1 Supplementary Note
2 Identification of 370 genetic loci for age at
3 first sex and birth linked to externalizing
4 behaviour
5
6 Melinda C. Mills1,2,†,*, Felix C. Tropf1,2,3,4,†, David M. Brazel1,2,†, Natalie van Zuydam5, Ahmad Vaez6,7, 7 eQTLGen Consortium, BIOS Consortium, Tune H. Pers8,9, Harold Snieder6, John R.B. Perry10, Ken K. 8 Ong10,†, Marcel den Hoed5,†, Nicola Barban11,†, and Felix R. Day10,†,* on behalf of the Human 9 Reproductive Behaviour Consortium 10 11 1 Leverhulme Centre for Demographic Science, University of Oxford, Oxford, United Kingdom 12 2 Nuffield College, University of Oxford, Oxford, United Kingdom 13 3 École Nationale de la Statistique et de L’administration Économique (ENSAE), Paris, France 14 4 Center for Research in Economics and Statistics (CREST), Paris, France 15 5 The Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University 16 and SciLifeLab, Uppsala, Sweden 17 6 Department of Epidemiology, University of Groningen, University Medical Center Groningen, 18 Groningen, The Netherlands 19 7 Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran 20 8 The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical 21 Sciences, University of Copenhagen, Copenhagen, Denmark 22 9 Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark 23 10 MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United 24 Kingdom 25 11 Department of Statistical Sciences, University of Bologna, Italy 26 27 † Denotes equal contribution 28 * Correspondence to Melinda C. Mills, [email protected], and Felix R. Day, 29 [email protected] 30
31 32 1 Contents 2 List of Supplementary Figures ...... 4 3 List of Supplementary Tables ...... 5 4 1. Background and Phenotype Definitions ...... 7 5 1.1 Background ...... 7 6 1.2 Phenotype Definitions ...... 7 7 2. Summary of Methods, Purpose of Analysis and Main Results ...... 8 8 3. Phenotypic and genotypic changes in the onset of human reproductive behaviour over time ...... 9 9 3.1 Phenotypic changes in the onset of human reproductive behaviour ...... 9 10 3.2 Heterogeneity in heritability across birth cohorts ...... 13 11 4. Overview of GWAS meta-analysis ...... 16 12 4.1 Participating cohorts ...... 16 13 4.2 Sample inclusion criteria ...... 17 14 4.3 Genotyping and imputation ...... 17 15 4.4 Models used to test for association ...... 18 16 4.5 Analysis of X chromosome ...... 18 17 4.6 Quality Control (QC): filters & diagnostic checks ...... 18 18 4.6.1 Filters ...... 19 19 4.6.2 Diagnostic graphs ...... 19 20 4.6.3 SNPs and cohorts excluded ...... 19 21 4.7 Meta Analyses ...... 20 22 4.8 MTAG results ...... 21 23 4.9 Summary of discovered loci and Manhattan plots ...... 21 24 5. Polygenic score calculation and prediction ...... 26 25 5.1 Calculation of polygenic scores ...... 26 26 5.2 Out of sample prediction ...... 26 27 5.3 Accounting for right-censoring and comparing top and bottom 5% PGS ...... 27 28 6. Testing population stratification and environmentally mediated parental genetic effects of 29 childhood socioeconomic status ...... 29 30 6.1 Testing Population Stratification: LD Score intercept test ...... 29 31 6.2 Polygenic score prediction by childhood socio-economic status ...... 30 32 7. Genetic correlations with related traits ...... 32 33 7.1 Genetic correlation with related traits ...... 32 1 7.2 Genetic correlations by sex ...... 33 2 8. Uncovering shared genetic etiology with Genomic SEM ...... 36 3 8.1 AFB and AFS regression educational attainment (EA) and trait X ...... 36 4 8.2 Reproductive biology and externalizing behaviour explanation of variance ...... 40 5 9. Bi-directional MR of reproductive behaviour, teenage behavioural disinhibition and onset of later 6 life disease...... 43 7 9.1 Background, methods and innovation ...... 43 8 9.2 Results MR ...... 43 9 10. Later age at first birth linked to parental longevity ...... 45 10 10.1 Background and innovation ...... 45 11 10.2 Data and measurement ...... 45 12 10.3 Methods of analysis ...... 45 13 10.4 Results: Later reproductive timing predicts parental longevity ...... 46 14 11. Gene prioritization ...... 47 15 11.1 Methods ...... 47 16 11.2 Results ...... 49 17 11.2.1 Candidate genes in brain ...... 49 18 11.2.2 Candidate genes in glands ...... 50 19 11.2.3 Candidate genes in female reproductive organs ...... 50 20 11.2.4 Candidate genes in male reproductive organs ...... 52 21 12. Sex-specific genetic effects ...... 53 22 12.1 Genetic overlap among sexes: LD score bivariate regression ...... 53 23 12.2 Sex specific loci ...... 56 24 12.2.1 Methods and identification of 10 additional associations for AFS and 1 for AFB ...... 56 25 12.2.2 Gene Prioritization Results AFS ...... 56 26 12.2.3 Gene Prioritization Results AFB ...... 56 27 13. Authorship ...... 57 28 14. Detailed Acknowledgments ...... 70 29 References ...... 76 30
