1
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Hernandez AE, Mahal B, Telonis AG, Figueroa M, Goel N. Response to: Comment on Genetic Ancestry-Specific Molecular and Survival Differences in Admixed Breast Cancer Patients. ANNALS OF SURGERY OPEN 2024; 5:e424. [PMID: 38911651 PMCID: PMC11191929 DOI: 10.1097/as9.0000000000000424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 03/26/2024] [Indexed: 06/25/2024] Open
Affiliation(s)
- Alexandra E. Hernandez
- From the Division of Surgical Oncology, Department of Surgery, University of Miami Miller School of Medicine, Miami, FL
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL
| | - Brandon Mahal
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL
- Department of Radiation Oncology, University of Miami, Miami, FL
| | - Aristeidis G. Telonis
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL
- Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, FL
| | - Maria Figueroa
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL
- Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL
- Department of Medicine, University of Miami Miller School of Medicine, Miami, FL
| | - Neha Goel
- From the Division of Surgical Oncology, Department of Surgery, University of Miami Miller School of Medicine, Miami, FL
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL
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2
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Fetter KC, Keller SR. Admixture mapping and selection scans identify genomic regions associated with stomatal patterning and disease resistance in hybrid poplars. Ecol Evol 2023; 13:e10579. [PMID: 37881228 PMCID: PMC10597741 DOI: 10.1002/ece3.10579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 09/18/2023] [Accepted: 09/21/2023] [Indexed: 10/27/2023] Open
Abstract
Variation in fitness components can be linked in some cases to variation in key traits. Metric traits that lie at the intersection of development, defense, and ecological interactions may be expected to experience environmental selection, informing our understanding of evolutionary and ecological processes. Here, we use quantitative genetic and population genomic methods to investigate disease dynamics in hybrid and non-hybrid populations. We focus our investigation on morphological and ecophysiological traits which inform our understanding of physiology, growth, and defense against a pathogen. In particular, we investigate stomata, microscopic pores on the surface of a leaf that regulate gas exchange during photosynthesis and are sites of entry for various plant pathogens. Stomatal patterning traits were highly predictive of disease risk. Admixture mapping identified a polygenic basis of disease resistance. Candidate genes for stomatal and disease resistance map to the same genomic regions and experienced positive selection. Genes with functions to guard cell homeostasis, the plant immune system, components of constitutive defenses, and growth-related transcription factors were identified. Our results indicate positive selection acted on candidate genes for stomatal patterning and disease resistance, potentially acting in concert to structure their variation in naturally formed backcrossing hybrid populations.
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Affiliation(s)
- Karl C. Fetter
- Department of Plant BiologyUniversity of VermontBurlingtonVermontUSA
- Department of Ecology and Evolutionary BiologyUniversity of ConnecticutStorrsConnecticutUSA
| | - Stephen R. Keller
- Department of Plant BiologyUniversity of VermontBurlingtonVermontUSA
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3
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Goel N, Hernandez A, Merchant N, Rebbeck T. Translational Epidemiology: Genetic Ancestry in Breast Cancer: What Is the Role of Genetic Ancestry and Socioeconomic Status in Triple-Negative Breast Cancer? Adv Surg 2023; 57:1-14. [PMID: 37536846 DOI: 10.1016/j.yasu.2023.03.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
Racial/ethnic and socioeconomic disparities seen in triple-negative breast cancer (TNBC) have prompted questions regarding the role of genetic ancestry in breast cancer (BC) subtype development, tumor biology, and ultimately prognosis. The causes of disparities in TNBC are influenced greatly by both sociopolitical factors and genetic ancestry, and now, the potential genomic underpinnings of social factors. To comprehensively understand disparities in TNBC, it is critical to take a translational epidemiologic approach that takes into account genomic and non-genomic factors. Understanding the interplay between genetic ancestry and social genomics and their proportional influence on outcomes can guide our priorities for screening, diagnosis, and interventions for this aggressive BC subtype.
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Affiliation(s)
- Neha Goel
- Department of Surgery, Division of Surgical Oncology, University of Miami Miller School of Medicine, 1120 Northwest 14th Street, 4th Floor, Miami, FL 31336, USA; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, 1120 Northwest 14th Street, 4th Floor, Miami, FL 31336, USA.
| | - Alexandra Hernandez
- Department of Surgery, Division of Surgical Oncology, University of Miami Miller School of Medicine, 1120 Northwest 14th Street, 4th Floor, Miami, FL 31336, USA
| | - Nipun Merchant
- Department of Surgery, Division of Surgical Oncology, University of Miami Miller School of Medicine, 1120 Northwest 14th Street, 4th Floor, Miami, FL 31336, USA; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, 1120 Northwest 14th Street, 4th Floor, Miami, FL 31336, USA
| | - Timothy Rebbeck
- Harvard T.H. Chan School of Public Health and Dana-Farber Cancer Institute, 1101 Dana. 450 Brookline Avenue, Boston, MA 02215, USA
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4
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Tan T, Atkinson EG. Strategies for the Genomic Analysis of Admixed Populations. Annu Rev Biomed Data Sci 2023; 6:105-127. [PMID: 37127050 PMCID: PMC10871708 DOI: 10.1146/annurev-biodatasci-020722-014310] [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] [Indexed: 05/03/2023]
Abstract
Admixed populations constitute a large portion of global human genetic diversity, yet they are often left out of genomics analyses. This exclusion is problematic, as it leads to disparities in the understanding of the genetic structure and history of diverse cohorts and the performance of genomic medicine across populations. Admixed populations have particular statistical challenges, as they inherit genomic segments from multiple source populations-the primary reason they have historically been excluded from genetic studies. In recent years, however, an increasing number of statistical methods and software tools have been developed to account for and leverage admixture in the context of genomics analyses. Here, we provide a survey of such computational strategies for the informed consideration of admixture to allow for the well-calibrated inclusion of mixed ancestry populations in large-scale genomics studies, and we detail persisting gaps in existing tools.
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Affiliation(s)
- Taotao Tan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA;
| | - Elizabeth G Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA;
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5
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Mester R, Hou K, Ding Y, Meeks G, Burch KS, Bhattacharya A, Henn BM, Pasaniuc B. Impact of cross-ancestry genetic architecture on GWASs in admixed populations. Am J Hum Genet 2023; 110:927-939. [PMID: 37224807 PMCID: PMC10257009 DOI: 10.1016/j.ajhg.2023.05.001] [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: 01/27/2023] [Revised: 05/04/2023] [Accepted: 05/04/2023] [Indexed: 05/26/2023] Open
Abstract
Genome-wide association studies (GWASs) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWASs in admixed populations, such as the need to correctly adjust for population stratification. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing a GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes, we find that controlling for and conditioning effect sizes on local ancestry can reduce statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs, HetLanc is not large enough for GWASs to benefit from modeling heterogeneity in this way.
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Affiliation(s)
- Rachel Mester
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yi Ding
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Gillian Meeks
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, Davis, CA 95616, USA
| | - Kathryn S Burch
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Brenna M Henn
- Department of Anthropology, Center for Population Biology and the Genome Center, University of California, Davis, Davis, CA 95616, USA
| | - Bogdan Pasaniuc
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Institute of Precision Health, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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6
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Mester R, Hou K, Ding Y, Meeks G, Burch KS, Bhattacharya A, Henn BM, Pasaniuc B. Impact of cross-ancestry genetic architecture on GWAS in admixed populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.20.524946. [PMID: 36747759 PMCID: PMC9900755 DOI: 10.1101/2023.01.20.524946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Genome-wide association studies (GWAS) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWAS in admixed populations, such as the need to correctly adjust for population stratification to balance type I error with statistical power. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes we find that modeling HetLanc in its absence reduces statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs HetLanc is not large enough for GWAS to benefit from modeling heterogeneity.
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Affiliation(s)
- Rachel Mester
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095 USA
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095 USA
| | - Yi Ding
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095 USA
| | - Gillian Meeks
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, Davis, CA, 95616 USA
| | - Kathryn S. Burch
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095 USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095 USA
| | - Brenna M. Henn
- Department of Anthropology, Center for Population Biology and the Genome Center, University of California, Davis, Davis, CA, 95616 USA
| | - Bogdan Pasaniuc
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095 USA
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095 USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095 USA
- Institute of Precision Health, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095 USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095 USA
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7
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Woodward AA, Urbanowicz RJ, Naj AC, Moore JH. Genetic heterogeneity: Challenges, impacts, and methods through an associative lens. Genet Epidemiol 2022; 46:555-571. [PMID: 35924480 PMCID: PMC9669229 DOI: 10.1002/gepi.22497] [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: 05/18/2022] [Revised: 07/06/2022] [Accepted: 07/19/2022] [Indexed: 01/07/2023]
Abstract
Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.
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Affiliation(s)
- Alexa A. Woodward
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ryan J. Urbanowicz
- Department of Computational BiomedicineCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Adam C. Naj
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jason H. Moore
- Department of Computational BiomedicineCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
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8
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Wu Y, Palmer JR, Rosenberg L, Ruiz-Narváez EA. Admixture mapping of anthropometric traits in the Black Women's Health Study: evidence of a shared African ancestry component with birth weight and type 2 diabetes. J Hum Genet 2022; 67:331-338. [PMID: 35017682 DOI: 10.1038/s10038-022-01010-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 12/27/2021] [Accepted: 12/29/2021] [Indexed: 11/09/2022]
Abstract
Prevalence of obesity, type 2 diabetes (T2D), and being born with low birth weight are much higher in African American women compared to U.S. white women. Genetic factors may contribute to the excess risk of these conditions. We conducted admixture mapping of body mass index (BMI) at age 18, adult BMI, and adult waist circumference and waist-to-hip ratio adjusted for BMI using 2918 ancestral informative markers in 2596 participants of the Black Women's Health Study. We also searched for evidence of shared African genetic ancestry components among the four examined anthropometric traits and among birth weight and T2D. We found that global percent African ancestry was associated with higher adult BMI. We also found that African ancestry at 9q34 was associated with lower BMI at age 18. Our shared ancestry analysis identified ten genomic regions with local African ancestry associated with multiple traits. Seven out of these ten genomic loci were related to T2D risk. Of special interest is the 12q14-21 region where local African ancestry was associated with low birth weight, low BMI, high BMI-adjusted waist-to-hip ratio, and high T2D risk. Findings in the 12q14-21 genomic locus are consistent with the fetal insulin hypothesis that postulates that low birth weight and T2D have a common genetic basis, and they support the hypothesis of a shared African genetic ancestry component linking low birth weight and T2D in African Americans. Future studies should identify the actual genetic variants responsible for the clustering of these conditions in African Americans.
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Affiliation(s)
- Yue Wu
- Department of Bioinformatics and Biostatistics, School of Life Science and Technology, Shanghai Jiao Tong University, Shanghai, China.,Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Julie R Palmer
- Slone Epidemiology Center at Boston University, Boston, MA, USA
| | - Lynn Rosenberg
- Slone Epidemiology Center at Boston University, Boston, MA, USA
| | - Edward A Ruiz-Narváez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA.
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9
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Beck EA, Healey HM, Small CM, Currey MC, Desvignes T, Cresko WA, Postlethwait JH. Advancing human disease research with fish evolutionary mutant models. Trends Genet 2022; 38:22-44. [PMID: 34334238 PMCID: PMC8678158 DOI: 10.1016/j.tig.2021.07.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 06/28/2021] [Accepted: 07/01/2021] [Indexed: 01/03/2023]
Abstract
Model organism research is essential to understand disease mechanisms. However, laboratory-induced genetic models can lack genetic variation and often fail to mimic the spectrum of disease severity. Evolutionary mutant models (EMMs) are species with evolved phenotypes that mimic human disease. EMMs complement traditional laboratory models by providing unique avenues to study gene-by-environment interactions, modular mutations in noncoding regions, and their evolved compensations. EMMs have improved our understanding of complex diseases, including cancer, diabetes, and aging, and illuminated mechanisms in many organs. Rapid advancements of sequencing and genome-editing technologies have catapulted the utility of EMMs, particularly in fish. Fish are the most diverse group of vertebrates, exhibiting a kaleidoscope of specialized phenotypes, many that would be pathogenic in humans but are adaptive in the species' specialized habitat. Importantly, evolved compensations can suggest avenues for novel disease therapies. This review summarizes current research using fish EMMs to advance our understanding of human disease.
