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Meitern R, Hõrak P. Survival costs and benefits of reproduction: A register-based study in 20th century Estonia. Ann N Y Acad Sci 2024; 1535:137-148. [PMID: 38536396 DOI: 10.1111/nyas.15127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/23/2024]
Abstract
Patterns of individual variation in lifespan and senescence depend on the associations between parental survival and reproductive rates. We studied the associations between parity and survival among 579,271 Estonians born between 1905 and 1945 and in a cohort with a completed lifespan born in 1905-1927. For this cohort, selection for increased lifespan operated on both sexes, but it was stronger in men than in women. However, the median lifespan increased between the subsequent cohorts in women but stagnated in men. Selection for longer lifespan was caused by the below-average lifespan of individuals with no or single offspring. Despite a general positive selection for lifespan, survival costs of reproduction were also detected among a relatively small proportion of individuals with high parities, as mothers of two and fathers of two and three children had the highest median lifespans. Fathers of more than six children had better survival than fathers of few children in their reproductive age, but this association reversed after age 70. The reversal of this association between survival and parity at old age indicates that relative mortality risks between those with lower versus higher parities change across ages, as predicted by the antagonistic pleiotropy theory of aging.
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Affiliation(s)
| | - Peeter Hõrak
- Department of Zoology, University of Tartu, Tartu, Estonia
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2
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Venkatesh SS, Wittemans LBL, Palmer DS, Baya NA, Ferreira T, Hill B, Lassen FH, Parker MJ, Reibe S, Elhakeem A, Banasik K, Bruun MT, Erikstrup C, Jensen BA, Juul A, Mikkelsen C, Nielsen HS, Ostrowski SR, Pedersen OB, Rohde PD, Sorensen E, Ullum H, Westergaard D, Haraldsson A, Holm H, Jonsdottir I, Olafsson I, Steingrimsdottir T, Steinthorsdottir V, Thorleifsson G, Figueredo J, Karjalainen MK, Pasanen A, Jacobs BM, Hubers N, Lippincott M, Fraser A, Lawlor DA, Timpson NJ, Nyegaard M, Stefansson K, Magi R, Laivuori H, van Heel DA, Boomsma DI, Balasubramanian R, Seminara SB, Chan YM, Laisk T, Lindgren CM. Genome-wide analyses identify 21 infertility loci and over 400 reproductive hormone loci across the allele frequency spectrum. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.19.24304530. [PMID: 38562841 PMCID: PMC10984039 DOI: 10.1101/2024.03.19.24304530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Genome-wide association studies (GWASs) may help inform treatments for infertility, whose causes remain unknown in many cases. Here we present GWAS meta-analyses across six cohorts for male and female infertility in up to 41,200 cases and 687,005 controls. We identified 21 genetic risk loci for infertility (P≤5E-08), of which 12 have not been reported for any reproductive condition. We found positive genetic correlations between endometriosis and all-cause female infertility (rg=0.585, P=8.98E-14), and between polycystic ovary syndrome and anovulatory infertility (rg=0.403, P=2.16E-03). The evolutionary persistence of female infertility-risk alleles in EBAG9 may be explained by recent directional selection. We additionally identified up to 269 genetic loci associated with follicle-stimulating hormone (FSH), luteinising hormone, oestradiol, and testosterone through sex-specific GWAS meta-analyses (N=6,095-246,862). While hormone-associated variants near FSHB and ARL14EP colocalised with signals for anovulatory infertility, we found no rg between female infertility and reproductive hormones (P>0.05). Exome sequencing analyses in the UK Biobank (N=197,340) revealed that women carrying testosterone-lowering rare variants in GPC2 were at higher risk of infertility (OR=2.63, P=1.25E-03). Taken together, our results suggest that while individual genes associated with hormone regulation may be relevant for fertility, there is limited genetic evidence for correlation between reproductive hormones and infertility at the population level. We provide the first comprehensive view of the genetic architecture of infertility across multiple diagnostic criteria in men and women, and characterise its relationship to other health conditions.
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Affiliation(s)
- Samvida S Venkatesh
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Laura B L Wittemans
- Novo Nordisk Research Centre Oxford, Oxford, United Kingdom
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, United Kingdom
| | - Duncan S Palmer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Nikolas A Baya
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Teresa Ferreira
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
| | - Barney Hill
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Frederik Heymann Lassen
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
| | - Melody J Parker
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Clinical Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Saskia Reibe
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Ahmed Elhakeem
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Obstetrics and Gynecology, Copenhagen University Hospital, Hvidovre, Copenhagen, Denmark
| | - Mie T Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Health, Aarhus University, Aarhus, Denmark
| | - Bitten A Jensen
- Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark
| | - Anders Juul
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen; Copenhagen, Denmark
- Department of Growth and Reproduction, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Christina Mikkelsen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, Copenhagen University, Copenhagen, Denmark
| | - Henriette S Nielsen
- Department of Obstetrics and Gynecology, The Fertility Clinic, Hvidovre University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ole B Pedersen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Kge, Denmark
| | - Palle D Rohde
- Genomic Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Erik Sorensen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Obstetrics and Gynecology, Copenhagen University Hospital, Hvidovre, Copenhagen, Denmark
| | - Asgeir Haraldsson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Children's Hospital Iceland, Landspitali University Hospital, Reykjavik, Iceland
| | - Hilma Holm
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Ingileif Jonsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali University Hospital, Reykjavik, Iceland
| | - Thora Steingrimsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Department of Obstetrics and Gynecology, Landspitali University Hospital, Reykjavik, Iceland
| | | | | | - Jessica Figueredo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Minna K Karjalainen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Finland
- Northern Finland Birth Cohorts, Arctic Biobank, Infrastructure for Population Studies, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Anu Pasanen
- Research Unit of Clinical Medicine, Medical Research Center Oulu, University of Oulu, and Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | - Benjamin M Jacobs
- Centre for Preventive Neurology, Wolfson Institute of Population Health, Queen Mary University London, London, EC1M 6BQ, United Kingdom
| | - Nikki Hubers
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Margaret Lippincott
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Abigail Fraser
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Mette Nyegaard
- Genomic Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Kari Stefansson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Reedik Magi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Hannele Laivuori
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Obstetrics and Gynecology, Tampere University Hospital, Finland
- Center for Child, Adolescent, and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University, Finland
| | - David A van Heel
- Blizard Institute, Queen Mary University London, London, E1 2AT, United Kingdom
| | - Dorret I Boomsma
- Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Ravikumar Balasubramanian
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Stephanie B Seminara
- Harvard Reproductive Sciences Center and Reproductive Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Yee-Ming Chan
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Endocrinology, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, United States of America
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Cecilia M Lindgren
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, United Kingdom
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
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Su Y, Xu Y, Hu Y, Chang Y, Wu F, Yang M, Peng Y. Late age at first birth is a protective factor for oesophageal cancer and gastro-oesophageal reflux: the evidence from the genetic study. Front Endocrinol (Lausanne) 2024; 14:1329763. [PMID: 38288469 PMCID: PMC10823002 DOI: 10.3389/fendo.2023.1329763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 12/28/2023] [Indexed: 02/01/2024] Open
Abstract
Objective The primary objective of this research endeavor was to examine the underlying genetic causality between the age at first birth (AFB) and four prevalent esophageal diseases, namely oesophageal obstruction (OO), oesophageal varices (OV), gastro-oesophageal reflux (GOR), and oesophageal cancer (OC). Methods We conducted a two-sample Mendelian randomization (MR) analysis to examine the causal association between AFB and four prevalent esophageal disorders. We employed eight distinct MR analysis techniques to evaluate causal relationships, encompassing random-effects inverse variance weighted (IVW), MR Egger, weighted median, simple mode, weighted mode, maximum likelihood, penalized weighted median, and fixed-effects IVW. The random-effects IVW method served as the primary approach for our analysis. Furthermore, we executed several sensitivity analyses to assess the robustness of the genetic causal inferences. Results The random-effects IVW analysis revealed a significant negative genetic causal association between AFB and both GOR (P < 0.001, Odds Ratio [OR] 95% Confidence Interval [CI] = 0.882 [0.828-0.940]) and OC (P < 0.001, OR 95% CI = 0.998 [0.998-0.999]). Conversely, there was insufficient evidence support to substantiate a genetic causal link between AFB and OO (P = 0.399, OR 95% CI = 0.873 [0.637-1.197]) or OV (P = 0.881, OR 95% CI = 0.978 [0.727-1.314]). The results of sensitivity analyses underscore the robustness and reliability of our MR analysis. Conclusion The findings of this investigation substantiate the notion that elevated AFB confers a protective effect against GOR and OC. In addition, no causative association was discerned between AFB and OO or OV at the genetic level.
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Affiliation(s)
- Yani Su
- Department of Clinical Laboratory Medicine, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Yiwei Xu
- Department of Clinical Laboratory Medicine, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Yunfeng Hu
- Department of Radiotherapy, Yan’an University Affiliated Hospital, Yan’an, China
| | - Yu Chang
- Department of Radiotherapy, Yan’an University Affiliated Hospital, Yan’an, China
| | - Fangcai Wu
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Mingyi Yang
- Department of Joint Surgery, HongHui Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Yuhui Peng
- Department of Clinical Laboratory Medicine, Cancer Hospital of Shantou University Medical College, Shantou, China
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4
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Gao Z. Unveiling recent and ongoing adaptive selection in human populations. PLoS Biol 2024; 22:e3002469. [PMID: 38236800 PMCID: PMC10796035 DOI: 10.1371/journal.pbio.3002469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2024] Open
Abstract
Genome-wide scans for signals of selection have become a routine part of the analysis of population genomic variation datasets and have resulted in compelling evidence of selection during recent human evolution. This Essay spotlights methodological innovations that have enabled the detection of selection over very recent timescales, even in contemporary human populations. By harnessing large-scale genomic and phenotypic datasets, these new methods use different strategies to uncover connections between genotype, phenotype, and fitness. This Essay outlines the rationale and key findings of each strategy, discusses challenges in interpretation, and describes opportunities to improve detection and understanding of ongoing selection in human populations.
