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Typical data in a microbiome study consist of the operational taxonomic unit (OTU) counts that have the characteristic of excess zeros, which are often ignored by investigators. In this paper, we compare the performance of different competing methods to model data with zero inflated features through extensive simulations and application to a microbiome study. These methods include standard parametric and non-parametric models, hurdle models, and zero inflated models. We examine varying degrees of zero inflation, with or without dispersion in the count component, as well as different magnitude and direction of the covariate effect on structural zeros and the count components. We focus on the assessment of type I error, power to detect the overall covariate effect, measures of model fit, and bias and effectiveness of parameter estimations. We also evaluate the abilities of model selection strategies using Akaike information criterion (AIC) or Vuong test to identify the correct model. The simulation studies show that hurdle and zero inflated models have well controlled type I errors, higher power, better goodness of fit measures, and are more accurate and efficient in the parameter estimation. Besides that, the hurdle models have similar goodness of fit and parameter estimation for the count component as their corresponding zero inflated models. However, the estimation and interpretation of the parameters for the zero components differs, and hurdle models are more stable when structural zeros are absent. We then discuss the model selection strategy for zero inflated data and implement it in a gut microbiome study of > 400 independent subjects.

An unexpectedly high proportion of SNPs on the X chromosome in the 1000 Genomes Project phase 3 data were identified with significant sex differences in minor allele frequencies (sdMAF). sdMAF persisted for many of these SNPs in the recently released high coverage whole genome sequence of the 1000 Genomes Project that was aligned to GRCh38, and it was consistent between the five super-populations. Among the 245,825 common (MAF>5%) biallelic X-chromosomal SNPs in the phase 3 data presumed to be of high quality, 2,039 have genome-wide significant sdMAF (p-value <5e-8). sdMAF varied by location: non-pseudo-autosomal region (NPR) = 0.83%, pseudo-autosomal regions (PAR1) = 0.29%, PAR2 = 13.1%, and X-transposed region (XTR)/PAR3 = 0.85% of SNPs had sdMAF, and they were clustered at the NPR-PAR boundaries, among others. sdMAF at the NPR-PAR boundaries are biologically expected due to sex-linkage, but have generally been ignored in association studies. For comparison, similar analyses found only 6, 1 and 0 SNPs with significant sdMAF on chromosomes 1, 7 and 22, respectively. Similar sdMAF results for the X chromosome were obtained from the high coverage whole genome sequence data from gnomAD V 3.1.2 for both the non-Finnish European and African/African American samples. Future X chromosome analyses need to take sdMAF into account.

Genetic effects can be sex-specific, particularly for traits such as testosterone, a sex hormone. While sex-stratified analysis provides easily interpretable sex-specific effect size estimates, the presence of sex-differences in SNP effect implies a SNP×sex interaction. This suggests the usage of the often overlooked joint test, testing for an SNP’s main and SNP×sex interaction effects simultaneously. Notably, even without individual-level data, the joint test statistic can be derived from sex-stratified summary statistics through an omnibus meta-analysis. Utilizing the available sex-stratified summary statistics of the UK Biobank, we performed such omnibus meta-analyses for 290 quantitative traits. Results revealed that this approach is robust to genetic effect heterogeneity and can outperform the traditional sex-stratified or sex-combined main effect-only tests. Therefore, we advocate using the omnibus meta-analysis that captures both the main and interaction effects. Subsequent sex-stratified analysis should be conducted for sex-specific effect size estimation and interpretation.

About the Authors: Andrew D. Paterson * E-mail: andrew.paterson@sickkids.ca Affiliations Genetics and Genome Biology, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada, Divisions of Epidemiology and Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada ORCID http://orcid.org/0000-0002-9169-118XCitation: Paterson AD (2017) HbA1c for type 2 diabetes diagnosis in Africans and African Americans: Personalized medicine NOW! Shift from glucose to HbA1c to diagnose type 2 diabetes: rs1050828 results in approximately 650,000 cases of undiagnosed type 2 diabetes among African Americans Just like hypertension and blood pressure, the diagnosis of type 2 diabetes (T2D) is just a threshold on a quantitative trait: the threshold has changed over time as data regarding the risk for long-term complications at various levels of glucose/HbA1c has been more accurately determined using long-term longitudinal epidemiologic studies. [...]genetic factors may also contribute to heterogeneous benefits and harms of intensive therapy in people with T2D, as has been observed in some clinical trials [29]. Genetic variants that confer resistance to malaria are associated with red blood cell traits in African-Americans: an electronic medical record-based genome-wide association study.

