Genomic prediction of cognitive traits in childhood and adolescence.

Recent advances in genomics are producing powerful DNA predictors of complex traits, especially cognitive abilities. Here, we leveraged summary statistics from the most recent genome-wide association studies of intelligence and educational attainment, with highly genetically correlated traits, to build prediction models of general cognitive ability and educational achievement. To this end, we compared the performances of multi-trait genomic and polygenic scoring methods. In a representative UK sample of 7,026 children at ages 12 and 16, we show that we can now predict up to 11% of the variance in intelligence and 16% in educational achievement. We also show that predictive power increases from age 12 to age 16 and that genomic predictions do not differ for girls and boys. We found that multi-trait genomic methods were effective in boosting predictive power. Prediction accuracy varied across polygenic score approaches, however results were similar for different multi-trait and polygenic score methods. We discuss general caveats of multi-trait methods and polygenic score prediction, and conclude that polygenic scores for educational attainment and intelligence are currently the most powerful predictors in the behavioural sciences.

Predicting educational achievement from DNA.

This corrects the article DOI: 10.1038/mp.2016.107.

Multi-polygenic score approach to trait prediction.

A primary goal of polygenic scores, which aggregate the effects of thousands of trait-associated DNA variants discovered in genome-wide association studies (GWASs), is to estimate individual-specific genetic propensities and predict outcomes. This is typically achieved using a single polygenic score, but here we use a multi-polygenic score (MPS) approach to increase predictive power by exploiting the joint power of multiple discovery GWASs, without assumptions about the relationships among predictors. We used summary statistics of 81 well-powered GWASs of cognitive, medical and anthropometric traits to predict three core developmental outcomes in our independent target sample: educational achievement, body mass index (BMI) and general cognitive ability. We used regularized regression with repeated cross-validation to select from and estimate contributions of 81 polygenic scores in a UK representative sample of 6710 unrelated adolescents. The MPS approach predicted 10.9% variance in educational achievement, 4.8% in general cognitive ability and 5.4% in BMI in an independent test set, predicting 1.1%, 1.1%, and 1.6% more variance than the best single-score predictions. As other relevant GWA analyses are reported, they can be incorporated in MPS models to maximize phenotype prediction. The MPS approach should be useful in research with modest sample sizes to investigate developmental, multivariate and gene-environment interplay issues and, eventually, in clinical settings to predict and prevent problems using personalized interventions.

Predicting educational achievement from DNA.

A genome-wide polygenic score (GPS), derived from a 2013 genome-wide association study (N=127,000), explained 2% of the variance in total years of education (EduYears). In a follow-up study (N=329,000), a new EduYears GPS explains up to 4%. Here, we tested the association between this latest EduYears GPS and educational achievement scores at ages 7, 12 and 16 in an independent sample of 5825 UK individuals. We found that EduYears GPS explained greater amounts of variance in educational achievement over time, up to 9% at age 16, accounting for 15% of the heritable variance. This is the strongest GPS prediction to date for quantitative behavioral traits. Individuals in the highest and lowest GPS septiles differed by a whole school grade at age 16. Furthermore, EduYears GPS was associated with general cognitive ability (~3.5%) and family socioeconomic status (~7%). There was no evidence of an interaction between EduYears GPS and family socioeconomic status on educational achievement or on general cognitive ability. These results are a harbinger of future widespread use of GPS to predict genetic risk and resilience in the social and behavioral sciences.

Phenome-wide analysis of genome-wide polygenic scores.

Genome-wide polygenic scores (GPS), which aggregate the effects of thousands of DNA variants from genome-wide association studies (GWAS), have the potential to make genetic predictions for individuals. We conducted a systematic investigation of associations between GPS and many behavioral traits, the behavioral phenome. For 3152 unrelated 16-year-old individuals representative of the United Kingdom, we created 13 GPS from the largest GWAS for psychiatric disorders (for example, schizophrenia, depression and dementia) and cognitive traits (for example, intelligence, educational attainment and intracranial volume). The behavioral phenome included 50 traits from the domains of psychopathology, personality, cognitive abilities and educational achievement. We examined phenome-wide profiles of associations for the entire distribution of each GPS and for the extremes of the GPS distributions. The cognitive GPS yielded stronger predictive power than the psychiatric GPS in our UK-representative sample of adolescents. For example, education GPS explained variation in adolescents' behavior problems (~0.6%) and in educational achievement (~2%) but psychiatric GPS were associated with neither. Despite the modest effect sizes of current GPS, quantile analyses illustrate the ability to stratify individuals by GPS and opportunities for research. For example, the highest and lowest septiles for the education GPS yielded a 0.5 s.d. difference in mean math grade and a 0.25 s.d. difference in mean behavior problems. We discuss the usefulness and limitations of GPS based on adult GWAS to predict genetic propensities earlier in development.

