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Li J, Daida YG, Bacong AM, Rosales AG, Frankland TB, Varga A, Chung S, Fortmann SP, Waitzfelder B, Palaniappan L. Trends in cigarette smoking and the risk of incident cardiovascular disease among Asian American, Pacific Islander, and multiracial populations. Am J Prev Cardiol 2024; 19:100688. [PMID: 39070025 PMCID: PMC11278113 DOI: 10.1016/j.ajpc.2024.100688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 05/15/2024] [Accepted: 06/12/2024] [Indexed: 07/30/2024] Open
Abstract
Background Cardiovascular disease (CVD) is the leading cause of death in the United States, and rates of CVD incidence vary widely by race and ethnicity. Cigarette smoking is associated with increased risk of CVD. The purpose of the study was: 1) to examine smoking prevalence over time across Asian and Pacific Islander (API) and multi-race API subgroups; 2) to determine whether the CVD risk associated with smoking differed among these subgroups. Methods We identified patients belonging to 7 single race/ethnicity groups, 4 multi-race/ethnicity groups, and a non-Hispanic White (NHW) comparison group at two large health systems in Hawaii and California. We estimated annual smoking prevalence from 2011 through 2018 by group and gender. We examined incidence of CVD events by smoking status and race/ethnicity, and computed hazard ratios for CVD events by age, gender, race/ethnicity, census block median household income, census block college degree, and study site using Cox regression. Results Of the 12 groups studied, the Asian Indian and Chinese American groups had the lowest smoking prevalence, and the Asian + Pacific Islander multiracial group had the highest smoking prevalence. The prevalence of smoking decreased from 2011 to 2018 for all groups. Multi-race/ethnicity groups had higher risk of CVD than the NHW group. There was no significant interaction between race/ethnicity and smoking in models predicting CVD, but the association between race/ethnicity and CVD incidence was attenuated after adjusting for smoking status. Conclusions There is considerable heterogeneity in smoking prevalence and the risk of CVD among API subgroups.
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Affiliation(s)
- Jiang Li
- Sutter Health Center for Health Systems Research/Palo Alto Medical Foundation Research Institute, Palo Alto, CA, USA
| | - Yihe G. Daida
- Center for Integrated Health Care Research, Kaiser Permanente Hawaii, USA
| | | | | | | | - Alexandra Varga
- Kaiser Permanente Center for Health Research, Portland, OR, USA
| | - Sukyung Chung
- Sutter Health Center for Health Systems Research/Palo Alto Medical Foundation Research Institute, Palo Alto, CA, USA
| | | | - Beth Waitzfelder
- Center for Integrated Health Care Research, Kaiser Permanente Hawaii, USA
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2
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Murray M, Kai J, Dentinger A, Kaplan L, Roman M, O'Brien E, Kearney J, Kaneshiro B, Zhu F, Fialkowski MK. Prenatal intention to human milk feed in the native Hawaiian population: predictors of any human milk feeding from birth to six months postpartum. Int Breastfeed J 2024; 19:54. [PMID: 39097709 PMCID: PMC11297697 DOI: 10.1186/s13006-024-00660-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 07/20/2024] [Indexed: 08/05/2024] Open
Abstract
BACKGROUND Rates of non-communicable diseases are disproportionately high among Native Hawaiian (NH) people, and the proportion of NH infants being fed human milk (HM) is the lowest among all ethnicities within the state of Hawai'i. The aim of this study was to explore biological, socio-economic, and psychosocial determinants of the initiation and duration of human milk feeding (HMF) among a study of NH mothers and infants. METHODS A sample of 85 NH mother-infant dyads who were participating in a larger prospective study were involved in this research. Recruitment for the parent was delayed due to the COVID-19 pandemic. Recruitment started in November 2020 and continued until April 2022. Questionnaires were distributed at birth, two-months, four-months, and six-months postpartum. Questionnaires addressed topics relating to maternal and infant characteristics and infant feeding practices. Descriptive statistics, comparative analysis, and multivariate logistic regression tests were conducted. RESULTS The majority of participating mothers were aged between 31 and 35 years, had some college education or more, were employed, and multiparous. The majority of infants were receiving HM at each timepoint (94% at birth, 78% at two-months postpartum, and 76% at four and six-months postpartum). Factors found to be significantly associated with HMF initiation and duration were prenatal intention to HMF, maternal educational attainment, Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) participation, and Supplemental Nutrition Assistance Program (SNAP) recipiency. A prenatal intention to HMF was found to be a strong predictor of HMF at birth (aOR = 64.18, 95% CI 2.94, 1400.28) and at two-months postpartum (aOR = 231.55, 95% CI 2.18, 2418.3). Participants not involved with WIC were more likely to be HMF at four-months postpartum (aOR = 6.83, 95% CI 1.01, 46.23). CONCLUSION This research supports existing evidence that prenatal intention to HMF and higher maternal educational attainment are positive predictors of HMF. WIC participation and being a SNAP recipient were found to be negatively associated with HMF which suggests a need for more culturally tailored support. Further research is required to reduce the gap in knowledge related to the determinants of HMF in NH.
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Affiliation(s)
- Méabh Murray
- School of Biological Sciences, Technological University Dublin, Grangegorman, Dublin, D02 HW71, Ireland
- Trinity College Dublin, College Green, The University of Dublin, Dublin, D02 PN40, Ireland
| | - Jessie Kai
- Department of Human Nutrition, Food, and Animal Sciences, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Amanda Dentinger
- Department of Human Nutrition, Food, and Animal Sciences, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Leah Kaplan
- Department of Human Nutrition, Food, and Animal Sciences, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Meliza Roman
- Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawai'i, Honolulu, HI, 96813, USA
| | - Eileen O'Brien
- School of Biological Sciences, Technological University Dublin, Grangegorman, Dublin, D02 HW71, Ireland
| | - John Kearney
- School of Biological Sciences, Technological University Dublin, Grangegorman, Dublin, D02 HW71, Ireland
| | - Bliss Kaneshiro
- Department of Obstetrics, and Women's Health, John A. Burns School of Medicine, University of Hawai'i, Honolulu, HI, 96813, USA
| | - Fengqing Zhu
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Marie K Fialkowski
- Population Sciences in the Pacific Program, University of Hawai'i Cancer Center, Honolulu, HI, 96813, USA.
