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Sports, Exercise, and Nutritional Genomics Sports, Exercise, and Nutritional Genomics Current Status and Future Directions Edited by DEBMALYA BARH Centre for Genomics and Applied Gene Technology Institute of Integrative Omics and Applied Biotechnology Nonakuri, Purba Medinipur, India ILDUS I. AHMETOV Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, United Kingdom Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom © 2019 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher's permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publi; sher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN 978-0-12-816193-7 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals Publisher: Andre Gerhard Wolff Acquisition Editor: Peter Linsley Editorial Project Manager: Rebeka Henry Production Project Manager: Sreejith Viswanathan Cover Designer: Mark Rogers Typeset by SPi Global, India Contributors David Abraham UCL-Division of Medicine, Royal Free Campus, London, United Kingdom Ildus I. Ahmetov Laboratory of Molecular Genetics, Kazan State Medical University, Kazan; Department of Molecular Biology and Genetics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow; Department of Physical Education, Plekhanov Russian University of Economics, Moscow; Sports Genetics Laboratory, St. Petersburg Research Institute of Physical Culture, St. Petersburg, Russia; Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom Fatima Al-Khelaifi Anti-Doping Laboratory Qatar, Doha, Qatar; UCL-Division of Medicine, Royal Free Campus, London, United Kingdom Caimari Antoni Eurecat, Technology Centre of Catalunya, Technological Unit of Nutrition and Health; Eurecat, Technology Centre of Catalunya, Centre for Omic Sciences (Joint Unit Eurecat-Universitat Rovira i Virgili), Unique Scientific and Technical Infrastructures (ICTS), Reus, Spain Philipp Baumert Research Institute for Sport & Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom; Exercise Biology Group, Faculty of Sport and Health Sciences, Technical University of Munich, Munich, Germany Alessandro Buriani Maria Paola Belloni Center for Personalized Medicine, Data Medica Group, Synlab Limited, Padova, Italy Stefano Cagnin Department of Biology; CRIBI Biotechnology Center; CIR-Myo Myology Center, University of Padova, Padua, Italy Carla Maria Calò Department of Life and Environmental Sciences, University of Cagliari, Cagliari, Italy Giada Caudullo Bendessere™ Study Center, Solgar Italia Multinutrient S.p.A., Padova, Italy Francesco Chemello Department of Biology, University of Padova, Padua, Italy Pawel Cięszczyk Unit of Molecular Biology; Faculty of Physical Education, Gdansk University of Physical Education and Sport, Gdansk, Poland Malcolm Collins Division of Exercise Science and Sports Medicine; Health Through Physical Activity, Lifestyle and Sport Research Centre, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa xvii Contributors xviii Marı́a J. Cuevas Institute of Biomedicine (IBIOMED), University of León, León, Spain Bruno A. Parenti de Oliveira Laboratory of Nutrigenomics Studies, Department of Internal Medicine, Ribeirão Preto Medical School, University of Sao Paulo, Sao Paulo, Brazil Ilhame Diboun Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Doha, Qatar Svitlana Drozdovska Biomedical Department, National University of Physical Education and Sport of Ukraine, Kyiv, Ukraine Mohamed A. Elrayess Anti-Doping Laboratory Qatar, Doha, Qatar; UCL-Division of Medicine, Royal Free Campus, London, United Kingdom Ahmed El-Sohemy Department of Nutritional Sciences, Faculty of Medicine, University of Toronto; Nutrigenomix Inc., Toronto, ON, Canada Karen Eny Nutrigenomix Inc., Toronto, ON, Canada Robert M. Erskine Research Institute for Sport & Exercise Sciences, Liverpool John Moores University, Liverpool; Institute of Sport, Exercise & Health, University College London, London, United Kingdom Brisamar Estebanez Institute of Biomedicine (IBIOMED), University of León, León, Spain Rodrigo Fernandez-Gonzalo Division of Clinical Physiology, Department of Laboratory Medicine, Karolinska Institute, and Unit of Clinical Physiology, Karolinska University Hospital, Stockholm, Sweden Sarah Mc Fie Department of Psychology, University of Cape Town, Cape Town, South Africa Stefano Fortinguerra Maria Paola Belloni Center for Personalized Medicine, Data Medica Group, Synlab Limited, Padova, Italy Noriyuki Fuku Graduate School of Health and Sports Science, Juntendo University, Inzai, Japan Bibiana Garcia-Bailo Department of Nutritional Sciences, Faculty of Medicine, University of Toronto; Nutrigenomix Inc., Toronto, ON, Canada Andrea Gibbon Division of Exercise Science and Sports Medicine; Health Through Physical Activity, Lifestyle and Sport Research Centre, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa Pietro Giusti Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy Contributors Javier González-Gallego Institute of Biomedicine (IBIOMED), University of León, León, Spain Nanci S. Guest Department of Nutritional Sciences, Faculty of Medicine, University of Toronto; Nutrigenomix Inc., Toronto, ON, Canada João Paulo L.F. Guilherme School of Physical Education and Sport, University of Sao Paulo, São Paulo, Brazil Elliott C. Hall Research Institute for Sport & Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom Hiroki Honma Nippon Sport Science University (NSSU), Tokyo, Japan Sara Joffre Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, ON, Canada Mayneris-Perxachs Jordi Eurecat, Technology Centre of Catalunya, Centre for Omic Sciences (Joint Unit EurecatUniversitat Rovira i Virgili), Unique Scientific and Technical Infrastructures (ICTS), Reus, Spain Michael Kalinski Department of Health and Fitness, School of Education, Human Ecology and Sport Studies, State University of New York (SUNY) at Oneonta, Oneonta, NY, United States Naoki Kikuchi Nippon Sport Science University (NSSU), Tokyo, Japan Hiroshi Kumagai Graduate School of Health and Sports Science, Juntendo University, Inzai; Japanese Society for the Promotion of Science, Tokyo, Japan Agata Leo nska-Duniec Faculty of Physical Education, Gdansk University of Physical Education and Sport, Gdansk, Poland Arola Lluı́s Eurecat, Technology Centre of Catalunya, Technological Unit of Nutrition and Health; Eurecat, Technology Centre of Catalunya, Centre for Omic Sciences (Joint Unit Eurecat-Universitat Rovira i Virgili), Unique Scientific and Technical Infrastructures (ICTS), Reus; Universitat Rovira i Virgili, Department of Biochemistry and Biotechnology, Nutrigenomics Research Group, Tarragona, Spain Alejandro Lucı́a School of Health Sciences, European University, Madrid, Spain Flavio Lucignano Department of Life and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy Agnieszka Maciejewska-Skrendo Unit of Molecular Biology, Gdansk University of Physical Education and Sport, Gdansk, Poland xix Contributors xx Eri Miyamoto-Mikami Graduate School of Health and Sports Science, Juntendo University, Inzai, Japan Haruka Murakami Department of Physical Activity Research, National Institutes of Biomedical Innovation, Health and Nutrition, Tokyo, Japan Myosotis Massidda Department of Life and Environmental Sciences, University of Cagliari, Cagliari; Italian Federation of Sports Medicine (FMSI), Rome, Italy Koichi Nakazato Nippon Sport Science University (NSSU), Tokyo, Japan Naokazu Miyamoto Graduate School of Health and Sports Science, Juntendo University, Inzai, Japan Carolina Ferreira Nicoletti Laboratory of Nutrigenomics Studies, Department of Internal Medicine, Ribeirão Preto Medical School, University of Sao Paulo, Sao Paulo, Brazil Boque Noemı́ Eurecat, Technology Centre of Catalunya, Technological Unit of Nutrition and Health, Reus, Spain Carla Barbosa Nonino Laboratory of Nutrigenomics Studies, Department of Internal Medicine, Ribeirão Preto Medical School, University of Sao Paulo, Sao Paulo, Brazil Canela Núria Eurecat, Technology Centre of Catalunya, Centre for Omic Sciences (Joint Unit EurecatUniversitat Rovira i Virgili), Unique Scientific and Technical Infrastructures (ICTS), Reus, Spain Marcela A. Souza Pinhel Laboratory of Nutrigenomics Studies, Department of Internal Medicine, Ribeirão Preto Medical School, University of Sao Paulo, Sao Paulo, Brazil Herrero Pol Eurecat, Technology Centre of Catalunya, Centre for Omic Sciences (Joint Unit EurecatUniversitat Rovira i Virgili), Unique Scientific and Technical Infrastructures (ICTS), Reus, Spain Francesc Puiggròs Eurecat, Technology Centre of Catalunya, Technological Unit of Nutrition and Health; Eurecat, Technology Centre of Catalunya, Centre for Omic Sciences (Joint Unit Eurecat-Universitat Rovira i Virgili), Unique Scientific and Technical Infrastructures (ICTS), Reus, Spain Masouda Rahim Division of Exercise Science and Sports Medicine; Health Through Physical Activity, Lifestyle and Sport Research Centre, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa Tim Rees Department of Sport and Physical Activity, Faculty of Management, Bournemouth University, Bournemouth, United Kingdom Contributors Paula Rodriguez-Miguelez Department of Kinesiology and Health Science, Virginia Commonwealth University, Richmond, VA, United States Samantha Beckley Department of Human Biology, Division of Exercise Science and Sports Medicine, University of Cape Town, Cape Town, South Africa Marek Sawczuk Laboratory of Physical Medicine, Chair of Sport, Gdansk University of Physical Education and Sport, Gdansk, Poland Robert A. Seaborne Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool; Institute for Science and Technology in Medicine (ISTM), School of Medicine, Keele University, Staffordshire; The Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom Ekaterina A. Semenova Department of Molecular Biology and Genetics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russia Alison V. September Division of Exercise Science and Sports