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Affiliation(s)
- Emily A Beck
- Data Science, University of Oregon, Eugene, OR 97403, USA; Institute of Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA.
| | - Hope M Healey
- Institute of Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA
| | - Clayton M Small
- Data Science, University of Oregon, Eugene, OR 97403, USA; Institute of Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA
| | - Mark C Currey
- Institute of Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA
| | - Thomas Desvignes
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA
| | - William A Cresko
- Data Science, University of Oregon, Eugene, OR 97403, USA; Institute of Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA
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10
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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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11
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Oleksyk TK, Wolfsberger WW, Weber AM, Shchubelka K, Oleksyk OT, Levchuk O, Patrus A, Lazar N, Castro-Marquez SO, Hasynets Y, Boldyzhar P, Neymet M, Urbanovych A, Stakhovska V, Malyar K, Chervyakova S, Podoroha O, Kovalchuk N, Rodriguez-Flores JL, Zhou W, Medley S, Battistuzzi F, Liu R, Hou Y, Chen S, Yang H, Yeager M, Dean M, Mills RE, Smolanka V. Genome diversity in Ukraine. Gigascience 2021; 10:6079618. [PMID: 33438729 PMCID: PMC7804371 DOI: 10.1093/gigascience/giaa159] [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: 08/04/2020] [Revised: 08/21/2020] [Accepted: 12/15/2020] [Indexed: 01/21/2023] Open
Abstract
Background The main goal of this collaborative effort is to provide genome-wide data for the previously underrepresented population in Eastern Europe, and to provide cross-validation of the data from genome sequences and genotypes of the same individuals acquired by different technologies. We collected 97 genome-grade DNA samples from consented individuals representing major regions of Ukraine that were consented for public data release. BGISEQ-500 sequence data and genotypes by an Illumina GWAS chip were cross-validated on multiple samples and additionally referenced to 1 sample that has been resequenced by Illumina NovaSeq6000 S4 at high coverage. Results The genome data have been searched for genomic variation represented in this population, and a number of variants have been reported: large structural variants, indels, copy number variations, single-nucletide polymorphisms, and microsatellites. To our knowledge, this study provides the largest to-date survey of genetic variation in Ukraine, creating a public reference resource aiming to provide data for medical research in a large understudied population. Conclusions Our results indicate that the genetic diversity of the Ukrainian population is uniquely shaped by evolutionary and demographic forces and cannot be ignored in future genetic and biomedical studies. These data will contribute a wealth of new information bringing forth a wealth of novel, endemic and medically related alleles.
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Affiliation(s)
- Taras K Oleksyk
- Department of Biological Sciences, Uzhhorod National University, 32 Voloshyna Str., Uzhhorod 88000, Ukraine.,Department of Biological Sciences,Oakland University, Dodge Hall, 118 Library Dr., Rochester, MI 48309, USA.,Departamento de Biología, Universidad de Puerto Rico, Mayagüez, PR 00682, USA
| | - Walter W Wolfsberger
- Department of Biological Sciences, Uzhhorod National University, 32 Voloshyna Str., Uzhhorod 88000, Ukraine.,Department of Biological Sciences,Oakland University, Dodge Hall, 118 Library Dr., Rochester, MI 48309, USA.,Departamento de Biología, Universidad de Puerto Rico, Mayagüez, PR 00682, USA
| | - Alexandra M Weber
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Khrystyna Shchubelka
- Department of Biological Sciences,Oakland University, Dodge Hall, 118 Library Dr., Rochester, MI 48309, USA.,Departamento de Biología, Universidad de Puerto Rico, Mayagüez, PR 00682, USA.,Department of Medicine, Uzhhorod National University, Uzhhorod 88000, Ukraine
| | - Olga T Oleksyk
- A. Novak Transcarpathian Regional Clinical Hospital, Uzhhorod 88000, Ukraine
| | | | | | | | - Stephanie O Castro-Marquez
- Department of Biological Sciences,Oakland University, Dodge Hall, 118 Library Dr., Rochester, MI 48309, USA.,Departamento de Biología, Universidad de Puerto Rico, Mayagüez, PR 00682, USA
| | - Yaroslava Hasynets
- Department of Biological Sciences, Uzhhorod National University, 32 Voloshyna Str., Uzhhorod 88000, Ukraine
| | - Patricia Boldyzhar
- Department of Medicine, Uzhhorod National University, Uzhhorod 88000, Ukraine
| | - Mikhailo Neymet
- Velyka Kopanya Family Hospital, Transcarpatia 90330, Ukraine
| | | | | | - Kateryna Malyar
- I.I.Mechnikov Dnipro Regional Clinical Hospital, Dnipro 49000, Ukraine
| | | | | | - Natalia Kovalchuk
- Rivne Regional Specialized Hospital of Radiation Protection, Rivne 33028, Ukraine
| | | | - Weichen Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sarah Medley
- Department of Biological Sciences,Oakland University, Dodge Hall, 118 Library Dr., Rochester, MI 48309, USA
| | - Fabia Battistuzzi
- Department of Biological Sciences,Oakland University, Dodge Hall, 118 Library Dr., Rochester, MI 48309, USA
| | - Ryan Liu
- BGI Shenzhen, Shenzhen, 518083, China
| | - Yong Hou
- BGI Shenzhen, Shenzhen, 518083, China
| | - Siru Chen
- BGI Shenzhen, Shenzhen, 518083, China
| | | | - Meredith Yeager
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Michael Dean
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Ryan E Mills
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.,Department of Human Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Volodymyr Smolanka
- Department of Medicine, Uzhhorod National University, Uzhhorod 88000, Ukraine
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12
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Mario-Vásquez JE, Naranjo-González CA, Montiel J, Zuluaga LM, Vásquez AM, Tobón-Castaño A, Bedoya G, Segura C. Association of variants in IL1B, TLR9, TREM1, IL10RA, and CD3G and Native American ancestry on malaria susceptibility in Colombian populations. INFECTION GENETICS AND EVOLUTION 2020; 87:104675. [PMID: 33316430 DOI: 10.1016/j.meegid.2020.104675] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 11/19/2020] [Accepted: 12/09/2020] [Indexed: 12/24/2022]
Abstract
Host genetics is an influencing factor in the manifestation of infectious diseases. In this study, the association of mild malaria with 28 variants in 16 genes previously reported in other populations and/or close to ancestry-informative markers (AIMs) selected was evaluated in an admixed 736 Colombian population sample. Additionally, the effect of genetic ancestry on phenotype expression was explored. For this purpose, the ancestral genetic composition of Turbo and El Bagre was determined. A higher Native American ancestry trend was found in the population with lower malaria susceptibility [odds ratio (OR) = 0.416, 95% confidence interval (95% CI) = 0.234-0.740, P = 0.003]. Three AIMs presented significant associations with the disease phenotype (MID1752, MID921, and MID1586). The first two were associated with greater malaria susceptibility (D/D, OR = 2.23, 95% CI = 1.06-4.69, P = 0.032 and I/D-I/I, OR = 2.14, 95% CI = 1.18-3.87, P = 0.011, respectively), and the latter has a protective effect on the appearance of malaria (I/I, OR = 0.18, 95% CI = 0.08-0.40, P < 0.0001). After adjustment by age, sex, municipality, and genetic ancestry, genotype association analysis showed evidence of association with malaria susceptibility for variants in or near IL1B, TLR9, TREM1, IL10RA, and CD3G genes: rs1143629-IL1B (G/A-A/A, OR = 0.41, 95% CI = 0.21-0.78, P = 0.0051), rs352139-TLR9 (T/T, OR = 0.28, 95% CI = 0.11-0.72, P = 0.0053), rs352140-TLR9 (C/C, OR = 0.41, 95% CI = 0.20-0.87, P = 0.019), rs2234237-TREM1 (T/A-A/A, OR = 0.43, 95% CI = 0.23-0.79, P = 0.0056), rs4252246-IL10RA (C/A-A/A, OR = 2.11, 95% CI = 1.18-3.75, P = 0.01), and rs1561966-CD3G (A/A, OR = 0.20, 95% CI = 0.06-0.69, P = 0.0058). The results showed the participation of genes involved in immunological processes and suggested an effect of ancestral genetic composition over the traits analyzed. Compared to the paisa population (Antioquia), Turbo and El Bagre showed a strong decrease in European ancestry and an increase in African and Native American ancestries. Also, a novel association of two single nucleotide polymorphisms with malaria susceptibility was identified in this study.
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Affiliation(s)
- Jorge Eliécer Mario-Vásquez
- Grupo Genética Molecular (GENMOL), Universidad de Antioquia, Carrera 53 No. 61-30, Lab 430. Medellín, Colombia
| | | | - Jehidys Montiel
- Grupo Malaria-Facultad de Medicina, Universidad de Antioquia, Carrera 53 No. 61-30, Lab 610, Medellín, Colombia
| | - Lina M Zuluaga
- Grupo Malaria-Facultad de Medicina, Universidad de Antioquia, Carrera 53 No. 61-30, Lab 610, Medellín, Colombia
| | - Ana M Vásquez
- Grupo Malaria-Facultad de Medicina, Universidad de Antioquia, Carrera 53 No. 61-30, Lab 610, Medellín, Colombia
| | - Alberto Tobón-Castaño
- Grupo Malaria-Facultad de Medicina, Universidad de Antioquia, Carrera 53 No. 61-30, Lab 610, Medellín, Colombia
| | - Gabriel Bedoya
- Grupo Genética Molecular (GENMOL), Universidad de Antioquia, Carrera 53 No. 61-30, Lab 430. Medellín, Colombia
| | - Cesar Segura
- Grupo Malaria-Facultad de Medicina, Universidad de Antioquia, Carrera 53 No. 61-30, Lab 610, Medellín, Colombia.
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13
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Fitak RR, Rinkevich SE, Culver M. Genome-Wide Analysis of SNPs Is Consistent with No Domestic Dog Ancestry in the Endangered Mexican Wolf (Canis lupus baileyi). J Hered 2019; 109:372-383. [PMID: 29757430 DOI: 10.1093/jhered/esy009] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Accepted: 02/28/2018] [Indexed: 11/13/2022] Open
Abstract
The Mexican gray wolf (Canis lupus baileyi) was historically distributed throughout the southwestern United States and northern Mexico. Extensive predator removal campaigns during the early 20th century, however, resulted in its eventual extirpation by the mid 1980s. At this time, the Mexican wolf existed only in 3 separate captive lineages (McBride, Ghost Ranch, and Aragón) descended from 3, 2, and 2 founders, respectively. These lineages were merged in 1995 to increase the available genetic variation, and Mexican wolves were reintroduced into Arizona and New Mexico in 1998. Despite the ongoing management of the Mexican wolf population, it has been suggested that a proportion of the Mexican wolf ancestry may be recently derived from hybridization with domestic dogs. In this study, we genotyped 87 Mexican wolves, including individuals from all 3 captive lineages and cross-lineage wolves, for more than 172000 single nucleotide polymorphisms. We identified levels of genetic variation consistent with the pedigree record and effects of genetic rescue. To identify the potential to detect hybridization with domestic dogs, we compared our Mexican wolf genotypes with those from studies of domestic dogs and other gray wolves. The proportion of Mexican wolf ancestry assigned to domestic dogs was only between 0.06% (SD 0.23%) and 7.8% (SD 1.0%) for global and local ancestry estimates, respectively; and was consistent with simulated levels of incomplete lineage sorting. Overall, our results suggested that Mexican wolves lack biologically significant ancestry with dogs and have useful implications for the conservation and management of this endangered wolf subspecies.