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Affiliation(s)
- Ziyue Gao
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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Zhuo C, Chen L, Wang Q, Cai H, Lin Z, Pan H, Wu M, Jin Y, Jin H, Zheng L. Association of age at first sexual intercourse and lifetime number of sexual partners with cardiovascular diseases: a bi-directional Mendelian randomization study. Front Cardiovasc Med 2023; 10:1267906. [PMID: 38146444 PMCID: PMC10749299 DOI: 10.3389/fcvm.2023.1267906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/03/2023] [Indexed: 12/27/2023] Open
Abstract
Background Limited studies have explored the association between sexual factors [age at first sexual intercourse (AFS) and lifetime number of sexual partners (LNSP)] and cardiovascular diseases (CVDs), leaving the causality inconclusive. Methods We performed a bi-directional Mendelian randomization (MR) study to investigate the causality between sexual factors and CVDs, including coronary artery disease, myocardial infarction, atrial fibrillation (AF), heart failure (HF), and ischemic stroke (IS). Single-nucleotide polymorphisms (SNPs) for sexual factors were extracted from the UK Biobank. Statistics for each CVD were derived from two different databases. MR estimates were calculated per outcome database and were combined through meta-analysis. Several complementary sensitivity analyses were also performed. Results The primary analysis suggested that AFS was causally associated with the risk of CVDs; the odds ratios (ORs) ranged from 0.686 [95% confidence interval (CI), 0.611-0.770] for HF to 0.798 (95% CI, 0.719-0.886) for AF. However, the association between AFS and IS (OR, 0.844; 95% CI, 0.632-1.126) was not consistent in the meta-analysis after excluding SNPs related to confounders. Moreover, non-significant associations were found between LNSP and CVDs. Reverse direction MR analysis showed that CVDs were not associated with sexual factors. Conclusions Genetic evidence suggested that AFS was causally associated with the risk of CVDs except for IS, whereas non-significant association of LNSP with CVDs was detected. Further investigation into AFS could be warranted in preventing the progression of CVDs.
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Affiliation(s)
- Chengui Zhuo
- Department of Cardiology, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, China
| | - Lei Chen
- Department of Cardiology, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, China
| | - Qiqi Wang
- Zhejiang Provincial Center for Drug and Medical Device Procurement, Hangzhou, China
| | - Haipeng Cai
- Department of Cardiology, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, China
| | - Zujin Lin
- Department of Cardiology, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, China
| | - Huili Pan
- Department of Cardiology, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, China
| | - Meicui Wu
- Department of Cardiology, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, China
| | - Yuxiang Jin
- Department of Cardiology, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, China
| | - Hong Jin
- Department of Cardiology, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, China
| | - Liangrong Zheng
- Department of Cardiology and Atrial Fibrillation Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
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Zhang S, Chen M, Liu J, Yang L, Li H, Hong L. The causal effect of educational attainment on stress urinary incontinence: a two-sample mendelian randomization study. BMC Womens Health 2023; 23:564. [PMID: 37915016 PMCID: PMC10621122 DOI: 10.1186/s12905-023-02724-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/21/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND Stress urinary incontinence (SUI) is characterized by involuntary urine leakage in response to increased abdominal pressure, such as coughing, laughing, or sneezing. It significantly affects women's quality of life and imposes a substantial disease burden. While pregnancy and childbirth have been previously identified as risk factors for SUI, educational attainment may also play a role. Therefore, this paper investigates the causal relationship between educational attainment and SUI using two-sample Mendelian randomization (TSMR) analysis, years of schooling (YOS), and college or university degree (CUD) as proxies. METHODS Summary statistics of YOS, CUD, and SUI were obtained from genome-wide association studies (GWAS), and TSMR analysis was applied to explore potential causal relationships between them. Causal effects were mainly estimated using the standard inverse variance weighting (IVW) method, and complementary and sensitivity analyses were also performed using multiple methods. RESULTS The results indicate that both YOS (OR = 0.994, 95% CI: 0.992-0.996; P = 7.764E-10) and CUD (OR = 0.987, 95% CI: 0.983-0.991; P = 1.217E-09) may have a negative causal effect on SUI. CONCLUSIONS Improving educational attainment may go some way towards reducing the risk of SUI. Therefore, it is important to increase efforts to improve the imbalance in educational development and safeguard women's health.
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Affiliation(s)
- Shufei Zhang
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People's Republic of China
| | - Mao Chen
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People's Republic of China
| | - Jianfeng Liu
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People's Republic of China
| | - Lian Yang
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People's Republic of China
| | - Hanyue Li
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People's Republic of China
| | - Li Hong
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People's Republic of China.
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7
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Mikhailova SV. Problems with studying directional natural selection in humans. Vavilovskii Zhurnal Genet Selektsii 2023; 27:684-693. [PMID: 38023807 PMCID: PMC10643113 DOI: 10.18699/vjgb-23-79] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/03/2023] [Accepted: 07/03/2023] [Indexed: 12/01/2023] Open
Abstract
The review describes the main methods for assessing directional selection in human populations. These include bioinformatic analysis of DNA sequences via detection of linkage disequilibrium and of deviations from the random distribution of frequencies of genetic variants, demographic and anthropometric studies based on a search for a correlation between fertility and phenotypic traits, genome-wide association studies on fertility along with genetic loci and polygenic risk scores, and a comparison of allele frequencies between generations (in modern samples and in those obtained from burials). Each approach has its limitations and is applicable to different periods in the evolution of Homo sapiens. The main source of error in such studies is thought to be sample stratification, the small number of studies on nonwhite populations, the impossibility of a complete comparison of the associations found and functionally significant causative variants, and the difficulty with taking into account all nongenetic determinants of fertility in contemporary populations. The results obtained by various methods indicate that the direction of human adaptation to new food products has not changed during evolution since the Neolithic; many variants of immunity genes associated with inflammatory and autoimmune diseases in modern populations have undergone positive selection over the past 2-3 thousand years owing to the spread of bacterial and viral infections. For some genetic variants and polygenic traits, an alteration of the direction of natural selection in Europe has been documented, e. g., for those associated with an immune response and cognitive abilities. Examination of the correlation between fertility and educational attainment yields conflicting results. In modern populations, to a greater extent than previously, there is selection for variants of genes responsible for social adaptation and behavioral phenotypes. In particular, several articles have shown a positive correlation of fertility with polygenic risk scores of attention deficit/hyperactivity disorder.
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Affiliation(s)
- S V Mikhailova
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
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8
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Mathieson I, Day FR, Barban N, Tropf FC, Brazel DM, Vaez A, van Zuydam N, Bitarello BD, Gardner EJ, Akimova ET, Azad A, Bergmann S, Bielak LF, Boomsma DI, Bosak K, Brumat M, Buring JE, Cesarini D, Chasman DI, Chavarro JE, Cocca M, Concas MP, Davey Smith G, Davies G, Deary IJ, Esko T, Faul JD, Franco O, Ganna A, Gaskins AJ, Gelemanovic A, de Geus EJC, Gieger C, Girotto G, Gopinath B, Grabe HJ, Gunderson EP, Hayward C, He C, van Heemst D, Hill WD, Hoffmann ER, Homuth G, Hottenga JJ, Huang H, Hyppӧnen E, Ikram MA, Jansen R, Johannesson M, Kamali Z, Kardia SLR, Kavousi M, Kifley A, Kiiskinen T, Kraft P, Kühnel B, Langenberg C, Liew G, Lind PA, Luan J, Mägi R, Magnusson PKE, Mahajan A, Martin NG, Mbarek H, McCarthy MI, McMahon G, Medland SE, Meitinger T, Metspalu A, Mihailov E, Milani L, Missmer SA, Mitchell P, Møllegaard S, Mook-Kanamori DO, Morgan A, van der Most PJ, de Mutsert R, Nauck M, Nolte IM, Noordam R, Penninx BWJH, Peters A, Peyser PA, Polašek O, Power C, Pribisalic A, Redmond P, Rich-Edwards JW, Ridker PM, Rietveld CA, Ring SM, Rose LM, Rueedi R, Shukla V, Smith JA, Stankovic S, Stefánsson K, Stöckl D, Strauch K, Swertz MA, Teumer A, Thorleifsson G, Thorsteinsdottir U, Thurik AR, Timpson NJ, Turman C, Uitterlinden AG, Waldenberger M, Wareham NJ, Weir DR, Willemsen G, Zhao JH, Zhao W, Zhao Y, Snieder H, den Hoed M, Ong KK, Mills MC, Perry JRB. Genome-wide analysis identifies genetic effects on reproductive success and ongoing natural selection at the FADS locus. Nat Hum Behav 2023; 7:790-801. [PMID: 36864135 DOI: 10.1038/s41562-023-01528-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/12/2023] [Indexed: 03/04/2023]
Abstract
Identifying genetic determinants of reproductive success may highlight mechanisms underlying fertility and identify alleles under present-day selection. Using data in 785,604 individuals of European ancestry, we identified 43 genomic loci associated with either number of children ever born (NEB) or childlessness. These loci span diverse aspects of reproductive biology, including puberty timing, age at first birth, sex hormone regulation, endometriosis and age at menopause. Missense variants in ARHGAP27 were associated with higher NEB but shorter reproductive lifespan, suggesting a trade-off at this locus between reproductive ageing and intensity. Other genes implicated by coding variants include PIK3IP1, ZFP82 and LRP4, and our results suggest a new role for the melanocortin 1 receptor (MC1R) in reproductive biology. As NEB is one component of evolutionary fitness, our identified associations indicate loci under present-day natural selection. Integration with data from historical selection scans highlighted an allele in the FADS1/2 gene locus that has been under selection for thousands of years and remains so today. Collectively, our findings demonstrate that a broad range of biological mechanisms contribute to reproductive success.