Medicine; Health Through Physical Activity, Lifestyle and Sport Research Centre, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa Adam P. Sharples Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool; Institute for Science and Technology in Medicine (ISTM), School of Medicine, Keele University, Staffordshire, United Kingdom Vincenzo Sorrenti Department of Pharmaceutical and Pharmacological Sciences, University of Padova; Maria Paola Belloni Center for Personalized Medicine, Data Medica Group, Synlab Limited; Bendessere™ Study Center, Solgar Italia Multinutrient S.p.A., Padova, Italy Elena V. Valeeva Laboratory of Molecular Genetics, Kazan State Medical University, Kazan, Russia Alun G. Williams MMU Sports Genomics Laboratory, Department of Sport and Exercise Sciences, Manchester Metropolitan University, Manchester; Institute of Sport, Exercise and Health, University College London, London, United Kingdom Hirofumi Zempo Department of Administrative Nutrition, Faculty of Health and Nutrition, Tokyo Seiei College, Tokyo; Graduate School of Health and Sports Science, Juntendo University, Inzai, Japan Morena Zusso Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy xxi About the Editors Debmalya Barh is a MSc (Applied Genetics), MTech (Biotechnology), MPhil (Biotechnology), PhD (Biotechnology), PhD (Bioinformatics), PostDoc (Bioinformatics), and PGDM (Postgraduate in Management). He is a honorary scientist at the Institute of Integrative Omics and Applied Biotechnology (IIOAB), India. Dr. Barh is blended with both academic and industrial research and is an expert in integrative omics-based biomarker discovery, molecular diagnosis, and precision medicine in various complex human diseases and traits. He works with 400 + scientists from more than 100 organizations across 40 + countries. Dr. Barh has published over 150 research publications, 32 + book chapters, and has edited 20+ cutting-edge, omicsrelated reference books published by Taylor & Francis, Elsevier, and Springer. He frequently reviews articles for Nature publications, Elsevier, AACR journals, NAR, BMC journals, PLOS ONE, and Frontiers, to name a few. He has been recognized by Who's Who in the World and Limca Book of Records for his significant contributions in managing advance scientific research. Dr. Ildus Ahmetov is a senior lecturer in Genetics and Epigenetics of the Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, UK. He worked for 15 years in the top research centers of sport science in Russia, collected DNA samples of more than 8000 professional athletes, and published over 150 peer-referred research papers in journals. Dr. Ahmetov's main research areas include exercise and sports genomics, exercise biochemistry and physiology, behavioral genetics, nutrigenetics, sports medicine, and genetics of obesity. His group was the first to publish a genome-wide association study on athletic performance. Dr. Ahmetov is a member of the Steering Committee of the Athlome Consortium. In addition, he is an associate editor of the journal Biology of Sport. xxiii CHAPTER ONE Introduction to genetics of sport and exercise João Paulo L.F. Guilhermea, Alejandro Lucíab a School of Physical Education and Sport, University of Sao Paulo, São Paulo, Brazil School of Health Sciences, European University, Madrid, Spain b 1.1 Nature vs nurture influence It is acceptable to assume that any individual who is highly committed and dedicated to physical training is able to improve performance provided that the stimulus is adequate. However, some individuals appear to be naturally gifted with superior baseline traits or better responses to training than others (Tucker and Collins, 2012). It has been common to observe in the scientific literature a large interindividual variance in the response to training in several phenotypes relevant to physical performance (Bouchard 2012). Even among elite athletes, there may be a wide phenotypic variation—the curve distribution of individual performances for a given sport discipline will certainly show a normal distribution with a limited number of individuals at the extremities. Few individuals seem to be exceptionally gifted and demonstrate extraordinarily high-performance levels. A number of variables can contribute to explain this interindividual variance in response to training (Mann et al., 2014). Human physical performance expresses at a given time point a complex phenomenon, which is the result of the interaction of numerous intrinsic and extrinsic factors (Guilherme et al., 2014). Indeed, many of the intrinsic factors known to contribute to performance-related traits are within their own complex phenotypes determined by both nature and nurture (Tucker and Collins, 2012). The relative contributions of genetics and environment to phenotypic variation in numerous behavioral and biological traits have long been a topic of interest (Bouchard and Malina, 2014). The degree to which performance potential is predetermined by inherited traits against the degree influenced by environmental factors (training, nutrition, motivation, development opportunities, and overall health conditions) has excited much debate, that is often framed as "nature vs nurture" (Gibson, 2016). The terms nature and nurture were first used in the late 19th century (Galton, 1875). Based on twins and pedigree records, Francis Galton proposed that there is no escape from the conclusion that nature prevails enormously over nurture when the Sports, Exercise, and Nutritional Genomics https://doi.org/10.1016/B978-0-12-816193-7.00001-4 © 2019 Elsevier Inc. All rights reserved. 3 4 João Paulo L.F. Guilherme and Alejandro Lucía differences of nurture do not exceed what is commonly to be found among persons of the same rank of society and in the same country (Galton, 2012). This implies that the upper limit for an individual performance among persons exposed to the same environment is predetermined by heritable characteristics which no amount of practice or other natural environmental factor could overcome. However, there is also evidence that environmental factors have remarkable influence on performance (Davids and Baker, 2007). Most of the understanding about environmental influences on exercise performance comes from social and psychological research, which claims that performance in a given task is learned and achieved through extended practice of a given skill (Yan et al., 2016). In 1993, it was proposed that when individuals engage in practice activities with full concentration on improving some specific aspect of performance, the so-called deliberate practice, there is a linear improvement of an individual performance through repetition and successive refinement (Ericsson et al., 1993, 2009). Thus, as a consequence of the accumulated deliberate practice, performance in many areas of expertise considerably increases and can bring out the best in you. In fact, top-level athletes accumulate years of a rigid and organized training schedule on the road to sports excellence, in which quality rather than the amount of training can differentiate athletes of different competitive levels (Ericsson, 2013). Moreover, it was proposed that expert (i.e., top-level) athletes accumulate more hours of training than nonexperts; in particular, they devote more time in activities deemed most relevant for their respective disciplines (Davids and Baker, 2007). Therefore, the deliberate practice appears to be an important contributing factor to performance achievement, but it is not the only one. A recent metaanalysis showed that deliberate practice accounted for only 18% of the variance in sports performance, leaving 82% of the variance explained by other factors (Macnamara et al., 2016). In fact, the environment influence on sports performance also includes motivation, family support, coaches' influence, local culture, relative age effect, and birthplace effect (Davids and Baker, 2007; Yan et al., 2016). All these environmental factors are undoubtedly critical to performance achievement, but the current literature does not support the idea that interindividual variance in sports performance is mainly due to environmental factors (Georgiades et al., 2017). Heritability studies have provided evidence that performance phenotypes are substantially inherited, since all functional capacities, morphological dimensions, motor attributes, personality traits, and cognitive abilities showed moderate to high heritability (Georgiades et al., 2017). In this sense, the current accumulated literature tips the balance in favor of the relative contribution of genetics (Georgiades et al., 2017); however, the genetic basis of performance phenotypes is still under intense investigation. Particularly, the understanding of the influence of genetics on physical performance have been challenging due to its complex nature (Guilherme et al., 2014). Two main classes of studies evaluating the genetic basis of performance phenotypes were conducted in the course of history. The first class was designed to explore genetic Introduction to genetics of sport and exercise epidemiology paradigms, focusing on the magnitude of the genetic contribution for a relevant phenotype and characteristics of inheritance patterns. These studies dominated the landscape of exercise genetics research from 1960s to the early 1990s, and were based on twins or members of nuclear families (Bouchard and Malina, 2014). The second class was designed to identify locus, genes, and DNA sequence variants contributing to phenotypic variation. For this, several research designs and technologies were used over the past two decades, such as: case-control association studies, genome-wide linkage scans, and genome-wide association studies (Bouchard and Malina, 2014). More recently, analyses of "omics" approaches (e.g., genomics and transcriptomics) have been introduced for the evaluation of performance phenotypes (Sarzynski et al., 2015). Based on these studies, more than 200 genetic markers have been shown to be associated with a performance-relevant trait in at least one study (Ahmetov et al., 2016; Bray et al., 2009). Of note, a genetic marker is a DNA sequence whose address is known and that can be used as a reference point in the human genome. Collective information of DNA markers has been used for the construction of genetic maps (Bray et al., 2009), allowing the identification of variants contributing to several phenotypic variations observed in human physical performance. The human gene map for performance-related traits continues to grow over the years (Ahmetov et al., 2016; Sarzynski et al., 2016). In the following sections of this introductory chapter, we will address brief historical and basic concepts, evidence on genetic contributions to performance phenotypes, main methods of investigation used, and future research perspectives. 