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Affiliation(s)
| | | | - Melanie Culver
- US Geological Survey Arizona Cooperative Fish and Wildlife Research Unit, School of Natural Resources and the Environment, University of Arizona, Tucson, AZ
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14
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Zhang C, Gao Y, Liu J, Xue Z, Lu Y, Deng L, Tian L, Feng Q, Xu S. PGG.Population: a database for understanding the genomic diversity and genetic ancestry of human populations. Nucleic Acids Res 2019; 46:D984-D993. [PMID: 29112749 PMCID: PMC5753384 DOI: 10.1093/nar/gkx1032] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 10/17/2017] [Indexed: 12/16/2022] Open
Abstract
There are a growing number of studies focusing on delineating genetic variations that are associated with complex human traits and diseases due to recent advances in next-generation sequencing technologies. However, identifying and prioritizing disease-associated causal variants relies on understanding the distribution of genetic variations within and among populations. The PGG.Population database documents 7122 genomes representing 356 global populations from 107 countries and provides essential information for researchers to understand human genomic diversity and genetic ancestry. These data and information can facilitate the design of research studies and the interpretation of results of both evolutionary and medical studies involving human populations. The database is carefully maintained and constantly updated when new data are available. We included miscellaneous functions and a user-friendly graphical interface for visualization of genomic diversity, population relationships (genetic affinity), ancestral makeup, footprints of natural selection, and population history etc. Moreover, PGG.Population provides a useful feature for users to analyze data and visualize results in a dynamic style via online illustration. The long-term ambition of the PGG.Population, together with the joint efforts from other researchers who contribute their data to our database, is to create a comprehensive depository of geographic and ethnic variation of human genome, as well as a platform bringing influence on future practitioners of medicine and clinical investigators. PGG.Population is available at https://www.pggpopulation.org.
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Affiliation(s)
- Chao Zhang
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Gao
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jiaojiao Liu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Zhe Xue
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China
| | - Yan Lu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China
| | - Lian Deng
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lei Tian
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qidi Feng
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuhua Xu
- Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.,Collaborative Innovation Center of Genetics and Development, Shanghai 200438, China
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15
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Abstract
Risk of disease is multifactorial and can be shaped by socio-economic, demographic, cultural, environmental and genetic factors. Our understanding of the genetic determinants of disease risk has greatly advanced with the advent of genome-wide association studies (GWAS), which detect associations between genetic variants and complex traits or diseases by comparing populations of cases and controls. However, much of this discovery has occurred through GWAS of individuals of European ancestry, with limited representation of other populations, including from Africa, The Americas, Asia and Oceania. Population demography, genetic drift and adaptation to environments over thousands of years have led globally to the diversification of populations. This global genomic diversity can provide new opportunities for discovery and translation into therapies, as well as a better understanding of population disease risk. Large-scale multi-ethnic and representative biobanks and population health resources provide unprecedented opportunities to understand the genetic determinants of disease on a global scale.
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16
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Haworth S, Mitchell R, Corbin L, Wade KH, Dudding T, Budu-Aggrey A, Carslake D, Hemani G, Paternoster L, Smith GD, Davies N, Lawson DJ, J Timpson N. Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis. Nat Commun 2019; 10:333. [PMID: 30659178 PMCID: PMC6338768 DOI: 10.1038/s41467-018-08219-1] [Citation(s) in RCA: 168] [Impact Index Per Article: 33.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 11/09/2018] [Indexed: 01/06/2023] Open
Abstract
Large studies use genotype data to discover genetic contributions to complex traits and infer relationships between those traits. Co-incident geographical variation in genotypes and health traits can bias these analyses. Here we show that single genetic variants and genetic scores composed of multiple variants are associated with birth location within UK Biobank and that geographic structure in genotype data cannot be accounted for using routine adjustment for study centre and principal components derived from genotype data. We find that major health outcomes appear geographically structured and that coincident structure in health outcomes and genotype data can yield biased associations. Understanding and accounting for this phenomenon will be important when making inference from genotype data in large studies.
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Affiliation(s)
- Simon Haworth
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Ruth Mitchell
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Laura Corbin
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Kaitlin H Wade
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Tom Dudding
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Ashley Budu-Aggrey
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - David Carslake
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Lavinia Paternoster
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Neil Davies
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Daniel J Lawson
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Nicholas J Timpson
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- Avon Longitudinal Study of Parents and Children, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
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17
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Hernandez-Pacheco N, Pino-Yanes M, Flores C. Genomic Predictors of Asthma Phenotypes and Treatment Response. Front Pediatr 2019; 7:6. [PMID: 30805318 PMCID: PMC6370703 DOI: 10.3389/fped.2019.00006] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 01/10/2019] [Indexed: 12/11/2022] Open
Abstract
Asthma is a complex respiratory disease considered as the most common chronic condition in children. A large genetic contribution to asthma susceptibility is predicted by the clustering of asthma and allergy symptoms among relatives and the large disease heritability estimated from twin studies, ranging from 55 to 90%. Genetic basis of asthma has been extensively investigated in the past 40 years using linkage analysis and candidate-gene association studies. However, the development of dense arrays for polymorphism genotyping has enabled the transition toward genome-wide association studies (GWAS), which have led the discovery of several unanticipated asthma genes in the last 11 years. Despite this, currently known risk variants identified using many thousand samples from distinct ethnicities only explain a small proportion of asthma heritability. This review examines the main findings of the last 2 years in genomic studies of asthma using GWAS and admixture mapping studies, as well as the direction of studies fostering integrative perspectives involving omics data. Additionally, we discuss the need for assessing the whole spectrum of genetic variation in association studies of asthma susceptibility, severity, and treatment response in order to further improve our knowledge of asthma genes and predictive biomarkers. Leveraging the individual's genetic information will allow a better understanding of asthma pathogenesis and will facilitate the transition toward a more precise diagnosis and treatment.
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Affiliation(s)
- Natalia Hernandez-Pacheco
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain.,Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, Santa Cruz de Tenerife, Spain
| | - Maria Pino-Yanes
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain.,Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, Santa Cruz de Tenerife, Spain.,CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | - Carlos Flores
- Research Unit, Hospital Universitario N.S. de Candelaria, Universidad de La Laguna, Santa Cruz de Tenerife, Spain.,CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain.,Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
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18
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Kruzel-Davila E, Wasser WG, Skorecki K. APOL1 Nephropathy: A Population Genetics and Evolutionary Medicine Detective Story. Semin Nephrol 2018; 37:490-507. [PMID: 29110756 DOI: 10.1016/j.semnephrol.2017.07.002] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Common DNA sequence variants rarely have a high-risk association with a common disease. When such associations do occur, evolutionary forces must be sought, such as in the association of apolipoprotein L1 (APOL1) gene risk variants with nondiabetic kidney diseases in populations of African ancestry. The variants originated in West Africa and provided pathogenic resistance in the heterozygous state that led to high allele frequencies owing to an adaptive evolutionary selective sweep. However, the homozygous state is disadvantageous and is associated with a markedly increased risk of a spectrum of kidney diseases encompassing hypertension-attributed kidney disease, focal segmental glomerulosclerosis, human immunodeficiency virus nephropathy, sickle cell nephropathy, and progressive lupus nephritis. This scientific success story emerged with the help of the tools developed over the past 2 decades in human genome sequencing and population genomic databases. In this introductory article to a timely issue dedicated to illuminating progress in this area, we describe this unique population genetics and evolutionary medicine detective story. We emphasize the paradox of the inheritance mode, the missing heritability, and unresolved associations, including cardiovascular risk and diabetic nephropathy. We also highlight how genetic epidemiology elucidates mechanisms and how the principles of evolution can be used to unravel conserved pathways affected by APOL1 that may lead to novel therapies. The APOL1 gene provides a compelling example of a common variant association with common forms of nondiabetic kidney disease occurring in a continental population isolate with subsequent global admixture. Scientific collaboration using multiple experimental model systems and approaches should further clarify pathomechanisms further, leading to novel therapies.
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Affiliation(s)
| | - Walter G Wasser
- Department of Nephrology, Rambam Health Care Campus, Haifa, Israel; Department of Nephrology, Mayanei HaYeshua Medical Center, Bnei Brak, Israel
| | - Karl Skorecki
- Department of Nephrology, Rambam Health Care Campus, Haifa, Israel; Department of Genetics and Developmental Biology, Rappaport Faculty of Medicine and Research Institute Technion-Israel Institute of Technology, Rambam Health Care Campus, Haifa, Israel.
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19
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Abstract
Signatures of recent historical admixture are ubiquitous in human populations. We present a mechanistic model of admixture with two source populations, encompassing recurrent admixture periods and study the distribution of admixture fractions for finite but arbitrary genome size. We provide simulation-based methods to estimate the introgression parameters and discuss the implications of reaching stationarity on estimability of parameters when there are recurrent admixture events with different rates.
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Affiliation(s)
- Erkan Ozge Buzbas
- Department of Statistical Science, University of Idaho, United States.
| | - Paul Verdu
- CNRS/MNHN/Université Paris Diderot/Sorbonne Paris Cité, France
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20
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Muzzio M, Motti JMB, Paz Sepulveda PB, Yee MC, Cooke T, Santos MR, Ramallo V, Alfaro EL, Dipierri JE, Bailliet G, Bravi CM, Bustamante CD, Kenny EE. Population structure in Argentina. PLoS One 2018; 13:e0196325. [PMID: 29715266 PMCID: PMC5929549 DOI: 10.1371/journal.pone.0196325] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 04/11/2018] [Indexed: 11/19/2022] Open
Abstract
We analyzed 391 samples from 12 Argentinian populations from the Center-West, East and North-West regions with the Illumina Human Exome Beadchip v1.0 (HumanExome-12v1-A). We did Principal Components analysis to infer patterns of populational divergence and migrations. We identified proportions and patterns of European, African and Native American ancestry and found a correlation between distance to Buenos Aires and proportion of Native American ancestry, where the highest proportion corresponds to the Northernmost populations, which is also the furthest from the Argentinian capital. Most of the European sources are from a South European origin, matching historical records, and we see two different Native American components, one that spreads all over Argentina and another specifically Andean. The highest percentages of African ancestry were in the Center West of Argentina, where the old trade routes took the slaves from Buenos Aires to Chile and Peru. Subcontinentaly, sources of this African component are represented by both West Africa and groups influenced by the Bantu expansion, the second slightly higher than the first, unlike North America and the Caribbean, where the main source is West Africa. This is reasonable, considering that a large proportion of the ships arriving at the Southern Hemisphere came from Mozambique, Loango and Angola.