[CD80-modified tumor cell lines: implications for cell-based vaccinations in breast cancer patients]. / Eine CD80-modifizierte Tumorzelllinie als therapeutische Vakzine beim Mammakarzinom--Vakzinedesign und Studienkonzeption.

A number of genetic alterations are required for malignant transformation. However, these mutations provide the source for tumor-associated antigens which can be recognized by cellular effectors of the immune system. Recent advances in tumor immunology - such as the improved understanding of antigen presentation as well as T cell activation - have opened new perspectives for cancer immunotherapy. The advantage of using tumor cell based vaccines is that these comprise the complete antigen pool of an individual tumor for activating polyclonal immune responses. However, the induction of antigen-specific immune responses is impaired by the fact that T cell activation is depending on additional nonspecific costimulatory signals provided by the antigen-presenting cell. The majority of solid human tumors does not express costimulatory molecules and is unable to deliver all signals required for T cell activation. In contrast, tumors often induce immunologic tolerance. Therefore, the introduction of genes encoding costimulatory molecules such as CD80 or cytokines is aimed at conferring the immunostimulatory potential of tumor cells. We have undertaken efforts at endowing a breast carcinoma cell line expressing at least seven known tumor associated antigens with immunostimulatory competence by CD80 gene transfer. In preclinical studies this cell line was demonstrated to induce specific immune responses. We designed a phase I/II trial to administer the CD80-modified cell line to patients with metastatic breast cancer to determine the toxicities of the vaccination protocol and nature of the vaccine-induced immune response.

A MAGE-A1 HLA-A A*0201 epitope identified by mass spectrometry.

Peptides presented by HLA-A*0201 molecules on the surface of the human breast carcinoma cell line KS24.22 after IFN-gamma induction were analyzed by the "Predict-Calibrate-Detect" approach, which combines epitope prediction and high-performance liquid chromatography mass spectrometry. One of the predicted epitopes, MAGE-A1(278-286) (KVLEYVIKV), was found to be presented by HLA-A*0201, with an estimated copy number of 18 molecules/cell. HLA-A*0201 transgenic mice (HHD mice) were used to generate CTL lines that stained positive with an HLA-A*0201 tetramer folded around the KVLEYVIKV peptide and killed peptide-loaded mouse target cells expressing HLA-A*0201. IFN-gamma-treated or -nontreated HLA-A*0201 expressing HeLa cells transiently transfected with a plasmid expressing the MAGE-A1 gene stimulated in vitro cytokine production by the CTL lines. Moreover, IFN-gamma-treated KS24.22 cells, but not IFN-gamma-treated HLA-A*0201(+) MAGE-A1(-) cells or IFN-gamma-treated HLA-A*0201(-) MAGE-A1(+) cells, were killed by these CTLS: Thus, the combination of HLA epitope prediction, peptide analysis, and immunological methods is a powerful approach for the identification of tumor-associated epitopes.

Mutation of a phosphorylation site in the DNA-binding domain is required for redox-independent transactivation of AP1-dependent genes by v-Jun.

The ability of the nuclear oncoprotein Jun to activate transcription is controlled both by level of DNA binding and by the activity of its transactivation domain. Control of DNA binding is achieved by two mechanisms: phosphorylation and redox regulation. Mutation of Ser-226 inhibits phosphorylation of the DNA binding, resulting in enhanced DNA-binding and transactivation activity of Jun. In contrast, mutation of Cys-252, which is the target for repression of DNA-binding activity under oxidative conditions, results in a strong decrease of Jun-specific activation of transcription. However, transactivation by c-Jun-Cys-252 is fully restored upon mutation of Ser-226. Both mutations are also found in the oncogenic counterpart of c-Jun, v-Jun, and are the only differences between these proteins in the DNA-binding domain, suggesting that v-Jun escapes down-modulation of DNA binding by both mechanisms. However, inhibition of phosphorylation of Ser-226 is absolutely required for the ability of v-Jun to activate transcription of AP-1-dependent genes in a redox-independent manner.