1.2 Genetics and olympic glory: early studies on olympic athletes The first attempt to identify differences in genetic markers for performance-related traits dates to the late 1960s (Bouchard and Malina, 2014). Due to their uniquely high levels of performance, elite athletes are considered to be predisposed for improving motor performance. In this sense, taking advantage of the 1968 Olympic Games in Mexico, an international group of researchers investigated 1265 competitors of both genders (although only 148 were women) and from almost all events in order to discriminate the body shape and the distribution of blood groups of Olympic athletes (de Garay et al., 1974). The study evaluated athletes of different ethnicities, sport disciplines, and competitive levels, however, at that time the methodologies available for the study of genetics were limited, and thus the expected conclusions were also limited. The authors addressed the inheritance of athleticism by estimating phenylthiocarbamide (PTC) threshold, red blood cell (RBC) antigens and enzymes, as well as chromosome patterns (karyotype). The PTC threshold was underrepresented among the athletes, and no woman with the XY chromosome was found, nor men with the XYY pattern occurred 5 6 João Paulo L.F. Guilherme and Alejandro Lucía among the 15 women and 227 men karyotyped (Tanner, 1976). Unfortunately, most of the findings were inconclusive (Evans Robson, 1977; Tanner, 1976). The effort to evaluate genetic characteristics of Olympic athletes was continued on the occasion of the 1976 Olympic Games in Montreal (Chagnon et al., 1984; Couture et al., 1986). In these studies, an attempt to associate genetic variants with performance was done by exploring RBC antigens and enzymes in a subsample of Caucasian athletes (n 79 individuals) who participated in endurance sports (track and field, canoeing, cycling, swimming, or rowing). Gene frequencies were reported and no significant differences were observed between athletes (i.e., cases) and a nonathletic reference population (i.e., controls). Taken together, none of these initial studies identified reliable and predictive genetic markers of superior physical performance (Bouchard and Malina, 2014). Among early studies, the most compelling data relating a genetic variant to superior athletic performance is the case of an Olympic gold medalist in cross-country skiing (Longmore, 1993). In the early 1990s, some members of a large family from Northern Europe were examined with naturally high RBC levels, although the cause of this erythrocytosis was not clear at the time ( Juvonen et al., 1991). After a deeper evaluation, it was demonstrated that erythrocytosis is segregating in this family, and as a consequence, these individuals present familial erythrocytosis––an inherited condition characterized by a very high number of RBC ( Juvonen et al., 1991). The aforementioned athlete is a member of this family and was evaluated along with other relatives. At the time of writing these studies, the athlete was 53 years old and has always been in excellent health, but had elevated hemoglobin levels (20 g/dL or greater) since childhood. Of note, he was one of the best cross-country skiers in the world, having won three Olympic gold medals, two Olympic silver medals, two bronze Olympic silver medals, besides five medals in World championships (competitions at distances between 10 and 30 km). The impact of erythrocytosis on the health of the affected members of this family in uncertain, but it seems to have caused few problems in them, being compatible with long life, good health, and exceptional physical fitness—probably due to an increased sensitivity to erythropoietin ( Juvonen et al., 1991). Has been shown that the affected individuals did not present altered serum erythropoietin concentration; however, these individuals may present high erythrocytosis even at low concentrations of erythropoietin ( Juvonen et al., 1991). The resulting high hemoglobin concentration leads to an increased oxygen-carrying capacity, potentially improving endurance performance since a high aerobic capacity is a result of an outstanding maximal cardiac output and a high oxygen-carrying capacity, together leading to a high level of maximal oxygen uptake and an optimal oxygen delivery to the exercising muscles (Lundby and Robach, 2015). Familial erythrocytosis has been reported to be a dominant inheritance trait that shows linkage to a polymorphic locus adjacent to the erythropoietin receptor (EPOR) gene (de la Chapelle et al., 1993a). Therefore, using genomic regions deposited in the Introduction to genetics of sport and exercise 7 GenBank database, a group of researchers designed primers to amplify the 30 region of the EPOR gene comprising exons 7 and 8 and the intron between them (de la Chapelle et al., 1993b). Sequencing of the region amplified revealed a G to A mutation at position 6002 in affected individuals, which alters for amino acid 439 of the EPOR protein from TGG encoding tryptophan to TAG (a stop codon); as a consequence, the resulting protein product is truncated for all 70 amino acids downstream of the mutation (de la Chapelle et al., 1993b). A total of 29 members of the family evaluated were classified as affected, and all 29 were carriers of the mutation (i.e., carriers of the heterozygous genotype); conversely, none of 21 clinically unaffected individuals had the mutation (de la Chapelle et al., 1993b). Thus, possessing only one allele of the mutation is sufficient to bring about a phenotypic effect (i.e., familial erythrocytosis). Other mutations cannot be formally excluded, but the G to A mutation showed an important biological relevance. It is noteworthy that familial forms of erythrocytosis are rare, and therefore, although the affected individuals of this family (with an Olympic champion among them) might possess an inherent advantage in aerobic-oriented events, few individuals around the world are carriers of this natural "advantage," especially under a favorable environmental condition. The remaining question is which other variants (whether rare or not) could predispose an individual to a better trainability of performance phenotypes. 1.3 The phenomenon of athletes from west and east africa An intriguing question in Sports Science is the dominance of specific population groups at the opposite ends of the competitive running spectrum (see Tables 1.1 and 1.2). Table 1.1 Men outdoor world records. Discipline Athlete Perf. Date 100 m 9.58 Aug. 16, 2009 Jamaica Usain Bolt Country Ethnicity AfricanAmerican 100 m hurdles Aries Merritt 12.80 Sep. 7, 2012 United States AfricanAmerican 200 m Usain Bolt 19.19 Aug. 20, 2009 Jamaica AfricanAmerican 1500 m Hicham El Guerrouj 3:26.00 Jul. 14, 1998 Morocco North Africa 3000 m Daniel Komen 7:20.67 Sep. 1, 1996 Kenya East Africa 5000 m Kenenisa Bekele 12:37.35 May 31, 2004 Ethiopia East Africa 10,000 m Kenenisa Bekele 26:17.53 Aug. 26, 2005 Ethiopia East Africa Half marathon Abraham Kiptum 58:18 Oct. 28, 2018 Kenya East Africa Marathon Eliud Kipchoge 02:01:39 Sep. 16, 2018 Kenya East Africa This table is based on information from the International Association of Athletics Federations (http://iaaf.org). Perf., performance. João Paulo L.F. Guilherme and Alejandro Lucía 8 Table 1.2 Women outdoor world records. Discipline Athlete Perf. Date 100 m Jul. 16, 1988 100 m hurdles 200 m 1500 m 3000 ma 5000 m 10,000 m Half marathon Marathon Florence Griffith-Joyner 10.49 Country Ethnicity United States AfricanAmerican Kendra Harrison 12.20 Jul. 22, 2016 United States AfricanAmerican Florence Griffith-Joyner 21.34 Sep. 29, 1988 United States AfricanAmerican Genzebe Dibaba 3:50.07 Jul. 17, 2015 Ethiopia East Africa Genzebe Dibaba 8:16.60 Feb. 6, 2014 Ethiopia East Africa Tirunesh Dibaba 14:11.15 Jun. 6, 2008 Ethiopia East Africa Almaz Ayana 29:17.45 Aug. 12, 2016 Ethiopia East Africa Netsanet Gudeta 01:06:11 Mar. 24, 2018 Ethiopia East Africa Mary Jepkosgei Keitany 02:17:01 Apr. 23, 2017 Kenya East Africa a Indoor performance. This table is based on information from the International Association of Athletics Federations (http://iaaf.org). Perf., performance. There is a distinct geographical concentration of success between sprint running events and distance running events. In sprint/power-oriented running events, there is a dominance of African-American athletes born in the United States or Caribbean Islands, especially Jamaica. In particular, most of these athletes are African-American with earlier ancestry from West Africa (Pitsiladis, 2011; Tucker et al., 2013). At the opposite end of the spectrum, that is, endurance events, we observed a dominance of athletes from Kenya and Ethiopia (i.e., of East African descent). Of the all-time top-50 male athletes in the Marathon, all come from East Africa; moreover, of the all-time top-100 male marathon runners, 92 come from East Africa (IAAF, 2018). Similarly, there is a dominance of the athletes from East African descent among female marathon runners (IAAF, 2018). Among the world's best runners (between