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

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

Updated Field Synopsis and Systematic Meta-Analyses of Genetic Association Studies in Cutaneous Melanoma: The MelGene Database

We updated a field synopsis of genetic associations of cutaneous melanoma (CM) by systematically retrieving and combining data from all studies in the field published as of August 31, 2013. Data were available from 197 studies, which included 83,343 CM cases and 187,809 controls and reported on 1,126 polymorphisms in 289 different genes. Random-effects meta-analyses of 81 eligible polymorphisms evaluated in >4 data sets confirmed 20 single-nucleotide polymorphisms across 10 loci (TYR, AFG3L1P, CDK10, MYH7B, SLC45A2, MTAP, ATM, CLPTM1L, FTO, and CASP8) that have previously been published with genome-wide significant evidence for association (P<5 × 10−8) with CM risk, with certain variants possibly functioning as proxies of already tagged genes. Four other loci (MITF, CCND1, MX2, and PLA2G6) were also significantly associated with 5 × 10−8<P<1 × 10−3. In supplementary meta-analyses derived from genome-wide association studies, one additional locus located 11 kb upstream of ARNT (chromosome 1q21) showed genome-wide statistical significance with CM. Our approach serves as a useful model in analyzing and integrating the reported germline alterations involved in CM

Infographic. How does exercise treatment compare with antihypertensive medications?

High systolic blood pressure (SBP) remains the major cause of premature death globally despite advances in pharmacological treatment.1 2 The global direct medical costs associated with hypertension treatment are estimated at $370 billion/year worldwide, with the healthcare savings from effective management of this condition projected at about$100 billion/year.3 Unfortunately, relatively little attention is given to non-pharmacological strategies, including structured exercise interventions. A recent network meta-analysis of randomised controlled trials (RCTs) published in the BJSM4 aimed to compare the effects of exercise interventions and medications on SBP. We highlight the key findings of this network meta-analysis that are particularly relevant for clinical practice and health policy.Sin financiación12.022 JCR (2019) Q1, 1/85 Sport Sciences3.712 SJR (2019) Q1, 48/2754 Medicine (miscellaneous), 1/284 Orthopedics and Sports Medicine, 1/207 Physical Therapy, Sports Therapy and Rehabilitation, 2/125 Sports ScienceNo data IDR 2019UE