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Affiliation(s)
- Marina Muzzio
- Instituto Multidisciplinario de Biología Celular (IMBICE) CCT-La Plata CONICET-CICPBA, La Plata, Buenos Aires, Argentina
- Facultad de Ciencias Naturales y Museo, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina
| | - Josefina M. B. Motti
- Universidad Nacional del Centro de la Provincia de Buenos Aires, FACSO, NEIPHPA, Quequén, Buenos Aires, Argentina
| | - Paula B. Paz Sepulveda
- Instituto Multidisciplinario de Biología Celular (IMBICE) CCT-La Plata CONICET-CICPBA, La Plata, Buenos Aires, Argentina
| | - Muh-ching Yee
- Stanford University, Stanford, California, United States of America
| | - Thomas Cooke
- Stanford University, Stanford, California, United States of America
| | - María R. Santos
- Instituto Multidisciplinario de Biología Celular (IMBICE) CCT-La Plata CONICET-CICPBA, La Plata, Buenos Aires, Argentina
- Facultad de Ciencias Naturales y Museo, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina
| | | | - Emma L. Alfaro
- INECOA (Instituto de Ecorregiones Andinas) UNJu-CONICET, Instituto de Biología de la Altura, Universidad Nacional de Jujuy, San Salvador de Jujuy, Jujuy, Argentina
| | - Jose E. Dipierri
- INECOA (Instituto de Ecorregiones Andinas) UNJu-CONICET, Instituto de Biología de la Altura, Universidad Nacional de Jujuy, San Salvador de Jujuy, Jujuy, Argentina
| | - Graciela Bailliet
- Instituto Multidisciplinario de Biología Celular (IMBICE) CCT-La Plata CONICET-CICPBA, La Plata, Buenos Aires, Argentina
| | - Claudio M. Bravi
- Instituto Multidisciplinario de Biología Celular (IMBICE) CCT-La Plata CONICET-CICPBA, La Plata, Buenos Aires, Argentina
- Facultad de Ciencias Naturales y Museo, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina
| | | | - Eimear E. Kenny
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States
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21
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Luikart G, Kardos M, Hand BK, Rajora OP, Aitken SN, Hohenlohe PA. Population Genomics: Advancing Understanding of Nature. POPULATION GENOMICS 2018. [DOI: 10.1007/13836_2018_60] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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22
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AdmixPower: Statistical Power and Sample Size Estimation for Mapping Genetic Loci in Admixed Populations. Genetics 2017; 207:873-882. [PMID: 28951529 DOI: 10.1534/genetics.117.300312] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 09/10/2017] [Indexed: 11/18/2022] Open
Abstract
Admixed populations result from recent admixture of two or more ancestral populations with divergent allele frequencies. The genome of each admixed individual is a mosaic of haplotypes inherited from the ancestral populations. Despite the substantial work to assess power and sample size requirements for association mapping in genetically homogeneous populations of European ancestry, power and sample size estimation methods for mapping genes in genetically heterogeneous admixed populations such as African Americans are lacking. Admixture mapping is a method that traces the ancestral origin of disease-susceptibility genetic loci in the admixed population. We developed AdmixPower, a freely available tool set based on the open-source R software, to perform power and sample size analysis for genetically heterogeneous admixed populations considering continuous or dichotomous outcomes with a case-only or case-control study design. AdmixPower can be used to compute the sample size required to achieve investigator-specified statistical power under several key parameters including ancestry odds ratio, genotype risk ratio, parental risk ratio, an underlying genetic risk model, trait type, and admixture model (hybrid-isolation or continuous gene flow model). We demonstrate that differences in the key parameters in the admixed population results in substantial differences in the sample size required to achieve adequate power in admixture mapping studies. Our tool provides a resource for researchers to develop a strategy to minimize cost and maximize the success of identifying disease-susceptibility loci in an admixed population. R code used in the sample size and power analysis is freely available from https://research.cchmc.org/mershalab/Tools.html.
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23
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Profaizer T, Lázár-Molnár E, Pole A, Delgado JC, Kumánovics A. HLA genotyping using the Illumina HLA TruSight next-generation sequencing kits: A comparison. Int J Immunogenet 2017; 44:164-168. [DOI: 10.1111/iji.12322] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 03/21/2017] [Accepted: 04/20/2017] [Indexed: 12/25/2022]
Affiliation(s)
- T. Profaizer
- Department of Pathology; ARUP Institute for Clinical and Experimental Pathology; University of Utah School of Medicine; Salt Lake City UT USA
| | - E. Lázár-Molnár
- Department of Pathology; ARUP Institute for Clinical and Experimental Pathology; University of Utah School of Medicine; Salt Lake City UT USA
- Histocompatibility & Immunogenetics Lab oratory; University of Utah Healthcare; Salt Lake City UT USA
| | - A. Pole
- Histocompatibility & Immunogenetics Lab oratory; University of Utah Healthcare; Salt Lake City UT USA
| | - J. C. Delgado
- Department of Pathology; ARUP Institute for Clinical and Experimental Pathology; University of Utah School of Medicine; Salt Lake City UT USA
- Histocompatibility & Immunogenetics Lab oratory; University of Utah Healthcare; Salt Lake City UT USA
| | - A. Kumánovics
- Department of Pathology; ARUP Institute for Clinical and Experimental Pathology; University of Utah School of Medicine; Salt Lake City UT USA
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24
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Quach H, Quintana-Murci L. Living in an adaptive world: Genomic dissection of the genus Homo and its immune response. J Exp Med 2017; 214:877-894. [PMID: 28351985 PMCID: PMC5379985 DOI: 10.1084/jem.20161942] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2016] [Revised: 02/14/2017] [Accepted: 03/06/2017] [Indexed: 12/14/2022] Open
Abstract
More than a decade after the sequencing of the human genome, a deluge of genome-wide population data are generating a portrait of human genetic diversity at an unprecedented level of resolution. Genomic studies have provided new insight into the demographic and adaptive history of our species, Homo sapiens, including its interbreeding with other hominins, such as Neanderthals, and the ways in which natural selection, in its various guises, has shaped genome diversity. These studies, combined with functional genomic approaches, such as the mapping of expression quantitative trait loci, have helped to identify genes, functions, and mechanisms of prime importance for host survival and involved in phenotypic variation and differences in disease risk. This review summarizes new findings in this rapidly developing field, focusing on the human immune response. We discuss the importance of defining the genetic and evolutionary determinants driving immune response variation, and highlight the added value of population genomic approaches in settings relevant to immunity and infection.
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Affiliation(s)
- Hélène Quach
- Human Evolutionary Genetics Unit, Department of Genomes and Genetics, Institut Pasteur, 75015 Paris, France.,Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, 75015 Paris, France.,Centre National de la Recherche Scientifique, URA3012, 75015 Paris, France
| | - Lluis Quintana-Murci
- Human Evolutionary Genetics Unit, Department of Genomes and Genetics, Institut Pasteur, 75015 Paris, France .,Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, 75015 Paris, France.,Centre National de la Recherche Scientifique, URA3012, 75015 Paris, France
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Inference of multiple-wave population admixture by modeling decay of linkage disequilibrium with polynomial functions. Heredity (Edinb) 2017; 118:503-510. [PMID: 28198814 DOI: 10.1038/hdy.2017.5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Revised: 01/17/2017] [Accepted: 01/19/2017] [Indexed: 01/11/2023] Open
Abstract
To infer the histories of population admixture, one important challenge with methods based on the admixture linkage disequilibrium (ALD) is to remove the effect of source LD (SLD), which is directly inherited from source populations. In previous methods, only the decay curve of weighted LD between pairs of sites whose genetic distance were larger than a certain starting distance was fitted by single or multiple exponential functions, for the inference of recent single- or multiple-wave admixture. However, the effect of SLD has not been well defined and no tool has been developed to estimate the effect of SLD on weighted LD decay. In this study, we defined the SLD in the formularized weighted LD statistic under the two-way admixture model and proposed a polynomial spectrum (p-spectrum) to study the weighted SLD and weighted LD. We also found that reference populations could be used to reduce the SLD in weighted LD statistics. We further developed a method, iMAAPs, to infer multiple-wave admixture by fitting ALD using a p-spectrum. We evaluated the performance of iMAAPs under various admixture models in simulated data and applied iMAAPs to the analysis of genome-wide single nucleotide polymorphism data from the Human Genome Diversity Project and the HapMap Project. We showed that iMAAPs is a considerable improvement over other current methods and further facilitates the inference of histories of complex population admixtures.
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Rishishwar L, Wang L, Clayton EA, Mariño-Ramírez L, McDonald JF, Jordan IK. Population and clinical genetics of human transposable elements in the (post) genomic era. Mob Genet Elements 2017; 7:1-20. [PMID: 28228978 PMCID: PMC5305044 DOI: 10.1080/2159256x.2017.1280116] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 01/03/2017] [Accepted: 01/04/2017] [Indexed: 10/26/2022] Open
Abstract
Recent technological developments-in genomics, bioinformatics and high-throughput experimental techniques-are providing opportunities to study ongoing human transposable element (TE) activity at an unprecedented level of detail. It is now possible to characterize genome-wide collections of TE insertion sites for multiple human individuals, within and between populations, and for a variety of tissue types. Comparison of TE insertion site profiles between individuals captures the germline activity of TEs and reveals insertion site variants that segregate as polymorphisms among human populations, whereas comparison among tissue types ascertains somatic TE activity that generates cellular heterogeneity. In this review, we provide an overview of these new technologies and explore their implications for population and clinical genetic studies of human TEs. We cover both recent published results on human TE insertion activity as well as the prospects for future TE studies related to human evolution and health.
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Affiliation(s)
- Lavanya Rishishwar
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA; PanAmerican Bioinformatics Institute, Cali, Colombia; Applied Bioinformatics Laboratory, Atlanta, GA, USA
| | - Lu Wang
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA; PanAmerican Bioinformatics Institute, Cali, Colombia
| | - Evan A Clayton
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA; Ovarian Cancer Institute, Atlanta, GA, USA
| | - Leonardo Mariño-Ramírez
- PanAmerican Bioinformatics Institute, Cali, Colombia; National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - John F McDonald
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA; Ovarian Cancer Institute, Atlanta, GA, USA
| | - I King Jordan
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA; PanAmerican Bioinformatics Institute, Cali, Colombia; Applied Bioinformatics Laboratory, Atlanta, GA, USA
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Ruiz-Narváez EA, Sucheston-Campbell L, Bensen JT, Yao S, Haddad S, Haiman CA, Bandera EV, John EM, Bernstein L, Hu JJ, Ziegler RG, Deming SL, Olshan AF, Ambrosone CB, Palmer JR, Lunetta KL. Admixture Mapping of African-American Women in the AMBER Consortium Identifies New Loci for Breast Cancer and Estrogen-Receptor Subtypes. Front Genet 2016; 7:170. [PMID: 27708667 PMCID: PMC5030764 DOI: 10.3389/fgene.2016.00170] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Accepted: 09/07/2016] [Indexed: 12/13/2022] Open
Abstract
Recent genetic admixture coupled with striking differences in incidence of estrogen receptor (ER) breast cancer subtypes, as well as severity, between women of African and European ancestry, provides an excellent rationale for performing admixture mapping in African American women with breast cancer risk. We performed the largest breast cancer admixture mapping study with in African American women to identify novel genomic regions associated with the disease. We conducted a genome-wide admixture scan using 2,624 autosomal ancestry informative markers (AIMs) in 3,629 breast cancer cases (including 1,968 ER-positive, 1093 ER-negative, and 601 triple-negative) and 4,658 controls from the African American Breast Cancer Epidemiology and Risk (AMBER) Consortium, a collaborative study of four large geographically different epidemiological studies of breast cancer in African American women. We used an independent case-control study to test for SNP association in regions with genome-wide significant admixture signals. We found two novel genome-wide significant regions of excess African ancestry, 4p16.1 and 17q25.1, associated with ER-positive breast cancer. Two regions known to harbor breast cancer variants, 10q26 and 11q13, were also identified with excess of African ancestry. Fine-mapping of the identified genome-wide significant regions suggests the presence of significant genetic associations with ER-positive breast cancer in 4p16.1 and 11q13. In summary, we identified three novel genomic regions associated with breast cancer risk by ER status, suggesting that additional previously unidentified variants may contribute to the racial differences in breast cancer risk in the African American population.