years 1999 and 2015), most male runners in the 10 km, half-marathon and marathon were Kenyans; similarly, most female runners in the 10 km and half-marathon were Kenyans (for the marathon, there was a similar proportion of Kenyans and Ethiopians) (Nikolaidis et al., 2017). The dominance of Kenya in distance running is increased by the observation that a large majority of Kenya's most successful runners originate from a single tribe, the Kalenjin (Tucker et al., 2013). Questionnaires administered to 404 elite Kenyan middle-distance/distance runners (i.e., from the 800 m to the marathon) revealed that most of these athletes came from the Rift Valley province, where the Kalenjin ethnic group lives; in particular, a considerable proportion of these athletes come from a subtribe of the Kalenjin known as the Nandi (Onywera et al., 2006). This information highlights that individuals who are born in particular places share genetic or environmental factors that may predispose them to success in a given Introduction to genetics of sport and exercise sport. The underlying factors enabling this concentrated performance achievement have been the subject of considerable research, and theories emerged that West and East Africans are genetically predisposed to sprint and distance running, respectively (Tucker et al., 2013). Although genetics might not be the unique determinant of population differences on performance, evidence suggests an important role of genetics (Pitsiladis and Scott, 2005). In specific populations, there may be selection for a particular phenotype such as superior sprint or endurance performance, if it offers a selective advantage in that environment (Pitsiladis, 2011). In this case, the genetic background beneficial to sprint or endurance can prevail and predispose the population for the type of favorable performance. For example, variations in somatotype and genetic patterns in muscle fiber characteristics, biochemical metabolic pathways or pulmonary physiology which result in favorable biological consequences for sprinting may have been concentrated by natural selection over the centuries in the Afrocentric peoples and displaced from West Africa to the New World during the slave trade (Morrison and Cooper, 2006). Alternatively, it was hypothesized that only the strongest slaves survived to the harsh living conditions of the displacement process from West African across the Atlantic (Pitsiladis, 2011). Although these hypotheses remain without adequate scientific proof, it has been postulated that superior sprint performances of African-Americans of primarily West African origin were due to favorable sprint biology. Conversely, it was hypothesized that the Nandi tribe in Kenya has been concentrated by selection over the centuries for endurance performance through cultural practices such as cattle theft, also known as cattle raiding (Manners, 1997). These hypotheses have potential theoretical underpinnings, but the genetic background responsible for the superior performance of African-Americans (or West Africans) and East Africans still needs to be identified. The first genetic approach conducted in African athletes was to investigate the genetic ancestry of Ethiopian and Kenyan distance runners using the uniparentally inherited genetic marker: the mitochondrial DNA (mtDNA) (Scott et al., 2009, 2005b). Typically, mtDNA is inherited matrilinearly (i.e., from mother to child), undergoing no recombination and only changing with the rise of new mutations (Pitsiladis, 2011). This results in the accumulation of linked complexes of DNA variants (i.e., "haplogroups" or "haplotypes") down different lines of descent from an ancestral mtDNA molecule, which can be used to trace the ancestry of individuals or populations (Scott et al., 2005b). Haplotype groups represent easily comparable units of genealogical information, that when found in other populations can be used as indicators of recent migrations. The studies conducted in Ethiopian and Kenyan athletes, revealed a wide distribution of mtDNA haplogroups, in contrast to the concept that these individuals are a genetically distinct group as defined by mtDNA, that is, these individuals did not remain isolated in East African but shows that there were migration events and subsequent admixture (Wilber and Pitsiladis, 2012). The theory that the elite East African runners do not arise 9 10 João Paulo L.F. Guilherme and Alejandro Lucía from a limited genetic isolate is further supported by the analysis of the Y chromosome haplogroups distribution in Ethiopians (Moran et al., 2004). Of note, the Y chromosome can be considered the male equivalent of mtDNA (Wilber and Pitsiladis, 2012). Apart from their role on the ancestry of individuals or populations, variants in mtDNA have been suggested to influence the variance in human physical performance as mtDNA encodes various subunits of enzyme complexes of oxidative phosphorylation, as well as components of the mitochondrial protein synthesis system (Pitsiladis, 2011). In context, although elite East African runners are not a mitochondrially distinct group (Scott et al., 2005b), international- and national-level runners from Kenya showed differences in mtDNA haplogroups distribution in comparison to the general Kenyan population (Scott et al., 2009). These findings suggest that mtDNA haplogroups contain DNA variants that can influence some aspects of endurance performance or its trainability, but it may not fully explain the Kenyan running performance (Wilber and Pitsiladis, 2012). Indeed, variants in the nuclear genome and environment factors can also be determinants of East African running success. Two variants in the nuclear genome have been widely studied in sports and exercise: the angiotensin-converting enzyme (ACE) I/D and the α-actinin-3 (ACTN3) R577X gene variants. The first is an insertion (I) or deletion (D) allele of 287 base pairs in intron 16 of the ACE gene (Puthucheary et al., 2011), which has been estimated to explain up to 47% of the variance in circulating ACE levels (Rigat et al., 1990)—an enzyme that cleaves the circulating angiotensin-I giving rise to a biologically highly active oligopeptide, the so-called angiotensin-II (Puthucheary et al., 2011). The I-allele might provide a potential advantage to endurance-oriented events, whereas the D-allele might benefit performance in strength/power-oriented events (Myerson et al., 1999). The second is a C to T mutation at position 1747 in exon 16, which alters for amino acid 577 of the α-actinin-3 protein from CGA encoding an arginine to TGA (a stop codon); as a consequence, the resulting protein product is truncated for all amino acids downstream of the mutation (North et al., 1999). The α-actinins are important structural proteins of skeletal muscle. In human skeletal muscles, there are two genes encoding α-actinins: ACTN2, which is expressed in all fibers and ACTN3, which is restricted to fast fibers (i.e., type II fibers) (North and Beggs, 1996). It has been proposed that the X-allele (i.e., related to the truncated protein) can provide an advantage to endurance-oriented events, whereas the R-allele (i.e., related to the normal protein translation) can be an advantage to strength/power-oriented events (Yang et al., 2003). Both gene variants (i.e., ACE I/D and ACTN3 R577X) were evaluated in East and West African athletes, however, no significant association between the variants and athlete status was found (Ash et al., 2011; Scott et al., 2005a, 2010; Yang et al., 2007). In summary, genotyping two of the key candidate genes for human physical performance in highly successful athletes revealed that these genes should not be expressive determinants of their success. Whether other nuclear variants can help to explain such Introduction to genetics of sport and exercise phenomena remains to be determined, however, the extraordinary performance achievement must rely on the successful integration of a number of physiological, biochemical, and biomechanical systems, which themselves are the product of a combination of variables (Wilber and Pitsiladis, 2012). Of note, altitude, training-related aspects, habitual diet, cultural aspects, and socioeconomic factors are also contributors of sports success (Tucker et al., 2013). 1.4 The human genome variation: basic concepts The complete set of genetic information that an organism possesses embodied in its complete DNA sequence is called genome. The genome carries the information for all the proteins and RNA molecules that the organism will ever synthesize, as well as products participating in the regulation of gene expression and other cellular events. The Human Genome Project, an international collaboration which operated from 1990 to 2003, worked to provide researchers with basic information about the sequences of the human genome. With the availability of the draft sequences providing a first overall view of the human genome in 2001 (Lander et al., 2001; Venter et al., 2001) and later the availability of the near-complete sequence of the human genome (International Human Genome Sequencing Consortium, 2004), one could begin to look for deviations from the reference sequence and to begin exploring in-depth interindividual differences at the DNA level (Bouchard, 2015). Nowadays, the exploration of interindividual differences at the DNA level was greatly facilitated by other major projects subsequent to the Human Genome Project, such as the International HapMap Project, the 1000 Genome Project and similar consortia. These consortia are intended to map the patterns of genetic diversity in the human genome, and consequently, establish a reference for genetic association studies. For example, the 1000 Genomes Project, which operated from 2008 to 2015, generated a data set that contains data of 2504 individuals from 26 populations, so creating the largest public catalog of human genetic diversity and genotype data (Sudmant et al., 2015). For