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Wang X, Hou K, Ricciuti B, Alessi JV, Li X, Pecci F, Dey R, Luo J, Awad MM, Gusev A, Lin X, Johnson BE, Christiani DC. Additional impact of genetic ancestry over race/ethnicity to prevalence of KRAS mutations and allele-specific subtypes in non-small cell lung cancer. HGG ADVANCES 2024; 5:100320. [PMID: 38902927 DOI: 10.1016/j.xhgg.2024.100320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 06/18/2024] [Accepted: 06/18/2024] [Indexed: 06/22/2024] Open
Abstract
The KRAS mutation is the most common oncogenic driver in patients with non-small cell lung cancer (NSCLC). However, a detailed understanding of how self-reported race and/or ethnicity (SIRE), genetically inferred ancestry (GIA), and their interaction affect KRAS mutation is largely unknown. Here, we investigated the associations between SIRE, quantitative GIA, and KRAS mutation and its allele-specific subtypes in a multi-ethnic cohort of 3,918 patients from the Boston Lung Cancer Survival cohort and the Chinese OrigiMed cohort with an independent validation cohort of 1,450 patients with NSCLC. This comprehensive analysis included detailed covariates such as age at diagnosis, sex, clinical stage, cancer histology, and smoking status. We report that SIRE is significantly associated with KRAS mutations, modified by sex, with SIRE-Asian patients showing lower rates of KRAS mutation, transversion substitution, and the allele-specific subtype KRASG12C compared to SIRE-White patients after adjusting for potential confounders. Moreover, GIA was found to correlate with KRAS mutations, where patients with a higher proportion of European ancestry had an increased risk of KRAS mutations, especially more transition substitutions and KRASG12D. Notably, among SIRE-White patients, an increase in European ancestry was linked to a higher likelihood of KRAS mutations, whereas an increase in admixed American ancestry was associated with a reduced likelihood, suggesting that quantitative GIA offers additional information beyond SIRE. The association of SIRE, GIA, and their interplay with KRAS driver mutations in NSCLC highlights the importance of incorporating both into population-based cancer research, aiming to refine clinical decision-making processes and mitigate health disparities.
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Affiliation(s)
- Xinan Wang
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, USA
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, 611 Charles E. Young Drive, Los Angeles, CA, USA
| | - Biagio Ricciuti
- Lowe Center for Thoracic Oncology and Center for Cancer Genomics, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, USA
| | - Joao V Alessi
- Lowe Center for Thoracic Oncology and Center for Cancer Genomics, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, USA
| | - Xihao Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC, USA; Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC, USA
| | - Federica Pecci
- Lowe Center for Thoracic Oncology and Center for Cancer Genomics, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, USA
| | - Rounak Dey
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, USA
| | - Jia Luo
- Lowe Center for Thoracic Oncology and Center for Cancer Genomics, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, USA
| | - Mark M Awad
- Lowe Center for Thoracic Oncology and Center for Cancer Genomics, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, USA
| | - Alexander Gusev
- McGraw/Patterson Center for Population Sciences, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, USA
| | - Bruce E Johnson
- Lowe Center for Thoracic Oncology and Center for Cancer Genomics, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, USA
| | - David C Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, USA; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA.
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Lin L, Andersen MK, Stæger FF, Li Z, Hanghøj K, Linneberg A, Grarup N, Jørgensen ME, Hansen T, Moltke I, Albrechtsen A. Analysis of admixed Greenlandic siblings shows that the mean genotypic values for metabolic phenotypes differ between Inuit and Europeans. Genome Med 2024; 16:71. [PMID: 38778393 PMCID: PMC11112775 DOI: 10.1186/s13073-024-01326-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 03/28/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Disease prevalence and mean phenotype values differ between many populations, including Inuit and Europeans. Whether these differences are partly explained by genetic differences or solely due to differences in environmental exposures is still unknown, because estimates of the genetic contribution to these means, which we will here refer to as mean genotypic values, are easily confounded, and because studies across genetically diverse populations are lacking. METHODS Leveraging the unique genetic properties of the small, admixed and historically isolated Greenlandic population, we estimated the differences in mean genotypic value between Inuit and European genetic ancestry using an admixed sibling design. Analyses were performed across 26 metabolic phenotypes, in 1474 admixed sibling pairs present in a cohort of 5996 Greenlanders. RESULTS After FDR correction for multiple testing, we found significantly lower mean genotypic values in Inuit genetic ancestry compared to European genetic ancestry for body weight (effect size per percentage of Inuit genetic ancestry (se), -0.51 (0.16) kg/%), body mass index (-0.20 (0.06) kg/m2/%), fat percentage (-0.38 (0.13) %/%), waist circumference (-0.42 (0.16) cm/%), hip circumference (-0.38 (0.11) cm/%) and fasting serum insulin levels (-1.07 (0.51) pmol/l/%). The direction of the effects was consistent with the observed mean phenotype differences between Inuit and European genetic ancestry. No difference in mean genotypic value was observed for height, markers of glucose homeostasis, or circulating lipid levels. CONCLUSIONS We show that mean genotypic values for some metabolic phenotypes differ between two human populations using a method not easily confounded by possible differences in environmental exposures. Our study illustrates the importance of performing genetic studies in diverse populations.
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Affiliation(s)
- Long Lin
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Mette K Andersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark
| | - Frederik Filip Stæger
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Zilong Li
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Kristian Hanghøj
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Allan Linneberg
- Centre for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, The Capital Region of Denmark, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark
| | - Marit Eika Jørgensen
- Centre for Public Health in Greenland, National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark
- Clinical Research, Copenhagen University Hospital - Steno Diabetes Center Copenhagen, Herlev, Denmark
- Steno Diabetes Center Greenland, Nuuk, Greenland
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark.
| | - Ida Moltke
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark.
| | - Anders Albrechtsen
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark.
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Cahoon JL, Rui X, Tang E, Simons C, Langie J, Chen M, Lo YC, Chiang CWK. Imputation accuracy across global human populations. Am J Hum Genet 2024; 111:979-989. [PMID: 38604166 PMCID: PMC11080279 DOI: 10.1016/j.ajhg.2024.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/14/2024] [Accepted: 03/15/2024] [Indexed: 04/13/2024] Open
Abstract
Genotype imputation is now fundamental for genome-wide association studies but lacks fairness due to the underrepresentation of references from non-European ancestries. The state-of-the-art imputation reference panel released by the Trans-Omics for Precision Medicine (TOPMed) initiative improved the imputation of admixed African-ancestry and Hispanic/Latino samples, but imputation for populations primarily residing outside of North America may still fall short in performance due to persisting underrepresentation. To illustrate this point, we imputed the genotypes of over 43,000 individuals across 123 populations around the world and identified numerous populations where imputation accuracy paled in comparison to that of European-ancestry populations. For instance, the mean imputation r-squared (Rsq) for variants with minor allele frequencies between 1% and 5% in Saudi Arabians (n = 1,061), Vietnamese (n = 1,264), Thai (n = 2,435), and Papua New Guineans (n = 776) were 0.79, 0.78, 0.76, and 0.62, respectively, compared to 0.90-0.93 for comparable European populations matched in sample size and SNP array content. Outside of Africa and Latin America, Rsq appeared to decrease as genetic distances to European-ancestry reference increased, as predicted. Using sequencing data as ground truth, we also showed that Rsq may over-estimate imputation accuracy for non-European populations more than European populations, suggesting further disparity in accuracy between populations. Using 1,496 sequenced individuals from Taiwan Biobank as a second reference panel to TOPMed, we also assessed a strategy to improve imputation for non-European populations with meta-imputation, but this design did not improve accuracy across frequency spectra. Taken together, our analyses suggest that we must ultimately strive to increase diversity and size to promote equity within genetics research.