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Affiliation(s)
| | - Lara Sucheston-Campbell
- College of Pharmacy, The Ohio State University, ColumbusOH, USA
- College of Veterinary Medicine, The Ohio State University, ColumbusOH, USA
| | - Jeannette T. Bensen
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel HillNC, USA
| | - Song Yao
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, BuffaloNY, USA
| | - Stephen Haddad
- Slone Epidemiology Center, Boston University, BostonMA, USA
| | - Christopher A. Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los AngelesCA, USA
| | | | - Esther M. John
- Cancer Prevention Institute of California, FremontCA, USA
| | - Leslie Bernstein
- Division of Cancer Etiology, Department of Population Science, Beckman Research Institute, City of Hope, DuarteCA, USA
| | - Jennifer J. Hu
- Sylvester Comprehensive Cancer Center and Department of Public Health Sciences, University of Miami Miller School of Medicine, MiamiFL, USA
| | - Regina G. Ziegler
- Epidemiology and Biostatistics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, BethesdaMD, USA
| | - Sandra L. Deming
- Vanderbilt Epidemiology Center, Vanderbilt University and the Vanderbilt-Ingram Cancer Center, NashvilleTN, USA
| | - Andrew F. Olshan
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel HillNC, USA
| | - Christine B. Ambrosone
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, BuffaloNY, USA
| | | | - Kathryn L. Lunetta
- Department of Biostatistics, Boston University School of Public Health, BostonMA, USA
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Wei YL, Sun QF, Li Q, Yi JL, Zhao L, Ou Y, Jiang L, Zhang T, Liu HB, Chen JG, Zhu BF, Ye J, Hu L, Li CX. Genetic structure and differentiation analysis of a Eurasian Uyghur population by use of 27 continental ancestry-informative SNPs. Int J Legal Med 2016; 130:897-903. [DOI: 10.1007/s00414-016-1335-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Accepted: 02/10/2016] [Indexed: 01/12/2023]
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Hejase HA, Liu KJ. Mapping the genomic architecture of adaptive traits with interspecific introgressive origin: a coalescent-based approach. BMC Genomics 2016; 17 Suppl 1:8. [PMID: 26819241 PMCID: PMC4895787 DOI: 10.1186/s12864-015-2298-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Recent studies of eukaryotes including human and Neandertal, mice, and butterflies have highlighted the major role that interspecific introgression has played in adaptive trait evolution. A common question arises in each case: what is the genomic architecture of the introgressed traits? One common approach that can be used to address this question is association mapping, which looks for genotypic markers that have significant statistical association with a trait. It is well understood that sample relatedness can be a confounding factor in association mapping studies if not properly accounted for. Introgression and other evolutionary processes (e.g., incomplete lineage sorting) typically introduce variation among local genealogies, which can also differ from global sample structure measured across all genomic loci. In contrast, state-of-the-art association mapping methods assume fixed sample relatedness across the genome, which can lead to spurious inference. We therefore propose a new association mapping method called Coal-Map, which uses coalescent-based models to capture local genealogical variation alongside global sample structure. Using simulated and empirical data reflecting a range of evolutionary scenarios, we compare the performance of Coal-Map against EIGENSTRAT, a leading association mapping method in terms of its popularity, power, and type I error control. Our empirical data makes use of hundreds of mouse genomes for which adaptive interspecific introgression has recently been described. We found that Coal-Map's performance is comparable or better than EIGENSTRAT in terms of statistical power and false positive rate. Coal-Map's performance advantage was greatest on model conditions that most closely resembled empirically observed scenarios of adaptive introgression. These conditions had: (1) causal SNPs contained in one or a few introgressed genomic loci and (2) varying rates of gene flow - from high rates to very low rates where incomplete lineage sorting dominated as a primary cause of local genealogical variation.
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Affiliation(s)
- Hussein A Hejase
- Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, 48824, MI, USA.
| | - Kevin J Liu
- Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, 48824, MI, USA.
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Wang H, Park SL, Stram DO, Haiman CA, Wilkens LR, Hecht SS, Kolonel LN, Murphy SE, Le Marchand L. Associations Between Genetic Ancestries and Nicotine Metabolism Biomarkers in the Multiethnic Cohort Study. Am J Epidemiol 2015; 182:945-51. [PMID: 26568573 DOI: 10.1093/aje/kwv138] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2014] [Accepted: 05/19/2015] [Indexed: 01/18/2023] Open
Abstract
Differences in internal dose of nicotine and tobacco-derived carcinogens among ethnic/racial groups have been observed. In this study, we explicitly examined the relationships between genetic ancestries (genome-wide average) and 19 tobacco-derived biomarkers in smokers from 3 admixed groups in the Multiethnic Cohort Study (1993-present), namely, African ancestry in African Americans (n = 362), Amerindian ancestry in Latinos (n = 437), and Asian and Native Hawaiian ancestries in Native Hawaiians (n = 300). After multiple comparison adjustment, both African and Asian ancestries were significantly related to a greater level of free cotinine; African ancestry was also significantly related to lower cotinine glucuronidation (P's < 0.00156). The predicted decrease in cotinine glucuronidation was 8.6% (P = 4.5 × 10(-6)) per a 20% increase in African ancestry. Follow-up admixture mapping revealed that African ancestry in a 12-Mb region on chromosome 4q was related to lower cotinine glucuronidation (P's < 2.7 × 10(-7), smallest P = 1.5 × 10(-9)), although this is the same region reported in our previous genome-wide association study. Our results implicate a genetic ancestral component in the observed ethnic/racial variation in nicotine metabolism. Further studies are needed to identify the underlying genetic variation that could potentially be ethnic/racial specific.
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Affiliation(s)
- Taras K Oleksyk
- Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia. University of Puerto Rico, Mayaguez, PR 00680, USA
| | - Vladimir Brukhin
- Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia
| | - Stephen J O'Brien
- Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia.
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Abstract
Background The human genome contains several active families of transposable elements (TE): Alu, L1 and SVA. Germline transposition of these elements can lead to polymorphic TE (polyTE) loci that differ between individuals with respect to the presence/absence of TE insertions. Limited sets of such polyTE loci have proven to be useful as markers of ancestry in human population genetic studies, but until this time it has not been possible to analyze the full genomic complement of TE polymorphisms in this way. Results For the first time here, we have performed a human population genetic analysis based on a genome-wide polyTE data set consisting of 16,192 loci genotyped in 2,504 individuals across 26 human populations. PolyTEs are found at very low frequencies, > 93 % of loci show < 5 % allele frequency, consistent with the deleteriousness of TE insertions. Nevertheless, polyTEs do show substantial geographic differentiation, with numerous group-specific polymorphic insertions. African populations have the highest numbers of polyTEs and show the highest levels of polyTE genetic diversity; Alu is the most numerous and the most diverse polyTE family. PolyTE genotypes were used to compute allele sharing distances between individuals and to relate them within and between human populations. Populations and continental groups show high coherence based on individuals’ polyTE genotypes, and human evolutionary relationships revealed by these genotypes are consistent with those seen for SNP-based genetic distances. The patterns of genetic diversity encoded by TE polymorphisms recapitulate broad patterns of human evolution and migration over the last 60–100,000 years. The utility of polyTEs as ancestry informative markers is further underscored by their ability to accurately predict both ancestry and admixture at the continental level. A genome-wide list of polyTE loci, along with their population group-specific allele frequencies and FST values, is provided as a resource for investigators who wish to develop panels of TE-based ancestry markers. Conclusions The genetic diversity represented by TE polymorphisms reflects known patterns of human evolution, and ensembles of polyTE loci are suitable for both ancestry and admixture analyses. The patterns of polyTE allelic diversity suggest the possibility that there may be a connection between TE-based genetic divergence and population-specific phenotypic differences. ᅟ ![]()
Electronic supplementary material The online version of this article (doi:10.1186/s13100-015-0052-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Lavanya Rishishwar
- School of Biology, Georgia Institute of Technology, 310 Ferst Drive, Atlanta, GA 30332-0230 USA ; PanAmerican Bioinformatics Institute, Cali, Valle del Cauca Colombia ; BIOS Centro de Bioinformática y Biología Computacional, Manizales, Caldas Colombia
| | - Carlos E Tellez Villa
- PanAmerican Bioinformatics Institute, Cali, Valle del Cauca Colombia ; Escuela de Ingeniería de Sistemas y Computación, Universidad del Valle, Santiago de Cali, Colombia
| | - I King Jordan
- School of Biology, Georgia Institute of Technology, 310 Ferst Drive, Atlanta, GA 30332-0230 USA ; PanAmerican Bioinformatics Institute, Cali, Valle del Cauca Colombia ; BIOS Centro de Bioinformática y Biología Computacional, Manizales, Caldas Colombia
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The Genome Russia project: closing the largest remaining omission on the world Genome map. Gigascience 2015; 4:53. [PMID: 26568821 PMCID: PMC4644275 DOI: 10.1186/s13742-015-0095-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Accepted: 11/05/2015] [Indexed: 11/20/2022] Open
Abstract
We are witnessing the great era of genome exploration of the world, as genetic variation in people is being detailed across multiple varied world populations in an effort unprecedented since the first human genome sequence appeared in 2001. However, these efforts have yet to produce a comprehensive mapping of humankind, because important regions of modern human civilization remain unexplored. The Genome Russia Project promises to fill one of the largest gaps, the expansive regions across the Russian Federation, informing not just medical genomics of the territories, but also the migration settlements of historic and pre-historic Eurasian peoples.
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Mersha TB. Mapping asthma-associated variants in admixed populations. Front Genet 2015; 6:292. [PMID: 26483834 PMCID: PMC4586512 DOI: 10.3389/fgene.2015.00292] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Accepted: 09/03/2015] [Indexed: 12/19/2022] Open
Abstract
Admixed populations arise when two or more previously isolated populations interbreed. Mapping asthma susceptibility loci in an admixed population using admixture mapping (AM) involves screening the genome of individuals of mixed ancestry for chromosomal regions that have a higher frequency of alleles from a parental population with higher asthma risk as compared with parental population with lower asthma risk. AM takes advantage of the admixture created in populations of mixed ancestry to identify genomic regions where an association exists between genetic ancestry and asthma (in contrast to between the genotype of the marker and asthma). The theory behind AM is that chromosomal segments of affected individuals contain a significantly higher-than-average proportion of alleles from the high-risk parental population and thus are more likely to harbor disease-associated loci. Criteria to evaluate the applicability of AM as a gene mapping approach include: (1) the prevalence of the disease differences in ancestral populations from which the admixed population was formed; (2) a measurable difference in disease-causing alleles between the parental populations; (3) reduced linkage disequilibrium (LD) between unlinked loci across chromosomes and strong LD between neighboring loci; (4) a set of markers with noticeable allele-frequency differences between parental populations that contributes to the admixed population (single nucleotide polymorphisms (SNPs) are the markers of choice because they are abundant, stable, relatively cheap to genotype, and informative with regard to the LD structure of chromosomal segments); and (5) there is an understanding of the extent of segmental chromosomal admixtures and their interactions with environmental factors. Although genome-wide association studies have contributed greatly to our understanding of the genetic components of asthma, the large and increasing degree of admixture in populations across the world create many challenges for further efforts to map disease-causing genes. This review, summarizes the historical context of admixed populations and AM, and considers current opportunities to use AM to map asthma genes. In addition, we provide an overview of the potential limitations and future directions of AM in biomedical research, including joint admixture and association mapping for asthma and asthma-related disorders.
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Affiliation(s)
- Tesfaye B Mersha
- Division of Asthma Research, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati Cincinnati, OH, USA
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Kassahun Y, Mattiangeli V, Ameni G, Hailu E, Aseffa A, Young DB, Hewinson RG, Vordermeier HM, Bradley DG. Admixture mapping of tuberculosis and pigmentation-related traits in an African-European hybrid cattle population. Front Genet 2015; 6:210. [PMID: 26124773 PMCID: PMC4467177 DOI: 10.3389/fgene.2015.00210] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2015] [Accepted: 05/30/2015] [Indexed: 12/03/2022] Open
Abstract
Admixture mapping affords a powerful approach to genetic mapping of complex traits and may be particularly suited to investigation in cattle where many breeds and populations are hybrids of the two divergent ancestral genomes, derived from Bos taurus and Bos indicus. Here we design a minimal genome wide SNP panel for tracking ancestry in recent hybrids of Holstein–Friesian and local Arsi zebu in a field sample from a region of high bovine tuberculosis (BTB) endemicity in the central Ethiopian highlands. We first demonstrate the utility of this approach by mapping the red coat color phenotype, uncovering a highly significant peak over the MC1R gene and a second peak with no previously known candidate gene. Secondly, we exploit the described differential susceptibility to BTB between the ancestral strains to identify a region in which Bos taurus ancestry associates, at suggestive significance, with skin test positivity. Interestingly, this association peak contains the toll-like receptor gene cluster on chromosome 6. With this work we have shown the potential of admixture mapping in hybrid domestic animals with divergent ancestral genomes, a recurring condition in domesticated species.