researchers, a catalog of human genetic variations of literally thousands of individuals available for further detailed analysis can be considered an important advance for science, because then we have a comparative baseline. Sequencing the human genome has revealed that the DNA sequence is more than 99% identical among individuals, and therefore, a small fraction of the human genome (0.1%) may be responsible for the wide phenotypic diversity observed in the population (International Human Genome Sequencing Consortium, 2004). Due to the all-size of the human genome, this small fraction can represent millions of base pairs that differ in the DNA sequence between individuals. Although most of these DNA variants appear to have little or no functional consequence, a set of these DNA differences may reflect the normal phenotypic variation observed in humans, including differences in anatomy, physiology, personality traits, and athletic ability (Guilherme et al., 2014). Therefore, 11 12 João Paulo L.F. Guilherme and Alejandro Lucía the aim of human genetics research is to identify specific DNA differences that are influencing a given phenotype. Performance phenotypes (e.g., maximal strength, sprinting, movement economy, and maximum oxygen uptake) are very complex traits, influenced by several genes on different tissues and organs and multiple nongenetic environmental factors (Guilherme et al., 2014). Regarding the genetic component, relevant phenotypes can be influenced by small-scale sequence changes in either the regulatory region of a gene or the DNA coding sequence. The simplest and most abundant type of genetic variation found in the human genome is the single-nucleotide polymorphism (SNP). About one nucleotide in every 200–300 of the DNA sequence is polymorphic (i.e., more than one form is common in the population) (Salisbury et al., 2003). The word polymorphism is used by molecular geneticists to describe a variant having a frequency in the population above 1%. Although other types of variant coexist in the human genome and can impact complex traits, SNPs are largely the easiest to ascertain and the most useful and widely applied markers in human genetic studies ( Johnson, 2009). The DNA is encoded by four different nucleotides [adenine (A), thymine (T), cytosine (C), and guanidine (G)] that, by binding in sequence, make a single DNA strand sequence (e.g., …AACGGT…) (Guilherme et al., 2014). SNPs consist of a change in a single nucleotide of the DNA sequence (Salisbury et al., 2003). For example, in the hypothetical sequence "AACGGT" the nucleotide G was identified as polymorphic, and some individuals have a nucleotide A rather than G, so the alternative sequence is "AACAGT." The different sequences that a particular polymorphism may take are called alleles. Given the very large number of SNPs, these variants are designated by numbers beginning with "rs" (e.g., rs1815739) and cataloged in the public dbSNP database (the NCBI database of SNPs and multiple small-scale variants). In DNA coding sequences, each sequence of three nucleotides (called codons) encodes for one specific amino acid in the peptide chain of the final gene product (Guilherme et al., 2014). Therefore, if the SNP occurs in DNA coding sequences, the final gene product can be affected, depending on the type of variant: synonymous, missense, or nonsense (Table 1.3). Synonymous or silent SNPs do not change an amino acid in the final gene product; in contrast, nonsynonymous SNPs result in an altered codon that specifies either a different amino acid (a missense variant) or a termination codon (a nonsense variant) (Bouchard et al., 2011a). A missense variant can induce either a conservative or nonconservative amino acid substitution. While a conservative missense SNP does not actually change the chemical properties of the molecule, the amino acid introduced by a nonconservative missense SNP has different chemical characteristics (Bouchard et al., 2011a). For instance, a nonconservative missense SNP can affect the shape of the final gene product. Regarding the nonsense variants, a classic example is the aforementioned ACTN3 R577X polymorphism. Introduction to genetics of sport and exercise 13 Table 1.3 The main classes of small-scale genetic variants. Variant type Alleles Coding sequence (CDS) Synonymous Missense Nonsense Short insertion Short deletion Common allele Alternative allele Common allele Alternative allele Common allele Alternative allele Common sequence Alternative sequence Common sequence Alternative sequence …ATC.TTA.AAA… …ATC.TTG.AAA… …ATC.TTA.AAA… …ATC.TTT.AAA… …ATC.TTA.AAA… …ATC.TAA.AAA… …ATC.TCT.CAA … …ATC.CAT.CTC… …ATC.TCT.CAA … …ATC.CTC.AAG… Encoded amino acids …Ile.Leu.Lys… …Ile.Leu.Lys… …Ile.Leu.Lys… …Ile.Phe.Lys… …Ile.Leu.Lys… …Ile.Stop.— …Ile.Ser.Gln… …Ile.His.Leu… …Ile.Ser.Gln… …Ile.Leu.Lys… Gln, glutamine; His, histidine; Ile, isoleucine; Leu, leucine; Lys, lysine; Phe, phenylalanine; Ser, serine. The second most common class of polymorphism found in the human genome is the insertions/deletions (indels) of one or a few nucleotides (Table 1.3). These variants may introduce frameshifts, ending the normal chain of amino acids and replacing the remainder with a different chain of amino acids (Gibson, 2016). Polymorphisms used to be viewed as functionally significant only if they affected the DNA coding sequence, however, variants in the noncoding sequence may also have significant effects on gene regulation (Bouchard et al., 2011a). Although 99.9% of variants consist of SNPs and short indels, other types of variants, including microsatellites, copy number variants, large indels, and epigenetic markers may also be important for phenotypic variation (Auton et al., 2015). 1.5 Research methodologies in genetics of sport and exercise Initial studies aimed at defining the genetics of human physical performance were essentially observational based on the pairs of twins (i.e., twin-studies) or members of nuclear families (i.e., familial aggregation studies), and focused on estimating the heritability of different traits of interest (Bouchard and Malina, 2014). In general, these studies have shown that there was less variance among more genetically related individuals (e.g., within pairs of monozygotic twins) than in less genetically related individuals, suggesting an important contribution of genetics to performance-related traits (Bouchard et al., 2011a). For example, Prud'homme et al. (1984) submitted 10 pairs of monozygotic twins to a 20-week endurance-training program and found that trainability of maximal aerobic power is largely influenced by heritability. The intraclass correlation coefficient computed from the amount of improvement in maximal aerobic performance (mL O2/min/kg) induced by training reached 0.74 (P < 0.01), indicating that monozygotic twin pairs yielded approximately the same responsiveness to training 14 João Paulo L.F. Guilherme and Alejandro Lucía (Prud'homme et al., 1984). Thereafter, findings from the classical HERITAGE family study also indicated based on the nuclear families an important heritability estimate of the maximum oxygen uptake (V̇O2max) (Bouchard et al. 1999). Following the 20-week endurance training program, a variance in V̇O2max response (mL O2/min) between individuals was observed, however, there were 2.5 times more variance (P ¼ 0.0001) between than within families, showing that the V̇O2max response to training aggregates in families (Bouchard et al., 1999). Muscle strength and mass phenotypes have also been quantified in twin and family studies, and the results also indicated a relatively high heritability estimates (Thomis and Aerssens, 2012). Although unquestionably important, these studies do not return information about which genes and gene variants are contributing to the relevant phenotypes (Guilherme et al., 2014). However, with the recent development of more advanced molecular biology techniques, genetic studies are no longer restricted exclusively to family studies but have been expanded to include the evaluation of locus, genes, and DNA sequence variants within populations or between distinct athletic groups (Pitsiladis and Wang, 2011). Over the past 20 years, there has been a great effort to identify at the DNAlevel polymorphisms that may contribute to performance phenotypes. The most frequent experimental approach used during this period was the genetic association study between a candidate polymorphism and relevant phenotypes. These studies can be divided into three main categories: (1) case-control association studies, which compare the frequency of a particular polymorphism (i.e., specific genotypes and their alleles) in a cohort of cases (individuals with the trait of interest) and controls (individuals without the trait of interest); (2) cross-sectional association studies, which compare selected physiological and/or performance data between specific genotype groups; and (3) longitudinal association studies, in which responses to a given intervention (e.g., exercise training or diet intervention) are compared between genotype groups (Guilherme et al., 2014). Of these, case-control association design remains the most common approach in sports and exercise genetics studies (Ahmetov and Fedotovskaya, 2015), mainly because they have a relatively low cost and are straightforward to do, which makes this approach interesting to evaluate potential candidate polymorphisms in different populations or cohorts. In a case-control association study, for example, if one genotype or allele of a given polymorphism is more frequent in elite athletes (i.e., the "cases") than in their nonathletic referents (i.e., the "controls"; usually adult nonathletes), thus an association between the polymorphism and the athletes can be proposed, but not a cause-effect relationship (Lucia et al., 2010). In order to better establish a physiological or rationale role between the variants and relevant phenotypes, cross-sectional