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Affiliation(s)
- Jordan L Cahoon
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, CA 90033, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, Los Angeles, CA 90089, USA; Department of Computer Science, University of Southern California, Los Angeles, Los Angeles, CA 90089, USA
| | - Xinyue Rui
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, CA 90033, USA
| | - Echo Tang
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, Los Angeles, CA 90089, USA
| | - Christopher Simons
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, Los Angeles, CA 90089, USA
| | - Jalen Langie
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, CA 90033, USA
| | - Minhui Chen
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, CA 90033, USA
| | - Ying-Chu Lo
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, CA 90033, USA
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, CA 90033, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, Los Angeles, CA 90089, USA; Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, Los Angeles, CA 90033, USA.
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6
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Dinh BL, Tang E, Taparra K, Nakatsuka N, Chen F, Chiang CWK. Recombination map tailored to Native Hawaiians may improve robustness of genomic scans for positive selection. Hum Genet 2024; 143:85-99. [PMID: 38157018 PMCID: PMC10794367 DOI: 10.1007/s00439-023-02625-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 11/25/2023] [Indexed: 01/03/2024]
Abstract
Recombination events establish the patterns of haplotypic structure in a population and estimates of recombination rates are used in several downstream population and statistical genetic analyses. Using suboptimal maps from distantly related populations may reduce the efficacy of genomic analyses, particularly for underrepresented populations such as the Native Hawaiians. To overcome this challenge, we constructed recombination maps using genome-wide array data from two study samples of Native Hawaiians: one reflecting the current admixed state of Native Hawaiians (NH map) and one based on individuals of enriched Polynesian ancestries (PNS map) with the potential to be used for less admixed Polynesian populations such as the Samoans. We found the recombination landscape to be less correlated with those from other continental populations (e.g. Spearman's rho = 0.79 between PNS and CEU (Utah residents with Northern and Western European ancestry) compared to 0.92 between YRI (Yoruba in Ibadan, Nigeria) and CEU at 50 kb resolution), likely driven by the unique demographic history of the Native Hawaiians. PNS also shared the fewest recombination hotspots with other populations (e.g. 8% of hotspots shared between PNS and CEU compared to 27% of hotspots shared between YRI and CEU). We found that downstream analyses in the Native Hawaiian population, such as local ancestry inference, imputation, and IBD segment and relatedness detections, would achieve similar efficacy when using the NH map compared to an omnibus map. However, for genome scans of adaptive loci using integrated haplotype scores, we found several loci with apparent genome-wide significant signals (|Z-score|> 4) in Native Hawaiians that would not have been significant when analyzed using NH-specific maps. Population-specific recombination maps may therefore improve the robustness of haplotype-based statistics and help us better characterize the evolutionary history that may underlie Native Hawaiian-specific health conditions that persist today.
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Affiliation(s)
- Bryan L Dinh
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Echo Tang
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Kekoa Taparra
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA
| | | | - Fei Chen
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Charleston W K Chiang
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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7
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Lo YC, Chan TF, Jeon S, Maskarinec G, Taparra K, Nakatsuka N, Yu M, Chen CY, Lin YF, Wilkens LR, Le Marchand L, Haiman CA, Chiang CWK. The accuracy of polygenic score models for anthropometric traits and Type II Diabetes in the Native Hawaiian Population. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.25.23300499. [PMID: 38234828 PMCID: PMC10793530 DOI: 10.1101/2023.12.25.23300499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Polygenic scores (PGS) are promising in stratifying individuals based on the genetic susceptibility to complex diseases or traits. However, the accuracy of PGS models, typically trained in European- or East Asian-ancestry populations, tend to perform poorly in other ethnic minority populations, and their accuracies have not been evaluated for Native Hawaiians. Using body mass index, height, and type-2 diabetes as examples of highly polygenic traits, we evaluated the prediction accuracies of PGS models in a large Native Hawaiian sample from the Multiethnic Cohort with up to 5,300 individuals. We evaluated both publicly available PGS models or genome-wide PGS models trained in this study using the largest available GWAS. We found evidence of lowered prediction accuracies for the PGS models in some cases, particularly for height. We also found that using the Native Hawaiian samples as an optimization cohort during training did not consistently improve PGS performance. Moreover, even the best performing PGS models among Native Hawaiians would have lowered prediction accuracy among the subset of individuals most enriched with Polynesian ancestry. Our findings indicate that factors such as admixture histories, sample size and diversity in GWAS can influence PGS performance for complex traits among Native Hawaiian samples. This study provides an initial survey of PGS performance among Native Hawaiians and exposes the current gaps and challenges associated with improving polygenic prediction models for underrepresented minority populations.
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Affiliation(s)
- Ying-Chu Lo
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Tsz Fung Chan
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Soyoung Jeon
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Gertraud Maskarinec
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i, Manoa, Honolulu, HI, USA
| | - Kekoa Taparra
- Standard Health Care, Department of Radiation Oncology, Palo Alto, CA, USA
| | | | - Mingrui Yu
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Chia-Yen Chen
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
- Biogen, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Yen-Feng Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
- Department of Public Health & Medical Humanities, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Lynne R Wilkens
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i, Manoa, Honolulu, HI, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawai'i Cancer Center, University of Hawai'i, Manoa, Honolulu, HI, USA
| | - Christopher A Haiman
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Cancer Epidemiology Program, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Cancer Epidemiology Program, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
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8
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Chan TF, Rui X, Conti DV, Fornage M, Graff M, Haessler J, Haiman C, Highland HM, Jung SY, Kenny EE, Kooperberg C, Le Marchand L, North KE, Tao R, Wojcik G, Gignoux CR, Chiang CWK, Mancuso N. Estimating heritability explained by local ancestry and evaluating stratification bias in admixture mapping from summary statistics. Am J Hum Genet 2023; 110:1853-1862. [PMID: 37875120 PMCID: PMC10645552 DOI: 10.1016/j.ajhg.2023.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 09/20/2023] [Accepted: 09/21/2023] [Indexed: 10/26/2023] Open
Abstract
The heritability explained by local ancestry markers in an admixed population (hγ2) provides crucial insight into the genetic architecture of a complex disease or trait. Estimation of hγ2 can be susceptible to biases due to population structure in ancestral populations. Here, we present heritability estimation from admixture mapping summary statistics (HAMSTA), an approach that uses summary statistics from admixture mapping to infer heritability explained by local ancestry while adjusting for biases due to ancestral stratification. Through extensive simulations, we demonstrate that HAMSTA hγ2 estimates are approximately unbiased and are robust to ancestral stratification compared to existing approaches. In the presence of ancestral stratification, we show a HAMSTA-derived sampling scheme provides a calibrated family-wise error rate (FWER) of ∼5% for admixture mapping, unlike existing FWER estimation approaches. We apply HAMSTA to 20 quantitative phenotypes of up to 15,988 self-reported African American individuals in the Population Architecture using Genomics and Epidemiology (PAGE) study. We observe hˆγ2 in the 20 phenotypes range from 0.0025 to 0.033 (mean hˆγ2 = 0.012 ± 9.2 × 10-4), which translates to hˆ2 ranging from 0.062 to 0.85 (mean hˆ2 = 0.30 ± 0.023). Across these phenotypes we find little evidence of inflation due to ancestral population stratification in current admixture mapping studies (mean inflation factor of 0.99 ± 0.001). Overall, HAMSTA provides a fast and powerful approach to estimate genome-wide heritability and evaluate biases in test statistics of admixture mapping studies.