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Affiliation(s)
- Yonas Kassahun
- Smurfit Institute of Genetics, Trinity College Dublin Dublin, Ireland
| | | | - Gobena Ameni
- Aklilu Lemma Institute of Pathobiology, Addis Ababa University Addis Ababa, Ethiopia ; Armauer Hansen Research Institute Addis Ababa, Ethiopia
| | - Elena Hailu
- Armauer Hansen Research Institute Addis Ababa, Ethiopia
| | | | - Douglas B Young
- Centre for Molecular Microbiology and Infection, Imperial College London London, UK
| | - R Glyn Hewinson
- TB Research Group, Animal and Plant Health Agency Addlestone, UK
| | | | - Daniel G Bradley
- Smurfit Institute of Genetics, Trinity College Dublin Dublin, Ireland
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Chen W, Ren C, Qin H, Archer KJ, Ouyang W, Liu N, Chen X, Luo X, Zhu X, Sun S, Gao G. A Generalized Sequential Bonferroni Procedure for GWAS in Admixed Populations Incorporating Admixture Mapping Information into Association Tests. Hum Hered 2015; 79:80-92. [PMID: 26087776 DOI: 10.1159/000381474] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2014] [Accepted: 03/09/2015] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE To develop effective methods for GWAS in admixed populations such as African Americans. METHODS We show that, when testing the null hypothesis that the test SNP is not in background linkage disequilibrium with the causal variants, several existing methods cannot control well the family-wise error rate (FWER) in the strong sense in GWAS. These existing methods include association tests adjusting for global ancestry and joint association tests that combine statistics from admixture mapping tests and association tests that correct for local ancestry. Furthermore, we describe a generalized sequential Bonferroni (smooth-GSB) procedure for GWAS that incorporates smoothed weights calculated from admixture mapping tests into association tests that correct for local ancestry. We have applied the smooth-GSB procedure to analyses of GWAS data on American Africans from the Atherosclerosis Risk in Communities (ARIC) Study. RESULTS Our simulation studies indicate that the smooth-GSB procedure not only control the FWER, but also improves statistical power compared with association tests correcting for local ancestry. CONCLUSION The smooth-GSB procedure can result in a better performance than several existing methods for GWAS in admixed populations.
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Affiliation(s)
- Wenan Chen
- Department of Biostatistics, Virginia Commonwealth University, Richmond, Va., USA
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Johnson RC, Nelson GW, Zagury JF, Winkler CA. ALDsuite: Dense marker MALD using principal components of ancestral linkage disequilibrium. BMC Genet 2015; 16:23. [PMID: 25886794 PMCID: PMC4408589 DOI: 10.1186/s12863-015-0179-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2014] [Accepted: 02/06/2015] [Indexed: 01/04/2023] Open
Abstract
Background Mapping by admixture linkage disequilibrium (MALD) is a whole genome gene mapping method that uses LD from extended blocks of ancestry inherited from parental populations among admixed individuals to map associations for diseases, that vary in prevalence among human populations. The extended LD queried for marker association with ancestry results in a greatly reduced number of comparisons compared to standard genome wide association studies. As ancestral population LD tends to confound the analysis of admixture LD, the earliest algorithms for MALD required marker sets sufficiently sparse to lack significant ancestral LD between markers. However current genotyping technologies routinely provide dense SNP data, which convey more information than sparse sets, if this information can be efficiently used. There are currently no software solutions that offer both local ancestry inference using dense marker data and disease association statistics. Results We present here an R package, ALDsuite, which accounts for local LD using principal components of haplotypes from surrogate ancestral population data, and includes tools for quality control of data, MALD, downstream analysis of results and visualization graphics. Conclusions ALDsuite offers a fast, accurate estimation of global and local ancestry and comes bundled with the tools needed for MALD, from data quality control through mapping of and visualization of disease genes.
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Affiliation(s)
- Randall C Johnson
- BSP CCR Genetics Core, Leidos Biomedical Research, Inc, Frederick National Laboratory, Frederick, MD, 21702, USA. .,Chaire de Bioinformatique, Conservatiore National des Arts et Metieèrs, Paris, 75003, France.
| | - George W Nelson
- BSP CCR Genetics Core, Leidos Biomedical Research, Inc, Frederick National Laboratory, Frederick, MD, 21702, USA.
| | - Jean-Francois Zagury
- Chaire de Bioinformatique, Conservatiore National des Arts et Metieèrs, Paris, 75003, France.
| | - Cheryl A Winkler
- Basic Research Laboratory, Leidos Biomedical Research, Inc, Frederick National Laboratory, Frederick, MD, 21702, USA.
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Shetty PB, Tang H, Feng T, Tayo B, Morrison AC, Kardia SLR, Hanis CL, Arnett DK, Hunt SC, Boerwinkle E, Rao DC, Cooper RS, Risch N, Zhu X. Variants for HDL-C, LDL-C, and triglycerides identified from admixture mapping and fine-mapping analysis in African American families. CIRCULATION. CARDIOVASCULAR GENETICS 2015; 8:106-13. [PMID: 25552592 PMCID: PMC4378661 DOI: 10.1161/circgenetics.114.000481] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
BACKGROUND Admixture mapping of lipids was followed-up by family-based association analysis to identify variants for cardiovascular disease in African Americans. METHODS AND RESULTS The present study conducted admixture mapping analysis for total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. The analysis was performed in 1905 unrelated African American subjects from the National Heart, Lung and Blood Institute's Family Blood Pressure Program (FBPP). Regions showing admixture evidence were followed-up with family-based association analysis in 3556 African American subjects from the FBPP. The admixture mapping and family-based association analyses were adjusted for age, age(2), sex, body mass index, and genome-wide mean ancestry to minimize the confounding caused by population stratification. Regions that were suggestive of local ancestry association evidence were found on chromosomes 7 (low-density lipoprotein cholesterol), 8 (high-density lipoprotein cholesterol), 14 (triglycerides), and 19 (total cholesterol and triglycerides). In the fine-mapping analysis, 52 939 single-nucleotide polymorphisms (SNPs) were tested and 11 SNPs (8 independent SNPs) showed nominal significant association with high-density lipoprotein cholesterol (2 SNPs), low-density lipoprotein cholesterol (4 SNPs), and triglycerides (5 SNPs). The family data were used in the fine-mapping to identify SNPs that showed novel associations with lipids and regions, including genes with known associations for cardiovascular disease. CONCLUSIONS This study identified regions on chromosomes 7, 8, 14, and 19 and 11 SNPs from the fine-mapping analysis that were associated with high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides for further studies of cardiovascular disease in African Americans.
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Affiliation(s)
- Priya B Shetty
- From the Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH (P.B.S., T.F., X.Z.); Department of Genetics, Stanford University School of Medicine, Stanford, CA (H.T.); Department of Public Health Sciences, Loyola University of Chicago Stritch School of Medicine, Maywood, IL (B.T., R.S.C.); Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health (A.C.M., C.L.H., E.B.); Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (S.L.R.K.); Department of Epidemiology, University of Alabama at Birmingham School of Public Health (D.K.A.); Cardiovascular Genetics Division, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City (S.C.H.); Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO (D.C. Rao); and Department of Epidemiology and Biostatistics, University of California, San Francisco (N.R.)
| | - Hua Tang
- From the Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH (P.B.S., T.F., X.Z.); Department of Genetics, Stanford University School of Medicine, Stanford, CA (H.T.); Department of Public Health Sciences, Loyola University of Chicago Stritch School of Medicine, Maywood, IL (B.T., R.S.C.); Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health (A.C.M., C.L.H., E.B.); Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (S.L.R.K.); Department of Epidemiology, University of Alabama at Birmingham School of Public Health (D.K.A.); Cardiovascular Genetics Division, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City (S.C.H.); Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO (D.C. Rao); and Department of Epidemiology and Biostatistics, University of California, San Francisco (N.R.)
| | - Tao Feng
- From the Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH (P.B.S., T.F., X.Z.); Department of Genetics, Stanford University School of Medicine, Stanford, CA (H.T.); Department of Public Health Sciences, Loyola University of Chicago Stritch School of Medicine, Maywood, IL (B.T., R.S.C.); Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health (A.C.M., C.L.H., E.B.); Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (S.L.R.K.); Department of Epidemiology, University of Alabama at Birmingham School of Public Health (D.K.A.); Cardiovascular Genetics Division, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City (S.C.H.); Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO (D.C. Rao); and Department of Epidemiology and Biostatistics, University of California, San Francisco (N.R.)
| | - Bamidele Tayo
- From the Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH (P.B.S., T.F., X.Z.); Department of Genetics, Stanford University School of Medicine, Stanford, CA (H.T.); Department of Public Health Sciences, Loyola University of Chicago Stritch School of Medicine, Maywood, IL (B.T., R.S.C.); Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health (A.C.M., C.L.H., E.B.); Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (S.L.R.K.); Department of Epidemiology, University of Alabama at Birmingham School of Public Health (D.K.A.); Cardiovascular Genetics Division, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City (S.C.H.); Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO (D.C. Rao); and Department of Epidemiology and Biostatistics, University of California, San Francisco (N.R.)
| | - Alanna C Morrison
- From the Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH (P.B.S., T.F., X.Z.); Department of Genetics, Stanford University School of Medicine, Stanford, CA (H.T.); Department of Public Health Sciences, Loyola University of Chicago Stritch School of Medicine, Maywood, IL (B.T., R.S.C.); Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health (A.C.M., C.L.H., E.B.); Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (S.L.R.K.); Department of Epidemiology, University of Alabama at Birmingham School of Public Health (D.K.A.); Cardiovascular Genetics Division, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City (S.C.H.); Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO (D.C. Rao); and Department of Epidemiology and Biostatistics, University of California, San Francisco (N.R.)
| | - Sharon L R Kardia
- From the Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH (P.B.S., T.F., X.Z.); Department of Genetics, Stanford University School of Medicine, Stanford, CA (H.T.); Department of Public Health Sciences, Loyola University of Chicago Stritch School of Medicine, Maywood, IL (B.T., R.S.C.); Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health (A.C.M., C.L.H., E.B.); Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (S.L.R.K.); Department of Epidemiology, University of Alabama at Birmingham School of Public Health (D.K.A.); Cardiovascular Genetics Division, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City (S.C.H.); Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO (D.C. Rao); and Department of Epidemiology and Biostatistics, University of California, San Francisco (N.R.)
| | - Craig L Hanis
- From the Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH (P.B.S., T.F., X.Z.); Department of Genetics, Stanford University School of Medicine, Stanford, CA (H.T.); Department of Public Health Sciences, Loyola University of Chicago Stritch School of Medicine, Maywood, IL (B.T., R.S.C.); Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health (A.C.M., C.L.H., E.B.); Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (S.L.R.K.); Department of Epidemiology, University of Alabama at Birmingham School of Public Health (D.K.A.); Cardiovascular Genetics Division, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City (S.C.H.); Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO (D.C. Rao); and Department of Epidemiology and Biostatistics, University of California, San Francisco (N.R.)
| | - Donna K Arnett
- From the Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH (P.B.S., T.F., X.Z.); Department of Genetics, Stanford University School of Medicine, Stanford, CA (H.T.); Department of Public Health Sciences, Loyola University of Chicago Stritch School of Medicine, Maywood, IL (B.T., R.S.C.); Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health (A.C.M., C.L.H., E.B.); Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (S.L.R.K.); Department of Epidemiology, University of Alabama at Birmingham School of Public Health (D.K.A.); Cardiovascular Genetics Division, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City (S.C.H.); Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO (D.C. Rao); and Department of Epidemiology and Biostatistics, University of California, San Francisco (N.R.)
