Identifying large sets of unrelated individuals and unrelated markers

Abraham, Kuruvilla Joseph; Díaz, Clara

doi:10.1186/1751-0473-9-6

Cited by 22 publications

(18 citation statements)

References 20 publications

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“…Principal components and ancestry were estimated by projecting all genotyped samples into the space of the principal components of the Human Genome Diversity Project reference panel using PLINK (938 unrelated individuals) [21, 22]. Pairwise kinship was assessed with the software KING [23], and the software fastindep was used to reduce the data to a maximal subset that contained no pairs of individuals with 3rd-or closer degree relationship [24]. We also removed patients not of recent European descent from the analysis, resulting in a final sample of 30,702 unrelated subjects.…”

Section: Methodsmentioning

confidence: 99%

“…The UK Biobank phenome was used as a replication dataset and was based on ICD9 and ICD10 code data of 408,961 White British [15], genotyped individuals that were aggregated to PheWAS traits in a similar fashion (as described elsewhere [9]). To remove related individuals and to retain larger sample sizes, we first selected a maximal set of unrelated cases for each phenotype (defined as no pairwise relationship of 3 rd degree or closer [24, 29]) before selecting a maximal set of unrelated controls unrelated to these cases. Similar to MGI, we matched up to 10 controls to each case using the R package “MatchIt” [28].…”

Section: Methodsmentioning

confidence: 99%

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Exploring various polygenic risk scores for skin cancer in the phenomes of the Michigan genomics initiative and the UK Biobank with a visual catalog: PRSWeb

et al. 2019

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Polygenic risk scores (PRS) are designed to serve as single summary measures that are easy to construct, condensing information from a large number of genetic variants associated with a disease. They have been used for stratification and prediction of disease risk. The primary focus of this paper is to demonstrate how we can combine PRS and electronic health records data to better understand the shared and unique genetic architecture and etiology of disease subtypes that may be both related and heterogeneous. PRS construction strategies often depend on the purpose of the study, the available data/summary estimates, and the underlying genetic architecture of a disease. We consider several choices for constructing a PRS using data obtained from various publicly-available sources including the UK Biobank and evaluate their abilities to predict not just the primary phenotype but also secondary phenotypes derived from electronic health records (EHR). This study was conducted using data from 30,702 unrelated, genotyped patients of recent European descent from the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort within Michigan Medicine. We examine the three most common skin cancer subtypes in the USA: basal cell carcinoma, cutaneous squamous cell carcinoma, and melanoma. Using these PRS for various skin cancer subtypes, we conduct a phenome-wide association study (PheWAS) within the MGI data to evaluate PRS associations with secondary traits. PheWAS results are then replicated using population-based UK Biobank data and compared across various PRS construction methods. We develop an accompanying visual catalog called PRSweb that provides detailed PheWAS results and allows users to directly compare different PRS construction methods.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Exploring various polygenic risk scores for skin cancer in the phenomes of the Michigan genomics initiative and the UK Biobank with a visual catalog: PRSWeb

et al. 2019

View full text Add to dashboard Cite

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“…We estimated r among individuals within each of the clusters identified at the uppermost hierarchical level using the TrioML estimator (Wang ) with 100 control samples and 1,000 bootstraps within COANCESTRY version 1.0.1.7 (Wang ). To retain the maximum number of individuals within each cluster, we identified an optimal set from a greedy heuristic in FastIndep (Abraham and Diaz ).…”

Section: Methodsmentioning

confidence: 99%

Hybridization and Genetic Structure of Neosho Smallmouth Bass in the Ozark Highlands

Taylor

Long

Schwemm

et al. 2018

N American J Fish Manag

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The Neosho Smallmouth Bass Micropterus dolomieu velox is endemic to Arkansas River tributaries originating in the Ozark Highlands and Boston Mountains. Although morphologically and genetically distinct from other populations of Smallmouth Bass M. dolomieu, the conservation-genetic status of Neosho Smallmouth Bass is largely unknown. To assist in filling this data gap, we quantified introgressive hybridization, genetic diversity, and population structure of Neosho Smallmouth Bass in two major river basins (Grand River and Illinois River) using seven polymorphic microsatellite markers. Introgressive hybridization with stocked Tennessee lake-strain Smallmouth Bass was most prevalent in the Illinois River, wherein the overall genomic proportion of Neosho Smallmouth Bass alleles was only 0.422. After accounting for hybrid individuals, genetic diversity of Neosho Smallmouth Bass was generally higher in larger rivers and lower in smaller, isolated streams. Three distinct population clusters were identified at the uppermost level of genetic structureone from the Illinois River basin and two from the Grand River basin. These three population boundaries accounted for approximately 7% of the hierarchical genetic variation within our data set, and substructure below the uppermost level accounted for an additional 2% of genetic variation. The population structure we discovered can provide a blueprint for management that conserves diversity within and among populations; for example, population boundaries can be used to determine brood source locations for supplemental stocking efforts to counter nonnative introgression. Introgressive hybridization with nonnative Smallmouth Bass appears to be the most pertinent threat to the Neosho Smallmouth Bass subspecies, which is of conservation value to the overall adaptability of Smallmouth Bass as a species.Smallmouth Bass Micropterus dolomieu is native to a broad region of North America, spanning portions of the upper Mississippi, Tennessee, Ohio, and Saint Lawrence-Great Lakes river basins (Scott and Crossman 1973;Brewer and Orth 2015). Across its range, the Smallmouth Bass inhabits a variety of habitats including streams,

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“…Similarly, heuristic approaches have been suggested to identify a maximal independent set of uncorrelated phenotypes among pairwise correlations between pairs of phenotypes . A popular method for identifying phenotypes is to aggregate ICD codes into a set of phenotype codes called “phecodes.” For example, using 1578 phecodes in MGI, we identified a maximal set of 981 phenotypes with no pairwise Pearson correlation above 0.1.…”

Section: Statistical Issues Related To Biobank Researchmentioning

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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Identifying large sets of unrelated individuals and unrelated markers

Cited by 22 publications

References 20 publications

Exploring various polygenic risk scores for skin cancer in the phenomes of the Michigan genomics initiative and the UK Biobank with a visual catalog: PRSWeb

Exploring various polygenic risk scores for skin cancer in the phenomes of the Michigan genomics initiative and the UK Biobank with a visual catalog: PRSWeb

Hybridization and Genetic Structure of Neosho Smallmouth Bass in the Ozark Highlands

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

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