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Affiliation(s)
- Tsz Fung Chan
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xinyue Rui
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - David V Conti
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Myriam Fornage
- Brown Foundation Institute for Molecular Medicine, The University of Texas Health Science Center, Houston, TX, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Christopher Haiman
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Heather M Highland
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Su Yon Jung
- Translational Sciences Section, School of Nursing, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Genevieve Wojcik
- Department of Epidemiology, Bloomberg School of Public Health, John Hopkins University, Baltimore, MD, USA
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
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9
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Cahoon JL, Rui X, Tang E, Simons C, Langie J, Chen M, Lo YC, Chiang CWK. Imputation Accuracy Across Global Human Populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.22.541241. [PMID: 37292811 PMCID: PMC10245797 DOI: 10.1101/2023.05.22.541241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Genotype imputation is now fundamental for genome-wide association studies but lacks fairness due to the underrepresentation of populations with non-European ancestries. The state-of-the-art imputation reference panel released by the Trans-Omics for Precision Medicine (TOPMed) initiative contains a substantial number of admixed African-ancestry and Hispanic/Latino samples to impute these populations with nearly the same accuracy as European-ancestry cohorts. However, imputation for populations primarily residing outside of North America may still fall short in performance due to persisting underrepresentation. To illustrate this point, we curated genome-wide array data from 23 publications published between 2008 to 2021. In total, we imputed over 43k individuals across 123 populations around the world. We identified a number of populations where imputation accuracy paled in comparison to that of European-ancestry populations. For instance, the mean imputation r-squared (Rsq) for 1-5% alleles in Saudi Arabians (N=1061), Vietnamese (N=1264), Thai (N=2435), and Papua New Guineans (N=776) were 0.79, 0.78, 0.76, and 0.62, respectively. In contrast, the mean Rsq ranged from 0.90 to 0.93 for comparable European populations matched in sample size and SNP content. Outside of Africa and Latin America, Rsq appeared to decrease as genetic distances to European reference increased, as predicted. Further analysis using sequencing data as ground truth suggested that imputation software may over-estimate imputation accuracy for non-European populations than European populations, suggesting further disparity between populations. Using 1496 whole genome sequenced individuals from Taiwan Biobank as a reference, we also assessed a strategy to improve imputation for non-European populations with meta-imputation, which can combine results from TOPMed with smaller population-specific reference panels. We found that meta-imputation in this design did not improve Rsq genome-wide. Taken together, our analysis suggests that with the current size of alternative reference panels, meta-imputation alone cannot improve imputation efficacy for underrepresented cohorts and we must ultimately strive to increase diversity and size to promote equity within genetics research.
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Affiliation(s)
- Jordan L. Cahoon
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
- Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA
| | - Xinyue Rui
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Echo Tang
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Christopher Simons
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Jalen Langie
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Minhui Chen
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Ying-Chu Lo
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Charleston W. K. Chiang
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
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10
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Dinh BL, Tang E, Taparra K, Nakatsuka N, Chen F, Chiang CWK. Recombination map tailored to Native Hawaiians improves robustness of genomic scans for positive selection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.12.548735. [PMID: 37503129 PMCID: PMC10370006 DOI: 10.1101/2023.07.12.548735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Recombination events establish the patterns of haplotypic structure in a population and estimates of recombination rates are used in several downstream population and statistical genetic analyses. Using suboptimal maps from distantly related populations may reduce the efficacy of genomic analyses, particularly for underrepresented populations such as the Native Hawaiians. To overcome this challenge, we constructed recombination maps using genome-wide array data from two study samples of Native Hawaiians: one reflecting the current admixed state of Native Hawaiians (NH map), and one based on individuals of enriched Polynesian ancestries (PNS map) with the potential to be used for less admixed Polynesian populations such as the Samoans. We found the recombination landscape to be less correlated with those from other continental populations (e.g. Spearman's rho = 0.79 between PNS and CEU (Utah residents with Northern and Western European ancestry) compared to 0.92 between YRI (Yoruba in Ibadan, Nigeria) and CEU at 50 kb resolution), likely driven by the unique demographic history of the Native Hawaiians. PNS also shared the fewest recombination hotspots with other populations (e.g. 8% of hotspots shared between PNS and CEU compared to 27% of hotspots shared between YRI and CEU). We found that downstream analyses in the Native Hawaiian population, such as local ancestry inference, imputation, and IBD segment and relatedness detections, would achieve similar efficacy when using the NH map compared to an omnibus map. However, for genome scans of adaptive loci using integrated haplotype scores, we found several loci with apparent genome-wide significant signals (|Z-score| > 4) in Native Hawaiians that would not have been significant when analyzed using NH-specific maps. Population-specific recombination maps may therefore improve the robustness of haplotype-based statistics and help us better characterize the evolutionary history that may underlie Native Hawaiian-specific health conditions that persist today.