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Affiliation(s)
- Iain Mathieson
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Felix R Day
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Nicola Barban
- Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Felix C Tropf
- Nuffield College, University of Oxford, Oxford, UK
- École Nationale de la Statistique et de L'administration Économique (ENSAE), Paris, France
- Center for Research in Economics and Statistics (CREST), Paris, France
| | - David M Brazel
- Nuffield College, University of Oxford, Oxford, UK
- Leverhulme Centre for Demographic Science, University of Oxford, Oxford, UK
| | - Ahmad Vaez
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Natalie van Zuydam
- Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden
| | - Bárbara D Bitarello
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Eugene J Gardner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Evelina T Akimova
- Nuffield College, University of Oxford, Oxford, UK
- Leverhulme Centre for Demographic Science, University of Oxford, Oxford, UK
| | - Ajuna Azad
- DNRF Center for Chromosome Stability, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Lawrence F Bielak
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Dorret I Boomsma
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, the Netherlands
| | | | - Marco Brumat
- Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
| | - Julie E Buring
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - David Cesarini
- Department of Economics, New York University, New York, NY, USA
- Research Institute for Industrial Economics, Stockholm, Sweden
- National Bureau of Economic Research, Cambridge, MA, USA
| | - Daniel I Chasman
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Jorge E Chavarro
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Massimiliano Cocca
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Maria Pina Concas
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | | | - Gail Davies
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Tõnu Esko
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Oscar Franco
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Andrea Ganna
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Audrey J Gaskins
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | | | - Eco J C de Geus
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Giorgia Girotto
- Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Bamini Gopinath
- Centre for Vision Research, Westmead Institute for Medical Research and Department of Ophthalmology, University of Sydney, Sydney, New South Wales, Australia
| | - Hans Jörgen Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Erica P Gunderson
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Chunyan He
- Markey Cancer Center, University of Kentucky, Lexington, KY, USA
- Department of Internal Medicine, Division of Medical Oncology, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - W David Hill
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Eva R Hoffmann
- DNRF Center for Chromosome Stability, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Hongyang Huang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Elina Hyppӧnen
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Rick Jansen
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Magnus Johannesson
- Department of Economics, Stockholm School of Economics, Stockholm, Sweden
| | - Zoha Kamali
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Sharon L R Kardia
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Annette Kifley
- Centre for Vision Research, Westmead Institute for Medical Research and Department of Ophthalmology, University of Sydney, Sydney, New South Wales, Australia
| | - Tuomo Kiiskinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Helsinki, Finland
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Brigitte Kühnel
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Gerald Liew
- Centre for Vision Research, Westmead Institute for Medical Research and Department of Ophthalmology, University of Sydney, Sydney, New South Wales, Australia
| | - Penelope A Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Nicholas G Martin
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Hamdi Mbarek
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Qatar Genome Programme, Qatar Foundation Research, Development and Innovation, Qatar Foundation, Doha, Qatar
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - George McMahon
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Andres Metspalu
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | | | - Lili Milani
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Stacey A Missmer
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Adolescent and Young Adult Medicine, Department of Medicine, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Obstetrics, Gynecology, and Reproductive Biology, College of Human Medicine, Michigan State University, Grand Rapids, MI, USA
| | - Paul Mitchell
- Centre for Vision Research, Westmead Institute for Medical Research and Department of Ophthalmology, University of Sydney, Sydney, New South Wales, Australia
| | - Stine Møllegaard
- Department of Sociology, University of Copenhagen, Copenhagen, Denmark
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands
| | - Anna Morgan
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, EMGO Institute for Health and Care Research and Neuroscience Campus Amsterdam, VU University Medical Center/GGZ inGeest, Amsterdam, the Netherlands
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Patricia A Peyser
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Ozren Polašek
- University of Split School of Medicine, Split, Croatia
- Algebra University College, Zagreb, Croatia
| | - Chris Power
- Population, Policy and Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK
| | | | - Paul Redmond
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Janet W Rich-Edwards
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Women's Health, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Paul M Ridker
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Cornelius A Rietveld
- Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, the Netherlands
- Department of Applied Economics, Erasmus School of Economics, Rotterdam, the Netherlands
| | - Susan M Ring
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | | | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Vallari Shukla
- DNRF Center for Chromosome Stability, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Stasa Stankovic
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | | | - Doris Stöckl
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Konstantin Strauch
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU, Munich, Germany
| | - Morris A Swertz
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | | | | | - A Roy Thurik
- Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, the Netherlands
- Department of Applied Economics, Erasmus School of Economics, Rotterdam, the Netherlands
- Montpellier Business School, Montpellier, France
| | | | - Constance Turman
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - André G Uitterlinden
- Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - David R Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Gonneke Willemsen
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Jing Hau Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Wei Zhao
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Yajie Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Marcel den Hoed
- Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden
| | - Ken K Ong
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Melinda C Mills
- Nuffield College, University of Oxford, Oxford, UK.
- Leverhulme Centre for Demographic Science, University of Oxford, Oxford, UK.
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
- Department of Economics, Econometrics and Finance, University of Groningen, Groningen, the Netherlands.
| | - John R B Perry
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
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9
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Requena M, Reher DS. Intergenerational transmission of fertility in Spain among cohorts born during the first half of twentieth century. ECONOMICS AND HUMAN BIOLOGY 2023; 50:101244. [PMID: 37148630 DOI: 10.1016/j.ehb.2023.101244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/22/2023] [Accepted: 04/08/2023] [Indexed: 05/08/2023]
Abstract
It is known that historically fertility is correlated between generations of the same family. These links tend to be explained either in terms of the biogenetic determinants of reproduction or by the transmission of intra-familial values associated with reproduction and family life. Less is known about the micro-determinants of these links or about the extent to which the progressive modernization of reproductive outcomes over the past century has affected behavior. In this paper, we will address these issues for Spain with data from the Socio-Demographic Survey (SDS) carried out in 1991 and including data on cohorts born between 1900 and 1946. These data enable us to explore the micro determinants of fertility at different points of time during this period. Our results point to the existence of a significant correlation between intergenerational reproductive outcomes that persists and strengthens throughout this period of demographic change. Results confirm the importance of birth order in large family groups where firstborn offspring are more likely to have larger families than subsequent siblings. There is also evidence that the strength of these intergenerational ties increases with the onset of more modern demographic behavior characterized by sharply declining fertility. The results presented here promise to condition future debates on the subject.
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Affiliation(s)
- Miguel Requena
- Grupo de Estudios 'Población y Sociedad', Spain; Departamento de Sociologia II, Universidad Nacional de Educación a Distancia, C/ Obispo Trejo 2, 28040 Madrid, Spain.
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10
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Ćalić I, Groen SC, Choi JY, Joly‐Lopez Z, Hamann E, Natividad MA, Dorph K, Cabral CLU, Torres RO, Vergara GV, Henry A, Purugganan MD, Franks SJ. The influence of genetic architecture on responses to selection under drought in rice. Evol Appl 2022; 15:1670-1690. [PMID: 36330294 PMCID: PMC9624088 DOI: 10.1111/eva.13419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 11/29/2022] Open
Abstract
Accurately predicting responses to selection is a major goal in biology and important for successful crop breeding in changing environments. However, evolutionary responses to selection can be constrained by such factors as genetic and cross-environment correlations, linkage, and pleiotropy, and our understanding of the extent and impact of such constraints is still developing. Here, we conducted a field experiment to investigate potential constraints to selection for drought resistance in rice (Oryza sativa) using phenotypic selection analysis and quantitative genetics. We found that traits related to drought response were heritable, and some were under selection, including selection for earlier flowering, which could allow drought escape. However, patterns of selection generally were not opposite under wet and dry conditions, and we did not find individual or closely linked genes that influenced multiple traits, indicating a lack of evidence that antagonistic pleiotropy, linkage, or cross-environment correlations would constrain selection for drought resistance. In most cases, genetic correlations had little influence on responses to selection, with direct and indirect selection largely congruent. The exception to this was seed mass under drought, which was predicted to evolve in the opposite direction of direct selection due to correlations. Because of this indirect effect on selection on seed mass, selection for drought resistance was not accompanied by a decrease in seed mass, and yield increased with fecundity. Furthermore, breeding lines with high fitness and yield under drought also had high fitness and yield under wet conditions, indicating that there was no evidence for a yield penalty on drought resistance. We found multiple genes in which expression influenced both water use efficiency (WUE) and days to first flowering, supporting a genetic basis for the trade-off between drought escape and avoidance strategies. Together, these results can provide helpful guidance for understanding and managing evolutionary constraints and breeding stress-resistant crops.
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Affiliation(s)
- Irina Ćalić
- Department of Biological SciencesFordham UniversityBronxNew YorkUSA
- Institute of BotanyUniversity of CologneCologneGermany
| | - Simon C. Groen
- Department of NematologyUniversity of California at RiversideRiversideCaliforniaUSA
- Department of Biology, Center for Genomics and Systems BiologyNew York UniversityNew YorkNew YorkUSA
| | - Jae Young Choi
- Department of Biology, Center for Genomics and Systems BiologyNew York UniversityNew YorkNew YorkUSA
| | - Zoé Joly‐Lopez
- Department of Biology, Center for Genomics and Systems BiologyNew York UniversityNew YorkNew YorkUSA
- Département de ChimieUniversité du Québec à MontréalQuébecCanada
| | - Elena Hamann
- Department of Biological SciencesFordham UniversityBronxNew YorkUSA
- Department of Genetics and Odum School of EcologyUniversity of GeorgiaAthensGeorgiaUSA
| | | | - Katherine Dorph
- Department of Biology, Center for Genomics and Systems BiologyNew York UniversityNew YorkNew YorkUSA
| | | | | | - Georgina V. Vergara
- International Rice Research InstituteLos BañosLagunaPhilippines
- Institute of Crop ScienceUniversity of the Philippines Los BañosLos BañosLagunaPhilippines
| | - Amelia Henry
- International Rice Research InstituteLos BañosLagunaPhilippines
| | - Michael D. Purugganan
- Department of Biology, Center for Genomics and Systems BiologyNew York UniversityNew YorkNew YorkUSA
- Center for Genomics and Systems BiologyNYU Abu Dhabi Research Institute, New York University Abu DhabiAbu DhabiUnited Arab Emirates
| | - Steven J. Franks
- Department of Biological SciencesFordham UniversityBronxNew YorkUSA
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11
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Fieder M, Huber S. Contemporary selection pressures in modern societies? Which factors best explain variance in human reproduction and mating? EVOL HUM BEHAV 2022. [DOI: 10.1016/j.evolhumbehav.2021.08.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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12
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OUP accepted manuscript. Hum Reprod Update 2022; 28:457-479. [DOI: 10.1093/humupd/dmac014] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 02/17/2022] [Indexed: 11/12/2022] Open
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13
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Mills MC, Tropf FC, Brazel DM, van Zuydam N, Vaez A, Pers TH, Snieder H, Perry JRB, Ong KK, den Hoed M, Barban N, Day FR. Identification of 371 genetic variants for age at first sex and birth linked to externalising behaviour. Nat Hum Behav 2021; 5:1717-1730. [PMID: 34211149 PMCID: PMC7612120 DOI: 10.1038/s41562-021-01135-3] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 05/14/2021] [Indexed: 02/06/2023]
Abstract
Age at first sexual intercourse and age at first birth have implications for health and evolutionary fitness. In this genome-wide association study (age at first sexual intercourse, N = 387,338; age at first birth, N = 542,901), we identify 371 single-nucleotide polymorphisms, 11 sex-specific, with a 5-6% polygenic score prediction. Heritability of age at first birth shifted from 9% [CI = 4-14%] for women born in 1940 to 22% [CI = 19-25%] for those born in 1965. Signals are driven by the genetics of reproductive biology and externalising behaviour, with key genes related to follicle stimulating hormone (FSHB), implantation (ESR1), infertility and spermatid differentiation. Our findings suggest that polycystic ovarian syndrome may lead to later age at first birth, linking with infertility. Late age at first birth is associated with parental longevity and reduced incidence of type 2 diabetes and cardiovascular disease. Higher childhood socioeconomic circumstances and those in the highest polygenic score decile (90%+) experience markedly later reproductive onset. Results are relevant for improving teenage and late-life health, understanding longevity and guiding experimentation into mechanisms of infertility.
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Affiliation(s)
- Melinda C Mills
- Leverhulme Centre for Demographic Science, University of Oxford, Oxford, United Kingdom.