31 1 List of Supplementary Figures 2 Figure S1. Summary and description of methods and main results, GWAS of age at first sex (AFS) and 3 age at first birth (AFB) 4 Figure S2. Correlation plot between Age at first birth and Age at first sex by birth cohort, UK Biobank 5 Figure S3. Age at first birth (AFB) panel A and Age at first sex (AFS) panel B by birth cohort, UK 6 Biobank 7 Figure S4A. SNP heritability for AFB, women in the UK Biobank 8 Figure S4B. SNP heritability for AFS, women and men in UK Biobank 9 Figure S5A-C. Manhattan plots, Age at first sex (AFS), Pooled (A), Women (B) and Men (C) 10 Figure S6A-C. Manhattan plots, Age at first birth (AFB), Pooled (A), Women (B) and Men (C) 11 Figure S7. Variance explained from Polygenic scores for Age at First Birth and Age at First Sex using 12 PRSice, LDPred and MTAG+LDPred in out-of-sample cohorts 13 Figure S8. Nelson-Aalen hazard estimates of first sex by age. Comparison between top 5% and 14 bottom 5% PGS of age at first sex 15 Figure S9. Nelson-Aalen hazard estimates of first birth by age. Comparison between top 5% and 16 bottom 5% PGS of age at first birth 17 Figure S10A. AFB PGS score by percentile groups and parent’s educational level 18 Figure S10B. AFS PGS score by percentile groups and parent’s educational level 19 Figure S11. Genetic correlations and SNP heritabilities between and among reproductive, 20 behavioural, psychiatric, substance use, personality and anthropometric traits 21 Figure S12A. A path diagram showing the structure of the genetic multiple regression model fit to EA 22 and AFB 23 Figure S12B. A path diagram showing the structure of the genetic multiple regression model fit to 24 AFS and EA 25 Figure S13. A heat map showing the genetic correlations between and among the fertility GWAS 26 phenotypes, the sex hormone phenotypes, and other phenotypes related to reproductive 27 biology, as calculated by LD score regression. 28 Figure S14. A path diagram for a GenomicSEM model of the relative associations of an externalizing 29 latent factor, age at menopause, and age at menarche with age at first birth in women 30 Figure S15. A heat map showing the genetic correlations between and among the fertility GWAS 31 phenotypes, the sex hormone phenotypes, and other phenotypes related to reproductive 32 biology, as calculated by LD score regression. 33 Figure S16. Coefficients (and Cis) of bi-directional MR of human reproductive behaviour (AFB, AFS), 34 age initiated smoking and educational attainment on Type 2 diabetes and Coronary Artery 35 Disease later in life 36 Figure S17. Protein-protein interactions identified using STRING for genes that are highly expressed 37 at the protein level in: A) brain and result in a nervous system or neurological behavior 38 phenotype in mutant mice; B) glands and result in an endocrine/exocrine phenotype in 39 mutant mice; C-D) female (C) or male (D) reproductive organs and result in a reproductive 40 phenotype in mutant mice. Pink lines highlight experimentally determined interactions. 41 Figure S18. Genetic overlap amongst the sexes for AFS and AFB, LD score bivariate regression 42 Figure S19. Gene prioritization of AFS and AFB by sex
43 1 List of Supplementary Tables 2 Table S1. Description of participating cohorts 3 Table S2 Cohort phenotype description 4 Table S3a. Sample size individuals with automsome chromosome information in participating 5 cohorts 6 Table S3b. Sample size individuals with sex chromosome information in participating cohorts 7 Table S4. Genotyping and imputation 8 Table S5. Description of SNP filtering and cohort exclusion for age at first birth (AFB) analyses for 9 women 10 Table S6. Description of SNP filtering and cohort exclusion for age at first sex (AFS) analyses for 11 women 12 Table S7. Description of SNP filtering and cohort exclusion for age at first birth (AFB) analyses for 13 women 14 Table S8. Description of SNP filtering and cohort exclusion for age at first sex (AFS) analyses for 15 women 16 Table S9. Association Results for 88 independent SNPs that reached genome-wide significance (P < 17 5×10-8) in the pooled-sex GWAS