Participating studies and self-employment measures

The analyses were performed within the Gentrepreneur Consortium [53], [54], which included two out of the five studies that participate in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium [63] and fourteen additional studies. The discovery studies included the Age, Gene/Environment Susceptibility–Reykjavik Study (AGES), the Austrian Stroke Prevention Study (ASPS), the Erasmus Rucphen Family study (ERF), the Gutenberg Health Study (GHS), Health 2000 (H2000), the Helsinki Birth Cohort Study (HBCS), the Health and Retirement Study (HRS), the Cooperative Health Research in the Region of Augsburg (KORA S4), the Northern Finland Birth Cohort 1966 (NFBC1966), the Netherlands Twin Register Cohort 1 (NTR1), the Netherlands Twin Register Cohort 2 (NTR2), the Rotterdam Study Baseline (RS-I), the Rotterdam Study Extension of Baseline (RS-II), the Rotterdam Study Young (RS-III), the SardiNIA Study of Aging (SardiNIA), the Study of Health in Pomerania (SHIP), The Hellenic study of Interactions between SNPs & Eating in Atherosclerosis Susceptibility (THISEAS), the UK Adult Twin Registry (TwinsUK), and the Cardiovascular Risk in Young Finns Study (YFS). The Swedish Twin Registry (STR) served as an in silico replication study, as genome-wide data were only available following the completion of the discovery stage.

The studies collected data regarding occupational status using questionnaires or interviews, from which self-employment status was distilled. Self-employment measures were defined in collaboration with the consortium leaders to minimize heterogeneity across participating studies. The cases were defined as individuals who were self-employed at least once, and the controls were defined as individuals who were never self-employed during their working life. However, for a number of studies, reliable data regarding work-life history were unavailable, possibly resulting in the inclusion of previously self-employed individuals in the control group. The details regarding the background and self-employment measures of each of the discovery studies and of the replication study are given in Table S1.

Ethics statement

All participating studies were approved by the relevant institutional review boards or the local research ethics committees, including the Icelandic National Bioethics Committee (VSN: 00-063), the Icelandic Data Protection Authority, and the Institutional Review Board for the National Institute on Aging (AGES); the Ethics Committee of the Medical Faculty of the University of Graz (ASPS); the Medical Ethics Committee at Erasmus University which approved the protocols for the ascertainment and examination of human subjects (ERF); the local ethics committee and data safety commissioner, the sampling design was approved by the federal data safety commissioner (GHS); the Ethics Committee for Epidemiology and Public Health in the Hospital District of Helsinki and Uusimaa in Finland, in accordance with the ethical standards of the Declaration of Helsinki (H2000); the Ethics Committee of Epidemiology and Public Health of the Hospital District of Helsinki and Uusimaa (HBCS); the Health Sciences Institutional Review Board at the University of Michigan (HRS); the Ethics Committee of the Bavarian Medical Association (KORA S4); the Ethics Committee of the University Hospital of Oulu (NFBC1966); the VU University Medical Ethical Committee (NTR); the Medical Ethics Committee of the Erasmus Medical Center (RS); the local Ethics Committee for the Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche and the MedStar Research Institute, responsible for intramural research at the National Institute of Aging (SardiNIA); the Ethics Committee of the University of Greifswald (SHIP), the Ethical Review Board in Stockholm (STR); the Bioethics Committee of the Harokopio University of Athens (THISEAS); the NRES Committee London-Westminster (TwinsUK); the local Ethics Committees of the participating universities (YFS). Written informed consent was provided by all of the participants.

Genotyping, imputation, and quality control

The seventeen participating studies used a variety of commercially available SNP genotyping platforms to genotype their participants. Each study performed quality control of their genotypic data and imputed the genotypes of each participant to a common set of approximately 2.5 million SNPs from the HapMap CEU population. The exceptions to this were THISEAS, which only supplied results for directly genotyped SNPs, and HRS, which imputed to the 1,000 Genomes Project Phase I v3 panel. Prior to the meta-analysis, we performed parallel quality control of the association results for each study. SNPs were excluded on the basis of minor allele frequency (MAF<0.01 or MAF<0.05 if deemed necessary) and if the imputation quality (a measure of the observed variance divided by the expected variance of the imputed allele dosage from the imputation software output) was less than 0.4. Following these exclusions, approximately 2.4 million SNPs remained. Study-specific details regarding the genotyping, imputation, and quality control are given in Table S2.

