The Molecular Genetic Architecture of Self-Employment

Economic variables such as income, education, and occupation are known to affect mortality and morbidity, such as cardiovascular disease, and have also been shown to be partly heritable. However, very little is known about which genes influence economic variables, although these genes may have both a direct and an indirect effect on health. We report results from the first large-scale collaboration that studies the molecular genetic architecture of an economic variable-entrepreneurship-that was operationalized using self-employment, a widely-available proxy. Our results suggest that common SNPs when considered jointly explain about half of the narrow-sense heritability of self-employment estimated in twin data (σg2/σP2= 25%, h2= 55%). However, a meta-analysis of genome-wide association studies across sixteen studies comprising 50,627 participants did not identify genome-wide significant SNPs. 58 SNPs with p<10-5were tested in a replication sample (n = 3,271), but none replicated. Furthermore, a gene-based test shows that none of the genes that were previously suggested in the literature to influence entrepreneurship reveal significant associations. Finally, SNP-based genetic scores that use results from the meta-analysis capture less than 0.2% of the variance in self-employment in an independent sample (p≥0.039). Our results are consistent with a highly polygenic molecular genetic architecture of self-employment, with many genetic variants of small effect. Although self-employment is a multi-faceted, heavily environmentally influenced, and biologically distal trait, our results are similar to those for other genetically complex and biologically more proximate outcomes, such as height, intelligence, personality, and several diseases

Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure.

Numerous genetic loci have been associated with systolic blood pressure (SBP) and diastolic blood pressure (DBP) in Europeans. We now report genome-wide association studies of pulse pressure (PP) and mean arterial pressure (MAP). In discovery (N = 74,064) and follow-up studies (N = 48,607), we identified at genome-wide significance (P = 2.7 × 10(-8) to P = 2.3 × 10(-13)) four new PP loci (at 4q12 near CHIC2, 7q22.3 near PIK3CG, 8q24.12 in NOV and 11q24.3 near ADAMTS8), two new MAP loci (3p21.31 in MAP4 and 10q25.3 near ADRB1) and one locus associated with both of these traits (2q24.3 near FIGN) that has also recently been associated with SBP in east Asians. For three of the new PP loci, the estimated effect for SBP was opposite of that for DBP, in contrast to the majority of common SBP- and DBP-associated variants, which show concordant effects on both traits. These findings suggest new genetic pathways underlying blood pressure variation, some of which may differentially influence SBP and DBP

The Molecular Genetic Architecture of Self-Employment

Peer reviewe

Heterogeneity in age-related white matter changes

White matter changes occur endemically in routine magnetic resonance imaging (MRI) scans of elderly persons. MRI appearance and histopathological correlates of white matter changes are heterogeneous. Smooth periventricular hyperintensities, including caps around the ventricular horns, periventricular lining and halos are likely to be of non-vascular origin. They relate to a disruption of the ependymal lining with subependymal widening of the extracellular space and have to be differentiated from subcortical and deep white matter abnormalities. For the latter a distinction needs to be made between punctate, early confluent and confluent types. Although punctate white matter lesions often represent widened perivascular spaces without substantial ischemic tissue damage, early confluent and confluent lesions correspond to incomplete ischemic destruction. Punctate abnormalities on MRI show a low tendency for progression, while early confluent and confluent changes progress rapidly. The causative and modifying pathways involved in the occurrence of sporadic age-related white matter changes are still incompletely understood, but recent microarray and genome-wide association approaches increased the notion of pathways that might be considered as targets for therapeutic intervention. The majority of differentially regulated transcripts in white matter lesions encode genes associated with immune function, cell cycle, proteolysis, and ion transport. Genome-wide association studies identified six SNPs mapping to a locus on chromosome 17q25 to be related to white matter lesion load in the general population. We also report first and preliminary data that demonstrate apolipoprotein E (ApoE) immunoreactivity in white matter lesions and support epidemiological findings indicating that ApoE is another factor possibly related to white matter lesion occurrence. Further insights come from modern MRI techniques, such as diffusion tensor and magnetization transfer imaging, as they provide tools for the characterization of normal-appearing brain tissue beyond what can be expected from standard MRI scans. There is a need for additional pre- and postmortem studies in humans, including these new imaging techniques

The association between lower educational attainment and depression owing to shared genetic effects? Results in ∼25 000 subjects: Results in ~25,000 subjects

An association between lower educational attainment (EA) and an increased risk for depression has been confirmed in various western countries. This study examines whether pleiotropic genetic effects contribute to this association. Therefore, data were analyzed from a total of 9662 major depressive disorder (MDD) cases and 14 949 controls (with no lifetime MDD diagnosis) from the Psychiatric Genomics Consortium with additional Dutch and Estonian data. The association of EA and MDD was assessed with logistic regression in 15 138 individuals indicating a significantly negative association in our sample with an odds ratio for MDD 0.78 (0.75-0.82) per standard deviation increase in EA. With data of 884 105 autosomal common single-nucleotide polymorphisms (SNPs), three methods were applied to test for pleiotropy between MDD and EA: (i) genetic profile risk scores (GPRS) derived from training data for EA (independent meta-analysis on ∼120 000 subjects) and MDD (using a 10-fold leave-one-out procedure in the current sample), (ii) bivariate genomic-relationship-matrix restricted maximum likelihood (GREML) and (iii) SNP effect concordance analysis (SECA). With these methods, we found (i) that the EA-GPRS did not predict MDD status, and MDD-GPRS did not predict EA, (ii) a weak negative genetic correlation with bivariate GREML analyses, but this correlation was not consistently significant, (iii) no evidence for concordance of MDD and EA SNP effects with SECA analysis. To conclude, our study confirms an association of lower EA and MDD risk, but this association was not because of measurable pleiotropic genetic effects, which suggests that environmental factors could be involved, for example, socioeconomic status

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

<p>Chr.: chromosome; Pos.: position; EAF: average effect allele frequency; In the column “direction”, the studies are in the following order: 1. AGES, 2. ASPS, 3. ERF, 4. GHS, 5. H2000, 6. HBCS, 7. HRS, 8. KORA, 9. NFBC1966, 10. NTR1, 11. NTR2, 12. RS-I, 13. RS-II, 14. RS-III, 15. SardINIA, 16. SHIP, 17. THISEAS, 18. TwinsUK (pooled and female sample)/YFS (male sample), 19. YFS (pooled and female sample); A question mark indicates that the SNP was not tested in that specific study; For SNPs that were located close together in the same region, only the most significant SNP is included in the table. The last column shows the number of neighboring SNPs that exceed the threshold for suggestive SNPs.</p

Prediction results.

<p>Variance explained (Nagelkerke pseudo-<i>R</i>² from logistic regression) vs. <i>p</i>-value threshold <i>p</i>_T for including SNPs in the score calculation.</p

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

<p>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 <i>p</i>-values.</p

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.

<p>The genetic relationships were estimated from 301,115 directly genotyped autosomal SNPs that were available in both studies. All analyses controlled for age, study, and the first 10 principal components of the genetic similarity matrix of the combined sample of RS-I and STR. In the pooled sample we also controlled for sex. The results did not change markedly when 4 or 20 principal components were included; <i>σ_g</i>²/<i>σ_P</i>²: proportion of phenotypic variance explained by the variance of the total additive genetic effects of the 301,115 autosomal SNPs; s.e.: standard error; <i>p</i>-value: <i>p</i>-value from a likelihood ratio (LR) test assuming that the LR is distributed as a 50∶50 mixture of zero and <i>χ</i>₁².</p