of Age at First birth (AFB), AFB Males and Females 18 Table S10. Association Results for 261 independent SNPs that reached genome-wide significance (P < 19 5×10-8) in the pooled-sex GWAS of Age at First Sex (AFS), AFS Males and Females 20 Table S11. Genetic correlations (rg) AFB and AFS with selected phenotypes 21 Table S12A . Unstandardized results from genetic multivariate regression models examining the 22 relationship between AFS in males and EA, accounting for the genetic correlation of AFS with a 23 third phenotype 24 Table S12B. Unstandardized results from genetic multivariate regression models examining the 25 relationship between EA and AFB, accounting for the genetic correlation of EA with a third 26 phenotype 27 Table S12C. Standardized results from genetic multivariate regression models examining the 28 relationship between AFS and EA, accounting for the genetic correlation of AFS with a third 29 phenotype 30 Table 12D. Unstandardized results from genetic multivariate regression models examining the 31 relationship between AFS and EA, accounting for the genetic correlation of AFS with a third 32 phenotype 33 Table S12E. Standardized results from genetic multivariate regression models examining the 34 relationship between EA and AFB in males, accounting for the genetic correlation of EA with a 35 third phenotype 36 Table S12F. Unstandardized results from genetic multivariate regression models examining the 37 relationship between EA and AFB in males, accounting for the genetic correlation of EA with a 38 third phenotype 39 Table S12G. Standardized results from genetic multivariate regression models examining the 40 relationship between EA and AFB in females, accounting for the genetic correlation of EA with 41 a third phenotype 42 Table 12H. Unstandardized results from genetic multivariate regression models examining the 43 relationship between EA and AFB in females, accounting for the genetic correlation of EA with 44 a third phenotype 45 Table 12I. Standardized results from genetic multivariate regression models examining the 46 relationship between AFS in males and EA, accounting for the genetic correlation of AFS with a 47 third phenotype 1 Table 12J. Unstandardized results from genetic multivariate regression models examining the 2 relationship between AFS in males and EA, accounting for the genetic correlation of AFS 3 with a third phenotype 4 Table 12K. Standardized results from genetic multivariate regression models examining the 5 relationship between AFS in females and EA, accounting for the genetic correlation of AFS 6 with a third phenotype 7 Table 12L. Unstandardized results from genetic multivariate regression models examining the 8 relationship between AFS in females and EA, accounting for the genetic correlation of AFS 9 with a third phenotype 10 Table 13A. Bi-Directional MR, Years of education and AFB and AFB/AFS with risk taking and age at 11 smoking initiation 12 Table S13B. Mendelian Randomization (MR) of age at first birth (AFB) to Coronary arterty disease 13 (CAD) and Type 2 diabetes (T2D) and age at first sex (AFS) to CAD and T2D, and 14 Educational Attainment to CAD and T2D 15 Table S14. Polygenic score (PGS) prediction of age at first birth (AFB), educational attainment (EA) 16 and risk on parental longevity 17 Table S15A. Results from CELLECT tissue enrichment analysis for age at first sex (AFS) 18 Table S15B. Results from CELLECT tissue enrichment analysis for age at first birth (AFB) 19 Table 15C. Results from CELLECT gene prioritization for age at first sex (AFS) 20 Table S15D. Results from CELLECT gene prioritization for age at first birth (AFB) 21 Table S15E. Results from CELLECT cell type enrichment analysis using mouse brain RNA sequencing 22 data for age at first sex (AFS) 23 Table S15F. Results from CELLECT cell type enrichment analysis using mouse brain RNA sequencing 24 data for age at first birth (AFB) 25 Table S15G. Results from CELLECT cell type enrichment analysis using tabula muris RNA sequencing 26 data for age at first sex (AFS) 27 Table S16A. Search terms used for the Phenolyzer analysis for the three areas of interest 28 Table S16B. Results of Phenolyzer analysis age at first birth (AFB) and age at first sex (AFS) 29 Table S17A. The results of in silico sequencing and in silico lookup of GWAS associations of AFB. 30 AF_EUR indicates the allele frequency of the alternative allele (A2) in the European 31 population. 