In genome-wide association studies (GWAS), it is often desirable to test for interactions, such as gene–environment ( G x E ) or gene–gene ( G x G ) interactions, between single-nucleotide polymorphisms (SNPs, G ’s) and environmental variables ( E ’s). However, directly accounting for interaction is often infeasible, because the interacting variable is latent or the computational burden is too large. For quantitative traits ( Y ) that are approximately normally distributed, it has been shown that indirect testing on GxE can be done by testing for heteroskedasticity of Y between genotypes. However, when traits are binary, the existing methodology based on testing the heteroskedasticity of the trait across genotypes cannot be generalized. In this paper, we propose an approach to indirectly test interaction effects for binary traits and subsequently propose a joint test that accounts for the main and interaction effects of each SNP during GWAS. The final method is straightforward to implement in practice—it simply involves adding a non-additive (i.e., dominance) term to standard GWAS additive models for binary traits and testing its significance. We illustrate the statistical features including type-I-error control and power of the proposed method through extensive numerical studies. Applying our method to the UK Biobank dataset, we showcase the practical utility of the proposed method, revealing SNPs and genes with strong potential for latent interaction effects.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected millions of people worldwide. While most infected individuals who survive do so with no long-term consequences, approximately 10 to 70% develop long-term sequelae. Of particular concern has been the development of autoimmune diseases. Viral triggers for autoimmune disease have been thoroughly studied for previous viral infections and several recent studies have sought to investigate the link between SARS-CoV-2 and new onset autoimmune disease. Several reviews have also been conducted on the topic, however, many of these reviews are limited in focus, emphasizing biological mechanisms and case reports, as opposed to estimates of risk. Further, these reviews do not capture more recent cohort studies that have been published investigating the association between SARS-CoV-2 and new onset autoimmune disease. Therefore, there is a need for a more comprehensive and temporally updated systematically conducted review of the literature to address the question What is the risk of incident (i.e., new onset) autoimmune disease following a SARS-CoV-2 infection among adults (≥18 years)?. A systematic search of MEDLINE, EMBASE, CINAHL, and grey literature will be conducted, with results screened in duplicate in two stages: 1) Title and abstract screening and 2) Full text screening. A standardized data extraction sheet will be used on any studies passing through both stages of screening to extract details on publication, study population, exposure, and outcomes. Narrative and tabular synthesis of overall findings will be conducted, with diversity and heterogeneity of included studies discussed. If possible, a meta-analysis will also be conducted to combine findings of risk across the included studies. This protocol has been registered to PROSPERO (registration number: CRD42024594446).

Next-generation sequencing has led to an explosion of genetic findings for many rare diseases. However, most of the variants identified are very rare and were also identified in small pedigrees, which creates challenges in terms of penetrance estimation and translation into genetic counselling in the setting of cascade testing. We use simulations to show that for a rare (dominant) disorder where a variant is identified in a small number of small pedigrees, the penetrance estimate can both have large uncertainty and be drastically inflated, due to underlying ascertainment bias. We have developed PenEst, an app that allows users to investigate the phenomenon across ranges of parameter settings. We also illustrate robust ascertainment corrections via the LOD (logarithm of the odds) score, and recommend a LOD-based approach to assessing pathogenicity of rare variants in the presence of reduced penetrance.

Aims/hypothesisEarlier studies have shown that skin autofluorescence measured with an AGE reader estimates the accumulation of AGEs in the skin, which increases with ageing and is associated with the metabolic syndrome and type 2 diabetes. In the present study, we examined whether the measurement of skin autofluorescence can predict 4 year risk of incident type 2 diabetes, cardiovascular disease (CVD) and mortality in the general population.MethodsFor this prospective analysis, we included 72,880 participants of the Dutch Lifelines Cohort Study, who underwent baseline investigations between 2007 and 2013, had validated baseline skin autofluorescence values available and were not known to have diabetes or CVD. Individuals were diagnosed with incident type 2 diabetes by self-report or by a fasting blood glucose ≥7.0 mmol/l or HbA1c ≥48 mmol/mol (≥6.5%) at follow-up. Participants were diagnosed as having incident CVD (myocardial infarction, coronary interventions, cerebrovascular accident, transient ischaemic attack, intermittent claudication or vascular surgery) by self-report. Mortality was ascertained using the Municipal Personal Records Database.ResultsAfter a median follow-up of 4 years (range 0.5–10 years), 1056 participants (1.4%) had developed type 2 diabetes, 1258 individuals (1.7%) were diagnosed with CVD, while 928 (1.3%) had died. Baseline skin autofluorescence was elevated in participants with incident type 2 diabetes and/or CVD and in those who had died (all p < 0.001), compared with individuals who survived and remained free of the two diseases. Skin autofluorescence predicted the development of type 2 diabetes, CVD and mortality, independent of several traditional risk factors, such as the metabolic syndrome, glucose and HbA1c.Conclusions/interpretationThe non-invasive skin autofluorescence measurement is of clinical value for screening for future risk of type 2 diabetes, CVD and mortality, independent of glycaemic measures and the metabolic syndrome.