- Nuffield College, University of Oxford, Oxford, United Kingdom.
| | - Felix C Tropf
- Leverhulme Centre for Demographic Science, University of Oxford, Oxford, United Kingdom
- Nuffield College, University of Oxford, Oxford, United Kingdom
- École Nationale de la Statistique et de L'administration Économique (ENSAE), Paris, France
- Center for Research in Economics and Statistics (CREST), Paris, France
| | - David M Brazel
- Leverhulme Centre for Demographic Science, University of Oxford, Oxford, United Kingdom
- Nuffield College, University of Oxford, Oxford, United Kingdom
| | - Natalie van Zuydam
- The Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden
| | - Ahmad Vaez
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Tune H Pers
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - John R B Perry
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Ken K Ong
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Marcel den Hoed
- The Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden
| | - Nicola Barban
- Department of Statistical Sciences, University of Bologna, Bologna, Italy
| | - Felix R Day
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom.
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14
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Međedović J. Phenotypic Signals of Sexual Selection and Fast Life History Dynamics for the Long-Term but Not Short-Term Mating. EVOLUTIONARY PSYCHOLOGY 2021; 19:14747049211057158. [PMID: 34841944 PMCID: PMC10461799 DOI: 10.1177/14747049211057158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 09/11/2021] [Accepted: 10/15/2021] [Indexed: 11/17/2022] Open
Abstract
Mating patterns are crucial for understanding selection regimes in current populations and highly implicative for sexual selection and life history theory. However, empirical data on the relations between mating and reproductive outcomes in contemporary humans are lacking. In the present research we examined the sexual selection on mating (with an emphasis on Bateman's third parameter - the association between mating and reproductive success) and life history dynamics of mating by examining the relations between mating patterns and a comprehensive set of variables which determine human reproductive ecology. We conducted two studies (Study 1: N = 398, Study 2: N = 996, the sample was representative for participants' sex, age, region, and settlement size). The findings from these studies were mutually congruent and complementary. In general, the data suggested that short-term mating was unrelated or even negatively related to reproductive success. Conversely, long-term mating was positively associated with reproductive success (number of children in Study 1; number of children and grandchildren in Study 2) and there were indices that the beneficial role of long-term mating is more pronounced in males, which is in accordance with Bateman's third principle. Observed age of first reproduction mediated the link between long-term mating and number of children but only in male participants (Study 2). There were no clear indications of the position of the mating patterns in human life history trajectories; however, the obtained data suggested that long-term mating has some characteristics of fast life history dynamics. Findings are implicative for sexual selection and life history theory in humans.
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Affiliation(s)
- Janko Međedović
- Institute of Criminological and Sociological
Research, Belgrade, Serbia
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15
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Warrington NM, Hwang LD, Nivard MG, Evans DM. Estimating direct and indirect genetic effects on offspring phenotypes using genome-wide summary results data. Nat Commun 2021; 12:5420. [PMID: 34521848 PMCID: PMC8440517 DOI: 10.1038/s41467-021-25723-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 08/26/2021] [Indexed: 01/12/2023] Open
Abstract
Estimation of direct and indirect (i.e. parental and/or sibling) genetic effects on phenotypes is becoming increasingly important. We compare several multivariate methods that utilize summary results statistics from genome-wide association studies to determine how well they estimate direct and indirect genetic effects. Using data from the UK Biobank, we contrast point estimates and standard errors at individual loci compared to those obtained using individual level data. We show that Genomic structural equation modelling (SEM) outperforms the other methods in accurately estimating conditional genetic effects and their standard errors. We apply Genomic SEM to fertility data in the UK Biobank and partition the genetic effect into female and male fertility and a sibling specific effect. We identify a novel locus for fertility and genetic correlations between fertility and educational attainment, risk taking behaviour, autism and subjective well-being. We recommend Genomic SEM be used to partition genetic effects into direct and indirect components when using summary results from genome-wide association studies.
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Affiliation(s)
- Nicole M Warrington
- Institute for Molecular Biosciences, The University of Queensland, St Lucia, QLD, Australia.
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, Australia.
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
- Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Liang-Dar Hwang
- Institute for Molecular Biosciences, The University of Queensland, St Lucia, QLD, Australia
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, Australia
| | - Michel G Nivard
- Department of Biological Psychology, Faculty of Behaviour and Movement Sciences, VU University, Amsterdam, The Netherlands
| | - David M Evans
- Institute for Molecular Biosciences, The University of Queensland, St Lucia, QLD, Australia
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, Australia
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
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16
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Long-term mating positively predicts both reproductive fitness and parental investment. J Biosoc Sci 2021; 54:912-923. [PMID: 34365983 DOI: 10.1017/s0021932021000407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Empirical data on the relations between mating and reproductive success are rare for humans, especially for industrial and post-industrial populations. Existing data show that mating (and especially long-term mating) can be beneficial for fitness, especially that of males. This finding is in line with the hypothesis of sexual selection operating in human populations. The present research expands on previous studies by: 1) analysing additional fitness indicators, including having children with different partners; 2) including parental investment in the analysis as another important marker of sexual selection; 3) analysing several mediators between mating, reproductive fitness and parental investment, i.e. age of first and last reproduction and desired number of children. The data were obtained in 2019 from a sample of parents living in Serbia (N=497). The findings showed that long-term mating (duration of longest partner relationship) was positively related to parental investment and number of offspring and grand-offspring. Furthermore, the link between long-term mating and reproductive success was completely mediated by the age of first reproduction and desired number of children. Short-term mating (number of sexual partners) was marginally positively related to the number of children participants had with different partners and negatively related to parental investment. No sex differences in the link between mating, fitness and parental investment were detected. In general, the signatures of sexual selection were weak in the present data, but those that were detected were in line with sexual selection theory. The present findings provide a deeper insight into the adaptive function of mating and also the mechanism of how mating is beneficial for fitness.
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17
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Van Winkle Z, Conley D. Genome-Wide Heritability Estimates for Family Life Course Complexity. Demography 2021; 58:1575-1602. [PMID: 34251430 DOI: 10.1215/00703370-9373608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Sequence analysis is an established method used to study the complexity of family life courses. Although individual and societal characteristics have been linked with the complexity of family trajectories, social scientists have neglected the potential role of genetic factors in explaining variation in family transitions and events across the life course. We estimate the genetic contribution to sequence complexity and a wide range of family demographic behaviors using genomic relatedness-based, restricted maximum likelihood models with data from the U.S. Health and Retirement Study. This innovative methodological approach allows us to provide the first estimates of the heritability of composite life course outcomes-that is, sequence complexity. We demonstrate that a number of family demographic indicators (e.g., the age at first birth and first marriage) are heritable and provide evidence that composite metrics can be influenced by genetic factors. For example, our results show that 11% of the total variation in the complexity of differentiated family sequences is attributable to genetic influences. Moreover, we test whether this genetic contribution varies by social environment as indexed by birth cohort over a period of rapid changes in family norms during the twentieth century. Interestingly, we find evidence that the complexity of fertility and differentiated family trajectories decreased across cohorts, but we find no evidence that the heritability of the complexity of partnership trajectories changed across cohorts. Therefore, our results do not substantiate claims that lower normative constraints on family demographic behavior increase the role of genes.
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Affiliation(s)
- Zachary Van Winkle
- Sciences Po, Observatoire Sociologique du Changement (OSC), CNRS, Paris, France.,Nuffield College, University of Oxford, Oxford, United Kingdom
| | - Dalton Conley
- Princeton University, Princeton, NJ, USA.,National Bureau of Economic Research, Cambridge, MA, USA
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18
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Unraveling the Balance between Genes, Microbes, Lifestyle and the Environment to Improve Healthy Reproduction. Genes (Basel) 2021; 12:genes12040605. [PMID: 33924000 PMCID: PMC8073673 DOI: 10.3390/genes12040605] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/08/2021] [Accepted: 04/17/2021] [Indexed: 12/16/2022] Open
Abstract
Humans’ health is the result of a complex and balanced interplay between genetic factors, environmental stimuli, lifestyle habits, and the microbiota composition. The knowledge about their single contributions, as well as the complex network linking each to the others, is pivotal to understand the mechanisms underlying the onset of many diseases and can provide key information for their prevention, diagnosis and therapy. This applies also to reproduction. Reproduction, involving almost 10% of our genetic code, is one of the most critical human’s functions and is a key element to assess the well-being of a population. The last decades revealed a progressive decline of reproductive outcomes worldwide. As a consequence, there is a growing interest in unveiling the role of the different factors involved in human reproduction and great efforts have been carried out to improve its outcomes. As for many other diseases, it is now clear that the interplay between the underlying genetics, our commensal microbiome, the lifestyle habits and the environment we live in can either exacerbate the outcome or mitigate the adverse effects. Here, we aim to analyze how each of these factors contribute to reproduction highlighting their individual contribution and providing supporting evidence of how to modify their impact and overall contribution to a healthy reproductive status.
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19
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Peng H, Wu X, Wen Y, Du X, Li C, Liang H, Lin J, Liu J, Ge F, Huo Z, He J, Liang W. Age at first birth and lung cancer: a two-sample Mendelian randomization study. Transl Lung Cancer Res 2021; 10:1720-1733. [PMID: 34012788 PMCID: PMC8107761 DOI: 10.21037/tlcr-20-1216] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background Growing evidence suggests that female reproductive factors, like age at first birth (AFB), may play a potential role in the progression of lung cancer (LC). However, previous studies are susceptible to confounding factors, inadequate attention to variation by histology or reverse causality. Few studies have comprehensively evaluated their association and the causal effect remains unclear. Methods We aimed to determine whether AFB is causally correlated with the risk of LC, by means of utilizing aggregated data from the large genome-wide association studies conducted on AFB (251,151 individuals) and data of LC from International Lung and Cancer Consortium (ILCCO, 11,348 cases and 15,861 controls). We used 10 AFB-related single nucleotide polymorphisms as instrument variables and applied several two-sample Mendelian randomization (MR) methods. Secondary results according to different histological subtypes of lung cancer were also implemented. Results Conventional inverse-variance weighted method indicated that genetic predisposition towards number unit (1 year) increase of AFB was associated with a 18% lower risk of LC [odds ratio (OR) =0.82, 95% confidence interval (CI): 0.69–0.97; P=0.029]. When results were examined by histotypes, an inverse association was observed between genetically predisposed number unit (1 year) increase of AFB and lung adenocarcinoma (OR =0.75, 95% CI: 0.59–0.97, P=0.017) but not with squamous cell lung cancer (OR =0.77, 95% CI: 0.57–1.05, P=0.103). The results demonstrated no association between number unit decrease of AFB and LC. Pleiotropy was not presented through sensitivity analyses including MR pleiotropy residual sum and outlier test (P=0.412). Genetic predisposition towards older AFB was additionally associated with longer years of schooling (OR =1.12, 95% CI: 1.08–1.16, P<0.001), lower body mass index (OR =0.93, 95% CI: 0.88–0.98, P=0.004) and less alcohol consumption (OR =0.99, 95% CI: 0.99–1.00, P=0.004). Conclusions Our study suggested that older AFB was a causal protective factor in the progression of LC. Further studies elucidating the potential mechanisms are needed.