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Affiliation(s)
- Bryan L Dinh
- Department of Quantitative and Computational Biology, University of Southern California
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Echo Tang
- Department of Quantitative and Computational Biology, University of Southern California
| | - Kekoa Taparra
- Department of Radiation Oncology, Stanford University, Palo Alto, California
| | | | - Fei Chen
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Charleston W K Chiang
- Department of Quantitative and Computational Biology, University of Southern California
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
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11
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Moors J, Krishnan M, Sumpter N, Takei R, Bixley M, Cadzow M, Major TJ, Phipps-Green A, Topless R, Merriman M, Rutledge M, Morgan B, Carlson JC, Zhang JZ, Russell EM, Sun G, Cheng H, Weeks DE, Naseri T, Reupena MS, Viali S, Tuitele J, Hawley NL, Deka R, McGarvey ST, de Zoysa J, Murphy R, Dalbeth N, Stamp L, Taumoepeau M, King F, Wilcox P, Rapana N, McCormick S, Minster RL, Merriman TR, Leask M. A Polynesian -specific missense CETP variant alters the lipid profile. HGG ADVANCES 2023; 4:100204. [PMID: 37250494 PMCID: PMC10209881 DOI: 10.1016/j.xhgg.2023.100204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 05/31/2023] Open
Abstract
Identifying population-specific genetic variants associated with disease and disease-predisposing traits is important to provide insights into the genetic determinants of health and disease between populations, as well as furthering genomic justice. Various common pan-population polymorphisms at CETP associate with serum lipid profiles and cardiovascular disease. Here, sequencing of CETP identified a missense variant rs1597000001 (p.Pro177Leu) specific to Māori and Pacific people that associates with higher HDL-C and lower LDL-C levels. Each copy of the minor allele associated with higher HDL-C by 0.236 mmol/L and lower LDL-C by 0.133 mmol/L. The rs1597000001 effect on HDL-C is comparable with CETP Mendelian loss-of-function mutations that result in CETP deficiency, consistent with our data, which shows that rs1597000001 lowers CETP activity by 27.9%. This study highlights the potential of population-specific genetic analyses for improving equity in genomics and health outcomes for population groups underrepresented in genomic studies.
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Affiliation(s)
- Jaye Moors
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Mohanraj Krishnan
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Nick Sumpter
- Division of Clinical Rheumatology and Immunology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Riku Takei
- Division of Clinical Rheumatology and Immunology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Matt Bixley
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Murray Cadzow
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Tanya J. Major
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | | | - Ruth Topless
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Marilyn Merriman
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Malcolm Rutledge
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Ben Morgan
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Jenna C. Carlson
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jerry Z. Zhang
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Emily M. Russell
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Guangyun Sun
- Department of Environmental and Public Health Sciences, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Hong Cheng
- Department of Environmental and Public Health Sciences, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Daniel E. Weeks
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Take Naseri
- Ministry of Health, Apia, Samoa
- International Health Institute, Department of Epidemiology, School of Public Health, Brown University, Providence, RI, USA
| | | | | | - John Tuitele
- Department of Public Health, Lyndon B. Johnson Tropical Medical Center, Faga’alu, American Samoa, USA
| | - Nicola L. Hawley
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA
| | - Ranjan Deka
- Department of Environmental and Public Health Sciences, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Stephen T. McGarvey
- International Health Institute, Department of Epidemiology, School of Public Health, Brown University, Providence, RI, USA
| | - Janak de Zoysa
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Rinki Murphy
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Nicola Dalbeth
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Lisa Stamp
- Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Mele Taumoepeau
- Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Frances King
- Ngāti Porou Hauora, Te Puia Springs, New Zealand
| | - Phillip Wilcox
- Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand
| | - Nuku Rapana
- Pukapukan Community Centre, Māngere, Auckland, New Zealand
| | - Sally McCormick
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Ryan L. Minster
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Tony R. Merriman
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
- Division of Clinical Rheumatology and Immunology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Megan Leask
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
- Division of Clinical Rheumatology and Immunology, University of Alabama at Birmingham, Birmingham, AL, USA
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12
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Chan TF, Rui X, Conti DV, Fornage M, Graff M, Haessler J, Haiman C, Highland HM, Jung SY, Kenny E, Kooperberg C, Marchland LL, North KE, Tao R, Wojcik G, Gignoux CR, Chiang CWK, Mancuso N. Estimating heritability explained by local ancestry and evaluating stratification bias in admixture mapping from summary statistics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.10.536252. [PMID: 37131817 PMCID: PMC10153181 DOI: 10.1101/2023.04.10.536252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The heritability explained by local ancestry markers in an admixed population h γ 2 provides crucial insight into the genetic architecture of a complex disease or trait. Estimation of h γ 2 can be susceptible to biases due to population structure in ancestral populations. Here, we present a novel approach, Heritability estimation from Admixture Mapping Summary STAtistics (HAMSTA), which uses summary statistics from admixture mapping to infer heritability explained by local ancestry while adjusting for biases due to ancestral stratification. Through extensive simulations, we demonstrate that HAMSTA h γ 2 estimates are approximately unbiased and are robust to ancestral stratification compared to existing approaches. In the presence of ancestral stratification, we show a HAMSTA-derived sampling scheme provides a calibrated family-wise error rate (FWER) of ~5% for admixture mapping, unlike existing FWER estimation approaches. We apply HAMSTA to 20 quantitative phenotypes of up to 15,988 self-reported African American individuals in the Population Architecture using Genomics and Epidemiology (PAGE) study. We observe h ˆ γ 2 in the 20 phenotypes range from 0.0025 to 0.033 (mean h ˆ γ 2 = 0.012 + / - 9.2 × 10 - 4 ), which translates to h ˆ 2 ranging from 0.062 to 0.85 (mean h ˆ 2 = 0.30 + / - 0.023 ). Across these phenotypes we find little evidence of inflation due to ancestral population stratification in current admixture mapping studies (mean inflation factor of 0.99 +/- 0.001). Overall, HAMSTA provides a fast and powerful approach to estimate genome-wide heritability and evaluate biases in test statistics of admixture mapping studies.
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Affiliation(s)
- Tsz Fung Chan
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Xinyue Rui
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - David V Conti
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Myriam Fornage
- Brown Foundation Institute for Molecular Medicine, The University of Texas Health Science Center, Houston, TX, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Christopher Haiman
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Heather M Highland
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Su Yon Jung
- Translational Sciences Section, School of Nursing, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, United States
| | - Eimear Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Loic Le Marchland
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Genevieve Wojcik
- Department of Epidemiology, Bloomberg School of Public Health, John Hopkins University, Baltimore, MD, USA
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
- Department of Quantitative and Computational Biology, University of Southern California
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
- Department of Quantitative and Computational Biology, University of Southern California
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13
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Carlson JC, Krishnan M, Rosenthal SL, Russell EM, Zhang JZ, Hawley NL, Moors J, Cheng H, Dalbeth N, de Zoysa JR, Watson H, Qasim M, Murphy R, Naseri T, Reupena MS, Viali S, Stamp LK, Tuitele J, Kershaw EE, Deka R, McGarvey ST, Merriman TR, Weeks DE, Minster RL. A stop-gain variant in BTNL9 is associated with atherogenic lipid profiles. HGG ADVANCES 2023; 4:100155. [PMID: 36340932 PMCID: PMC9630829 DOI: 10.1016/j.xhgg.2022.100155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 10/10/2022] [Indexed: 11/12/2022] Open
Abstract
Current understanding of lipid genetics has come mainly from studies in European-ancestry populations; limited effort has focused on Polynesian populations, whose unique population history and high prevalence of dyslipidemia may provide insight into the biological foundations of variation in lipid levels. Here, we performed an association study to fine map a suggestive association on 5q35 with high-density lipoprotein cholesterol (HDL-C) seen in Micronesian and Polynesian populations. Fine-mapping analyses in a cohort of 2,851 Samoan adults highlighted an association between a stop-gain variant (rs200884524; c.652C>T, p.R218∗; posterior probability = 0.9987) in BTNL9 and both lower HDL-C and greater triglycerides (TGs). Meta-analysis across this and several other cohorts of Polynesian ancestry from Samoa, American Samoa, and Aotearoa New Zealand confirmed the presence of this association (βHDL-C = -1.60 mg/dL, p HDL-C = 7.63 × 10-10; βTG = 12.00 mg/dL, p TG = 3.82 × 10-7). While this variant appears to be Polynesian specific, there is also evidence of association from other multiancestry analyses in this region. This work provides evidence of a previously unexplored contributor to the genetic architecture of lipid levels and underscores the importance of genetic analyses in understudied populations.
