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      The impact of global and local Polynesian genetic ancestry on complex traits in Native Hawaiians

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          Abstract

          Epidemiological studies of obesity, Type-2 diabetes (T2D), cardiovascular diseases and several common cancers have revealed an increased risk in Native Hawaiians compared to European- or Asian-Americans living in the Hawaiian islands. However, there remains a gap in our understanding of the genetic factors that affect the health of Native Hawaiians. To fill this gap, we studied the genetic risk factors at both the chromosomal and sub-chromosomal scales using genome-wide SNP array data on ~4,000 Native Hawaiians from the Multiethnic Cohort. We estimated the genomic proportion of Native Hawaiian ancestry (“global ancestry,” which we presumed to be Polynesian in origin), as well as this ancestral component along each chromosome (“local ancestry”) and tested their respective association with binary and quantitative cardiometabolic traits. After attempting to adjust for non-genetic covariates evaluated through questionnaires, we found that per 10% increase in global Polynesian genetic ancestry, there is a respective 8.6%, and 11.0% increase in the odds of being diabetic ( P = 1.65×10 −4) and having heart failure ( P = 2.18×10 −4), as well as a 0.059 s.d. increase in BMI ( P = 1.04×10 −10). When testing the association of local Polynesian ancestry with risk of disease or biomarkers, we identified a chr6 region associated with T2D. This association was driven by an uniquely prevalent variant in Polynesian ancestry individuals. However, we could not replicate this finding in an independent Polynesian cohort from Samoa due to the small sample size of the replication cohort. In conclusion, we showed that Polynesian ancestry, which likely capture both genetic and lifestyle risk factors, is associated with an increased risk of obesity, Type-2 diabetes, and heart failure, and that larger cohorts of Polynesian ancestry individuals will be needed to replicate the putative association on chr6 with T2D.

          Author summary

          Native Hawaiians are one of the fastest growing ethnic minorities in the U.S., and exhibit increased risk for metabolic and cardiovascular diseases. However, they are generally understudied, especially from a genetic perspective. To fill this gap, we studied the association of Polynesian genetic ancestry, at genomic and subgenomic scales, with quantitative and binary traits in self-identified Native Hawaiians. We showed that Polynesian ancestry, which likely captures both genetic and non-genetic risk factors related to Native Hawaiian people and culture, is associated with increased risk for obesity, type-2 diabetes, and heart failure. While we do not endorse utilizing genetic information to supplant current standards of defining community membership through self-identity or genealogical records, our results suggest future studies could identify population-specific genetic susceptibility factors that may elucidate underlying biological mechanisms and reducing the disparity in disease risks in Polynesian populations.

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          Most cited references59

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          The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019

          Abstract The GWAS Catalog delivers a high-quality curated collection of all published genome-wide association studies enabling investigations to identify causal variants, understand disease mechanisms, and establish targets for novel therapies. The scope of the Catalog has also expanded to targeted and exome arrays with 1000 new associations added for these technologies. As of September 2018, the Catalog contains 5687 GWAS comprising 71673 variant-trait associations from 3567 publications. New content includes 284 full P-value summary statistics datasets for genome-wide and new targeted array studies, representing 6 × 109 individual variant-trait statistics. In the last 12 months, the Catalog's user interface was accessed by ∼90000 unique users who viewed >1 million pages. We have improved data access with the release of a new RESTful API to support high-throughput programmatic access, an improved web interface and a new summary statistics database. Summary statistics provision is supported by a new format proposed as a community standard for summary statistics data representation. This format was derived from our experience in standardizing heterogeneous submissions, mapping formats and in harmonizing content. Availability: https://www.ebi.ac.uk/gwas/.
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            Robust relationship inference in genome-wide association studies.

            Genome-wide association studies (GWASs) have been widely used to map loci contributing to variation in complex traits and risk of diseases in humans. Accurate specification of familial relationships is crucial for family-based GWAS, as well as in population-based GWAS with unknown (or unrecognized) family structure. The family structure in a GWAS should be routinely investigated using the SNP data prior to the analysis of population structure or phenotype. Existing algorithms for relationship inference have a major weakness of estimating allele frequencies at each SNP from the entire sample, under a strong assumption of homogeneous population structure. This assumption is often untenable. Here, we present a rapid algorithm for relationship inference using high-throughput genotype data typical of GWAS that allows the presence of unknown population substructure. The relationship of any pair of individuals can be precisely inferred by robust estimation of their kinship coefficient, independent of sample composition or population structure (sample invariance). We present simulation experiments to demonstrate that the algorithm has sufficient power to provide reliable inference on millions of unrelated pairs and thousands of relative pairs (up to 3rd-degree relationships). Application of our robust algorithm to HapMap and GWAS datasets demonstrates that it performs properly even under extreme population stratification, while algorithms assuming a homogeneous population give systematically biased results. Our extremely efficient implementation performs relationship inference on millions of pairs of individuals in a matter of minutes, dozens of times faster than the most efficient existing algorithm known to us. Our robust relationship inference algorithm is implemented in a freely available software package, KING, available for download at http://people.virginia.edu/∼wc9c/KING.
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              Clinical use of current polygenic risk scores may exacerbate health disparities