Statistical analysis

Tetrachoric correlations were used to calculate self-employment correlations for MZ and DZ twin pairs. This analysis assumes a latent normally distributed tendency to engage in self-employment. We estimated the heritability of the tendency to engage in self-employment in the replication study using standard twin study methods, which were implemented in the program Mx [64]. Only complete twin pairs with data regarding self-employment status were included in the analysis and opposite-sex DZ twin pairs were excluded, resulting in a final sample size of 4,464 individuals. Specifically, for pooled males and females, males only, and females only, we fitted the three following nested models using the maximum likelihood approach on the raw data: (1) a model including an additive genetic effect, a shared common environment effect, and an individual-specific environment effect (the ACE model); (2) a model that included only an additive genetic and an individual-specific environment effect (the AE model); and (3) a model including only a common environment effect and an individual-specific environment effect (the CE model). For all of the samples, we controlled for a z-score of age by estimating age-specific thresholds. For the pooled sample, we additionally controlled for sex in a similar way.

We used the method that was recently developed by Yang et al. [52] to estimate the proportion of variance in the tendency to engage in self-employment that is explained by all of the common genotyped SNPs. The method is implemented in the GCTA software [65] and hinges on the assumption that in a sample of unrelated individuals, environmental factors segregate independently in the pedigree from the degree of genetic relatedness. In contrast to the twin study design, genetic relatedness is not inferred from the pedigree but is estimated directly from genome-wide SNP data. Under the assumption of no confounding by environmental variables, we can then estimate the accounted-for variance by relating the estimated genetic relatedness between pairs of individuals to their phenotypic correlation. The resulting estimate is actually a lower bound of the heritability that is estimated from classic twin and family studies. The reason for this is that twin and family studies capture the variation that is due to all of the additive causal variants, whereas the more recently developed method only captures the variants that are either directly genotyped or in linkage disequilibrium.

We used a combined sample of individuals from one of the discovery studies (RS-I) and the replication study (STR) to estimate the accounted-for variance. We restricted the sample from each study to individuals for whom data regarding self-employment were available. Additionally, we included only one randomly selected individual from each family in the STR sample. A second round of quality control of the genotypic data was then performed for both studies. In the RS-I sample, we excluded 3,748 SNPs because they failed a test of Hardy-Weinberg equilibrium at p<1×10⁻⁶. We removed 24,993 SNPs with minor allele frequencies that were lower than 0.01 and another 6,665 due to data missingness greater than 5%. In total, 5,374 individuals and 561,466 autosomal SNPs were included in the analysis. In the STR sample, we removed two SNPs because they failed a test of Hardy-Weinberg equilibrium at p<1×10⁻⁶. Another 628 SNPs with a minor allele frequency lower than 0.01 were removed, as were two SNPs with data missingness greater than 5%. Therefore, 643,924 autosomal SNPs and 2,589 individuals were included in the analysis.

We then estimated the genetic relationships among 7,963 individuals in the combined sample from the 301,115 common autosomal SNPs. We dropped one of any pair of individuals with an estimated genetic relationship that was >0.025 while maximizing the remaining sample size to exclude the possibility of ascribing shared environmental effects to genetic effects and/or including the effects of causal variants not correlated with the genotyped SNPs but captured by the pedigree. The maximum relatedness in the remaining sample of 6,223 individuals therefore approximately corresponds to cousins two to three times removed [52].

Next, the linear mixed model y = μ+g+e was fitted, where y is the binary phenotype, g the total additive genetic effect of the SNPs, and e is a residual effect. The restricted maximum likelihood (REML) was used to estimate the variance of the total additive genetic effect σ_g² of the SNPs by fitting the genetic relationships as the covariance structure. Because the analyzed phenotype is binary, σ_g² is the variance of the total additive genetics effects on the observed 0–1 scale. A latent normally distributed tendency to engage in self-employment was assumed when transforming the explained variance from the observed 0–1 scale to the latent scale using the transformation that is derived in the appendix of Dempster and Lerner [66]. For all of the analyses, we controlled for a z-score of age, study, and the first ten principal components of the genetic relationships of the combined sample. In the pooled sample, we also controlled for sex.