32 Table S17B. The results of in silico sequencing and in silico lookup of GWAS associations of AFS. 33 AF_EUR indicates the allele frequency of the alternative allele (A2) in the European 34 population. 35 Table S18A. Summary data-based Mendelian Randomization (SMR) for age at first sex (AFS) 36 Table S18B. Summary data-based Mendelian Randomization (SMR) for age at first birth (AFB) 37 Table S19A. Summary of gene prioritization results across all approaches for age at first sex (AFS) 38 Table S19B. Summary of gene prioritization results across all approaches for age at first birth (AFB) 39 Table S19C. Summary of gene prioritization results across all approaches for age at first sex (AFS) and 40 age at first birth (AFB) 1 1. Background and Phenotype Definitions
2 1.1 Background 3 Previous studies have shown that the onset of human reproductive behaviour – age at first sexual 4 intercourse (AFS) and age at first birth (AFB) – have a genetic basis. AFB has a SNP-heritability of 5 15%1 and AFS 15-17% (see Section 3), with two genome-wide association studies (GWAS) in 2016 6 identifying 10 genetic loci linked to AFB2 and 38 associated with AFS.3 A detailed discussion of the 7 motivation behind the study of these traits including the evolutionary causes of genetic variance in 8 reproductive behaviour, additive and dominant genetic variation and environmental variation in 9 fertility behaviour can be found in the Supplementary Note of Barban et al. (2016).2 A description of 10 the data and methods used in this study can be found in the online Methods section appended to 11 the article.
12 The current analysis extends previous work in several appreciable ways. First, this study has a 13 sizeable increase in sample size, making this the largest GWAS to date on these phenotypes. 14 Previous work on AFS3 examined a small sample of 125,667 individuals from the UK Biobank with a 15 study on AFB2 examining 251,151 individuals. The current study is considerably larger for both AFS 16 (N=397,338 pooled; N=214,547 women; N=182,791) and AFB (N=542,901 pooled; N=418,758 17 women; N=124,008 men). A second extension is that we use 1000G imputed genotype data, which 18 in addition to the larger sample, allows us to detect considerably more signals. Third, we include an 19 X-Chromosome analysis, allowing us to uncover additional novel loci. A fourth advance is the ability 20 to find markedly more biological signals. Fifth, our extensive analyses of the correlation and 21 underlying etiology of these traits reveals an underlying genetic basis of AFS and AFB with other 22 traits. This includes externalizing behaviour and substance use for early AFS and AFB and links to 23 internalizing traits and infertility disease for later AFS and AFB. Sixth, we show that AFB is a stronger 24 predictor for late age onset of disease and parental longevity, even beyond known standard 25 predictors such as educational attainment. Finally, we demonstrate how that our polygenic scores 26 are sensitive to gene-environment correlation (rGE) and childhood socioeconomic status.
27 1.2 Phenotype Definitions 28 An overview of participating cohorts is found in Table S1, with a description provided shortly in 29 Section 3. The detailed phenotype definitions and questions drawn from each of the cohorts are 30 included in Supplementary Table S2.
31 Age at first sexual intercourse (AFS) is treated as a continuous measure and assessed using 32 questions such as What was your age when you first had sexual intercourse? This is often defined by 33 more detailed divisions such as (sexual intercourse includes vaginal, oral or anal intercourse). Ages 34 less than 12 are normally excluded. The UKBiobank requires confirmation of ages in the range 4-12, 35 and excludes all answers that were less than 4. For out of sample replication, other studies include 36 12 as the minimum age, if they do not have a study specific lower limit. Age at first sexual 37 intercourse tends to have a markedly non-normal distribution, so a within-sex inverse rank normal 38 transformation is required. 1 Age at first birth (AFB) is treated as a continuous measure either asked directly or created from 2 several survey questions (e.g., birthdate of participant and date of birth of first child). The most 3 common question was: How old were you when you had your first child? Or What is the date of birth 4 of your first child? Individuals were eligible for inclusion if they were assessed for AFB and had given 5 birth to a child.