| | - Steven C Hunt
- From the Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH (P.B.S., T.F., X.Z.); Department of Genetics, Stanford University School of Medicine, Stanford, CA (H.T.); Department of Public Health Sciences, Loyola University of Chicago Stritch School of Medicine, Maywood, IL (B.T., R.S.C.); Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health (A.C.M., C.L.H., E.B.); Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (S.L.R.K.); Department of Epidemiology, University of Alabama at Birmingham School of Public Health (D.K.A.); Cardiovascular Genetics Division, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City (S.C.H.); Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO (D.C. Rao); and Department of Epidemiology and Biostatistics, University of California, San Francisco (N.R.)
| | - Eric Boerwinkle
- From the Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH (P.B.S., T.F., X.Z.); Department of Genetics, Stanford University School of Medicine, Stanford, CA (H.T.); Department of Public Health Sciences, Loyola University of Chicago Stritch School of Medicine, Maywood, IL (B.T., R.S.C.); Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health (A.C.M., C.L.H., E.B.); Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (S.L.R.K.); Department of Epidemiology, University of Alabama at Birmingham School of Public Health (D.K.A.); Cardiovascular Genetics Division, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City (S.C.H.); Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO (D.C. Rao); and Department of Epidemiology and Biostatistics, University of California, San Francisco (N.R.)
| | - Dabeeru C Rao
- From the Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH (P.B.S., T.F., X.Z.); Department of Genetics, Stanford University School of Medicine, Stanford, CA (H.T.); Department of Public Health Sciences, Loyola University of Chicago Stritch School of Medicine, Maywood, IL (B.T., R.S.C.); Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health (A.C.M., C.L.H., E.B.); Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (S.L.R.K.); Department of Epidemiology, University of Alabama at Birmingham School of Public Health (D.K.A.); Cardiovascular Genetics Division, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City (S.C.H.); Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO (D.C. Rao); and Department of Epidemiology and Biostatistics, University of California, San Francisco (N.R.)
| | - Richard S Cooper
- From the Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH (P.B.S., T.F., X.Z.); Department of Genetics, Stanford University School of Medicine, Stanford, CA (H.T.); Department of Public Health Sciences, Loyola University of Chicago Stritch School of Medicine, Maywood, IL (B.T., R.S.C.); Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health (A.C.M., C.L.H., E.B.); Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (S.L.R.K.); Department of Epidemiology, University of Alabama at Birmingham School of Public Health (D.K.A.); Cardiovascular Genetics Division, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City (S.C.H.); Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO (D.C. Rao); and Department of Epidemiology and Biostatistics, University of California, San Francisco (N.R.)
| | - Neil Risch
- From the Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH (P.B.S., T.F., X.Z.); Department of Genetics, Stanford University School of Medicine, Stanford, CA (H.T.); Department of Public Health Sciences, Loyola University of Chicago Stritch School of Medicine, Maywood, IL (B.T., R.S.C.); Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health (A.C.M., C.L.H., E.B.); Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (S.L.R.K.); Department of Epidemiology, University of Alabama at Birmingham School of Public Health (D.K.A.); Cardiovascular Genetics Division, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City (S.C.H.); Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO (D.C. Rao); and Department of Epidemiology and Biostatistics, University of California, San Francisco (N.R.)
| | - Xiaofeng Zhu
- From the Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH (P.B.S., T.F., X.Z.); Department of Genetics, Stanford University School of Medicine, Stanford, CA (H.T.); Department of Public Health Sciences, Loyola University of Chicago Stritch School of Medicine, Maywood, IL (B.T., R.S.C.); Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health (A.C.M., C.L.H., E.B.); Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (S.L.R.K.); Department of Epidemiology, University of Alabama at Birmingham School of Public Health (D.K.A.); Cardiovascular Genetics Division, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City (S.C.H.); Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO (D.C. Rao); and Department of Epidemiology and Biostatistics, University of California, San Francisco (N.R.).
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Li YR, Keating BJ. Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations. Genome Med 2014; 6:91. [PMID: 25473427 PMCID: PMC4254423 DOI: 10.1186/s13073-014-0091-5] [Citation(s) in RCA: 124] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Genome-wide association studies (GWASs) are the method most often used by geneticists to interrogate the human genome, and they provide a cost-effective way to identify the genetic variants underpinning complex traits and diseases. Most initial GWASs have focused on genetically homogeneous cohorts from European populations given the limited availability of ethnic minority samples and so as to limit population stratification effects. Transethnic studies have been invaluable in explaining the heritability of common quantitative traits, such as height, and in examining the genetic architecture of complex diseases, such as type 2 diabetes. They provide an opportunity for large-scale signal replication in independent populations and for cross-population meta-analyses to boost statistical power. In addition, transethnic GWASs enable prioritization of candidate genes, fine-mapping of functional variants, and potentially identification of SNPs associated with disease risk in admixed populations, by taking advantage of natural differences in genomic linkage disequilibrium across ethnically diverse populations. Recent efforts to assess the biological function of variants identified by GWAS have highlighted the need for large-scale replication, meta-analyses and fine-mapping across worldwide populations of ethnically diverse genetic ancestries. Here, we review recent advances and new approaches that are important to consider when performing, designing or interpreting transethnic GWASs, and we highlight existing challenges, such as the limited ability to handle heterogeneity in linkage disequilibrium across populations and limitations in dissecting complex architectures, such as those found in recently admixed populations.
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Affiliation(s)
- Yun R Li
- />The Center for Applied Genomics, 1,016 Abramson Building, The Children’s Hospital of Philadelphia, Philadelphia, 19104 PA USA
- />Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
| | - Brendan J Keating
- />The Center for Applied Genomics, 1,016 Abramson Building, The Children’s Hospital of Philadelphia, Philadelphia, 19104 PA USA
- />Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
- />Department of Surgery, Division of Transplantation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
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Bhatia G, Tandon A, Patterson N, Aldrich M, Ambrosone C, Amos C, Bandera E, Berndt S, Bernstein L, Blot W, Bock C, Caporaso N, Casey G, Deming S, Diver W, Gapstur S, Gillanders E, Harris C, Henderson B, Ingles S, Isaacs W, De Jager P, John E, Kittles R, Larkin E, McNeill L, Millikan R, Murphy A, Neslund-Dudas C, Nyante S, Press M, Rodriguez-Gil J, Rybicki B, Schwartz A, Signorello L, Spitz M, Strom S, Tucker M, Wiencke J, Witte J, Wu X, Yamamura Y, Zanetti K, Zheng W, Ziegler R, Chanock S, Haiman C, Reich D, Price A. Genome-wide scan of 29,141 African Americans finds no evidence of directional selection since admixture. Am J Hum Genet 2014; 95:437-44. [PMID: 25242497 DOI: 10.1016/j.ajhg.2014.08.011] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Accepted: 08/22/2014] [Indexed: 10/24/2022] Open
Abstract
The extent of recent selection in admixed populations is currently an unresolved question. We scanned the genomes of 29,141 African Americans and failed to find any genome-wide-significant deviations in local ancestry, indicating no evidence of selection influencing ancestry after admixture. A recent analysis of data from 1,890 African Americans reported that there was evidence of selection in African Americans after their ancestors left Africa, both before and after admixture. Selection after admixture was reported on the basis of deviations in local ancestry, and selection before admixture was reported on the basis of allele-frequency differences between African Americans and African populations. The local-ancestry deviations reported by the previous study did not replicate in our very large sample, and we show that such deviations were expected purely by chance, given the number of hypotheses tested. We further show that the previous study's conclusion of selection in African Americans before admixture is also subject to doubt. This is because the FST statistics they used were inflated and because true signals of unusual allele-frequency differences between African Americans and African populations would be best explained by selection that occurred in Africa prior to migration to the Americas.
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Schielzeth H, Husby A. Challenges and prospects in genome-wide quantitative trait loci mapping of standing genetic variation in natural populations. Ann N Y Acad Sci 2014; 1320:35-57. [PMID: 24689944 DOI: 10.1111/nyas.12397] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
A considerable challenge in evolutionary genetics is to understand the genetic mechanisms that facilitate or impede evolutionary adaptation in natural populations. For this, we must understand the genetic loci contributing to trait variation and the selective forces acting on them. The decreased costs and increased feasibility of obtaining genotypic data on a large number of individuals have greatly facilitated gene mapping in natural populations, particularly because organisms whose genetics have been historically difficult to study are now within reach. Here we review the methods available to evolutionary ecologists interested in dissecting the genetic basis of traits in natural populations. Our focus lies on standing genetic variation in outbred populations. We present an overview of the current state of research in the field, covering studies on both plants and animals. We also draw attention to particular challenges associated with the discovery of quantitative trait loci and discuss parallels to studies on crops, livestock, and humans. Finally, we point to some likely future developments in genetic mapping studies.
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Affiliation(s)
- Holger Schielzeth
- Department of Evolutionary Biology, Bielefeld University, Bielefeld, Germany
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Admixture fine-mapping in African Americans implicates XAF1 as a possible sarcoidosis risk gene. PLoS One 2014; 9:e92646. [PMID: 24663488 PMCID: PMC3963923 DOI: 10.1371/journal.pone.0092646] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Accepted: 02/25/2014] [Indexed: 12/23/2022] Open
Abstract
Sarcoidosis is a complex, multi-organ granulomatous disease with a likely genetic component. West African ancestry confers a higher risk for sarcoidosis than European ancestry. Admixture mapping provides the most direct method to locate genes that underlie such ethnic variation in disease risk. We sought to identify genetic risk variants within four previously-identified ancestry-associated regions—6p24.3–p12.1, 17p13.3–13.1, 2p13.3–q12.1, and 6q23.3–q25.2—in a sample of 2,727 African Americans. We used logistic regression fit by generalized estimating equations and the MIX score statistic to determine which variants within ancestry-associated regions were associated with risk and responsible for the admixture signal. Fine mapping was performed by imputation, based on a previous genome-wide association study; significant variants were validated by direct genotyping. Within the 6p24.3–p12.1 locus, the most significant ancestry-adjusted SNP was rs74318745 (p = 9.4*10−11), an intronic SNP within the HLA-DRA gene that did not solely explain the admixture signal, indicating the presence of more than a single risk variant within this well-established sarcoidosis risk region. The locus on chromosome 17p13.3–13.1 revealed a novel sarcoidosis risk SNP, rs6502976 (p = 9.5*10−6), within intron 5 of the gene X-linked Inhibitor of Apoptosis Associated Factor 1 (XAF1) that accounted for the majority of the admixture linkage signal. Immunohistochemical expression studies demonstrated lack of expression of XAF1 and a corresponding high level of expression of its downstream target, X-linked Inhibitor of Apoptosis (XIAP) in sarcoidosis granulomas. In conclusion, ancestry and association fine mapping revealed a novel sarcoidosis susceptibility gene, XAF1, which has not been identified by previous genome-wide association studies. Based on the known biology of the XIAP/XAF1 apoptosis pathway and the differential expression patterns of XAF1 and XIAP in sarcoidosis granulomas, we suggest that this pathway may play a role in the maintenance of sarcoidosis granulomas.
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Abstract
We present a two-layer hidden Markov model to detect the structure of haplotypes for unrelated individuals. This allows us to model two scales of linkage disequilibrium (one within a group of haplotypes and one between groups), thereby taking advantage of rich haplotype information to infer local ancestry of admixed individuals. Our method outperforms competing state-of-the-art methods, particularly for regions of small ancestral track lengths. Applying our method to Mexican samples in HapMap3, we found two regions on chromosomes 6 and 8 that show significant departure of local ancestry from the genome-wide average. A software package implementing the methods described in this article is freely available at http://bcm.edu/cnrc/mcmcmc.