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Affiliation(s)
- Haoxin Peng
- Department of Thoracic Oncology and Surgery, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.,Nanshan School, Guangzhou Medical University, Guangzhou, China
| | - Xiangrong Wu
- Department of Thoracic Oncology and Surgery, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.,Nanshan School, Guangzhou Medical University, Guangzhou, China
| | - Yaokai Wen
- Department of Thoracic Oncology and Surgery, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.,Nanshan School, Guangzhou Medical University, Guangzhou, China
| | - Xiaoqin Du
- Department of Thoracic Oncology and Surgery, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Caichen Li
- Department of Thoracic Oncology and Surgery, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Hengrui Liang
- Department of Thoracic Oncology and Surgery, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jinsheng Lin
- Department of Thoracic Oncology and Surgery, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.,Nanshan School, Guangzhou Medical University, Guangzhou, China
| | - Jun Liu
- Department of Thoracic Oncology and Surgery, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Fan Ge
- Department of Thoracic Oncology and Surgery, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.,First Clinical School, Guangzhou Medical University, Guangzhou, China
| | - Zhenyu Huo
- Department of Thoracic Oncology and Surgery, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.,Nanshan School, Guangzhou Medical University, Guangzhou, China
| | - Jianxing He
- Department of Thoracic Oncology and Surgery, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Wenhua Liang
- Department of Thoracic Oncology and Surgery, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.,Medical Oncology, The First People's Hospital of Zhaoqing, Zhaoqing, China
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20
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McGrath IM, Mortlock S, Montgomery GW. Genetic Regulation of Physiological Reproductive Lifespan and Female Fertility. Int J Mol Sci 2021; 22:2556. [PMID: 33806348 PMCID: PMC7961500 DOI: 10.3390/ijms22052556] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/23/2021] [Accepted: 03/02/2021] [Indexed: 12/30/2022] Open
Abstract
There is substantial genetic variation for common traits associated with reproductive lifespan and for common diseases influencing female fertility. Progress in high-throughput sequencing and genome-wide association studies (GWAS) have transformed our understanding of common genetic risk factors for complex traits and diseases influencing reproductive lifespan and fertility. The data emerging from GWAS demonstrate the utility of genetics to explain epidemiological observations, revealing shared biological pathways linking puberty timing, fertility, reproductive ageing and health outcomes. The observations also identify unique genetic risk factors specific to different reproductive diseases impacting on female fertility. Sequencing in patients with primary ovarian insufficiency (POI) have identified mutations in a large number of genes while GWAS have revealed shared genetic risk factors for POI and ovarian ageing. Studies on age at menopause implicate DNA damage/repair genes with implications for follicle health and ageing. In addition to the discovery of individual genes and pathways, the increasingly powerful studies on common genetic risk factors help interpret the underlying relationships and direction of causation in the regulation of reproductive lifespan, fertility and related traits.
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Affiliation(s)
| | | | - Grant W. Montgomery
- Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, St Lucia, QLD 4072, Australia; (I.M.M.); (S.M.)
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21
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Human Life Histories as Dynamic Networks: Using Network Analysis to Conceptualize and Analyze Life History Data. EVOLUTIONARY PSYCHOLOGICAL SCIENCE 2020. [DOI: 10.1007/s40806-020-00252-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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22
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Hong Z. Modelling the on-going natural selection of educational attainment in contemporary societies. J Theor Biol 2020; 493:110210. [PMID: 32092304 DOI: 10.1016/j.jtbi.2020.110210] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2019] [Revised: 02/18/2020] [Accepted: 02/19/2020] [Indexed: 12/19/2022]
Abstract
There has been substantial increase in education attainment (EA) in both developing and developed countries over the past century. I present a simulation model to examine the potential evolutionary trajectories of EA under current selective pressure in western populations. With the assumption that EA is negatively correlated with fitness and has both a genetic component and a cultural component, I show that when prestige-biased transmission of the EA (i.e. people with more education are more likely to be copied) is present, the phenotype of EA is likely to keep increasing in the short term, yet the genetic component of EA may undergo a constant decline and become the limiting factor in further phenotypic increase.
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Affiliation(s)
- Ze Hong
- Department of Human Evolutionary Biology, Harvard University, 11 Divinity Avenue, Cambridge MA, 02138, USA.
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23
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The strength and pattern of natural selection on gene expression in rice. Nature 2020; 578:572-576. [PMID: 32051590 DOI: 10.1038/s41586-020-1997-2] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 12/13/2019] [Indexed: 01/12/2023]
Abstract
Levels of gene expression underpin organismal phenotypes1,2, but the nature of selection that acts on gene expression and its role in adaptive evolution remain unknown1,2. Here we assayed gene expression in rice (Oryza sativa)3, and used phenotypic selection analysis to estimate the type and strength of selection on the levels of more than 15,000 transcripts4,5. Variation in most transcripts appears (nearly) neutral or under very weak stabilizing selection in wet paddy conditions (with median standardized selection differentials near zero), but selection is stronger under drought conditions. Overall, more transcripts are conditionally neutral (2.83%) than are antagonistically pleiotropic6 (0.04%), and transcripts that display lower levels of expression and stochastic noise7-9 and higher levels of plasticity9 are under stronger selection. Selection strength was further weakly negatively associated with levels of cis-regulation and network connectivity9. Our multivariate analysis suggests that selection acts on the expression of photosynthesis genes4,5, but that the efficacy of selection is genetically constrained under drought conditions10. Drought selected for earlier flowering11,12 and a higher expression of OsMADS18 (Os07g0605200), which encodes a MADS-box transcription factor and is a known regulator of early flowering13-marking this gene as a drought-escape gene11,12. The ability to estimate selection strengths provides insights into how selection can shape molecular traits at the core of gene action.
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24
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Međedović J, Kovačević U. Personality as a state-dependent behavior: Do childhood poverty and pregnancy planning moderate the link between personality and fitness? PERSONALITY AND INDIVIDUAL DIFFERENCES 2020. [DOI: 10.1016/j.paid.2019.109625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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25
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Abstract
In recent years there have been attempts to explain religiousness from an evolutionary viewpoint. However, empirical data on this topic are still lacking. In the present study, the behavioural ecological theoretical framework was used to explore the relations between religiousness, harsh environment, fitness (reproductive success and parental investment) and fitness-related outcomes (age at first birth, desired number of children and the romantic relationship duration). The data were collected from 461 individuals from a community sample who were near the end of their reproductive phase (54% females, Mage = 51.75; SD = 6.56). Positive links between religiousness, harsh environment, fitness and fitness-related outcomes were expected, with the exception of age at first birth, for which a negative association was hypothesized. Hence, the main assumption of the study was that religiousness has some attributes of fast life-history phenotypes - that it emerges from a harsh environment and enables earlier reproduction. The study findings partially confirmed these hypotheses. Religiousness was positively related to environmental harshness but only on a zero-order level. Religious individuals had higher reproductive success (this association was especially pronounced in males) but religiousness did not show associations with parental investment. Religiousness was positively associated with desired number of children and negatively associated with age at first birth, although the latter association was only marginally significant in the multivariate analyses. Finally, path analysis showed that desired number of children and age at first birth completely mediated the relation between religiousness and reproductive success. The data confirmed the biologically adaptive function of religiousness in contemporary populations and found the mediating processes that facilitate fitness in religious individuals. Furthermore, the findings initiate a more complex view of religiousness in a life-history context which could be fruitful for future research: a proposal labelled as 'ontogeny-dependent life-history theory of religiousness'.
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26
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Gupta MK, Vadde R. Genetic Basis of Adaptation and Maladaptation via Balancing Selection. ZOOLOGY 2019; 136:125693. [PMID: 31513936 DOI: 10.1016/j.zool.2019.125693] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 07/03/2019] [Indexed: 10/26/2022]
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27
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Ni G, Amare AT, Zhou X, Mills N, Gratten J, Lee SH. The genetic relationship between female reproductive traits and six psychiatric disorders. Sci Rep 2019; 9:12041. [PMID: 31427629 PMCID: PMC6700195 DOI: 10.1038/s41598-019-48403-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 07/31/2019] [Indexed: 12/31/2022] Open
Abstract
Female reproductive behaviours have important implications for evolutionary fitness and health of offspring. Here we used the second release of UK Biobank data (N = 220,685) to evaluate the association between five female reproductive traits and polygenic risk scores (PRS) projected from genome-wide association study summary statistics of six psychiatric disorders (N = 429,178). We found that the PRS of attention-deficit/hyperactivity disorder (ADHD) were strongly associated with age at first birth (AFB) (genetic correlation of -0.68 ± 0.03), age at first sexual intercourse (AFS) (-0.56 ± 0.03), number of live births (NLB) (0.36 ± 0.04) and age at menopause (-0.27 ± 0.04). There were also robustly significant associations between the PRS of eating disorder (ED) and AFB (0.35 ± 0.06), ED and AFS (0.19 ± 0.06), major depressive disorder (MDD) and AFB (-0.27 ± 0.07), MDD and AFS (-0.27 ± 0.03) and schizophrenia and AFS (-0.10 ± 0.03). These associations were mostly explained by pleiotropic effects and there was little evidence of causal relationships. Our findings can potentially help improve reproductive health in women, hence better child outcomes. Our findings also lend partial support to the evolutionary hypothesis that causal mutations underlying psychiatric disorders have positive effects on reproductive success.
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Affiliation(s)
- Guiyan Ni
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA, 5000, Australia
- School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia
| | - Azmeraw T Amare
- South Australian Academic Health Science and Translation centre, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia
- Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, SA, Australia
| | - Xuan Zhou
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA, 5000, Australia
| | - Natalie Mills
- Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, SA, Australia
| | - Jacob Gratten
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
- Mater Research Institute, University of Queensland, Brisbane, Queensland, 4072, Australia
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA, 5000, Australia.