              Polygenic risk scores (PRS) are poised to improve biomedical outcomes via precision medicine. However, the major ethical and scientific challenge surrounding clinical implementation of PRS is that those available today are several times more accurate in individuals of European ancestry than other ancestries. This disparity is an inescapable consequence of Eurocentric biases in genome-wide association studies, thus highlighting that-unlike clinical biomarkers and prescription drugs, which may individually work better in some populations but do not ubiquitously perform far better in European populations-clinical uses of PRS today would systematically afford greater improvement for European-descent populations. Early diversifying efforts show promise in leveling this vast imbalance, even when non-European sample sizes are considerably smaller than the largest studies to date. To realize the full and equitable potential of PRS, greater diversity must be prioritized in genetic studies, and summary statistics must be publically disseminated to ensure that health disparities are not increased for those individuals already most underserved.
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                Author and article information

                Contributors
                Role: Formal analysisRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: VisualizationRole: Writing – original draft
                Role: Formal analysisRole: ValidationRole: Writing – review & editing
                Role: Data curationRole: SupervisionRole: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: Data curationRole: Formal analysis
                Role: Data curationRole: Resources
                Role: Data curationRole: Resources
                Role: ResourcesRole: Writing – review & editing
                Role: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: ResourcesRole: Writing – review & editing
                Role: SupervisionRole: Writing – review & editing
                Role: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                11 February 2021
                February 2021
                : 17
                : 2
                : e1009273
                Affiliations
                [1 ] Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
                [2 ] Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
                [3 ] Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, United States of America
                [4 ] Ministry of Health, Government of Samoa, Apia, Samoa
                [5 ] Lutia i Puava ae Mapu i Fagalele, Apia, Samoa
                [6 ] Epidemiology Program, University of Hawai‘i Cancer Center, University of Hawai‘i, Manoa, Honolulu, Hawaii, United States of America
                [7 ] Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America
                Georgia Institute of Technology, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                ¶ Membership of Samoan Obesity, Lifestyle, and Genetic Adaptations (OLaGA) Study Group is provided in the Acknowledgement.

                Author information
                https://orcid.org/0000-0003-0083-8471
                https://orcid.org/0000-0003-4603-0718
                https://orcid.org/0000-0001-9151-0310
                https://orcid.org/0000-0001-7382-6717
                https://orcid.org/0000-0003-3438-4068
                https://orcid.org/0000-0002-5351-6145
                https://orcid.org/0000-0003-4132-2893
                https://orcid.org/0000-0002-0668-7865
                Article
                PGENETICS-D-20-00764
                10.1371/journal.pgen.1009273
                7877570
                33571193
                28b79218-43a4-42ad-ac1c-f35ac13d6052
                © 2021 Sun et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 12 May 2020
                : 18 November 2020
                Page count
                Figures: 3, Tables: 4, Pages: 26
                Funding
                The Multiethnic Cohort was funded through grants from the National Cancer Institute (U01CA164973, P01CA168530) and National Human Genome Research Institute (U01HG007397). Whole genome sequencing (WGS) for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). NHLBI TOPMed: Genome-wide Association Study of Adiposity in Samoans was funded by NHLBI (R01HL093093 [S.T. McGarvey] and (R01 HL133040 [R.L.M.]); WGS (phs000972) was performed at the University of Washington Northwest Genomics Center (HHSN268201100037C) and the New York Genome Center (HHSN268201500016C). Centralized read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Phenotype harmonization, data management, sample-identity QC, and general study coordination were provided by the TOPMed Data Coordinating Center (3R01HL-120393-02S1; contract HHSN268201800001I). This research is also supported by Samoan Ministry of Health and the Samoa Bureau of Statistics. Computation for this work is supported by the University of Southern California's Center for Advanced Research Computing ( https://carc.usc.edu). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                People and Places
                Population Groupings
                Ethnicities
                Austronesian People
                Polynesian People
                Biology and Life Sciences
                Genetics
                Genomics
                Biology and Life Sciences
                Anatomy
                Biological Tissue
                Connective Tissue
                Adipose Tissue
                Medicine and Health Sciences
                Anatomy
                Biological Tissue
                Connective Tissue
                Adipose Tissue
                Biology and Life Sciences
                Genetics
                Genetics of Disease
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Obesity
                Medicine and Health Sciences
                Medical Conditions
                Cardiovascular Diseases
                Cardiovascular Disease Risk
                Medicine and Health Sciences
                Cardiology
                Cardiovascular Medicine
                Cardiovascular Diseases
                Cardiovascular Disease Risk
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Body Mass Index
                Medicine and Health Sciences
                Cardiology
                Heart Failure
                Custom metadata
                All genotype data are available from phs000220.v2.p2 ( https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000220.v2.p2).

                Genetics
                Genetics

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