Meta-Analysis in Genome-Wide Association Datasets: Strategies and Application in Parkinson Disease

BACKGROUND: Genome-wide association studies hold substantial promise for identifying common genetic variants that regulate susceptibility to complex diseases. However, for the detection of small genetic effects, single studies may be underpowered. Power may be improved by combining genome-wide datasets with meta-analytic techniques. METHODOLOGY/PRINCIPAL FINDINGS: Both single and two-stage genome-wide data may be combined and there are several possible strategies. In the two-stage framework, we considered the options of (1) enhancement of replication data and (2) enhancement of first-stage data, and then, we also considered (3) joint meta-analyses including all first-stage and second-stage data. These strategies were examined empirically using data from two genome-wide association studies (three datasets) on Parkinson disease. In the three strategies, we derived 12, 5, and 49 single nucleotide polymorphisms that show significant associations at conventional levels of statistical significance. None of these remained significant after conservative adjustment for the number of performed analyses in each strategy. However, some may warrant further consideration: 6 SNPs were identified with at least 2 of the 3 strategies and 3 SNPs [rs1000291 on chromosome 3, rs2241743 on chromosome 4 and rs3018626 on chromosome 11] were identified with all 3 strategies and had no or minimal between-dataset heterogeneity (I(2) = 0, 0 and 15%, respectively). Analyses were primarily limited by the suboptimal overlap of tested polymorphisms across different datasets (e.g., only 31,192 shared polymorphisms between the two tier 1 datasets). CONCLUSIONS/SIGNIFICANCE: Meta-analysis may be used to improve the power and examine the between-dataset heterogeneity of genome-wide association studies. Prospective designs may be most efficient, if they try to maximize the overlap of genotyping platforms and anticipate the combination of data across many genome-wide association studies

Statistical significance and publication reporting bias in abstracts of reproductive medicine studies

Funding Information: We thank Dr David Chavalarias from Complex Systems Institute of Paris Ile-de-France for sharing scripts in extracting P-values. B.W.M. is supported by an National Health and Medical Research Council (NHMRC) Investigator grant (GNT1176437); B.W.M. reports consultancy, research grants, and travel support from Merck. W.L. is supported by an NHMRC Investigator Grant (GNT2016729). Q.F. reports receiving a PhD scholarship from Merck. The other author has no conflict of interest to declare. Funding Information: B.W.M. is supported by an National Health and Medical Research Council (NHMRC) Investigator grant (GNT1176437); B.W.M. reports consultancy, research grants, and travel support from Merck. W.L. is supported by an NHMRC Investigator Grant (GNT2016729). Q.F. reports receiving a PhD scholarship from Merck. The other author has no conflict of interest to declare. Publisher Copyright: © 2024 Oxford University Press. All rights reserved.Peer reviewe

Electrocardiogram-gated single-photonemission computed tomography versus cardiacmagnetic resonance imaging for the assessmentof left ventricular volumes and ejection fraction A meta-analysis

AbstractObjectivesThe purpose of this study was to evaluate the accuracy of electrocardiogram (ECG)-gated single-photon emission computed tomography (SPECT) for assessment of left ventricular (LV) end-diastolic volume (EDV), end-systolic volume (ESV) and ejection fraction (EF) compared with the gold standard of cardiac magnetic resonance imaging (MRI).BackgroundSeveral comparisons of ECG-gated SPECT with cardiac MRI have been performed for evaluation of LV volumes and EF, but each has considered few subjects, thus leaving uncertainty about the frequency of discrepancies between the two methods.MethodsWe performed a meta-analysis of data on 164 subjects from nine studies comparing ECG-gated SPECT versus cardiac MRI. Data were pooled in correlation and regression analyses relating ECG-gated SPECT and cardiac MRI measurements. The frequency of discrepancies of at least 30 ml in EDV, 20 ml in ESV and 5% or 10% in EF and concordance for EF ≤40% versus >40% were determined.ResultsThere was an overall excellent correlation between ECG-gated SPECT and cardiac MRI for EDV (r = 0.89), ESV (r = 0.92) and EF (r = 0.87). However, rates of discrepancies for individual subjects were considerable (37% [95% confidence interval {CI}, 26% to 50%] for at least 30 ml in EDV; 35% [95% CI, 23% to 49%] for at least 20 ml in ESV; 52% [95% CI, 37% to 63%] for at least 5% in EF; and 23% [95% CI, 11% to 42%] for at least 10% in EF). The misclassification rate for the 40% EF cutoff was 11%.ConclusionsElectrocardiogram-gated SPECT measurements of EDV, ESV and EF show high correlation with cardiac MRI measurements, but substantial errors may occur in individual patients. Electrocardiogram-gated SPECT offers useful functional information, but cardiac MRI should be used when accurate measurement is required

Local Literature Bias in Genetic Epidemiology: An Empirical Evaluation of the Chinese Literature

BACKGROUND: Postulated epidemiological associations are subject to several biases. We evaluated whether the Chinese literature on human genome epidemiology may offer insights on the operation of selective reporting and language biases. METHODS AND FINDINGS: We targeted 13 gene-disease associations, each already assessed by meta-analyses, including at least 15 non-Chinese studies. We searched the Chinese Journal Full-Text Database for additional Chinese studies on the same topics. We identified 161 Chinese studies on 12 of these gene-disease associations; only 20 were PubMed-indexed (seven English full-text). Many studies (14–35 per topic) were available for six topics, covering diseases common in China. With one exception, the first Chinese study appeared with a time lag (2–21 y) after the first non-Chinese study on the topic. Chinese studies showed significantly more prominent genetic effects than non-Chinese studies, and 48% were statistically significant per se, despite their smaller sample size (median sample size 146 versus 268, p < 0.001). The largest genetic effects were often seen in PubMed-indexed Chinese studies (65% statistically significant per se). Non-Chinese studies of Asian-descent populations (27% significant per se) also tended to show somewhat more prominent genetic effects than studies of non-Asian descent (17% significant per se). CONCLUSION: Our data provide evidence for the interplay of selective reporting and language biases in human genome epidemiology. These biases may not be limited to the Chinese literature and point to the need for a global, transparent, comprehensive outlook in molecular population genetics and epidemiologic studies in general

Few randomized trials in preterm birth prevention meet predefined usefulness criteria

Funding Information: Funding: The study was funded by a grant from the Netherlands Organization for Health Research and Development (ZonMw Rubicon grand #452182306). The funder had no involvement in any phase of this study. Meta-Research Innovation Center at Stanford (METRICS), Stanford University is supported by a grant from the Laura and John Arnold Foundation. JvtH is supported by postdoctoral grant from the Netherlands Organization for Health Research and Development (Rubicon grand 452,182,306). C.A. is supported by postdoctoral grants from the Knut and Alice Wallenberg Foundation (K.A.W. 2019.0561), Uppsala University, and the Sweden-America Foundation. B.M. is supported by an NHMRC Investigator grant (GNT1176437). B.M. reports consultancy for Guerbet, has been a member of the ObsEva advisory board and holds Stock options for ObsEva. The work of J.I. has been supported by an unrestricted gift from Sue and Bob O'Donnell. J.I. is a team member of the editorial board of JCE. Publisher Copyright: © 2023 The AuthorsPeer reviewe