Background Vitamin B12 deficiency can lead to pernicious anemia and has been associated with various neuropsychiatric diseases and cognitive decline. However, it is unclear whether increasing serum vitamin B12 levels can help to prevent the onset of psychiatric disorders and cognitive impairment in the general population. Methods Leveraging large-scale genome-wide association studies (GWASs), we conducted Mendelian randomization (MR) and sensitivity analyses to estimate the potential effects of serum vitamin B12 levels on eight psychiatric disorders, educational attainment and cognitive performance. We conducted additional MR analyses utilizing within-sibship studies to mitigate potential residual confounding effects. Results As a positive control, we confirm that a one standard deviation increase in genetically increased vitamin B12 levels is strongly protective against pernicious anemia (odds ratio, OR = 0.24; 95% CI: 0.15–0.40; p -value = 2.1×10 -8 ). In contrast, MR estimates of vitamin B12 effects on all eight psychiatric disorders, educational attainment and cognitive performance largely overlap with the null. For example, a one standard deviation increase in genetically predicted vitamin B12 levels is associated with an OR of 1.02 for depression (95% CI: 1.00 – 1.04; p -value = 0.11), a 0.0077 standard deviation increase in educational attainment (95% CI: −0.010 – 0.025; p -value = 0.39) and a 0.013 standard deviation increase in cognitive performance (95% CI: −0.0088 – 0.035; p -value = 0.24). No significant associations are identified in sensitivity analyses excluding pleiotropic genetic instruments or MR analyses based on within-sibship studies. Conclusions Our findings suggest that increasing overall vitamin B12 levels may not meaningfully protect against the investigated psychiatric disorders or cognitive impairment in the general population. Plain Language Summary Low vitamin B12 levels are linked to pernicious anemia and have been associated with several psychiatric and cognitive conditions. However, it is unclear whether increasing B12 levels through supplementation can help to prevent these outcomes. Using genetic data from large studies, we found strong evidence that higher B12 levels protect against pernicious anemia, but no evidence that they reduce the risk of psychiatric disorders or cognitive impairment. Our findings suggest that increasing vitamin B12 levels is unlikely to meaningfully improve mental health or cognitive function in the general population. Lu et al. evaluate the potential impact of serum vitamin B12 levels on major psychiatric disorders and cognitive impairment. Findings suggest that higher overall serum vitamin B12 levels are unlikely to offer meaningful protection against these outcomes in the general population.

We aimed to assess the association of SAF with cardiovascular mortality in the general population and the possible association between SAF with other disease-specific mortality rates. We evaluated 77,143 participants without known diabetes or cardiovascular disease. The cause of death was ascertained by the municipality database. The associations between SAF and all-cause mortality, cardiovascular mortality and cancer mortality were assessed with Cox proportional hazard analysis.After a median follow-up of 115 months, 1447 participants were deceased (1.9%). SAF and age-adjusted SAF-z score were higher in all mortality groups. Cox regression analysis revealed that the highest quartile of SAF was associated with increased odds of cardiovascular mortality, (HR) 12.6 (7.3–21.7) and after adjusting for age (HR 1.8 (1.0–3.2)). Significance was lost after additional adjustments for sex, smoking status, and BMI (HR 1.4 (0.8–2.5). For cancer-related mortality the highest quartile of SAF was associated with higher probability of mortality in all models (unadjusted HR 8.6 (6.6–11.3), adjusted for age HR 2.1 (1.6–2.8)), adjusted for age, sex, smoking status, and BMI HR 1.7 (1.3–2.4)). SAF is associated with all-cause mortality as well as cardiovascular and cancer-related mortality in the general population.