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Geographical, environmental and pathophysiological influences on the human blood transcriptome. CURRENT GENETIC MEDICINE REPORTS 2013; 1:203-211. [PMID: 25830076 DOI: 10.1007/s40142-013-0028-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Gene expression variation provides a read-out of both genetic and environmental influences on gene activity. Geographical, genomic and sociogenomic studies have highlighted how life circumstances of an individual modify the expression of hundreds and in some cases thousands of genes in a co-ordinated manner. This review places such results in the context of a conserved set of 90 transcripts known as Blood Informative Transcripts (BIT) that capture the major conserved components of variation in the peripheral blood transcriptome. Pathophysiological states are also shown to associate with the perturbation of transcript abundance along the major axes. Discussion of false negative rates leads us to argue that simple significance thresholds provide a biased perspective on assessment of differential expression that may cloud the interpretation of studies with small sample sizes.
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Chimusa ER, Daya M, Möller M, Ramesar R, Henn BM, van Helden PD, Mulder NJ, Hoal EG. Determining ancestry proportions in complex admixture scenarios in South Africa using a novel proxy ancestry selection method. PLoS One 2013; 8:e73971. [PMID: 24066090 PMCID: PMC3774743 DOI: 10.1371/journal.pone.0073971] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2013] [Accepted: 07/25/2013] [Indexed: 02/03/2023] Open
Abstract
Admixed populations can make an important contribution to the discovery of disease susceptibility genes if the parental populations exhibit substantial variation in susceptibility. Admixture mapping has been used successfully, but is not designed to cope with populations that have more than two or three ancestral populations. The inference of admixture proportions and local ancestry and the imputation of missing genotypes in admixed populations are crucial in both understanding variation in disease and identifying novel disease loci. These inferences make use of reference populations, and accuracy depends on the choice of ancestral populations. Using an insufficient or inaccurate ancestral panel can result in erroneously inferred ancestry and affect the detection power of GWAS and meta-analysis when using imputation. Current algorithms are inadequate for multi-way admixed populations. To address these challenges we developed PROXYANC, an approach to select the best proxy ancestral populations. From the simulation of a multi-way admixed population we demonstrate the capability and accuracy of PROXYANC and illustrate the importance of the choice of ancestry in both estimating admixture proportions and imputing missing genotypes. We applied this approach to a complex, uniquely admixed South African population. Using genome-wide SNP data from over 764 individuals, we accurately estimate the genetic contributions from the best ancestral populations: isiXhosa [Formula: see text], ‡Khomani SAN [Formula: see text], European [Formula: see text], Indian [Formula: see text], and Chinese [Formula: see text]. We also demonstrate that the ancestral allele frequency differences correlate with increased linkage disequilibrium in the South African population, which originates from admixture events rather than population bottlenecks. NOMENCLATURE The collective term for people of mixed ancestry in southern Africa is "Coloured," and this is officially recognized in South Africa as a census term, and for self-classification. Whilst we acknowledge that some cultures may use this term in a derogatory manner, these connotations are not present in South Africa, and are certainly not intended here.
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Affiliation(s)
- Emile R. Chimusa
- Computational Biology Group, Department of Clinical Laboratory Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, Cape Town, South Africa
| | - Michelle Daya
- MRC Centre for Molecular and Cellular Biology, DST/NRF Centre of Excellence for Biomedical TB Research, Division of Molecular Biology and Human Genetics, Faculty of Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Marlo Möller
- MRC Centre for Molecular and Cellular Biology, DST/NRF Centre of Excellence for Biomedical TB Research, Division of Molecular Biology and Human Genetics, Faculty of Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Raj Ramesar
- MRC Human Genetics Research Unit, Division of Human Genetics, Department of Clinical Laboratory Sciences, Institute for Infectious Diseases and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Brenna M. Henn
- Department of Genetics, Stanford University, Stanford, California, United States of America
- Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York, United States of America
| | - Paul D. van Helden
- MRC Centre for Molecular and Cellular Biology, DST/NRF Centre of Excellence for Biomedical TB Research, Division of Molecular Biology and Human Genetics, Faculty of Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Nicola J. Mulder
- Computational Biology Group, Department of Clinical Laboratory Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, Cape Town, South Africa
| | - Eileen G. Hoal
- MRC Centre for Molecular and Cellular Biology, DST/NRF Centre of Excellence for Biomedical TB Research, Division of Molecular Biology and Human Genetics, Faculty of Health Sciences, Stellenbosch University, Tygerberg, South Africa
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Abstract
Admixture mapping is a popular tool to identify regions of the genome associated with traits in a recently admixed population. Existing methods have been developed primarily for identification of a single locus influencing a dichotomous trait within a case-control study design. We propose a generalized admixture mapping (GLEAM) approach, a flexible and powerful regression method for both quantitative and qualitative traits, which is able to test for association between the trait and local ancestries in multiple loci simultaneously and adjust for covariates. The new method is based on the generalized linear model and uses a quadratic normal moment prior to incorporate admixture prior information. Through simulation, we demonstrate that GLEAM achieves lower type I error rate and higher power than ANCESTRYMAP both for qualitative traits and more significantly for quantitative traits. We applied GLEAM to genome-wide SNP data from the Illumina African American panel derived from a cohort of black women participating in the Healthy Pregnancy, Healthy Baby study and identified a locus on chromosome 2 associated with the averaged maternal mean arterial pressure during 24 to 28 weeks of pregnancy.
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Liu J, Lewinger JP, Gilliland FD, Gauderman WJ, Conti DV. Confounding and heterogeneity in genetic association studies with admixed populations. Am J Epidemiol 2013; 177:351-60. [PMID: 23334005 DOI: 10.1093/aje/kws234] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Association studies among admixed populations pose many challenges including confounding of genetic effects due to population substructure and heterogeneity due to different patterns of linkage disequilibrium (LD). We use simulations to investigate controlling for confounding by indicators of global ancestry and the impact of including a covariate for local ancestry. In addition, we investigate the use of an interaction term between a single-nucleotide polymorphism (SNP) and local ancestry to capture heterogeneity in SNP effects. Although adjustment for global ancestry can control for confounding, additional adjustment for local ancestry may increase power when the induced admixture LD is in the opposite direction as the LD in the ancestral population. However, if the induced LD is in the same direction, there is the potential for reduced power because of overadjustment. Furthermore, the inclusion of a SNP by local ancestry interaction term can increase power when there is substantial differential LD between ancestry populations. We examine these approaches in genome-wide data using the University of Southern California's Children's Health Study investigating asthma risk. The analysis highlights rs10519951 (P = 8.5 × 10(-7)), a SNP lacking any evidence of association from a conventional analysis (P = 0.5).
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Affiliation(s)
- Jinghua Liu
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
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Variants in CXADR and F2RL1 are associated with blood pressure and obesity in African-Americans in regions identified through admixture mapping. J Hypertens 2013; 30:1970-6. [PMID: 22914544 DOI: 10.1097/hjh.0b013e3283578c80] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE Genetic variants in 296 genes in regions identified through admixture mapping of hypertension, BMI, and lipids were assessed for association with hypertension, blood pressure (BP), BMI, and high-density lipoprotein cholesterol (HDL-C). METHODS This study identified coding SNPs identified from HapMap2 data that were located in genes on chromosomes 5, 6, 8, and 21, wherein ancestry association evidence for hypertension, BMI, or HDL-C was identified in previous admixture mapping studies. Genotyping was performed in 1733 unrelated African-Americans from the National Heart, Lung and Blood Institute's Family Blood Pressure Project, and gene-based association analyses were conducted for hypertension, SBP, DBP, BMI, and HDL-C. A gene score based on the number of minor alleles of each SNP in a gene was created and used for gene-based regression analyses, adjusting for age, age, sex, local marker ancestry, and BMI, as applicable. An individual's African ancestry estimated from 2507 ancestry-informative markers was also adjusted for to eliminate any confounding due to population stratification. RESULTS CXADR (rs437470) on chromosome 21 was associated with SBP and DBP with or without adjusting for local ancestry (P < 0.0006). F2RL1 (rs631465) on chromosome 5 was associated with BMI (P = 0.0005). Local ancestry in these regions was associated with the respective traits as well. CONCLUSION This study suggests that CXADR and F2RL1 likely play important roles in BP and obesity variation, respectively; and these findings are consistent with those of other studies, so replication and functional analyses are necessary.
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Slavov G, Allison G, Bosch M. Advances in the genetic dissection of plant cell walls: tools and resources available in Miscanthus. FRONTIERS IN PLANT SCIENCE 2013; 4:217. [PMID: 23847628 PMCID: PMC3701120 DOI: 10.3389/fpls.2013.00217] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2013] [Accepted: 06/05/2013] [Indexed: 05/19/2023]
Abstract
Tropical C4 grasses from the genus Miscanthus are believed to have great potential as biomass crops. However, Miscanthus species are essentially undomesticated, and genetic, molecular and bioinformatics tools are in very early stages of development. Furthermore, similar to other crops targeted as lignocellulosic feedstocks, the efficient utilization of biomass is hampered by our limited knowledge of the structural organization of the plant cell wall and the underlying genetic components that control this organization. The Institute of Biological, Environmental and Rural Sciences (IBERS) has assembled an extensive collection of germplasm for several species of Miscanthus. In addition, an integrated, multidisciplinary research programme at IBERS aims to inform accelerated breeding for biomass productivity and composition, while also generating fundamental knowledge. Here we review recent advances with respect to the genetic characterization of the cell wall in Miscanthus. First, we present a summary of recent and on-going biochemical studies, including prospects and limitations for the development of powerful phenotyping approaches. Second, we review current knowledge about genetic variation for cell wall characteristics of Miscanthus and illustrate how phenotypic data, combined with high-density arrays of single-nucleotide polymorphisms, are being used in genome-wide association studies to generate testable hypotheses and guide biological discovery. Finally, we provide an overview of the current knowledge about the molecular biology of cell wall biosynthesis in Miscanthus and closely related grasses, discuss the key conceptual and technological bottlenecks, and outline the short-term prospects for progress in this field.
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Affiliation(s)
- Gancho Slavov
- *Correspondence: Gancho Slavov, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Plas Gogerddan, Aberystwyth, Ceredigion, Wales SY23 3EB, UK e-mail:
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Jin W, Wang S, Wang H, Jin L, Xu S. Exploring population admixture dynamics via empirical and simulated genome-wide distribution of ancestral chromosomal segments. Am J Hum Genet 2012; 91:849-62. [PMID: 23103229 DOI: 10.1016/j.ajhg.2012.09.008] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2012] [Revised: 06/01/2012] [Accepted: 09/11/2012] [Indexed: 12/22/2022] Open
Abstract
The processes of genetic admixture determine the haplotype structure and linkage disequilibrium patterns of the admixed population, which is important for medical and evolutionary studies. However, most previous studies do not consider the inherent complexity of admixture processes. Here we proposed two approaches to explore population admixture dynamics, and we demonstrated, by analyzing genome-wide empirical and simulated data, that the approach based on the distribution of chromosomal segments of distinct ancestry (CSDAs) was more powerful than that based on the distribution of individual ancestry proportions. Analysis of 1,890 African Americans showed that a continuous gene flow model, in which the African American population continuously received gene flow from European populations over about 14 generations, best explained the admixture dynamics of African Americans among several putative models. Interestingly, we observed that some African Americans had much more European ancestry than the simulated samples, indicating substructures of local ancestries in African Americans that could have been caused by individuals from some particular lineages having repeatedly admixed with people of European ancestry. In contrast, the admixture dynamics of Mexicans could be explained by a gradual admixture model in which the Mexican population continuously received gene flow from both European and Amerindian populations over about 24 generations. Our results also indicated that recent gene flows from Sub-Saharan Africans have contributed to the gene pool of Middle Eastern populations such as Mozabite, Bedouin, and Palestinian. In summary, this study not only provides approaches to explore population admixture dynamics, but also advances our understanding on population history of African Americans, Mexicans, and Middle Eastern populations.
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Affiliation(s)
- Wenfei Jin
- Max Planck Independent Research Group on Population Genomics, Chinese Academy of Sciences and Max Planck Society (CAS-MPG) Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
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