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28
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The genetics of human fertility. Curr Opin Psychol 2019; 27:41-45. [DOI: 10.1016/j.copsyc.2018.07.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 07/25/2018] [Accepted: 07/30/2018] [Indexed: 12/15/2022]
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29
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Lawn RB, Sallis HM, Taylor AE, Wootton RE, Smith GD, Davies NM, Hemani G, Fraser A, Penton-Voak IS, Munafò MR. Schizophrenia risk and reproductive success: a Mendelian randomization study. ROYAL SOCIETY OPEN SCIENCE 2019; 6:181049. [PMID: 31031992 PMCID: PMC6458425 DOI: 10.1098/rsos.181049] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 01/07/2019] [Indexed: 06/09/2023]
Abstract
Schizophrenia is a debilitating and heritable mental disorder associated with lower reproductive success. However, the prevalence of schizophrenia is stable over populations and time, resulting in an evolutionary puzzle: how is schizophrenia maintained in the population, given its apparent fitness costs? One possibility is that increased genetic liability for schizophrenia, in the absence of the disorder itself, may confer some reproductive advantage. We assessed the correlation and causal effect of genetic liability for schizophrenia with number of children, age at first birth and number of sexual partners using data from the Psychiatric Genomics Consortium and UK Biobank. Linkage disequilibrium score regression showed little evidence of genetic correlation between genetic liability for schizophrenia and number of children (r g = 0.002, p = 0.84), age at first birth (r g = -0.007, p = 0.45) or number of sexual partners (r g = 0.007, p = 0.42). Mendelian randomization indicated no robust evidence of a causal effect of genetic liability for schizophrenia on number of children (mean difference: 0.003 increase in number of children per doubling in the natural log odds ratio of schizophrenia risk, 95% confidence interval (CI): -0.003 to 0.009, p = 0.39) or age at first birth (-0.004 years lower age at first birth, 95% CI: -0.043 to 0.034, p = 0.82). We find some evidence of a positive effect of genetic liability for schizophrenia on number of sexual partners (0.165 increase in the number of sexual partners, 95% CI: 0.117-0.212, p = 5.30×10-10). These results suggest that increased genetic liability for schizophrenia does not confer a fitness advantage but does increase mating success.
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Affiliation(s)
- Rebecca B. Lawn
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
- School of Psychological Science, University of Bristol, Bristol BS8 1TU, UK
| | - Hannah M. Sallis
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
- School of Psychological Science, University of Bristol, Bristol BS8 1TU, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Amy E. Taylor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
- School of Psychological Science, University of Bristol, Bristol BS8 1TU, UK
| | - Robyn E. Wootton
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
- School of Psychological Science, University of Bristol, Bristol BS8 1TU, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Neil M. Davies
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
| | - Abigail Fraser
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
| | - Ian S. Penton-Voak
- School of Psychological Science, University of Bristol, Bristol BS8 1TU, UK
| | - Marcus R. Munafò
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK
- School of Psychological Science, University of Bristol, Bristol BS8 1TU, UK
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30
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The heritability of fertility makes world population stabilization unlikely in the foreseeable future. EVOL HUM BEHAV 2019. [DOI: 10.1016/j.evolhumbehav.2018.09.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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31
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Gajbhiye R, Fung JN, Montgomery GW. Complex genetics of female fertility. NPJ Genom Med 2018; 3:29. [PMID: 30345074 PMCID: PMC6185946 DOI: 10.1038/s41525-018-0068-1] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 08/13/2018] [Accepted: 09/12/2018] [Indexed: 01/10/2023] Open
Abstract
Variation in reproductive lifespan and female fertility have implications for health, population size and ageing. Fertility declines well before general signs of menopause and is also adversely affected by common reproductive diseases, including polycystic ovarian syndrome (PCOS) and endometriosis. Understanding the factors that regulate the timing of puberty and menopause, and the relationships with fertility are important for individuals and for policy. Substantial genetic variation exists for common traits associated with reproductive lifespan and for common diseases influencing female fertility. Genetic studies have identified mutations in genes contributing to disorders of reproduction, and in the last ten years, genome-wide association studies (GWAS) have transformed our understanding of common genetic contributions to these complex traits and diseases. These studies have made great progress towards understanding the genetic factors contributing to variation in traits and diseases influencing female fertility. The data emerging from GWAS demonstrate the utility of genetics to explain epidemiological observations, revealing shared biological pathways linking puberty timing, fertility, reproductive ageing and health outcomes. Many variants implicate DNA damage/repair genes in variation in the age at menopause with implications for follicle health and ageing. In addition to the discovery of individual genes and pathways, the increasingly powerful studies on common genetic risk factors help interpret the underlying relationships and direction of causation in the regulation of reproductive lifespan, fertility and related traits.
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Affiliation(s)
- Rahul Gajbhiye
- Institute for Molecular Bioscience, University of Queensland, St. Lucia, QLD 4072 Australia
- Department of Clinical Research, ICMR-National Institute for Research in Reproductive Health, J. M. Street, Parel Mumbai, 400012 India
| | - Jenny N. Fung
- Institute for Molecular Bioscience, University of Queensland, St. Lucia, QLD 4072 Australia
| | - Grant W. Montgomery
- Institute for Molecular Bioscience, University of Queensland, St. Lucia, QLD 4072 Australia
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32
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Ni G, Gratten J, Wray NR, Lee SH. Age at first birth in women is genetically associated with increased risk of schizophrenia. Sci Rep 2018; 8:10168. [PMID: 29977057 PMCID: PMC6033923 DOI: 10.1038/s41598-018-28160-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 06/14/2018] [Indexed: 11/10/2022] Open
Abstract
Previous studies have shown an increased risk for mental health problems in children born to both younger and older parents compared to children of average-aged parents. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age at first birth in women (AFB). Here, we use independent data from the UK Biobank (N = 38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, and to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value = 1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value = 3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is -0.16 (SE = 0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE = 0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia in the UK Biobank sample. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health.
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Affiliation(s)
- Guiyan Ni
- Australian Center for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA, 5000, Australia
- School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia
| | - Jacob Gratten
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Sang Hong Lee
- Australian Center for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA, 5000, Australia.
- School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia.
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, 4072, Australia.
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33
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Leveraging GWAS for complex traits to detect signatures of natural selection in humans. Curr Opin Genet Dev 2018; 53:9-14. [PMID: 29913353 DOI: 10.1016/j.gde.2018.05.012] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 05/29/2018] [Accepted: 05/31/2018] [Indexed: 02/08/2023]
Abstract
Natural selection can shape the genetic architecture of complex traits. In human populations, signals of positive selection at genetic loci have been detected through a variety of genome-wide scanning approaches without the knowledge of how genes affect traits or fitness. In the past decade, genome-wide association studies (GWAS) have provided unprecedented insights into the genetic basis of quantitative variation in complex traits. Summary statistics generated from these GWAS have been shown to be an extraordinary data source that can be utilized to detect and quantify natural selection in the genetic architecture of complex traits. In this review, we focus on recent discoveries about selection on genetic variants associated with human complex traits based on GWAS-facilitated methods.
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34
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Corbett S, Courtiol A, Lummaa V, Moorad J, Stearns S. The transition to modernity and chronic disease: mismatch and natural selection. Nat Rev Genet 2018; 19:419-430. [DOI: 10.1038/s41576-018-0012-3] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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35
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Zickert N, Geuze RH, van der Feen FE, Groothuis TG. Fitness costs and benefits associated with hand preference in humans: A large internet study in a Dutch sample. EVOL HUM BEHAV 2018. [DOI: 10.1016/j.evolhumbehav.2018.01.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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36
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Abstract
Modern molecular genetic datasets, primarily collected to study the biology of human health and disease, can be used to directly measure the action of natural selection and reveal important features of contemporary human evolution. Here we leverage the UK Biobank data to test for the presence of linear and nonlinear natural selection in a contemporary population of the United Kingdom. We obtain phenotypic and genetic evidence consistent with the action of linear/directional selection. Phenotypic evidence suggests that stabilizing selection, which acts to reduce variance in the population without necessarily modifying the population mean, is widespread and relatively weak in comparison with estimates from other species.
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37
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When genes and environment disagree: Making sense of trends in recent human evolution. Proc Natl Acad Sci U S A 2017; 113:7693-5. [PMID: 27402758 DOI: 10.1073/pnas.1608532113] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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38
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Tropf FC, Mandemakers JJ. Is the Association Between Education and Fertility Postponement Causal? The Role of Family Background Factors. Demography 2017; 54:71-91. [PMID: 28070853 DOI: 10.1007/s13524-016-0531-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
A large body of literature has demonstrated a positive relationship between education and age at first birth. However, this relationship may be partly spurious because of family background factors that cannot be controlled for in most research designs. We investigate the extent to which education is causally related to later age at first birth in a large sample of female twins from the United Kingdom (N = 2,752). We present novel estimates using within-identical twin and biometric models. Our findings show that one year of additional schooling is associated with about one-half year later age at first birth in ordinary least squares (OLS) models. This estimate reduced to only a 1.5-month later age at first birth for the within-identical twin model controlling for all shared family background factors (genetic and family environmental). Biometric analyses reveal that it is mainly influences of the family environment-not genetic factors-that cause spurious associations between education and age at first birth. Last, using data from the Office for National Statistics, we demonstrate that only 1.9 months of the 2.74 years of fertility postponement for birth cohorts 1944-1967 could be attributed to educational expansion based on these estimates. We conclude that the rise in educational attainment alone cannot explain differences in fertility timing between cohorts.
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Affiliation(s)
- Felix C Tropf
- Department of Sociology/Nuffield College, University of Oxford, Manor Road, Oxford, OX13UQ, UK.
- University of Groningen/ICS, Grote Rozenstraat 31a, 9712 TG, Groningen, The Netherlands.
| | - Jornt J Mandemakers
- Department of Social Sciences, Wageningen University, Wageningen, The Netherlands
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39
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Tropf FC, Lee SH, Verweij RM, Stulp G, van der Most PJ, de Vlaming R, Bakshi A, Briley DA, Rahal C, Hellpap R, Iliadou AN, Esko T, Metspalu A, Medland SE, Martin NG, Barban N, Snieder H, Robinson MR, Mills MC. Hidden heritability due to heterogeneity across seven populations. Nat Hum Behav 2017; 1:757-765. [PMID: 29051922 PMCID: PMC5642946 DOI: 10.1038/s41562-017-0195-1] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Meta-analyses of genome-wide association studies (GWAS), which dominate genetic discovery are based on data from diverse historical time periods and populations. Genetic scores derived from GWAS explain only a fraction of the heritability estimates obtained from whole-genome studies on single populations, known as the ‘hidden heritability’ puzzle. Using seven sampling populations (N=35,062), we test whether hidden heritability is attributed to heterogeneity across sampling populations and time, showing that estimates are substantially smaller from across compared to within populations. We show that the hidden heritability varies substantially: from zero (height), to 20% for BMI, 37% for education, 40% for age at first birth and up to 75% for number of children. Simulations demonstrate that our results more likely reflect heterogeneity in phenotypic measurement or gene-environment interaction than genetic heterogeneity. These findings have substantial implications for genetic discovery, suggesting that large homogenous datasets are required for behavioural phenotypes and that gene-environment interaction may be a central challenge for genetic discovery.
