide important clinical context to outcome data. Mortality was associated with increasing age. Most deaths were anticipated and occurred in patients with advance decisions on ceilings of treatment. [ABSTRACT FROM AUTHOR]

Housing insecurity is an escalating problem in the UK but there is limited evidence about its health impacts. Using nationally representative panel data and causally focussed methods, we examined the effect of insecure housing on mental health, sleep and blood pressure, during a period of government austerity. We used longitudinal survey data (2009–2019, n = 11,164 individuals with annual data) from the UK Household Longitudinal Study. Outcomes were probable common mental disorder (GHQ-12), sleep disturbance due to worry, and new diagnoses of hypertension. The primary exposure was housing payment problems in the past year. Using doubly robust marginal structural models with inverse probability of treatment weights, we estimated absolute and relative health effects of housing payment problems, and population attributable fractions. In stratified analyses we assessed potentially heterogeneous impacts across the population, and potential modifying effects of government austerity measures. A negative control analysis was conducted to detect bias due to unmeasured confounding. Housing payment problems were associated with a 2.5 percentage point increased risk of experiencing a common mental disorder (95% CI 1.1%, 3.8%) and 2.0% increased risk of sleep disturbance (95% CI 0.7%, 3.3%). Estimates were larger for renters, younger people, less educated, households with children, and people living in areas most affected by austerity-related cuts to housing support services. We did not find consistent evidence for an association with hypertension (risk difference = 0.4%; 95% CI -0.1%, 0.9%). The negative control analysis was not indicative of unmeasured confounding. Housing payment problems were associated with worse mental health and sleep disturbance in a large UK sample. Households at risk of falling into rent or mortgage arrears need more support, especially in areas where housing support services have been diminished. Substantial investment is urgently needed to improve supply of social and affordable housing. • Housing payment problems were associated with worse mental health and sleep. • We used doubly robust marginal structural models with IPTW. • No indication of bias from unmeasured confounding in a negative control analysis. • Larger estimates for renters, younger people, households with children. • Mental health effect larger in areas with disinvestment in housing support services. [ABSTRACT FROM AUTHOR]