and longitudinal approaches are necessary. For example, the first polymorphism associated with human physical performance (i.e., the ACE I/D polymorphism) was identified in 1998 (Gayagay et al., 1998; Montgomery et al., 1998). In the first two published papers about the ACE I/D polymorphism, elite British mountaineers (Montgomery et al., 1998) and Australian national Introduction to genetics of sport and exercise rowers (Gayagay et al., 1998) were genotyped and had the polymorphism frequency compared with nonathletic referents—a classical case-control association study. In both studies, it was found a higher frequency of the I-allele among athletes, that is, an association was found between the polymorphism and athletic status. Therefore, a possible relevance of this variant for elite endurance athletes was proposed, but not a cause-effect relationship. Regarding the ACE I/D polymorphism, it was shown that the D-allele (i.e., deletion allele) rather than the I-allele (i.e., insertion allele) allele is associated with higher circulating ACE levels in healthy Caucasians (Rigat et al., 1990) and elite Ironman triathletes (Domingo et al., 2013). Moreover, there was a positive correlation between plasma ACE activity and ACE protein concentration, and a positive association between plasma ACE activity and overall finishing time within the South African-born participants who completed the 2000 and/or 2001 South African Ironman Triathlon in under 15 h (Domingo et al., 2013). Furthermore, based on a muscle biopsy study, it was suggested that I-allele carriers (seven healthy nonathletic individuals) may have an amplified adjustment of myocellular organelles and gene transcripts related to mitochondrial lipid metabolism after a bicycle endurance training (Vaughan et al., 2013). Although a more solid conclusion is still lacking, these are examples of attempts aimed to establish a cause-effect relationship between the polymorphism and relevant phenotypes. Similar attempts have also been explored for other polymorphisms, such as the ACTN3 R577X variant (Vincent et al., 2007, 2012). One major disadvantage of these association studies with candidate polymorphisms is that only one or few gene variants can be assessed at a time. To improve the search for relevant variants, new methods exploring a greater extent of the human genome were developed. In this sense, genome-wide linkage study (GWLS) was the first approach proposed (Bouchard et al., 2011a). GWLS identifies chromosomal regions that harbor genes affecting quantitative traits over generations, and therefore, requires familial data not. The identification of these chromosomal regions, referred to as quantitative trait loci (QTL), is the first step in attempt to associate genetic variations with relevant phenotypes. Because a typical QTL may span millions of DNA base pairs and a large number of genes, progressing from the QTL to the causal DNA sequence and alleles requires additional analyzes such as positional cloning (Bouchard et al., 2011a). Technological advances have made the GWLS almost obsolete since the introduction of microarray-based, high-throughput SNP-genotyping methods, with the latter having drastically increased the ability to capture the existing variation in the genome of individuals (Bouchard and Malina, 2014). The so-called genome-wide association study (GWAS) is able to evaluate hundreds of thousands of SNPs rather than genomic loci, and individual data rather than familial data. Therefore, this approach is becoming increasingly popular in the search for polymorphisms that contribute to complex traits (Visscher et al., 2012). 15 16 João Paulo L.F. Guilherme and Alejandro Lucía In 2011, the first GWAS for the trainability of V̇O2max was undertaken based on 324,611 SNPs, and interestingly, almost 50% of the variance in V̇O2max response to an endurance training program was found to be predicted with a panel of 21 SNPs (Bouchard et al., 2011b). More recently, the GWAS approach was undertaken to identify variants associated with endurance athletic status (Ahmetov et al., 2015; Rankinen et al., 2016). In the first study, 1,140,419 SNPs were evaluated in 80 international-level Russian endurance athletes, in which six polymorphisms were more significantly associated with the endurance athletes (Ahmetov et al., 2015). In the second study, 195,000 and 700,000 SNPs were evaluated in two cohorts of elite endurance athletes (GENATHLETE and Japanese endurance runners, respectively), from which a panel of 45 promising SNPs was highlighted (Rankinen et al., 2016). Interestingly and also intriguing, seven additional cohorts of endurance athletes and controls were subsequently used for replication of the highlighted SNPs, through case-control association studies, and only one SNP (rs558129 at GALNTL6 locus) showed the same direction of association in all replication cohorts (Rankinen et al., 2016). An empirical evaluation of the early experience with GWAS suggests that nonreplicating results and inconsistency in the magnitude of results are common features of this approach (Ioannidis, 2007). An expressive amount of information is generated by genome-wide approaches; however, some possibilities need to be considered. Like any other study of genetic association, the following situations are possible: (i) a significant association is found, and the result is replicated in other cohorts, which can indicate a true-positive result; (ii) a significant association is found, but replication studies do not confirm the association, which can indicate a false-positive result; or (iii) the replication study was not sensitive enough to detect previous associations, which can indicate a methodological flaw (Ioannidis, 2007). For all variants in which an initial association was observed (regardless of the method used), replication studies are of paramount importance. Consistent associations strengthen the evidence of the influence of the polymorphism on phenotype. However, even if the replication occurs in more than one study, it does not mean that the same association will be found in every cohort or population (Guilherme et al., 2014). In the last version of the human gene map for performance and health-related fitness phenotypes, a total of 221 variants in the nuclear genome and 18 mtDNA markers have been shown to be associated with a relevant phenotype in at least one study (Bray et al., 2009). Regarding athlete genetics, a more recent literature review revealed that at least 155 genetic markers (i.e., nuclear genomic variants and mtDNA markers; 93 endurancerelated, and 62 power/strength-related genetic markers) have been shown to be associated with a relevant phenotype in at least one study (Ahmetov et al., 2016). Of note, several of these variants were not replicated in additional studies and may be falsepositive. Interestingly, 41 variants were identified very recently due to new GWAS approaches—indicating that GWASs represent a promising and productive method Introduction to genetics of sport and exercise (Ahmetov et al., 2016). However, it is noteworthy that GWAS captures most of the common SNPs found in the human genome, but do not cover all types of variants, such as the copy number variants. Moreover, the genomic architecture of complex traits also includes rare variants (Bouchard, 2015), for which we still have little information because they are hard to study. For instance, to uncover rare variants relevant to performance phenotypes, it may be necessary to sequence the genome of a large number of individuals on which the relevant trait has been measured (Bouchard, 2015). Although genetics has advanced in the recent years, there is still much to evolve to better understand the genetics of sports and exercise. 1.6 Research gaps and the athlome project consortium Complex traits such as physical performance are influenced by both genetic and environmental factors, the aforementioned: nature and nurture influence. Although deliberate practice and environmental factors (i.e., nurture aspects) are undoubtedly both critical to performance, they do not in themselves produce outstanding physical performances such as highly elite athletes—some evidence has favored an important role of nature over nurture (Georgiades et al., 2017). In fact, several studies comparing variations at the DNA level have suggested that certain specific genes and variants are involved in athletic performance achievement (Ahmetov et al., 2016; Ahmetov and Fedotovskaya, 2015); however, the use of the traditional genetic association studies over the past decades in sports and exercise has had limited success to feature relevant gene variants with consistent replications in different cohorts (Tanaka et al., 2016). Genetic associations with performance phenotypes should be initially interpreted carefully and replicated in independent populations before being accepted as valid (Gibson, 2016). Of note, methodological characteristics between studies (e.g., sample size, ethnic background, and sample heterogeneity) have favored some inconsistency in the findings (Guilherme et al., 2014). It is noteworthy that each variant can probably explain a very small proportion of the phenotypic variation, especially common variants such as SNPs (Bouchard, 2015). Therefore, very large sample sizes are needed to detect true associations in sports genetics. However, it is difficult to reconcile this premise with the scarce number of athletic champions worldwide for a given ethnicity and sport event, and thus, collaborations and data sharing between research centers worldwide have been recommended to circumvent this limitation and improve the sample size and quality (Eynon et al., 2011; Mattsson et al., 2016). Since physical performance is the result of a combination of many different phenotypes, it will be interesting if future studies base genetic analyses on more detailed phenotyping (i.e., relevant variants and physiological