6 2. Summary of Methods, Purpose of Analysis and Main Results 7 The Methods section in the main article describes all methods used in this paper in detail, 8 summarised in Figure S1. In this figure, dark grey boxes indicate the method of analysis, light grey 9 the purpose of the analysis and white a summary of the main results.
10 Historical changes. We first examined historical and phenotypic changes in the age distributions for 11 these phenotypes, followed by genotypic changes, estimating heritability by sex and across birth 12 cohorts.
13 Polygenic score (PGS) construction and prediction. Following the GWAS, we then engaged in a 14 variety of techniques to interrogate the PGS prediction. We constructed the PGS using multiple 15 techniques and test out of sample prediction. We applied the LD Score intercept test to test for 16 population stratification followed by survival models to examine the impact of right-censoring and 17 sex differences in PGS prediction. We also studied the sensitivity of our PGSs by childhood 18 socioeconomic status.
19 Correlation, etiology, causality and prediction. This was followed by five additional analyses to 20 explore various substantive and methodological questions. We used LD score regression to examine 21 the genetic correlations between traits. We engaged in Genomic SEM to understand the shared 22 genetic etiology of related traits. Bi-directional Mendelian Randomization was used to measure 23 causal pathways of our phenotypes with educational attainment, age at initiation of smoking and 24 risk taking and whether our PGSs had independent effects on later life diseases (type 2 diabetes, 25 coronary artery disease), once educational attainment and BMI were controlled for. Exploratory 26 Factor Analysis allowed us to breakdown the underlying components of reproductive behaviour into 27 those related to externalizing or disinhibition versus biological components. Finally, we estimated 28 survival models to examine whether reproductive timing was linked to parental longevity.
29 Biological annotation. We also carried out a variety of biological analyses. This included DEPICT for 30 candidate gene identification, CELLECT RNAseq mouse brain and Tabula muris RNAseq to identify 31 enriched mouse nervous system cell types. Phenolyzer was used to prioritize candidates using prior 32 knowledge of these phenotypes using machine learning on seed genes and predicted gene rankings. 33 In silico sequencing allowed us to identify non-synonymous variants and summary-based Mendelian 34 Randomization (SMR), HEIDI and eQTL to find evidence of gene expression. Sex-specific effects were 35 identified using LD score regression. We then integrated of these biological results to prioritize the 36 genes from all approaches and examine protein expression. This allowed us to identify the key genes 37 related to reproduction and externalizing behaviour as well as gene prioritization of sex-specific loci. 1 3. Phenotypic and genotypic changes in the onset of human 2 reproductive behaviour over time
3 3.1 Phenotypic changes in the onset of human reproductive behaviour 4 Over the past forty years, there has been a rapid postponement of age at first birth (AFB) by 4-5 5 years to a mean AFB for women around 29 many advanced societies.4 The biological ability to 6 conceive a child already starts to decline for many women as early as 25, with around 50% of 7 women sterile by the age of 40.5 This postponement has been related to multiple social, economic, 8 and cultural factors, which has been documented in several detailed reviews.4,6 A central factor is 9 the introduction of effective contraception and ability to control fertility and engage in individual 10 choice since the late 1960s. Another key factor is the well-documented association between 11 women’s gains in educational attainment and that relationship with later fertility, particularly for 12 more recent birth cohorts. This is related to women’s stronger labour market attachment and their 13 realization that fertility postponement avoids large motherhood wage penalties. In fact, by each a 14 year a woman delays motherhood, she increases her career earnings by 9%.6 Other factors are the 15 strong cultural and ideational changes and norms surrounding sexual behaviour, entry into 16 parenthood and the role of children who are often no longer strongly required for economic and 17 labour support to parents. Finally, multiple structural factors such as the availability of childcare, 18 gender equity, housing and resources all play a vital role.