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Affiliation(s)
- Jenna C. Carlson
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mohanraj Krishnan
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Samantha L. Rosenthal
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Craniofacial and Dental Genetics, Department of Oral Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Emily M. Russell
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jerry Z. Zhang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Nicola L. Hawley
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA
| | - Jaye Moors
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Hong Cheng
- Department of Environmental Health, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Nicola Dalbeth
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Janak R. de Zoysa
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Huti Watson
- Ngāti Porou Hauora Charitable Trust, Te Puia Springs, Tairāwhiti East Coast, New Zealand
| | - Muhammad Qasim
- Ngāti Porou Hauora Charitable Trust, Te Puia Springs, Tairāwhiti East Coast, New Zealand
| | - Rinki Murphy
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand
| | - Take Naseri
- Ministry of Health, Government of Samoa, Apia, Samoa
| | | | | | - Lisa K. Stamp
- Department of Medicine, University of Otago Christchurch, Christchurch, New Zealand
| | - John Tuitele
- Department of Public Health, Government of American Samoa, Pago Pago, American Samoa
| | - Erin E. Kershaw
- Division of Endocrinology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ranjan Deka
- Department of Environmental Health, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Stephen T. McGarvey
- International Health Institute, Department of Epidemiology, Brown University, Providence, RI, USA
- Department of Anthropology, Brown University, Providence, RI, USA
| | - Tony R. Merriman
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand
| | - Daniel E. Weeks
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ryan L. Minster
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
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14
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Andrasik MP, Maunakea AK, Oseso L, Rodriguez-Diaz CE, Wallace S, Walters K, Yukawa M. Awakening: The Unveiling of Historically Unaddressed Social Inequities During the COVID-19 Pandemic in the United States. Infect Dis Clin North Am 2022; 36:295-308. [PMID: 35636901 PMCID: PMC8806123 DOI: 10.1016/j.idc.2022.01.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The violence and victimization brought by colonization and slavery and justified for over a century by race-based science have resulted in enduring inequities for black, Indigenous and people of color (BIPOC) across the United States. This is particularly true if BIPOC individuals have other intersecting devalued identities. We highlight how such longstanding inequities paved the way for the disproportionate burdens of coronavirus disease 2019 (COVID-19) among the BIPOC populations across the country and provide recommendations on how to improve COVID-19 mitigation strategies with the goal of eliminating disparities.
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Affiliation(s)
- Michele P Andrasik
- Vaccine and Infectious Disease Division, Fred Hutchinson, 1100 Eastlake Avenue, E3-300, Seattle, WA 98109, USA.
| | - Alika K Maunakea
- Department of Anatomy, Biochemistry, and Physiology, John A. Burns School of Medicine, University of Hawaii, 651 Ilalo Street, BSB-222K, Honolulu, HI 96813, USA
| | - Linda Oseso
- Vaccine and Infectious Disease Division, Fred Hutchinson, 1100 Eastlake Avenue, E3-300, Seattle, WA 98109, USA
| | - Carlos E Rodriguez-Diaz
- Department of Prevention and Community Health, Milken Institute School of Public Health, The George Washington University, 950 New Hampshire Avenue NW, Suite 300, Washington, DC 20052, USA
| | - Stephaun Wallace
- Vaccine and Infectious Disease Division, Fred Hutchinson, 1100 Eastlake Avenue, E3-300, Seattle, WA 98109, USA
| | - Karina Walters
- Indigenous Wellness Research Institute, University of Washington, 4101 15th Avenue NE Box 354900, Seattle, WA 98105, USA
| | - Michi Yukawa
- Division of Geriatrics, University of California San Francisco, San Francisco, CA 94121, USA; Geriatric Palliative and Extended Care, San Francisco VA Medical Center, 490 Illinois Street, Floor 8, UCSF BOX 1265, San Francisco, CA 94143, USA
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15
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Kanaya AM, Hsing AW, Panapasa SV, Kandula NR, Araneta MRG, Shimbo D, Wang P, Gomez SL, Lee J, Narayan KMV, Mau MKLM, Bose S, Daviglus ML, Hu FB, Islam N, Jackson CL, Kataoka-Yahiro M, Kauwe JSK, Liu S, Ma GX, Nguyen T, Palaniappan L, Setiawan VW, Trinh-Shevrin C, Tsoh JY, Vaidya D, Vickrey B, Wang TJ, Wong ND, Coady S, Hong Y. Knowledge Gaps, Challenges, and Opportunities in Health and Prevention Research for Asian Americans, Native Hawaiians, and Pacific Islanders: A Report From the 2021 National Institutes of Health Workshop. Ann Intern Med 2022; 175:574-589. [PMID: 34978851 PMCID: PMC9018596 DOI: 10.7326/m21-3729] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Asian Americans (AsA), Native Hawaiians, and Pacific Islanders (NHPI) comprise 7.7% of the U.S. population, and AsA have had the fastest growth rate since 2010. Yet the National Institutes of Health (NIH) has invested only 0.17% of its budget on AsA and NHPI research between 1992 and 2018. More than 40 ethnic subgroups are included within AsA and NHPI (with no majority subpopulation), which are highly diverse culturally, demographically, linguistically, and socioeconomically. However, data for these groups are often aggregated, masking critical health disparities and their drivers. To address these issues, in March 2021, the National Heart, Lung, and Blood Institute, in partnership with 8 other NIH institutes, convened a multidisciplinary workshop to review current research, knowledge gaps, opportunities, barriers, and approaches for prevention research for AsA and NHPI populations. The workshop covered 5 domains: 1) sociocultural, environmental, psychological health, and lifestyle dimensions; 2) metabolic disorders; 3) cardiovascular and lung diseases; 4) cancer; and 5) cognitive function and healthy aging. Two recurring themes emerged: Very limited data on the epidemiology, risk factors, and outcomes for most conditions are available, and most existing data are not disaggregated by subgroup, masking variation in risk factors, disease occurrence, and trajectories. Leveraging the vast phenotypic differences among AsA and NHPI groups was identified as a key opportunity to yield novel clues into etiologic and prognostic factors to inform prevention efforts and intervention strategies. Promising approaches for future research include developing collaborations with community partners, investing in infrastructure support for cohort studies, enhancing existing data sources to enable data disaggregation, and incorporating novel technology for objective measurement. Research on AsA and NHPI subgroups is urgently needed to eliminate disparities and promote health equity in these populations.