data must be integrated) (Mattsson et al., 2016). Furthermore, to identify rare and other unknown variants further studies focused on genomic and mtDNA sequencing will serve as a reference panel for 17 João Paulo L.F. Guilherme and Alejandro Lucía 18 future investigations. Nevertheless, the whole-genome sequencing of elite athletes alone will not be sufficient to meaningfully understand the mechanisms underlying the biology of physical performance. Indeed, this will require genomic and epigenomic information that can be potentially linked with transcriptomic and proteomic information (Tanaka et al., 2016). Ultimately, definitive proof that gene variants really favor relevant phenotypes would require a long-term prospective study. Taken together, all these approaches will help us to better understand the biology of physical performance. In this regard, a recent consensus emerged among several research centers worldwide to collectively study the genotype and phenotype data currently available on elite athletes, in adaptation to exercise training and on exercise-related musculoskeletal injuries, named the Athlome Project Consortium (Pitsiladis et al., 2016). The main aim of the Athlome project is to characterize the genetics and biology of sports and exercise. To achieve this, several steps are set out as described in detail (Pitsiladis et al., 2016). Data generated from the Athlome Project will be made publically available for sharing of resources with the wider scientific community (http://athlomeconsortium.org/). The Athlome Project is a unique and highly ambitious attempt to uncover genetic variations and genomic process underlying sports and exercise biology in the coming years (Wang et al., 2016). References Ahmetov, I.I., Fedotovskaya, O.N., 2015. Current progress in sports genomics. Adv. Clin. 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CHAPTER TWO Heritability estimates of physical performance-related phenotypes Hirofumi Zempoa,b, Eri Miyamoto-Mikamib, Noriyuki Fukub, Haruka Murakamic a Department of Administrative Nutrition, Faculty of Health and Nutrition, Tokyo Seiei College, Tokyo, Japan Graduate School of Health and Sports Science, Juntendo University, Inzai, Japan c Department of Physical Activity Research, National Institutes of Biomedical Innovation, Health and Nutrition, Tokyo, Japan b 2.1 Introduction Twins share many features such as face structure, height, and weight and children resemble their parents. Aren't such phenomena recognized by everyone? Monozygotic (MZ) twins are similar to clones and have exactly the same genome sequence. Thus, families share much matching genetic information. However, most families also share the same living environment such as living in the same house and consuming the same meals. Therefore, families share both very similar genetic information and environments. To determine whether genetic and environmental factors have a greater influence on phenotype, studies should compare MZ twin populations with nearly perfect agreement of genomic information at the same age and in the same living environment to a population of dizygotic (DZ) twins who share the same age and living environment and have some genetic similarity. Comparing these populations would determine whether similarities in these individuals are conferred by genes. Fig. 2.1 shows the correlations of quantitative traits in twin pairs. A solid circle indicates an MZ twin pair. While an open circle indicates a DZ twin pair. Fig. 2.1A shows that the data for the pairs of MZ formed nearly a straight line with a gradient of 1 compared to the pairs of DZ. We assumed that inheritance affects phenotype. In contrast, Fig. 2.1B shows that the plots for MZ and DZ were similar, indicating the low impact of genetic involvement. Reed et al. (1991) evaluated differences on in grip strength between twins. The grip strength of MZ showed an r ¼ 0.62, while that of DZ was r ¼ 0.39. Thus, MZ with greater genetic similarity exhibited a higher concordance rate with grip strength compared to DZ. The correlation coefficients for their twins showed that heredity affects physical fitness. However, this result alone does not clarify whether genetics has the stronger influence on physical fitness than environment. Accordingly, we calculate "heritability" as an indicator of genetic factors for comparison to "environmentality." Sports, Exercise, and Nutritional Genomics https://doi.org/10.1016/B978-0-12-816193-7.00002-6 © 2019 Elsevier Inc. All rights reserved. 23 Hirofumi Zempo et al. 24 Fig. 2.1 Interpair comparison of quantitative trait for MZ and DZ twins. Solid circle indicates a pair of MZ. Open circle indicates a pair of DZ. (A) The correlation is higher in MZ than in DZ. It suggests inheritance affects phenotype. On the other hand, in (B), the plots for MZ and DZ were similar, indicating the low impact of genetic involvement. In the fields of sports science and exercise, studies have been conducted to evaluate genetic factors affecting phenotypes related to sports performance and physical fitness (muscle strength, endurance capacity, etc.) (Bouchard et al., 1997). Regarding heritability as an indicator of the influence of genetic factors on phenotype, this paper focuses on muscular strength and endurance capacity, which are major factors affecting physical fitness. We also review the relationship between these factors and athletic status. Moreover, there are individual differences in trainability, and it has been reported that genetic factors are involved in these individual differences. In athletes, identifying the genetic factors affecting muscle strength and endurance capacity, which are the foundation of athletic performance, is considered to be a critical factor in competition selection and training methods suited to each individual. On the other hand, even among ordinary people, a high level of physical activity, exercise, and greater physical fitness are correlated with a reduced risk of disease and mortality. Thus, the importance of exercise and improving physical fitness has been recognized not only from the perspective of sporting competition, but also from that of health. Therefore, it is important to identify the genetic factors affecting physical fitness, physical activities, and exercise, as well as the extent of these effects and specific genes involved. 2.2 What is heritability estimate? The anatomical structure and function of the human body (¼ phenotype) as well as the response to various external factors exhibit individual differences. In addition to environmental factors (living and regional environments, etc.) and socioeconomic factors (education, income, social support, etc.), genetic factors cause individual differences. Most human quantitative traits such as height (cm) and weight (kg) are determined partly by genetic variation (e.g., single-nucleotide polymorphism). However, these traits are polygenic and the involvement of a single-nucleotide polymorphisms on phenotype is small. Heritability estimates of physical performance-related phenotypes 25 Therefore, in population genetics, rather than evaluating the effects of single gene, researchers measure the effects of an entire locus (heritability) using statistical models. The classical method for calculating the heritability estimate is derived from the difference in correlation within a pair of MZ and within a pair of DZ, or from the variance between twins. In the recent years, rather than the classical method, structural equation modeling (SEM) has become a widely used method of calculating the heritability estimate. First, we explain the concept of heritability. A genetic factor refers to how genetic diversity can account for individual differences in human phenotypes. Heritability is a σ 2 numerical representation of this factor and is the ratio (σ G2 ) of the genetic variance P (σ 2G) to the total variance of the phenotype (σ 2P), known as broad-sense heritability (H2). We can express the relative ratio of genetic variance and environmental variance (σ 2E) to phenotypic variance as well as compare the influence of genetic and environmental influences causing individual differences. However, if the heritability of muscle strength is 60% when a person grip strength 50 kg, it does not mean that 30 kg of this 60% is made by heredity and the remaining 20 kg is made in the environment. In other word, the heritability estimate merely explains the contribution ratio of genetic factors to the variation of phenotype in population. G is divided into an additive genetic effect (a) and a nonadditive genetic effect (d), and variance can be included as σ 2G ¼ σ 2a + σ 2d . Additive genetic effects are the additive effects of alleles at different loci. Nonadditive genetic effects include dominant effects and gene-gene interactions (epistasis effects). In addition, σ 2 the ratio of σ 2a to σ 2P (σ a 2 ), or the condition under which the effect of σ 2d is neglected in P genetic factors, is known as narrow-sense heritability and is expressed as h2. Because nonadditive genetic effects (d) are determined by a combination of genes, inheritance of these effects is unlikely, and a greater h2 means the more closely resembled parent and child are. Environmental factors can be classified into common environmental factors (c) shared among family and nonshared environmental factors (e) experienced by individuals (σ 2E ¼ σ 2c + σ 2e ). Nonshared environmental factor includes measurement errors. Next, we explain the classical method of estimating heritability, which is very simple using sample variance (s2). The variance of measured values in twin pairs is considered to indicate phenotypic variation. Heritability can be estimated by comparing the variance of MZ and DZ. H2 ¼ sDZ 2 sMZ2 sDZ 2 (2.1) If sMZ 2 ¼ sDZ 2, the numerator of Eq. (2.1) becomes 0 and the heritability estimate is 0. If sMZ 2 ¼ 0, the heritability estimate is 1, indicating that 100% inheritance affects the phenotype. Next, we describe the method for estimating heritability based on the correlation coefficient for some phenotypes between twins. For both twin phenotypes, the influence of Hirofumi Zempo et al. 26 nonadditive genetic effects (d) is ignored in the ACE model (A, additive genetic factors; C, common environmental factors; E, nonshared environmental factors). Genetic factors (a) and shared environments (c) between MZ twins are equal, and the only difference is the individual's own nonshared environment (e). Therefore, the degree of similarity of the phenotype between twins is explained by the genetic factors and shared environment. The correlation coefficient "r" of two variables equals their covariance divided by the product of their individual standard deviations. However, the r of two standardized variables can be expressed by using only their covariance: Thus, we can express P ¼ a + c + e. rMZ ¼ Cov ðPtwin1 , Ptwin2 Þ ¼ Σ ai + ci + etwin1 iÞ ðai + ci + etwin2 iÞ ð 2 2 ¼ Σ ai + ci + 2Σ aici + Σðaietwin1 i + cietwin1 i + aietwin2 i + cietwin1 i + etwin1 i etwin2 iÞ a and c are independent of e, and the relationship between a and c is omitted here; rMZ ¼ Σa2i + Σci2 (2.2) DZ share half of their genes inherited from the parents (the expected value of concordance is an average of 50%), and only the shared environment is equal between twins. 