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Affiliation(s)
- Felix C Tropf
- Department of Sociology/Nuffield College, University of Oxford, Oxford, OX1 3UQ, UK.
| | - S Hong Lee
- School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia
| | - Renske M Verweij
- Department of Sociology/Interuniversity Center for Social Science Theory and Methodology, University of Groningen, Groningen, 9712 TG, The Netherlands
| | - Gert Stulp
- Department of Sociology/Interuniversity Center for Social Science Theory and Methodology, University of Groningen, Groningen, 9712 TG, The Netherlands
| | - Peter J van der Most
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, 9700 RB, The Netherlands
| | - Ronald de Vlaming
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, 3062 PA, The Netherlands.,Department of Complex Trait Genetics, University Amsterdam, Amsterdam, The Netherlands
| | - Andrew Bakshi
- Institute of Molecular Biosciences, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Daniel A Briley
- Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, 61820-9998, USA
| | - Charles Rahal
- Department of Sociology/Nuffield College, University of Oxford, Oxford, OX1 3UQ, UK
| | - Robert Hellpap
- Department of Sociology/Nuffield College, University of Oxford, Oxford, OX1 3UQ, UK
| | - Anastasia N Iliadou
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, PO Box 281, Stockholm, SE-171 77, Sweden
| | - Tõnu Esko
- Estonian Genome Center, University of Tartu, 51010, Tartu, Estonia
| | - Andres Metspalu
- Estonian Genome Center, University of Tartu, 51010, Tartu, Estonia
| | - Sarah E Medland
- Quantitative Genetics Laboratory, Queensland Institute of Medical Research Berghofer Medical Research Institute, Brisbane, QLD, 4029, Australia
| | - Nicholas G Martin
- Quantitative Genetics Laboratory, Queensland Institute of Medical Research Berghofer Medical Research Institute, Brisbane, QLD, 4029, Australia
| | - Nicola Barban
- Department of Sociology/Nuffield College, University of Oxford, Oxford, OX1 3UQ, UK
| | - Harold Snieder
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, 9700 RB, The Netherlands
| | - Matthew R Robinson
- Institute of Molecular Biosciences, The University of Queensland, Brisbane, QLD, 4072, Australia.,Department of Computational Biology, University of Lausanne, Lausanne, CH-1015, Switzerland
| | - Melinda C Mills
- Department of Sociology/Nuffield College, University of Oxford, Oxford, OX1 3UQ, UK
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40
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Mostafavi H, Berisa T, Day FR, Perry JRB, Przeworski M, Pickrell JK. Identifying genetic variants that affect viability in large cohorts. PLoS Biol 2017; 15:e2002458. [PMID: 28873088 PMCID: PMC5584811 DOI: 10.1371/journal.pbio.2002458] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 08/03/2017] [Indexed: 12/20/2022] Open
Abstract
A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness. As a step towards this goal, we developed a method to examine whether individual genetic variants, or sets of genetic variants, currently influence viability. The approach consists in testing whether the frequency of an allele varies across ages, accounting for variation in ancestry. We applied it to the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort and to the parents of participants in the UK Biobank. Across the genome, we found only a few common variants with large effects on age-specific mortality: tagging the APOE ε4 allele and near CHRNA3. These results suggest that when large, even late-onset effects are kept at low frequency by purifying selection. Testing viability effects of sets of genetic variants that jointly influence 1 of 42 traits, we detected a number of strong signals. In participants of the UK Biobank of British ancestry, we found that variants that delay puberty timing are associated with a longer parental life span (P~6.2 × 10−6 for fathers and P~2.0 × 10−3 for mothers), consistent with epidemiological studies. Similarly, variants associated with later age at first birth are associated with a longer maternal life span (P~1.4 × 10−3). Signals are also observed for variants influencing cholesterol levels, risk of coronary artery disease (CAD), body mass index, as well as risk of asthma. These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry. We also found marked differences between males and females, most notably at the CHRNA3 locus, and variants associated with risk of CAD and cholesterol levels. Beyond our findings, the analysis serves as a proof of principle for how upcoming biomedical data sets can be used to learn about selection effects in contemporary humans. Our global understanding of adaptation in humans is limited to indirect statistical inferences from patterns of genetic variation, which are sensitive to past selection pressures. We introduced a method that allowed us to directly observe ongoing selection in humans by identifying genetic variants that affect survival to a given age (i.e., viability selection). We applied our approach to the GERA cohort and parents of the UK Biobank participants. We found viability effects of variants near the APOE and CHRNA3 genes, which are associated with the risk of Alzheimer disease and smoking behavior, respectively. We also tested for the joint effect of sets of genetic variants that influence quantitative traits. We uncovered an association between longer life span and genetic variants that delay puberty timing and age at first birth. We also detected detrimental effects of higher genetically predicted cholesterol levels, body mass index, risk of coronary artery disease (CAD), and risk of asthma on survival. Some of the observed effects differ between males and females, most notably those at the CHRNA3 gene and variants associated with risk of CAD and cholesterol levels. Beyond this application, our analysis shows how large biomedical data sets can be used to study natural selection in humans.
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Affiliation(s)
- Hakhamanesh Mostafavi
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
- * E-mail: (HM); (MP); (JKP)
| | - Tomaz Berisa
- New York Genome Center, New York, New York, United States of America
| | - Felix R. Day
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - John R. B. Perry
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
| | - Molly Przeworski
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
- Department of Systems Biology, Columbia University, New York, New York, United States of America
- * E-mail: (HM); (MP); (JKP)
| | - Joseph K. Pickrell
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
- New York Genome Center, New York, New York, United States of America
- * E-mail: (HM); (MP); (JKP)
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41
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Lee SH, Weerasinghe WMSP, van der Werf JHJ. Genotype-environment interaction on human cognitive function conditioned on the status of breastfeeding and maternal smoking around birth. Sci Rep 2017; 7:6087. [PMID: 28729621 PMCID: PMC5519601 DOI: 10.1038/s41598-017-06214-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Accepted: 06/08/2017] [Indexed: 11/23/2022] Open
Abstract
We estimated genotype by environment interaction (G × E) on later cognitive performance and educational attainment across four unique environments, i.e. 1) breastfed without maternal smoking, 2) breastfed with maternal smoking, 3) non-breastfed without maternal smoking and 4) non-breastfed with maternal smoking, using a novel design and statistical approach that was facilitated by the availability of datasets with the genome-wide single nucleotide polymorphisms (SNPs). There was significant G × E for both fluid intelligence (p-value = 1.0E-03) and educational attainment (p-value = 8.3E-05) when comparing genetic effects in the group of individuals who were breastfed without maternal smoking with those not breastfed without maternal smoking. There was also significant G × E for fluid intelligence (p-value = 3.9E-05) when comparing the group of individuals who were breastfed with maternal smoking with those not breastfed without maternal smoking. Genome-wide significant SNPs were different between different environmental groups. Genomic prediction accuracies were significantly higher when using the target and discovery sample from the same environmental group than when using those from the different environmental groups. This finding demonstrates G × E has important implications for future studies on the genetic architecture, genome-wide association studies and genomic predictions.
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Affiliation(s)
- S Hong Lee
- School of Environmental and Rural Science, University of New England, Armidale, New South Wales 2351, Australia.
| | - W M Shalanee P Weerasinghe
- School of Environmental and Rural Science, University of New England, Armidale, New South Wales 2351, Australia
| | - Julius H J van der Werf
- School of Environmental and Rural Science, University of New England, Armidale, New South Wales 2351, Australia
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42
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Verweij RM, Mills MC, Tropf FC, Veenstra R, Nyman A, Snieder H. Sexual dimorphism in the genetic influence on human childlessness. Eur J Hum Genet 2017; 25:1067-1074. [PMID: 28794429 PMCID: PMC5555389 DOI: 10.1038/ejhg.2017.105] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Revised: 05/11/2017] [Accepted: 05/30/2017] [Indexed: 01/19/2023] Open
Abstract
Previous research has found a genetic component of human reproduction and childlessness. Others have argued that the heritability of reproduction is counterintuitive due to a frequent misinterpretation that additive genetic variance in reproductive fitness should be close to zero. Yet it is plausible that different genetic loci operate in male and female fertility in the form of sexual dimorphism and that these genes are passed on to the next generation. This study examines the extent to which genetic factors influence childlessness and provides an empirical test of genetic sexual dimorphism. Data from the Swedish Twin Register (N=9942) is used to estimate a classical twin model, a genomic-relatedness-matrix restricted maximum likelihood (GREML) model on twins and estimates polygenic scores of age at first birth on childlessness. Results show that the variation in individual differences in childlessness is explained by genetic differences for 47% in the twin model and 59% for women and 56% for men using the GREML model. Using a polygenic score (PGS) of age at first birth (AFB), the odds of remaining childless are around 1.25 higher for individuals with 1 SD higher score on the AFB PGS, but only for women. We find that different sets of genes influence childlessness in men and in women. These findings provide insight into why people remain childless and give evidence of genetic sexual dimorphism.
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Affiliation(s)
- Renske M Verweij
- Department of Sociology and ICS, University of Groningen, Groningen, The Netherlands
| | - Melinda C Mills
- Department of Sociology and Nuffield College, University of Oxford, Oxford, UK
| | - Felix C Tropf
- Department of Sociology and Nuffield College, University of Oxford, Oxford, UK
| | - René Veenstra
- Department of Sociology and ICS, University of Groningen, Groningen, The Netherlands
| | - Anastasia Nyman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Harold Snieder
- Department of Epidemiology, University Medical Center Groningen, Groningen, The Netherlands
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43
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Cho JI, Basnyat B, Jeong C, Di Rienzo A, Childs G, Craig SR, Sun J, Beall CM. Ethnically Tibetan women in Nepal with low hemoglobin concentration have better reproductive outcomes. EVOLUTION MEDICINE AND PUBLIC HEALTH 2017; 2017:82-96. [PMID: 28567284 PMCID: PMC5442430 DOI: 10.1093/emph/eox008] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Accepted: 03/12/2017] [Indexed: 12/24/2022]
Abstract
Background and objectives: Tibetans have distinctively low hemoglobin concentrations at high altitudes compared with visitors and Andean highlanders. This study hypothesized that natural selection favors an unelevated hemoglobin concentration among Tibetans. It considered nonheritable sociocultural factors affecting reproductive success and tested the hypotheses that a higher percent of oxygen saturation of hemoglobin (indicating less stress) or lower hemoglobin concentration (indicating dampened response) associated with higher lifetime reproductive success. Methodology: We sampled 1006 post-reproductive ethnically Tibetan women residing at 3000–4100 m in Nepal. We collected reproductive histories by interviews in native dialects and noninvasive physiological measurements. Regression analyses selected influential covariates of measures of reproductive success: the numbers of pregnancies, live births and children surviving to age 15. Results: Taking factors such as marriage status, age of first birth and access to health care into account, we found a higher percent of oxygen saturation associated weakly and an unelevated hemoglobin concentration associated strongly with better reproductive success. Women who lost all their pregnancies or all their live births had hemoglobin concentrations significantly higher than the sample mean. Elevated hemoglobin concentration associated with a lower probability a pregnancy progressed to a live birth. Conclusions and implications: These findings are consistent with the hypothesis that unelevated hemoglobin concentration is an adaptation shaped by natural selection resulting in the relatively low hemoglobin concentration of Tibetans compared with visitors and Andean highlanders.