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Affiliation(s)
- Alka M Kanaya
- University of California, San Francisco, San Francisco, California (A.M.K., S.L.G., T.N., J.Y.T.)
| | - Ann W Hsing
- Stanford University, Stanford, California (A.W.H., P.W., L.P.)
| | | | | | | | - Daichi Shimbo
- Columbia University Irving Medical Center, New York, New York (D.S.)
| | - Paul Wang
- Stanford University, Stanford, California (A.W.H., P.W., L.P.)
| | - Scarlett L Gomez
- University of California, San Francisco, San Francisco, California (A.M.K., S.L.G., T.N., J.Y.T.)
| | - Jinkook Lee
- University of Southern California, Los Angeles, California (J.L., V.W.S.)
| | | | | | - Sonali Bose
- Icahn School of Medicine at Mount Sinai, New York, New York (S.B., B.V.)
| | | | - Frank B Hu
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts (F.B.H.)
| | - Nadia Islam
- New York University Grossman School of Medicine, New York, New York (N.I., C.T.)
| | - Chandra L Jackson
- National Institute of Environmental Health Sciences, National Institutes of Health, Bethesda, Maryland (C.L.J.)
| | | | | | - Simin Liu
- Brown University, Providence, Rhode Island (S.L.)
| | - Grace X Ma
- Temple University, Philadelphia, Pennsylvania (G.X.M.)
| | - Tung Nguyen
- University of California, San Francisco, San Francisco, California (A.M.K., S.L.G., T.N., J.Y.T.)
| | | | - V Wendy Setiawan
- University of Southern California, Los Angeles, California (J.L., V.W.S.)
| | - Chau Trinh-Shevrin
- New York University Grossman School of Medicine, New York, New York (N.I., C.T.)
| | - Janice Y Tsoh
- University of California, San Francisco, San Francisco, California (A.M.K., S.L.G., T.N., J.Y.T.)
| | | | - Barbara Vickrey
- Icahn School of Medicine at Mount Sinai, New York, New York (S.B., B.V.)
| | - Thomas J Wang
- University of Texas Southwestern Medical Center, Dallas, Texas (T.J.W.)
| | - Nathan D Wong
- University of California, Irvine, Irvine, California (N.D.W.)
| | - Sean Coady
- National Heart, Lung, and Blood Institute, Bethesda, Maryland (S.C., Y.H.)
| | - Yuling Hong
- National Heart, Lung, and Blood Institute, Bethesda, Maryland (S.C., Y.H.)
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16
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Watanabe LM, Seale LA. Challenging Aspects to Precise Health Strategies in Native Hawaiian and Other Pacific Islanders Using Statins. Front Public Health 2022; 10:799731. [PMID: 35296045 PMCID: PMC8918550 DOI: 10.3389/fpubh.2022.799731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 01/31/2022] [Indexed: 12/03/2022] Open
Abstract
Cardiometabolic disorders (CD), including cardiovascular disease (CVD), diabetes, and obesity, are the leading cause of health concern in the United States (U.S.), disproportionately affecting indigenous populations such a Native Hawaiian and Other Pacific Islanders (NHOPI). Dyslipidemia, a prevalent risk factor for the development and progression of CVD, is more prone to occur in NHOPI than other populations in the U.S. High-intensity statin therapy to reduce low-density lipoprotein cholesterol is associated with the prevention of CVD events. However, significant side-effects, such as muscle disorders, have been associated with its use. Different ethnic groups could experience variation in the prevalence of statin side effects due to sociodemographic, behavioral, and/or biological factors. Therefore, identifying the most impactful determinants that can be modified to prevent or reduce statin side effects for individuals from high-risk ethnic minority groups, such as NHOPI, can lead to more effective strategies to reduce health disparities. Thus, our Mini-Review explores the challenging aspects of public health precise strategies in NHOPI taking statins, including a culturally informed additional therapy that could positively impact the NHOPI population.
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Affiliation(s)
- Ligia M. Watanabe
- Division of Nutrition and Metabolism, Department of Health Sciences, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Lucia A. Seale
- Pacific Biosciences Research Center, School of Ocean and Earth Science and Technology, University of Hawaii at Mānoa, Honolulu, HI, United States
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17
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Lord BD, Martini RN, Davis MB. Understanding how genetic ancestry may influence cancer development. Trends Cancer 2022; 8:276-279. [PMID: 35027335 DOI: 10.1016/j.trecan.2021.12.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 12/10/2021] [Accepted: 12/14/2021] [Indexed: 01/12/2023]
Abstract
Of the multifactorial determinants that lead to cancer health disparities among race groups, quantified genetic ancestry has begun to expand our knowledge beyond self-reported race. However, it is essential to study these biological determinants in the context of social determinants to truly improve clinical tools and achieve equitable survival outcomes.
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Affiliation(s)
- Brittany D Lord
- Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA
| | - Rachel N Martini
- Department of Surgery, Weill Cornell Medical College, New York, NY, USA; Meyer Cancer Center, Weill Cornell Medicine and New York Presbyterian Hospital, New York, NY, USA
| | - Melissa B Davis
- Department of Surgery, Weill Cornell Medical College, New York, NY, USA; Englander Institute of Precision Medicine, Weill Cornell Medical College, New York, NY, USA; New York Genome Center, New York, NY, USA; Meyer Cancer Center, Weill Cornell Medicine and New York Presbyterian Hospital, New York, NY, USA.
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18
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Benny P, Ahn HJ, Burlingame J, Lee MJ, Miller C, Chen J, Urschitz J. Genetic risk factors associated with gestational diabetes in a multi-ethnic population. PLoS One 2021; 16:e0261137. [PMID: 34928995 PMCID: PMC8687569 DOI: 10.1371/journal.pone.0261137] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 11/24/2021] [Indexed: 11/19/2022] Open
Abstract
AIMS Genome-wide association studies have shown an increased risk of type-2-diabetes (T2DM) in patients who carry single nucleotide polymorphisms in several genes. We investigated whether the same gene loci confer a risk for gestational diabetes mellitus (GDM) in women from Hawaii, and in particular, Pacific Islander and Filipino populations. METHODS Blood was collected from 291 women with GDM and 734 matched non-diabetic controls (Pacific Islanders: 71 GDM, 197 non-diabetic controls; Filipinos: 162 GDM, 395 controls; Japanese: 58 GDM, 142 controls). Maternal DNA was used to genotype and show allele frequencies of 25 different SNPs mapped to 18 different loci. RESULTS After adjusting for age, BMI, parity and gravidity by multivariable logistic regression, several SNPs showed significant associations with GDM and were ethnicity specific. In particular, SNPs rs1113132 (EXT2), rs1111875 (HHEX), rs2237892 (KCNQ1), rs2237895 (KCNQ1), rs10830963 (MTNR1B) and rs13266634 (SLC30A8) showed significant associations with GDM in Filipinos. For Japanese, SNPs rs4402960 (IGFBP2) and rs2237892 (KCNQ1) were significantly associated with GDM. For Pacific Islanders, SNPs rs10830963 (MTNR1B) and rs13266634 (SLC30A8) showed significant associations with GDM. Individually, none of the SNPs showed a consistent association with GDM across all three investigated ethnicities. CONCLUSION Several SNPs associated with T2DM are found to confer increased risk for GDM in a multiethnic cohort in Hawaii.
