Longitudinal analytical approaches to genetic data

Chiu, Yen‐Feng; Justice, Anne E.; Melton, Phillip E.

doi:10.1186/s12863-015-0312-y

Cited by 20 publications

(23 citation statements)

References 28 publications

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“…[61][62][63] Recent works propose phenotyping strategies to overcome hurdles using multiple data sources to more accurately ascertain disease status. [64][65][66][67][68][69][70][71][72] However, future work is needed to provide statistical methods for incorporating data of different types for phenome generation. For a detailed review of phenotyping procedures, see Bush et al 7 Figure S8 provides some examples of the types of structured and unstructured EHR information that can be used to construct phenotypes.…”

Section: 13mentioning

confidence: 99%

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

Beesley

Salvatore

Fritsche

et al. 2019

Statistics in Medicine

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Biobanks linked to electronic health records provide rich resources for health‐related research. With improvements in administrative and informatics infrastructure, the availability and utility of data from biobanks have dramatically increased. In this paper, we first aim to characterize the current landscape of available biobanks and to describe specific biobanks, including their place of origin, size, and data types. The development and accessibility of large‐scale biorepositories provide the opportunity to accelerate agnostic searches, expedite discoveries, and conduct hypothesis‐generating studies of disease‐treatment, disease‐exposure, and disease‐gene associations. Rather than designing and implementing a single study focused on a few targeted hypotheses, researchers can potentially use biobanks' existing resources to answer an expanded selection of exploratory questions as quickly as they can analyze them. However, there are many obvious and subtle challenges with the design and analysis of biobank‐based studies. Our second aim is to discuss statistical issues related to biobank research such as study design, sampling strategy, phenotype identification, and missing data. We focus our discussion on biobanks that are linked to electronic health records. Some of the analytic issues are illustrated using data from the Michigan Genomics Initiative and UK Biobank, two biobanks with two different recruitment mechanisms. We summarize the current body of literature for addressing these challenges and discuss some standing open problems. This work complements and extends recent reviews about biobank‐based research and serves as a resource catalog with analytical and practical guidance for statisticians, epidemiologists, and other medical researchers pursuing research using biobanks.

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Section: 13mentioning

confidence: 99%

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

Beesley

Salvatore

Fritsche

et al. 2019

Statistics in Medicine

View full text Add to dashboard Cite

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“…Although longitudinal genetic data analyses have been reported previously, only a few of these reports focused on rare variants 13–16 . The kernel machine method based on the LM framework 15 was extended to rare variants in longitudinal data for a family-based study, but only applicable to continuous traits.…”

Section: Discussionmentioning

confidence: 99%

“…Very few methods have been developed or extended to detect rare variants associated with longitudinal disease traits 13–16 . Yan et al .…”

Section: Introductionmentioning

confidence: 99%

Longitudinal data analysis for rare variants detection with penalized quadratic inference function

Cao

Zhi

Huang

et al. 2017

Sci Rep

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Longitudinal genetic data provide more information regarding genetic effects over time compared with cross-sectional data. Coupled with next-generation sequencing technologies, it becomes reality to identify important genes containing both rare and common variants in a longitudinal design. In this work, we adopted a weighted sum statistic (WSS) to collapse multiple variants in a gene region to form a gene score. When multiple genes in a pathway were considered together, a penalized longitudinal model under the quadratic inference function (QIF) framework was applied for efficient gene selection. We evaluated the estimation accuracy and model selection performance under different model settings, then applied the method to a real dataset from the Genetic Analysis Workshop 18 (GAW18). Compared with the unpenalized QIF method, the penalized QIF (pQIF) method achieved better estimation accuracy and higher selection efficiency. The pQIF remained optimal even when the working correlation structure was mis-specified. The real data analysis identified one important gene, angiotensin II receptor type 1 (AGTR1), in the Ca2+/AT-IIR/α-AR signaling pathway. The estimated effect implied that AGTR1 may have a protective effect for hypertension. Our pQIF method provides a general tool for longitudinal sequencing studies involving large numbers of genetic variants.

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“…Some groups concentrated on approaches to dealing with multiple testing in these high dimensional sequence data by filtering sequence variants or placing informative priors for association analyses [18], by pathway-based approaches for gene localization [19], or by other variant collapsing approaches [20]. Other contributions focused on utilizing unique aspects of the GAW19 family data set, including genetic analyses of longitudinal data [21], and analysis of gene expression data [22]. The variety of topics addressed in these GAW19 contributions illustrate the utility and versatility of the GAW19 data.…”

Section: Discussionmentioning

confidence: 99%

Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19

et al. 2016

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BackgroundThe Genetic Analysis Workshops (GAW) are a forum for development, testing, and comparison of statistical genetic methods and software. Each contribution to the workshop includes an application to a specified data set. Here we describe the data distributed for GAW19, which focused on analysis of human genomic and transcriptomic data.MethodsGAW19 data were donated by the T2D-GENES Consortium and the San Antonio Family Heart Study and included whole genome and exome sequences for odd-numbered autosomes, measures of gene expression, systolic and diastolic blood pressures, and related covariates in two Mexican American samples. These two samples were a collection of 20 large families with whole genome sequence and transcriptomic data and a set of 1943 unrelated individuals with exome sequence. For each sample, simulated phenotypes were constructed based on the real sequence data. ‘Functional’ genes and variants for the simulations were chosen based on observed correlations between gene expression and blood pressure. The simulations focused primarily on additive genetic models but also included a genotype-by-medication interaction. A total of 245 genes were designated as ‘functional’ in the simulations with a few genes of large effect and most genes explaining < 1 % of the trait variation. An additional phenotype, Q1, was simulated to be correlated among related individuals, based on theoretical or empirical kinship matrices, but was not associated with any sequence variants. Two hundred replicates of the phenotypes were simulated. The GAW19 data are an expansion of the data used at GAW18, which included the family-based whole genome sequence, blood pressure, and simulated phenotypes, but not the gene expression data or the set of 1943 unrelated individuals with exome sequence.

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Longitudinal analytical approaches to genetic data

Cited by 20 publications

References 28 publications

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

Longitudinal data analysis for rare variants detection with penalized quadratic inference function

Omics-squared: human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19

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