19 Figure S2 documents how sexual debut was linked to first childbirth in earlier birth cohorts (<1941, 20 r=0.60) to a relative uncoupling in more recent birth cohorts (>1960, r=0.31). Related to this is a 21 large body of demographic work that has examined the decoupling of sex with marriage.6
22 Figure S2 illustrates the gradual decoupling of sexual initiation with reproduction. Here we see that 23 the correlation or timing between AFS and AFB was concentrated and closer together in earlier birth 24 cohorts whereas with more recent birth cohorts, it is increasingly more widely distributed over time. 25 In other words, the classic association of sexual behaviour with marriage and childbearing held in 26 earlier cohorts has waned over time, largely due to the introduction of effective contraception and 27 changes in social norms about sexual behaviour outside of a marital union.6 Note that the 28 implausible ‘immaculate conception’ outliers (i.e., age at first birth before first sex) shown in this 29 figure were removed prior to GWAS analysis.
30 Figure S3 (Panel A) examines phenotypic data from the UK Biobank and shows the shift in the 31 distribution of AFB not only to later to ages, but also a wider spread in the distribution itself. Figure 32 S3 (Panel B) of AFS shows that in earlier cohorts, there was a bi-modal distribution, one which had 33 earlier sexual intercourse often tied to socio-economic circumstances, problem or risky behaviour.7 34 The other group engaging in later sexual initiation, has been found to be tied to higher educational 35 goals and achievement with early sexual intercourse tied to with higher calculated risk of pregnancy, 36 which would disturb longer-term life planning and career goals.8 The panel also shows a narrowing 37 of the distribution over time to earlier ages. 1 Figure S1. Summary and description of methods and main results, GWAS of age at first sex (AFS) and age at first birth (AFB)
2 3 Dark grey indicates analysis method, light grey the purpose of the analysis and white the main results. AFB (age at first birth); AFS (age at first sex); PGS (polygenic score); MR 4 (Mendelian Randomisation); EA (Educational Attainment); SES (socioeconomic status); EFA (Exploratory Factor Analysis); BMI (Body Mass Index); PCOS (polycystic ovarian 5 syndrome); SD (Standard Deviation); F (females); M (males); SEM (structural equation model); LD (linkage disequilibrium) 6 1 Figure S1. Continued, Summary and description of methods and main results, GWAS of age at first sex (AFS) and age at first birth (AFB).
2
3 Dark grey indicates analysis method, light grey the purpose of the analysis and white the main results. AFB (age at first birth); AFS (age at first sex); PGS (polygenic score); MR 4 (Mendelian Randomisation); EA (Educational Attainment); SES (socioeconomic status); EFA (Exploratory Factor Analysis); BMI (Body Mass Index); PCOS (polycystic ovarian 5 syndrome); SD (Standard Deviation); F (females); M (males); SEM (structural equation model); LD (linkage disequilibrium) 1 Figure S2. Correlation plot between Age at first birth and Age at first sex by birth cohort, UK Biobank
2 1
2 Figure S3. Age at first birth (AFB) panel A and Age at first sex (AFS) panel B by birth cohort, UK 3 Biobank
4 3.2 Heterogeneity in heritability across birth cohorts 5 A recent study demonstrated that estimates from GWAS discoveries are substantially smaller across 6 populations compared to within populations.9 Simulations showed that the results reflected 7 heterogeneity in gene – environment interaction rather than genetic heterogeneity. In other words, 8 particularly for complex traits and diseases such as reproductive behaviour or others such as 9 educational attainment or BMI, it is more difficult to determine the influence of genetic versus 10 socio-environmental factors. That study demonstrated that although GWA studies combine data 11 from individuals across different time periods, it is implausible to assume that genetic effects are 12 uniform across time.
13 To test whether this was a concern with our current analysis, Figures S4A and B show how the SNP 14 heritability estimates change over time for AFB and AFS using the UK Biobank. Our SNP heritability
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1 estimate refers to the proportion of the additive genetic variance explained by common SNPs across 2 the genome over the overall phenotypic variance ( ) of the trait: 10 = 3 ℎ 4 The phenotypic variance is the sum of additive genetic and environmental variance, i.e., + 5 , where is the additive genetic variance explained by all common SNPs across the genome and = 6 is the residual variance. The methods we applied have been detailed elsewhere. 10–14 Briefly, we 7 applied a linear mixed model 8