A Powerful Statistical Framework for Generalization Testing in GWAS, with Application to the HCHS/SOL

In GWAS, “generalization” is the replication of genotype-phenotype association in a population with different ancestry than the population in which it was first identified. The standard for reporting findings from a GWAS requires a two-stage design, in which discovered associations are replicated in an independent follow-up study. Current practices for declaring generalizations rely on testing associations while controlling the Family Wise Error Rate (FWER) in the discovery study, then separately controlling error measures in the follow-up study. While this approach limits false generalizations, we show that it does not guarantee control over the FWER or False Discovery Rate (FDR) of the generalization null hypotheses. In addition, it fails to leverage the two-stage design to increase power for detecting generalized associations. We develop a formal statistical framework for quantifying the evidence of generalization that accounts for the (in)consistency between the directions of associations in the discovery and follow-up studies. We develop the directional generalization FWER (FWERg) and FDR (FDRg) controlling r-values, which are used to declare associations as generalized. This framework extends to generalization testing when applied to a published list of SNP-trait associations. We show that our framework accommodates various SNP selection rules for generalization testing based on p-values in the discovery study, and still control FWERg or FDRg. A key finding is that it is often beneficial to use a more lenient p-value threshold then the genome-wide significance threshold. For instance, in a GWAS of Total Cholesterol (TC) in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), when testing all SNPs with p-values\u3c 5 × 10−8 (15 genomic regions) for generalization in a large GWAS of whites, we generalized SNPs from 15 regions. But when testing all SNPs with p-values\u3c 6.6×10−5 (89 regions), we generalized SNPs from 27 regions

Effectiveness of empirical <i>Helicobacter pylori</i> eradication therapy with furazolidone in Russia: results from the European Registry on <i>Helicobacter pylori</i> Management (Hp-EuReg)

Background. First-line therapy does not always provide a high level of Helicobacter pylori eradication due to the increase of H. pylori resistance to antibiotics; therefore, it remains necessary to identify the most effective rescue treatments. The purpose of this study was to evaluate the efficacy and safety of empirical H. pylori furazolidone-containing regimens. Materials and methods. Adult H. pylori infected patients empirically treated with furazolidone-containing eradication regimens were registered in an international, prospective, multicenter non-intervention European registry on H. pylori management (Hp-EuReg). Data were collected at AEG-REDCap e-CRF from 2013 to 2021 and the quality was reviewed. Modified intention-to-treat (mITT) effectiveness analyses were performed. Results. Overall 106 patients received empirical furazolidone-containing therapy in Russia. Furazolidone was prescribed in a sequential scheme along with amoxicillin, clarithromycin and a proton pump inhibitor in 68 (64%) cases, triple regimens were prescribed in 28 (26%) patients and quadruple regimens in 10 (9.4%). Treatment duration of 7 days was assigned to 2 (1.9%) patients, 10-day eradication therapy in case of 80 (75%) and 14 days in 24 (23%) patients. Furazolidone was mainly used in first- (79%) and second-line (21%) regimens. The methods used to diagnose H. pylori infection were: histology (81%), stool antigen test (64%), 13C-urea breath test (6.6%), and rapid urease test (1.9%). The mITT effectiveness of sequential therapy was 100%; 93% with the triple therapy and 75.5% with quadruple therapy. Compliance was reported in 98% of cases. Adverse events were revealed in 5.7% of patients, mostly nausea (3.8%). No serious adverse events were reported. Conclusion. Furazolidone containing eradication regimens appear to be an effective and safe empirical therapy in Russia

Characterization of Expression Quantitative Trait Loci in Pedigrees from Colombia and Costa Rica Ascertained for Bipolar Disorder

The observation that variants regulating gene expression (expression quantitative trait loci, eQTL) are at a high frequency among SNPs associated with complex traits has made the genome-wide characterization of gene expression an important tool in genetic mapping studies of such traits. As part of a study to identify genetic loci contributing to bipolar disorder and other quantitative traits in members of 26 pedigrees from Costa Rica and Colombia, we measured gene expression in lymphoblastoid cell lines derived from 786 pedigree members. The study design enabled us to comprehensively reconstruct the genetic regulatory network in these families, provide estimates of heritability, identify eQTL, evaluate missing heritability for the eQTL, and quantify the number of different alleles contributing to any given locus. In the eQTL analysis, we utilize a recently proposed hierarchical multiple testing strategy which controls error rates regarding the discovery of functional variants. Our results elucidate the heritability and regulation of gene expression in this unique Latin American study population and identify a set of regulatory SNPs which may be relevant in future investigations of complex disease in this population. Since our subjects belong to extended families, we are able to compare traditional kinship-based estimates with those from more recent methods that depend only on genotype information.National Institutes for Health/[R01 HG006695]/NIH/Estados UnidosNational Institutes for Health/[R01 MH101782]/NIH/Estados UnidosNational Institutes for Health/[R01 MH075007]/NIH/Estados UnidosIsrael Science Foundation/[1112/14]/ISF/IsraelUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigación en Biología Celular y Molecular (CIBCM

Replicability Across Multiple Studies

Meta-analysis is routinely performed in many scientific disciplines. This analysis is attractive since discoveries are possible even when all the individual studies are underpowered. However, the meta-analytic discoveries may be entirely driven by signal in a single study, and thus non-replicable. Although the great majority of meta-analyses carried out to date do not infer on the replicability of their findings, it is possible to do so. We provide a selective overview of analyses that can be carried out towards establishing replicability of the scientific findings. We describe methods for the setting where a single outcome is examined in multiple studies (as is common in systematic reviews of medical interventions), as well as for the setting where multiple studies each examine multiple features (as in genomics applications). We also discuss some of the current shortcomings and future directions.Comment: 30 page