In addition to the Yang et al. [52] method, we employed a novel method developed by So et al. [67] that serves the same purpose, i.e., estimating the proportion of variance in the tendency to engage in self-employment that is explained by all of the common SNPs. However, in contrast to the Yang et al. [52] method, So et al.'s method does not require raw genotype data but attempts to recover the accounted-for variance from the meta-analysis results. Using PLINK [68], we restricted the meta-analysis results to SNPs that were present in the HapMap Phase II CEU panel (release 23a) and pruned those in strong linkage disequilibrium with other SNPs using a pairwise r² threshold of 0.25 in a window of 100 SNPs that slides in 25 SNP increments. After this procedure 172,742, 175,970, and 172,989 SNPs remained in the pooled males and females, males only, and females only sample, respectively. We used the Gaussian Kernel function, considered under the null-hypothesis of no association, and ran the simulation 500 times in each sample.

The genome-wide association analysis of self-employment was independently performed by each study according to a predefined analysis plan. The analyses were performed for pooled males and females, males only, and females only using an additive genetic model, controlling for age (≤29 [reference]; 30–39; 40–49;≥50) and sex in the pooled sample. To control for population stratification, the first four principal components of the genotypic data were also included if available. We provide details regarding the statistical analysis within each study in Table S2.

Following the association analyses, the genomic inflation factor λ was calculated for each sample to quantify any remaining population stratification or cryptic relatedness. The lowest inflation factor was 0.989, and the highest was 1.156, although this latter value was for a study that did not include the first four principal components of the genotypic data in the analysis (Table S3). Genomic control [69] was applied in samples with inflation factors that were greater than one by adjusting the test statistics.

We next performed fixed-effect meta-analyses of the association results from the discovery studies for pooled males and females, males only, and females only using METAL software [70]. Although the phenotype was defined as self-employment in each participating study, we could not harmonize the exact wording of the question on which the self-employment measure was based. In addition, the connotations of self-employment may depend to some extent on the level of economic development and culture. This may lead to unobserved gene-environment interactions that could introduce additional noise in the GWAS results pooled across studies. We combined the association results using weighted z-scores that were based on the p-values and the direction of the effects. This method first computes a per-study signed z-score for each SNP based on its p-value and the effect direction. The z-scores are then summed with weights that are proportional to the square root of the sample size of each study. Following the meta-analyses, only autosomal SNPs that were present in the Hapmap Phase II CEU panel (release 22, NCBI build 36) and in at least half of the contributing samples in each meta-analysis were retained prior to both reporting p-values and the creation of the Q–Q and Manhattan plots. We a priori set the genome-wide significance threshold to p<5×10⁻⁸. SNPs with p<1×10⁻⁵ were considered suggestive and also carried forward to the replication stage. The heterogeneity of the test statistics between the studies was assessed using the I² metric [71], [72] and Cochran's Q statistic [73].

Replication was attempted for significant and suggestive SNPs from each meta-analysis using an in silico replication study comprising 3,271 individuals. The association results for these SNPs were looked up in the replication study and meta-analyzed together with the discovery samples for pooled males and females, males only, and females only. To adjust for family relationships in the replication study, we performed family-based association tests implemented in the MERLIN software [74].

We used the discovery meta-analyses results to calculate gene-based p-values using the VEGAS program [75]. The positions of the UCSC Genome Browser hg18 assembly were employed to assign SNPs to genes, which included regions that were ±50 kb from the 5′ and 3′ UTRs.

For the prediction analyses, we followed the approach that was pioneered by The International Schizophrenia Consortium [76] and used the association results from the discovery meta-analyses to predict self-employment in the STR. Specifically, twelve overlapping sets of SNPs that were nominally associated in the discovery meta-analyses were created for different significance thresholds (p_T<0.01, p_T<0.05, p_T<0.1, p_T<0.2, p_T<0.3, p_T<0.4, p_T<0.5, p_T<0.6, p_T<0.7, p_T<0.8, p_T<0.9, and p_T≤1). These sets were used as inputs for score calculation in the STR. We restricted the STR sample to individuals for whom data regarding self-employment were available and included only one randomly selected individual from each family, resulting in a final sample size of 2,589 individuals for the prediction analyses.

