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New models for large prospective studies: is there a better way?

ABSTRACT: Large prospective cohort studies are critical for identifying etiologic factors for disease, but they require substantial long-term research investment. Such studies can be conducted as multisite consortia of academic medical centers, combinations of smaller ongoing studies, or a single large site such as a dominant regional health-care provider. Still another strategy relies upon centralized conduct of most or all aspects, recruiting through multiple temporary assessment centers. This is the approach used by a large-scale national resource in the United Kingdom known as the "UK Biobank," which completed recruitment/examination of 503,000 participants between 2007 and 2010 within budget and ahead of schedule. A key lesson from UK Biobank and similar studies is that large studies are not simply small studies made large but, rather, require fundamentally different approaches in which "process" expertise is as important as scientific rigor. Embedding recruitment in a structure that facilitates outcome determination, utilizing comprehensive and flexible information technology, automating biospecimen processing, ensuring broad consent, and establishing essentially autonomous leadership with appropriate oversight are all critical to success. Whether and how these approaches may be transportable to the United States remain to be explored, but their success in studies such as UK Biobank makes a compelling case for such explorations to begin.

SUBMITTER: Manolio TA

PROVIDER: S-EPMC3339313 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

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New models for large prospective studies: is there a better way?

Manolio Teri A TA Weis Brenda K BK Cowie Catherine C CC Hoover Robert N RN Hudson Kathy K Kramer Barnett S BS Berg Chris C Collins Rory R Ewart Wendy W Gaziano J Michael JM Hirschfeld Steven S Marcus Pamela M PM Masys Daniel D McCarty Catherine A CA McLaughlin John J Patel Alpa V AV Peakman Tim T Pedersen Nancy L NL Schaefer Catherine C Scott Joan A JA Sprosen Timothy T Walport Mark M Collins Francis S FS

American journal of epidemiology 20120312 9

Large prospective cohort studies are critical for identifying etiologic factors for disease, but they require substantial long-term research investment. Such studies can be conducted as multisite consortia of academic medical centers, combinations of smaller ongoing studies, or a single large site such as a dominant regional health-care provider. Still another strategy relies upon centralized conduct of most or all aspects, recruiting through multiple temporary assessment centers. This is the ap ...[more]

PMID: 22411865

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Project description:Accurate evaluation of SNP effects is important for genome wide association studies and for genomic prediction. The genetic architecture of quantitative traits differs widely, with some traits exhibiting few if any quantitative trait loci (QTL) with large effects, while other traits have one or several easily detectable QTL with large effects.Body weight in broilers and egg weight in layers are two examples of traits that have QTL of large effect. A commonly used method for genome wide association studies is to fit a mixture model such as BayesB that assumes some known proportion of SNP effects are zero. In contrast, the most commonly used method for genomic prediction is known as GBLUP, which involves fitting an animal model to phenotypic data with the variance-covariance or genomic relationship matrix among the animals being determined by genome wide SNP genotypes. Genotypes at each SNP are typically weighted equally in determining the genomic relationship matrix for GBLUP. We used the equivalent marker effects model formulation of GBLUP for this study. We compare these two classes of models using egg weight data collected over 8 generations from 2,324 animals genotyped with a 42 K SNP panel.Using data from the first 7 generations, both BayesB and GBLUP found the largest QTL in a similar well-recognized QTL region, but this QTL was estimated to account for 24 % of genetic variation with BayesB and less than 1 % with GBLUP. When predicting phenotypes in generation 8 BayesB accounted for 36 % of the phenotypic variation and GBLUP for 25 %. When using only data from any one generation, the same QTL was identified with BayesB in all but one generation but never with GBLUP. Predictions of phenotypes in generations 2 to 7 based on only 295 animals from generation 1 accounted for 10 % phenotypic variation with BayesB but only 6 % with GBLUP. Predicting phenotype using only the marker effects in the 1 Mb region that accounted for the largest effect on egg weight from generation 1 data alone accounted for almost 8 % variation using BayesB but had no predictive power with GBLUP.In conclusion, In the presence of large effect QTL, BayesB did a better job of QTL detection and its genomic predictions were more accurate and persistent than those from GBLUP.

Project description:BackgroundThe obesity paradigm has been a health concern globally for many years, its meaning is controversial. In this study, we assess the characteristics and causes of obesity paradigm and detail the mediation of obesity and inflammation on survival.MethodsThe original cohort included participants from the US National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018, a prospective cohort of a nationally representative sample of adult participants; the oncology validation cohort included patients from the Investigation on Nutrition Status and Clinical Outcome of Common Cancers (INSCOC) from 2013 to 2021, a prospective cohort of Chinese patients with cancer. Survival analysis was performed using weighted (NHANES) or unweighted (INSCOC) Cox survival analyses. The normal BMI group was used as a reference for all comparisons. Systemic inflammation was defined as neutrophil-to-lymphocyte ratio (NLR) > 3. Model-based causal mediation analysis was used to identify the mediators.ResultsA total of 52 270 (weighted population: 528506229) participants of the NHANES [mean follow-up times: 10.2 years; mean age (SD): 47 (19.16) years] were included in the original cohort; and a total of 17 418 patients with cancer of INSCOC [mean follow-up times: 2.9 years; mean age (SD): 57.37 (11.66) years] were included in the validation cohort. In the subgroups of all the participants, the obesity paradigm was more apparent in older participants and participants with disease [HR (95% CI): age ≥ 65 years, 0.84 (0.76, 0.93); with cancer, 0.84 (0.71, 0.99); with CVD, 0.74 (0.65, 0.85)]. As aged, the protective effect of a high BMI on survival gradually increased and a high BMI showed the effect of a protective factor on older participants [for obese II, HR (95% CI): young adults, 1.91 (1.40, 2.62); middle age, 1.56 (1.28, 1.91); old adults, 0.85 (0.76, 0.96]). The aged-related obesity paradigm in patients with cancer from the NHANES was verified in the INSCOC cohorts [for obese, HR (95%CI): 0.65 (0.52, 0.81)]. The NLR is an important mediator of the effect of BMI on survival (proportion of mediation = 15.4%).ConclusionsThe obesity paradigm has a strong correlation with age. Relative to normal weight, obese in young people was association with higher all-cause mortality, and obese in elderly people was not association with higher mortality. The protection of obesity is association with systemic inflammation.

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New models for large prospective studies: is there a better way?

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New models for large prospective studies: is there a better way?

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