1 rDZ ¼ Σa2i + Σci2 2 (2.3) By solving these two equations simultaneously, h2 is calculated as follows (Falconer's formula) (Falconer 1960): h2 ¼ 2 ðrMZ rDZ Þ (2.4) For example, with respect to the correlation of grip strength between twins described by Reed et al. (1991) that introduced above, because rMZ ¼ 0.618 and rDZ ¼ 0.385, h2 is 0.47. In addition, a (additive genetic factor) is 0.47, c (¼ 2rDZ – rMZ) is 0.15, and e (¼ 1 – rMZ) is 0.38. The contribution ratio of the additive genetic factor and common environment in twins is shown in Fig. 2.2. Fig. 2.2 The contribution ratio of additive genetic factor and common environment in twins. Heritability estimates of physical performance-related phenotypes 27 If the correlation coefficient of the phenotype between MZ is large even after doubling the correlation coefficient of DZ (2rDZ < rMZ), the similarity of MZ is very high. There are two reasons for this: the effect of a special common environment that makes MZ similar or nonadditive genetic effects (d) are involved. If there is an effect of a specific common environment, the "equal environment assumption," where the degree of the common environment between pairs is the same both in MZ and DZ, has not been established. However, the equal environment assumption has been established (Koenig et al., 2010). Therefore, a specific common environment that makes MZ similar is unlikely. In addition, because common environment variance σ 2c can be calculated as 2rDZ – rMZ, the effects of the common environment on the phenotype are negligible. In case of 2rDZ < rMZ, the value for common environment (2rDZ – rMZ) becomes negative, and thus the model for the ACE is not unconformable. This suggests that the nonadditive genetic effect (d) affects the phenotype, the common environment is not considered in the genetic model (ADE model: A, additive genetic factors; D, nonadditive genetic factors; E, nonshared environment factors). The correlation coefficient of MZ is shown as rMZ ¼ Σa2i + Σdi2 (2.5) In contrast, in the case of DZ, the concordance rate of the gene combination as the dominant effect is 1/4 of MZ (combination of 1/2 from paternal inheritance and 1/2 from maternal inheritance). This is determined as follows: 1 1 rDZ ¼ Σa2i + Σdi2 2 4 (2.6) Calculation of the heritability estimate using the correlation coefficient does not consider measurement errors. In the recent years, rather than the correlation coefficient, a method for calculating the heritability estimate using SEM has become widely used. SEM is a combination of path analysis and the factor analysis and estimates the covariance structure of actual data using a hypothetical model. Furthermore, in multivariate genetic analysis using SEM, it is possible to simultaneously calculate genetic factors and environmental factors common to multiple phenotypes, as well as genetic factors and environmental factors specifically involved in individual phenotypes. In addition, genetic and environmental factors in longitudinal data such as when determining the influence of development and aging can also be evaluated. Several different genetic models (ACE, ADE, AE, CE, E) are used in this calculation, and the model that best matches the observed pattern of similarity in MZ and DZ is chosen using a goodness-of-fit index. χ2, Akaike's information criterion (AIC), Bayesian information criterion (BIC), etc. can be used as goodness-of-fit indices. Then, heritability is determined from the path coefficient in the selected genetic model. In a recent metaanalysis of genetic factors for 17,804 traits (skeletal structure, metabolism, psychology and social interaction, etc.) in 14,558,903 individuals (twins/family) 28 Hirofumi Zempo et al. from 2748 papers, the heritability was found to be approximately 50% for all traits (Polderman et al., 2015). The trait with the highest heritability showed a value of 71.2% in the ophthalmologic system, followed by 63.7% in the ears, nose, and throat, 60.4% in the skin, and 59.1% in the skeletal structure. Therefore, phenotypes are affected by genetic factors. Candidate genetic approaches and hypothetical-free approaches are being carried used to identify the specific genes related to these genetic factors. Particularly, the genome-wide association study (GWAS), which comprehensively analyze single-nucleotide polymorphisms in all chromosomal regions, has been widely conducted. However, the h2 calculated by GWAS is problematic in that it is lower than the estimate, which is referred to as "missing heritability" (Eichler et al., 2010). Models have been constructed in which genetic and environmental factors are independent (σ 2P ¼ σ 2G + σ 2E). However, a "gene-environment interaction" may occur between genetic and environmental factors. In the recent years, a method for detecting this interaction using SEM has been developed (Purcell, 2002), and studies using this model are ongoing. 2.3 Heritability estimates of muscle strength-related phenotypes Skeletal muscle strength and power are, respectively, defined as the force produced by muscle contraction and product of force and velocity. They are important factors in health, activities of daily living, and athletic performance. Recently, a large longitudinal population study (139,691 participants from 17 countries) showed that grip strength is a stronger predictor of cardiovascular and all-cause mortality than systolic blood pressure (Leong et al., 2015). Moreover, a decrease in muscle strength (i.e., sarcopenia and dynapenia) is considered an important social issue because of the globally growing elderly populations in high-income societies. Muscle strength-related phenotypes have been reported to be influenced by genetic and many environmental factors, such as exercise habits, diet, and geographic factors. The heredity of muscle strength-related phenotypes was first examined in 1970 (Venerando and Milani-Comparetti, 1970). In the 1980s, researchers applied SEM to calculate heritability estimates. Nevertheless, the heritability estimates of muscle strength-related phenotypes (H2-msp), defined as the proportion of genetic variation per total trait variation, varied widely from 0% to 98%. Therefore, we evaluated the H2-msp by systematic review and meta-analysis derived from studies of twins and families (Zempo et al., 2017). A systematic literature search was conducted using PubMed (through August 22, 2016) using the following keywords: (heredity or heritability) and ("muscle strength" or "muscle contraction" or endurance or athlete* or fitness or exercise) and (twin* or family). We searched English or Japanese language studies involving human subjects. Studies reporting the H2-msp for healthy subjects in a sedentary state were included. We identified 1988 articles from the searches with filtering. After screening of titles Heritability estimates of physical performance-related phenotypes and abstracts, 84 articles were subjected to full-text screening, and an additional 34 studies were added by manual searching and based on the reference lists of the screened 84 articles. After reading the articles in detail, 94 articles were excluded: reviews, other measures of physical performance, lack of quantitatively comparable data, and duplicate reports of the same cohort. A total of 24 articles were selected for the metaanalysis, representing 58 strength-related phenotype measurements (Fig. 2.3). These measurements were classified into six phenotypic categories: 12 isometric grip strength, 16 other isometric strength, 9 isotonic strength, 9 isokinetic strength, 8 jumping ability, and 4 other power measurements. A total of 330,902 participants from 24 articles were included in the analysis. One of 24 articles included 316,832 participants (Silventoinen et al., 2008). The average age of the subjects was less than 20 years in 8 articles (33.3%), 20–39 years in 9 articles (37.5%), and over 40 years in 7 articles (29.2%). Examination of the heritability rate by muscle force demonstration style revealed a wide distribution of the heritage rate in both cases. Of the 38 measurements obtained using SEM models, the ADCE component was estimated for muscle strength-related phenotypes. In all, 21 studies used AE models and 12 used ACE models. Only two measurements showed a nonadditive genetic effect (D). The common environmental effect (C) ranged from 0 to 0.53. Random-effects models were used to calculate the weighted mean heritability estimates. In all, 24 articles including 58 measurements were included in the meta-analysis. Fig. 2.3 Forest plot of a metaanalysis of the heritability estimates of the