Prior to calculating the scores for each individual in the STR, we followed [76] and selected all of the autosomal SNPs, pruning those in strong linkage disequilibrium with other SNPs. This process was performed using a pairwise r² threshold of 0.25 in a window of 200 SNPs that slides in five SNP increments. Following this exclusion process, 135,823 SNPs remained. The PLINK [68] ‘score’ function was then used to calculate the total score for each individual in the STR. The score is defined as the sum of the number of score alleles, weighted by the estimated coefficients from the discovery meta-analyses, divided by the number of non-missing genotypes. If an individual was missing a genotype, it was imputed as the mean genotype based on the score allele frequency in the STR. On average, the score was calculated from approximately 120,000 SNPs given that (1) the coefficients were only estimated for SNPs in the HapMap CEU population in the discovery meta-analyses, and (2) the overlap with the genotyped SNPs was not perfect. Lastly, we regressed self-employment onto the score using a logistic regression model. The variance that was explained by the score was estimated using the Nagelkerke pseudo-R² of the fitted model. We also calculated the area under the receiver operating characteristic curve (AUC) to evaluate the prediction accuracy.

Heritability of self-employment and the degree of variance that is accounted for by common SNPs

We used data from the Swedish Twin Registry (STR) and the classical twin design to estimate the heritability of the tendency to engage in self-employment. We computed the tetrachoric correlations between the tendencies to engage in self-employment within monozygotic (MZ) and dizygotic (DZ) twin pairs. Table 1 indicates that the correlations within the MZ twin pairs were consistently higher than within the DZ twin pairs for males only, for females only, and for pooled males and females. We note that the correlation within DZ twin pairs in the pooled sample was higher than for the DZ correlations in males and females when the two sexes are considered separately. This effect most likely results from imprecise estimation of the tetrachoric correlations due to the small number of cases. When we computed Pearson correlations, the pooled DZ twin pairs correlation was in between the male and female DZ twin pairs correlations. Applying Falconer's formula [77] to the correlations in Table 1, yields h² estimates of 0.39 for pooled males and females, 0.69 for males only, and 0.34 for females only.

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Table 1. Tetrachoric correlations in the tendency to engage in self-employment for MZ and DZ twin pairs in STR for pooled males and females, males only, and females only.

https://doi.org/10.1371/journal.pone.0060542.t001

A maximum likelihood approach was employed to estimate the relative contributions of the additive genetic (A), shared common environment (C), and individual-specific environment (E) components. This approach was performed using an ACE model and two nested submodels for pooled males and females, males only, and females only. Table 2 gives the estimates of the A component as 0.54 for pooled males and females, 0.67 for males only, and 0.38 for females only. The estimates of the C component were 0.01 for pooled males and females, 0.00 for males only, and 0.02 for females only. The A component was significant at the 95% confidence level for pooled males and females, and for males only, although the confidence intervals were very wide. This component was not significant for the females only analysis. However, the χ² test for goodness-of-fit and Akaike information criterion indicated that the AE model was the best-fitting model in all samples. In this submodel, the estimate for the A component for females only did not change markedly compared to the ACE model but was significant at the 95% confidence level. The estimates of the A component for pooled males and females, and males only were 0.55 and 0.67, respectively; these results were significant.

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Table 2. Results of fitting ACE, AE, and CE models to the tendency to engage in self-employment in STR for pooled males and females, males only, and females only.

https://doi.org/10.1371/journal.pone.0060542.t002

The recently developed method by Yang et al. [52] was employed to estimate the degree of variance in the tendency to engage in self-employment that is explained by all of the genotyped autosomal SNPs in the GWAS datasets. The proportion of the explained variance was estimated for pooled males and females, males only, and females only. To maximize the power of the analysis, we used a combined sample of one of the discovery studies (Rotterdam Study Baseline [RS-I]) and the STR. We estimated that 25% (p = 0.032) of the variance in the tendency to engage in self-employment could be explained by the common genotyped autosomal SNPs for pooled males and females (Table 3). The variance that could be explained for males only and for females only was 25% (p = 0.152) and 0% (p = 0.499), respectively. The estimates for males and females separately were not significantly different from one other. The fact that the variance that is explained was zero for females is most likely due to the very low number of female cases (n = 353) compared to the number of controls (n = 3,482). The estimation of the explained variance is therefore very imprecise. We also estimated the variance that was explained for pooled males and females, males only, and females only in the RS-I and the STR separately. The estimates were not significant because the standard errors of these estimates depend heavily on the sample size. However, considered in their entirety, the results were consistent with the estimates that we present for the combined RS-I and STR samples. Overall, the results for pooled males and females and for males indicated that the degree of variance in the tendency to engage in self-employment that is explained by all of the common autosomal SNPs simultaneously is only approximately half of the narrow-sense heritability that is estimated using the STR and the classical twin design. Furthermore, estimates using the method developed by So et al. [67] also provide non-zero estimates for heritability. Specifically, the accounted-for variance was 7% for pooled males and females, 21% for males only, and 15% for females only. However, confidence intervals and standard errors could not be calculated for these estimates because not all raw genotype data were available, prohibiting further interpretation of these results.