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Affiliation(s)
- Paula Benny
- Department of Obstetrics, Gynecology, and Women’s Health, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, United States of America
| | - Hyeong Jun Ahn
- Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, United States of America
| | - Janet Burlingame
- Department of Obstetrics, Gynecology, and Women’s Health, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, United States of America
| | - Men-Jean Lee
- Department of Obstetrics, Gynecology, and Women’s Health, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, United States of America
| | - Corrie Miller
- Department of Obstetrics, Gynecology, and Women’s Health, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, United States of America
| | - John Chen
- Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, United States of America
| | - Johann Urschitz
- Department of Anatomy, Biochemistry and Physiology, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, United States of America
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19
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Chiang CWK. The Opportunities and Challenges of Integrating Population Histories Into Genetic Studies for Diverse Populations: A Motivating Example From Native Hawaiians. Front Genet 2021; 12:643883. [PMID: 34646295 PMCID: PMC8503554 DOI: 10.3389/fgene.2021.643883] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 08/19/2021] [Indexed: 11/25/2022] Open
Abstract
There is a well-recognized need to include diverse populations in genetic studies, but several obstacles continue to be prohibitive, including (but are not limited to) the difficulty of recruiting individuals from diverse populations in large numbers and the lack of representation in available genomic references. These obstacles notwithstanding, studying multiple diverse populations would provide informative, population-specific insights. Using Native Hawaiians as an example of an understudied population with a unique evolutionary history, I will argue that by developing key genomic resources and integrating evolutionary thinking into genetic epidemiology, we will have the opportunity to efficiently advance our knowledge of the genetic risk factors, ameliorate health disparity, and improve healthcare in this underserved population.
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Affiliation(s)
- Charleston W K Chiang
- Department of Population and Public Health Sciences, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.,Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, United States
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20
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Mau MKLM, Minami CM, Stotz SA, Albright CL, Kana'iaupuni SM, Guth HK. Qualitative study on voyaging and health: perspectives and insights from the medical officers during the Worldwide Voyage. BMJ Open 2021; 11:e048767. [PMID: 34233995 PMCID: PMC8264866 DOI: 10.1136/bmjopen-2021-048767] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVE To examine the potential association of ocean voyaging with human health and well-being from the perspectives and experiences of the medical officers (MOs) who served during the Worldwide Voyage (WWV). DESIGN Using a phenomenology framework, focus group and individual interviews were conducted and analysed by three diverse core researchers and then reviewed by three external researchers to enhance triangulation. Analysis used the Framework Method and Atlas-ti software (V.8.4.4) to facilitate coding, identify categories and develop an analytical matrix. The matrix was applied to all data using the constant comparative method to construct major themes and subthemes. Synthesised member checking was performed. SETTING In 2014-2017, the WWV began in Hawai'i on a traditional voyaging canoe, known as Hōkūle'a, using a non-instrument navigational method, 'wayfinding', powered only by natural forces and guided by traditional ecological knowledge. Each segment of the voyage included ~12 individuals, including an MO physician. The entire WWV included 172 ports-of-call, 36 legs and 250+ crew members. PARTICIPANTS We purposively sampled all MO physicians who participated in the WWV and enrolled 87% of eligible MOs (n=20 of 23). We conducted two focus groups (n=17=11+6, 85%) and three individual informant interviews (n=3, 15%). RESULTS The four major themes: (1) Relationships; (2) Preventive Care to Enhance Health; (3) Holistic Health and Wellbeing beyond Voyaging and (4) Spiritual Transformative Experience, strongly suggest that ocean voyaging aboard a traditional voyaging canoe enhanced human health and well-being. The overall impact to perceived health and well-being extended beyond any increase in physical exercise. Essentially, traditional Polynesian ocean voyaging provided a cultural-based context for holistic health and well-being that influenced multiple levels and multiple dimensions. CONCLUSION Polynesian ocean voyaging was perceived as positively associated with holistic health and overall well-being and it may offer a new approach to confront complex health disparities.
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Affiliation(s)
- Marjorie K Leimomi Mala Mau
- Native Hawaiian Health, John A. Burns School of Medicine, University of Hawai'i at Mānoa, Honolulu, Hawai'i, USA
| | - Christina Mie Minami
- Native Hawaiian Health, John A. Burns School of Medicine, University of Hawai'i at Mānoa, Honolulu, Hawai'i, USA
| | - Sarah A Stotz
- Centers for American Indian and Alaska Native Health, Colorado School of Public Health, Aurora, Colorado, USA
| | - Cheryl L Albright
- School of Nursing & Dental Hygiene, University of Hawai'i at Mānoa, Honolulu, Hawai'i, USA
| | - Shawn Malia Kana'iaupuni
- Partners in Development Foundation, Honolulu, Hawai'i, USA
- Policy Analysis & System Evaluation, Kamehameha Schools - Kapālama Campus, Honolulu, Hawai'i, USA
| | - Heidi Kai Guth
- Native Hawaiian Health, John A. Burns School of Medicine, University of Hawai'i at Mānoa, Honolulu, Hawai'i, USA
- Kai Ho'oulu, LLC, Honolulu, Hawai'i, USA
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21
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Genetic Ancestry Inference and Its Application for the Genetic Mapping of Human Diseases. Int J Mol Sci 2021; 22:ijms22136962. [PMID: 34203440 PMCID: PMC8269095 DOI: 10.3390/ijms22136962] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 06/24/2021] [Accepted: 06/25/2021] [Indexed: 12/21/2022] Open
Abstract
Admixed populations arise when two or more ancestral populations interbreed. As a result of this admixture, the genome of admixed populations is defined by tracts of variable size inherited from these parental groups and has particular genetic features that provide valuable information about their demographic history. Diverse methods can be used to derive the ancestry apportionment of admixed individuals, and such inferences can be leveraged for the discovery of genetic loci associated with diseases and traits, therefore having important biomedical implications. In this review article, we summarize the most common methods of global and local genetic ancestry estimation and discuss the use of admixture mapping studies in human diseases.
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