0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Joint Analysis of Phenotypic and Genomic Diversity Sheds Light on the Evolution of Xenobiotic Metabolism in Humans

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Variation in genes involved in the absorption, distribution, metabolism, and excretion of drugs (ADME) can influence individual response to a therapeutic treatment. The study of ADME genetic diversity in human populations has led to evolutionary hypotheses of adaptation to distinct chemical environments. Population differentiation in measured drug metabolism phenotypes is, however, scarcely documented, often indirectly estimated via genotype-predicted phenotypes. We administered seven probe compounds devised to target six cytochrome P450 enzymes and the P-glycoprotein (P-gp) activity to assess phenotypic variation in four populations along a latitudinal transect spanning over Africa, the Middle East, and Europe (349 healthy Ethiopian, Omani, Greek, and Czech volunteers). We demonstrate significant population differentiation for all phenotypes except the one measuring CYP2D6 activity. Genome-wide association studies (GWAS) evidenced that the variability of phenotypes measuring CYP2B6, CYP2C9, CYP2C19, and CYP2D6 activity was associated with genetic variants linked to the corresponding encoding genes, and additional genes for the latter three. Instead, GWAS did not indicate any association between genetic diversity and the phenotypes measuring CYP1A2, CYP3A4, and P-gp activity. Genome scans of selection highlighted multiple candidate regions, a few of which included ADME genes, but none overlapped with the GWAS candidates. Our results suggest that different mechanisms have been shaping the evolution of these phenotypes, including phenotypic plasticity, and possibly some form of balancing selection. We discuss how these contrasting results highlight the diverse evolutionary trajectories of ADME genes and proteins, consistent with the wide spectrum of both endogenous and exogenous molecules that are their substrates.

          Related collections

          Most cited references117

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          A global reference for human genetic variation

          The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Fast model-based estimation of ancestry in unrelated individuals.

            Population stratification has long been recognized as a confounding factor in genetic association studies. Estimated ancestries, derived from multi-locus genotype data, can be used to perform a statistical correction for population stratification. One popular technique for estimation of ancestry is the model-based approach embodied by the widely applied program structure. Another approach, implemented in the program EIGENSTRAT, relies on Principal Component Analysis rather than model-based estimation and does not directly deliver admixture fractions. EIGENSTRAT has gained in popularity in part owing to its remarkable speed in comparison to structure. We present a new algorithm and a program, ADMIXTURE, for model-based estimation of ancestry in unrelated individuals. ADMIXTURE adopts the likelihood model embedded in structure. However, ADMIXTURE runs considerably faster, solving problems in minutes that take structure hours. In many of our experiments, we have found that ADMIXTURE is almost as fast as EIGENSTRAT. The runtime improvements of ADMIXTURE rely on a fast block relaxation scheme using sequential quadratic programming for block updates, coupled with a novel quasi-Newton acceleration of convergence. Our algorithm also runs faster and with greater accuracy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program FRAPPE. Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture coefficients and ancestral allele frequencies are as accurate as structure's Bayesian estimates. On real-world data sets, ADMIXTURE's estimates are directly comparable to those from structure and EIGENSTRAT. Taken together, our results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Next-generation genotype imputation service and methods.

              Genotype imputation is a key component of genetic association studies, where it increases power, facilitates meta-analysis, and aids interpretation of signals. Genotype imputation is computationally demanding and, with current tools, typically requires access to a high-performance computing cluster and to a reference panel of sequenced genomes. Here we describe improvements to imputation machinery that reduce computational requirements by more than an order of magnitude with no loss of accuracy in comparison to standard imputation tools. We also describe a new web-based service for imputation that facilitates access to new reference panels and greatly improves user experience and productivity.
                Bookmark

                Author and article information

                Contributors
                Role: Associate Editor
                Journal
                Genome Biol Evol
                Genome Biol Evol
                gbe
                Genome Biology and Evolution
                Oxford University Press (US )
                1759-6653
                December 2022
                29 November 2022
                29 November 2022
                : 14
                : 12
                : evac167
                Affiliations
                Department of Genetics and Evolution (GENEV), University of Geneva , Geneva, Switzerland
                Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals and University of Geneva , Geneva, Switzerland
                Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals and University of Geneva , Geneva, Switzerland
                Institute of Archaeology of the Academy of Sciences of the Czech Republic , Prague, Czech Republic
                Department of Pharmacology and Clinical Pharmacy, College of Health Sciences, Addis Ababa University , Addis Ababa, Ethiopia
                College of Pharmacy, National University of Science and Technology , Muscat, Sultanate of Oman
                Department of Molecular Biology and Genetics, Democritus University of Thrace , Alexandroupolis, Greece
                Department of Medicine, Democritus University of Thrace Medical School , Alexandroupolis, Greece
                Department of Genetics, Sultan Qaboos University Hospital , Muscat, Sultanate of Oman
                Department of Medical Genetics, Third Faculty of Medicine, Charles University , Prague, Czech Republic
                Department of Pharmacology and Clinical Pharmacy, College of Health Sciences, Addis Ababa University , Addis Ababa, Ethiopia
                Center for Innovative Drug Development and Therapeutic Trials for Africa, College of Health Sciences, Addis Ababa University , Addis Ababa, Ethiopia
                Department of Molecular Biology and Genetics, Democritus University of Thrace , Alexandroupolis, Greece
                Department of Genetics, College of Medicine and Health Sciences, Sultan Qaboos University , Muscat, Sultanate of Oman
                Center for Global Genomics & Health Equity, Department of Genetics, Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania, USA
                Department of Anthropology and Human Genetics, Faculty of Science, Charles University , Prague, Czech Republic
                Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals and University of Geneva , Geneva, Switzerland
                Department of Genetics and Evolution (GENEV), University of Geneva , Geneva, Switzerland
                Institute of Genetics and Genomics of Geneva (iGE3) , Geneva, Switzerland
                Author notes
                Author information
                https://orcid.org/0000-0002-5951-7130
                https://orcid.org/0000-0002-8391-9383
                https://orcid.org/0000-0002-8073-6610
                https://orcid.org/0000-0001-5776-8107
                https://orcid.org/0000-0002-8018-3454
                https://orcid.org/0000-0003-4638-8787
                https://orcid.org/0000-0003-1613-3943
                https://orcid.org/0000-0001-7518-4615
                https://orcid.org/0000-0002-1208-5588
                https://orcid.org/0000-0002-9259-5288
                https://orcid.org/0000-0002-3162-5375
                https://orcid.org/0000-0001-8217-6445
                https://orcid.org/0000-0002-1469-837X
                https://orcid.org/0000-0003-1197-6634
                https://orcid.org/0000-0003-2702-1795
                https://orcid.org/0000-0002-8465-003X
                Article
                evac167
                10.1093/gbe/evac167
                9750130
                36445690
                74387076-7802-434e-93b6-c2440331d293
                © The Author(s) 2022. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 November 2022
                : 14 December 2022
                Page count
                Pages: 23
                Funding
                Funded by: Swiss National Science Foundation, doi 10.13039/501100001711;
                Award ID: 320030_159669
                Funded by: Institute of Genetics and Genomics of Geneva, doi 10.13039/501100016199;
                Funded by: Czech Science Foundation, doi 10.13039/501100001824;
                Award ID: 18-23889S
                Categories
                Research Article
                AcademicSubjects/SCI01130
                AcademicSubjects/SCI01140

                Genetics
                adme genes,drug metabolism phenotypes,genome-wide association studies,genome-wide selection scans,human evolution

                Comments

                Comment on this article