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Table 3. Variance in the tendency to engage in self-employment explained by all autosomal SNPs in a combined sample of RS-I and STR for pooled males and females, males only, and females only.

https://doi.org/10.1371/journal.pone.0060542.t003

Meta-analyses of genome-wide association studies

We performed genome-wide association analyses of self-employment using the data from sixteen discovery studies. These studies comprised 7,734 participants who had been self-employed at least once and 42,893 participants who did not report being self-employed. Table 4 includes the descriptive statistics for the studies. The mean ages in the pooled samples of males and females ranged from 31 to 68.8 years, and the average age across all of the studies was 53.4 years. Following independent association analyses for each study, we performed a fixed-effect meta-analysis of the study-level results for approximately 2.4 million SNPs using a pooled z-score approach.

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Table 4. Descriptive statistics of the sixteen discovery studies and the replication study.

https://doi.org/10.1371/journal.pone.0060542.t004

The discovery meta-analysis Q–Q plot (Figure 1A) did not indicate a strong deviation for the lowest p-values. However, no confounding issues related to population stratification, cryptic relatedness, or genotyping errors were detected, as no systematic deviation from the expectation under the null hypothesis of no association was observed [78]. As illustrated in the Manhattan plot (Figure 2A), we observed twenty SNPs with 4.1×10⁻⁶≤p<1×10⁻⁵ (Tables 5 and S4). The SNP with the lowest p-value, rs6906622 (p = 4.10×10⁻⁶), was located near the RNF144B gene, with most studies indicating that the minor allele increased the probability of being self-employed (Table 5).

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Figure 1. Q–Q plots of the self-employment discovery meta-analyses.

Q–Q plot of the self-employment discovery meta-analysis for (A) pooled males and females, (B) males only, and (C) females only. The grey shaded areas in the Q–Q plots represent the 95% confidence bands around the p-values.

https://doi.org/10.1371/journal.pone.0060542.g001

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Figure 2. Manhattan plots of the self-employment discovery meta-analyses.

Manhattan plot of the self-employment discovery meta-analysis for (A) pooled males and females, (B) males only, and (C) females only. SNPs are plotted on the x-axis according to their position on each chromosome against association with self-employment on the y-axis (shown as −log10 p-value). The solid line indicates the threshold for genome-wide significance (p<5×10⁻⁸) and the dashed line the threshold for suggestive SNPs (p<1×10⁻⁵).

https://doi.org/10.1371/journal.pone.0060542.g002

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Table 5. Top SNPs (p<1×10⁻⁵) from the self-employment discovery meta-analyses for pooled males and females, males only, and females only.

https://doi.org/10.1371/journal.pone.0060542.t005

We next attempted to replicate in silico the twenty suggestive SNPs in the STR (n = 3,271). Two of the twenty SNPs associated with self-employment were statistically significant at the 5% level in the replication study. However, the SNP effects were not in the same direction as in the majority of the discovery studies (Table S4), indicating that these SNPs were potential false positives. We then performed a combined meta-analysis of the discovery and replication studies. For all SNPs, the p-values were larger in the combined sample than in the discovery sample and did not reach genome-wide significance (Table S4).

The Q–Q plot for the male only meta-analysis (Figure 1B) gave a certain degree of suggestive evidence of association; however, no evidence of population stratification, cryptic relatedness, or genotyping errors was observed, as only certain SNPs–those with particularly low p-values–deviated from their expectation under the null hypothesis of no association. The female only meta-analysis Q–Q plot (Figure 1C) did not indicate a strong deviation for the lowest p-values and no evidence of population stratification, cryptic relatedness, or genotyping errors was observed. No SNPs reached genome-wide significance in the sex-stratified meta-analyses (Table 5), as can be observed in the Manhattan plots (Figures 2B and C). The male meta-analysis resulted in 22 suggestive SNPs with p<1×10⁻⁵, and the female meta-analysis resulted in sixteen suggestive SNPs (Tables 5, S5, and S6). The top SNP in males, rs6738407 (p = 1.52×10⁻⁷), was located in the HECW2 gene, and most studies reported that carrying the minor allele decreased the probability of being self-employed. The top SNP in females, rs2331548 (p = 1.93×10⁻⁶), was located near the CBR4 gene, and most studies estimated that carrying the minor allele decreased the probability of being self-employed.