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Affiliation(s)
- Jang Ik Cho
- Department of Epidemiology and Biostatistics, Case Western Reserve University, School of Medicine, Cleveland, OH 44109, USA
| | - Buddha Basnyat
- Patan Hospital, Oxford University Clinical Research Unit-Nepal, Kathmandu, Nepal and Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Choongwon Jeong
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Anna Di Rienzo
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Geoff Childs
- Department of Anthropology, Washington University, St. Louis, MO 63130, USA
| | - Sienna R Craig
- Department of Anthropology, Dartmouth College, Hanover, NH 03755, USA
| | - Jiayang Sun
- Department of Epidemiology and Biostatistics, Case Western Reserve University, School of Medicine, Cleveland, OH 44109, USA
| | - Cynthia M Beall
- Department of Anthropology, Case Western Reserve University, Cleveland, OH 44106, USA
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44
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Stulp G, Sear R, Schaffnit SB, Mills MC, Barrett L. The Reproductive Ecology of Industrial Societies, Part II : The Association between Wealth and Fertility. HUMAN NATURE-AN INTERDISCIPLINARY BIOSOCIAL PERSPECTIVE 2017; 27:445-470. [PMID: 27670437 PMCID: PMC5107208 DOI: 10.1007/s12110-016-9272-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Studies of the association between wealth and fertility in industrial populations have a rich history in the evolutionary literature, and they have been used to argue both for and against a behavioral ecological approach to explaining human variability. We consider that there are strong arguments in favor of measuring fertility (and proxies thereof) in industrial populations, not least because of the wide availability of large-scale secondary databases. Such data sources bring challenges as well as advantages, however. The purpose of this article is to illustrate these by examining the association between wealth and reproductive success in the United States, using the National Longitudinal Study of Youth 1979. We conduct a broad-based exploratory analysis of the relationship between wealth and fertility, employing both cross-sectional and longitudinal approaches, and multiple measures of both wealth (income and net worth) and fertility (lifetime reproductive success and transitions to first, second and third births). We highlight the kinds of decisions that have to be made regarding sample selection, along with the selection and construction of explanatory variables and control measures. Based on our analyses, we find a positive effect of both income and net worth on fertility for men, which is more pronounced for white men and for transitions to first and second births. Income tends to have a negative effect on fertility for women, while net worth is more likely to positively predict fertility. Different reproductive strategies among different groups within the same population highlight the complexity of the reproductive ecology of industrial societies. These results differ in a number of respects from other analyses using the same database. We suggest this reflects the impossibility of producing a definitive analysis, rather than a failure to identify the “correct” analytical strategy. Finally, we discuss how these findings inform us about (mal)adaptive decision-making.
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Affiliation(s)
- Gert Stulp
- Department of Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.,Department of Sociology, University of Groningen / Inter-university Center for Social Science Theory and Methodology (ICS), Grote Rozenstraat 31, 9712, TG , Groningen, The Netherlands
| | - Rebecca Sear
- Department of Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
| | - Susan B Schaffnit
- Department of Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Melinda C Mills
- Department of Sociology and Nuffield College, University of Oxford, Manor Road, Oxford, OX1 3UQ, UK
| | - Louise Barrett
- Department of Psychology, University of Lethbridge, Lethbridge, AB, T1K 3M4, Canada
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45
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The Reproductive Ecology of Industrial Societies, Part I : Why Measuring Fertility Matters. HUMAN NATURE-AN INTERDISCIPLINARY BIOSOCIAL PERSPECTIVE 2017; 27:422-444. [PMID: 27670436 PMCID: PMC5107203 DOI: 10.1007/s12110-016-9269-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Is fertility relevant to evolutionary analyses conducted in modern industrial societies? This question has been the subject of a highly contentious debate, beginning in the late 1980s and continuing to this day. Researchers in both evolutionary and social sciences have argued that the measurement of fitness-related traits (e.g., fertility) offers little insight into evolutionary processes, on the grounds that modern industrial environments differ so greatly from those of our ancestral past that our behavior can no longer be expected to be adaptive. In contrast, we argue that fertility measurements in industrial society are essential for a complete evolutionary analysis: in particular, such data can provide evidence for any putative adaptive mismatch between ancestral environments and those of the present day, and they can provide insight into the selection pressures currently operating on contemporary populations. Having made this positive case, we then go on to discuss some challenges of fertility-related analyses among industrialized populations, particularly those that involve large-scale databases. These include “researcher degrees of freedom” (i.e., the choices made about which variables to analyze and how) and the different biases that may exist in such data. Despite these concerns, large datasets from multiple populations represent an excellent opportunity to test evolutionary hypotheses in great detail, enriching the evolutionary understanding of human behavior.
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Selection against variants in the genome associated with educational attainment. Proc Natl Acad Sci U S A 2017; 114:E727-E732. [PMID: 28096410 DOI: 10.1073/pnas.1612113114] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Epidemiological and genetic association studies show that genetics play an important role in the attainment of education. Here, we investigate the effect of this genetic component on the reproductive history of 109,120 Icelanders and the consequent impact on the gene pool over time. We show that an educational attainment polygenic score, POLYEDU, constructed from results of a recent study is associated with delayed reproduction (P < 10-100) and fewer children overall. The effect is stronger for women and remains highly significant after adjusting for educational attainment. Based on 129,808 Icelanders born between 1910 and 1990, we find that the average POLYEDU has been declining at a rate of ∼0.010 standard units per decade, which is substantial on an evolutionary timescale. Most importantly, because POLYEDU only captures a fraction of the overall underlying genetic component the latter could be declining at a rate that is two to three times faster.
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Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat Genet 2016; 48:1462-1472. [PMID: 27798627 PMCID: PMC5695684 DOI: 10.1038/ng.3698] [Citation(s) in RCA: 159] [Impact Index Per Article: 19.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2016] [Accepted: 09/22/2016] [Indexed: 12/16/2022]
Abstract
The genetic architecture of human reproductive behavior-age at first birth (AFB) and number of children ever born (NEB)-has a strong relationship with fitness, human development, infertility and risk of neuropsychiatric disorders. However, very few genetic loci have been identified, and the underlying mechanisms of AFB and NEB are poorly understood. We report a large genome-wide association study of both sexes including 251,151 individuals for AFB and 343,072 individuals for NEB. We identified 12 independent loci that are significantly associated with AFB and/or NEB in a SNP-based genome-wide association study and 4 additional loci associated in a gene-based effort. These loci harbor genes that are likely to have a role, either directly or by affecting non-local gene expression, in human reproduction and infertility, thereby increasing understanding of these complex traits.
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How Cognitive Genetic Factors Influence Fertility Outcomes: A Mediational SEM Analysis. Twin Res Hum Genet 2016; 19:628-637. [DOI: 10.1017/thg.2016.82] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Utilizing a newly released cognitive Polygenic Score (PGS) from Wave IV of Add Health (n = 1,886), structural equation models (SEMs) examining the relationship between PGS and fertility (which is approximately 50% complete in the present sample), employing measures of verbal IQ and educational attainment as potential mediators, were estimated. The results of indirect pathway models revealed that verbal IQ mediates the positive relationship between PGS and educational attainment, and educational attainment in turn mediates the negative relationship between verbal IQ and a latent fertility measure. The direct path from PGS to fertility was non-significant. The model was robust to controlling for age, sex, and race; furthermore, the results of a multigroup SEM revealed no significant differences in the estimated path coeficients across sex. These results indicate that those predisposed towards higher verbal IQ by virtue of higher PGS values are also predisposed towards trading fertility against time spent in education, which contributes to those with higher PGS values producing fewer offspring at this stage in their life course.
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The effects of resource availability and the demographic transition on the genetic correlation between number of children and grandchildren in humans. Heredity (Edinb) 2016; 118:186-192. [PMID: 27624115 DOI: 10.1038/hdy.2016.81] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2016] [Revised: 07/29/2016] [Accepted: 08/01/2016] [Indexed: 11/08/2022] Open
Abstract
Studies of evolutionary change require an estimate of fitness, and lifetime reproductive success is widely used for this purpose. However, many species face a trade-off between the number and quality of offspring and in such cases number of grandoffspring may better represent the genetic contribution to future generations. Here, we apply quantitative genetic methods to a genealogical data set on humans from Finland to address how the genetic correlation between number of children and grandchildren is influenced by the severity of the trade-off between offspring quality and quantity, as estimated by different levels of resource access among individuals in the population. Further, we compare the genetic correlation before and after the demographic transition to low mortality and fertility rates. The genetic correlation was consistently high (0.79-0.92) with the strongest correlations occurring in individuals with higher access to resources and before the demographic transition, and a tendency for lower correlations in resource poor individuals and after the transition. These results indicate that number of grandoffspring is a slightly better predictor of long-term genetic fitness than number of offspring in a human population across a range of environmental conditions, and more generally, that patterns of resource availability need to be taken into account when estimating genetic covariances with fitness.
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What Explains the Heritability of Completed Fertility? Evidence from Two Large Twin Studies. Behav Genet 2016; 47:36-51. [PMID: 27522223 DOI: 10.1007/s10519-016-9805-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Accepted: 08/08/2016] [Indexed: 10/21/2022]
Abstract
In modern societies, individual differences in completed fertility are linked with genotypic differences between individuals. Explaining the heritability of completed fertility has been inconclusive, with alternative explanations centering on family formation timing, pursuit of education, or other psychological traits. We use the twin subsample from the Midlife Development in the United States study and the TwinsUK study to examine these issues. In total, 2606 adult twin pairs reported on their completed fertility, age at first birth and marriage, level of education, Big Five personality traits, and cognitive ability. Quantitative genetic Cholesky models were used to partition the variance in completed fertility into genetic and environmental variance that is shared with other phenotypes and residual variance. Genetic influences on completed fertility are strongly related to family formation timing and less strongly, but significantly, with psychological traits. Multivariate models indicate that family formation, demographic, and psychological phenotypes leave no residual genetic variance in completed fertility in either dataset. Results are largely consistent across U.S. and U.K. sociocultural contexts.
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