The replication strategy for the 38 suggestive SNPs from the sex-stratified meta-analysis that were carried forward into the replication stage was similar to that used for the meta-analysis replication of the pooled data. We performed an in silico replication study using the data from the STR. None of the SNPs reached nominal significance (p<0.05) in the replication study for males only (n = 1,409, Table S5) and females only (n = 1,862, Table S6). In addition, for the majority of the suggestive SNPs, the direction of the effect was not consistently in the same direction as was reported in the majority of the discovery studies, again indicating that these SNPs were potential false positives. We meta-analyzed the results from the sex-stratified discovery meta-analysis and the replication study in a combined meta-analysis. For males, five SNPs had lower p-values compared to the male discovery meta-analysis, although none reached genome-wide significance (Table S5). In the combined meta-analysis for females, we observed that one SNP, rs562487, had a smaller p-value in this combined meta-analysis; however, this SNP did not reach genome-wide significance (p = 4.01×10⁻⁶; Table S6).

Gene-based association analyses

The findings from the discovery meta-analyses were used to perform gene-based association tests for seventeen genes that have been previously suggested to be candidate genes for entrepreneurship [48], [79], including ADORA2A, ADRA2A, COMT, DDC, DRD1, DRD2, DRD3, DRD4, DRD5, DYX1C1, HTR1B, HTR1E, HTR2A, KIAA0319 (DYX2), ROBO1, SLC6A3 (DAT1), and SNAP25. Genes with p<0.003 (0.05/17 genes) were considered significant, but none of the candidate genes reached this level (Table S7).

To identify novel genes that may be associated with self-employment, we tested 17,697 genes for pooled males and females, 17,698 genes for males only, and 17,699 genes for females only, implying a significance level of p<2.8×10⁻⁶. None of the analyzed genes reached this predetermined significance level (Tables S8, S9, and S10). The gene with the lowest p-value was SLC15A3 for the pooled male and female analysis (p = 1.63×10⁻⁴). For males only, the lowest p-value was for TMEM156 (1.61×10⁻⁴), and for females only, the lowest p-value was for PCP4 (p = 4.70×10⁻⁵).

We also sought to replicate the association that was reported by Nicolaou et al. [48] to exist between a common variant, rs1486011, which is located in the DRD3 gene, and the tendency to be an entrepreneur. The SNP was nominally significant in the discovery meta-analysis (p = 0.011; Table S11); however, most studies reported a positive effect of the C allele–opposite to that reported by Nicolaou et al. [48], corroborating the results from an earlier replication study [49]. We also sought to replicate this SNP in the sex-stratified discovery meta-analyses. In this analysis, we observed a certain degree of evidence for a positive effect of the C allele in males (p = 0.046; Table S11) but not in females (p = 0.112; Table S11).

Predicting self-employment from genotype data

We examined whether the results from the discovery meta-analyses could be used to predict self-employment in the replication study [76]. We pruned the set of autosomal SNPs to a subset of approximately 120,000 SNPs that are in approximate linkage equilibrium. In an initial prediction analysis, we included only the subset of these 120,000 SNPs that reached a 1% significance level. We calculated a predictive score for each individual in the replication study by determining, for each SNP, the product of the individual's number of effect alleles and the estimated regression coefficient from the discovery meta-analysis. This product was then summed across the included SNPs and divided by the number of included SNPs. We evaluated the predictive power of the SNPs by calculating the degree of variance in the tendency to engage in self-employment that was explained by the score and the area under the receiver operating characteristic curve (AUC). We repeated this prediction analysis eleven additional times, each time with a less stringent significance threshold required for a SNP to be included in the score. Hence, each time this analysis was performed, a larger subset of the 120,000 SNPs was analyzed.

For the pooled analysis of males and females (n = 2,589), the variance that was explained by the score reached a maximum of 0.184% when all SNPs were included (p = 0.039; Table S12). The scores for males only (n = 1,110) and for females only (n = 1,479) showed no evidence for association with self-employment (all p≥0.144, Table S12). Furthermore, we did not observe a consistent positive relationship between the variance in the tendency to engage in self-employment that was explained by the score and the significance threshold p_T (Figure 3).

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Figure 3. Prediction results.

Variance explained (Nagelkerke pseudo-R² from logistic regression) vs. p-value threshold p_T for including SNPs in the score calculation.

https://doi.org/10.1371/journal.pone.0060542.g003