Yanchun Bao

Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals.

Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11–13% of the variance in educational attainment and 7–10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.Gene discovery and polygenic predictions from a genome-wide association study of educational attainment in 1.1 million individuals.

Accounting for immunoprecipitation efficiencies in the statistical analysis of ChIP-seq data

BackgroundImmunoPrecipitation (IP) efficiencies may vary largely between different antibodies and between repeated experiments with the same antibody. These differences have a large impact on the quality of ChIP-seq data: a more efficient experiment will necessarily lead to a higher signal to background ratio, and therefore to an apparent larger number of enriched regions, compared to a less efficient experiment. In this paper, we show how IP efficiencies can be explicitly accounted for in the joint statistical modelling of ChIP-seq data.ResultsWe fit a latent mixture model to eight experiments on two proteins, from two laboratories where different antibodies are used for the two proteins. We use the model parameters to estimate the efficiencies of individual experiments, and find that these are clearly different for the different laboratories, and amongst technical replicates from the same lab. When we account for ChIP efficiency, we find more regions bound in the more efficient experiments than in the less efficient ones, at the same false discovery rate. A priori knowledge of the same number of binding sites across experiments can also be included in the model for a more robust detection of differentially bound regions among two different proteins.ConclusionsWe propose a statistical model for the detection of enriched and differentially bound regions from multiple ChIP-seq data sets. The framework that we present accounts explicitly for IP efficiencies in ChIP-seq data, and allows to model jointly, rather than individually, replicates and experiments from different proteins, leading to more robust biological conclusions.

Joint modeling of ChIP-seq data via a Markov random field model

Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein-binding sites. In this paper, we present a Markov random field model for the joint analysis of multiple ChIP-seq experiments. The proposed model naturally accounts for spatial dependencies in the data, by assuming first-order Markov dependence and, for the large proportion of zero counts, by using zero-inflated mixture distributions. In contrast to all other available implementations, the model allows for the joint modeling of multiple experiments, by incorporating key aspects of the experimental design. In particular, the model uses the information about replicates and about the different antibodies used in the experiments. An extensive simulation study shows a lower false non-discovery rate for the proposed method, compared with existing methods, at the same false discovery rate. Finally, we present an analysis on real data for the detection of histone modifications of two chromatin modifiers from eight ChIP-seq experiments, including technical replicates with different IP efficiencies.

The predictive ability of blood pressure in elderly trial patients

Objectives: To assess the impact of the blood pressure (BP) profile on cardiovascular risk in the Medical Research Council (UK) elderly trial; investigate whether the effects of hypertensive drugs in reducing event rates are solely a product of systolic pressure reduction. Methods: Using longitudinal BP data from 4396 hypertensive patients, the general trend over time was estimated using a first-stage multilevel model. We then investigated how BP acted alongside other BP-related covariates in a second-stage ‘time-to-event’ statistical model, assessing risk for stroke events and coronary heart disease (CHD). Differences in outcome prediction between diuretic, &bgr;-blocker and placebo treatment arms were investigated. Results: The &bgr;-blocker arm experienced comparatively poor control of current SBP, episodic peaks and variability in BP levels. After adjusting for the mean level, variability in SBP over time was significant: risk ratio was 1.15 [95% confidence interval (CI): 1.01–1.31] across all patients for stroke events. The risk ratio for current SBP was 1.36 (95% CI: 1.16–1.58). Current DBP and variability in DBP also predicted stroke independently: risk ratios was 1.43 and 1.18, respectively. The risk factors exhibited weaker associations with CHD risk; only the highest measured value and variability in SBP showed a statistically significant association: risk ratios were 1.26 and 1.16, respectively. Conclusion: Individual risk characterization could be augmented with additional prognostic information, besides current SBP, including current diastolic pressure, temporal variability over and above general trends and historical measurements.

Joint longitudinal and survival-cure models in tumour xenograft experiments.

In tumour xenograft experiments, treatment regimens are administered, and the tumour volume of each individual is measured repeatedly over time. Survival data are recorded because of the death of some individuals during the observation period. Also, cure data are observed because of a portion of individuals who are completely cured in the experiments. When modelling these data, certain constraints have to be imposed on the parameters in the models to account for the intrinsic growth of the tumour in the absence of treatment. Also, the likely inherent association of longitudinal and survival-cure data has to be taken into account in order to obtain unbiased estimators of parameters. In this paper, we propose such models for the joint modelling of longitudinal and survival-cure data arising in xenograft experiments. Estimators of parameters in the joint models are obtained using a Markov chain Monte Carlo approach. Real data analysis of a xenograft experiment is carried out, and simulation studies are also conducted, showing that the proposed joint modelling approach outperforms the separate modelling methods in the sense of mean squared errors.

Multivariate Genome-Wide and Integrated Transcriptome and Epigenome-Wide Analyses of the Well-being Spectrum.

Several phenotypes related to well-being (e.g., life satisfaction, positive affect, neuroticism, and depressive symptoms), are genetically highly correlated (| rg | > .75). Multivariate analyses of these traits, collectively referred to as the well-being spectrum, reveals 24 genome-wide significant loci. We integrated the genetic findings with large human transcriptome and epigenome datasets. Integrated analyses implicate gene expression at 48 additional loci and CpG methylation at 28 additional loci in the etiology of well-being.

Gene-environment interactions between education and body mass: Evidence from the UK and Finland

More education is associated with a lower body mass index (BMI) and likelihood of being overweight. However, since a large proportion of the variation in body mass is due to genetic makeup, it has been hypothesized that education may moderate the genetic risk. We estimate main associations between (i) education, (ii) genetic risk, and (iii) interactions between education and genetic risk on BMI and the probability of being overweight in the UK and Finland. The estimates show that education is negatively associated with BMI and overweightness, and genetic risk is positively associated. However, the interactions between education and genetic risk are small and statistically insignificant.

Genome-wide analysis of health-related biomarkers in the UK Household Longitudinal Study reveals novel associations

Serum biomarker levels are associated with the risk of complex diseases. Here, we aimed to gain insights into the genetic architecture of biomarker traits which can reflect health status. We performed genome-wide association analyses for twenty serum biomarkers involved in organ function and reproductive health. 9,961 individuals from the UK Household Longitudinal Study were genotyped using the Illumina HumanCoreExome array and variants imputed to the 1000 Genomes Project and UK10K haplotypes. We establish a polygenic heritability for all biomarkers, confirm associations of fifty-four established loci, and identify five novel, replicating associations at genome-wide significance. A low-frequency variant, rs28929474, (beta = 0.04, P = 2 × 10−10) was associated with levels of alanine transaminase, an indicator of liver damage. The variant is located in the gene encoding serine protease inhibitor, low levels of which are associated with alpha-1 antitrypsin deficiency which leads to liver disease. We identified novel associations (rs78900934, beta = 0.05, P = 6 × 10−12; rs2911280, beta = 0.09, P = 6 × 10−10) for dihydroepiandrosterone sulphate, a precursor to major sex-hormones, and for glycated haemoglobin (rs12819124, beta = −0.03, P = 4 × 10−9; rs761772, beta = 0.05, P = 5 × 10−9). rs12819124 is nominally associated with risk of type 2 diabetes. Our study offers insights into the genetic architecture of well-known and less well-studied biomarkers.

A joint modelling approach for clustered recurrent events and death events

In dental implant research studies, events such as implant complications including pain or infection may be observed recurrently before failure events, i.e. the death of implants. It is natural to assume that recurrent events and failure events are correlated to each other, since they happen on the same implant (subject) and complication times have strong effects on the implant survival time. On the other hand, each patient may have more than one implant. Therefore these recurrent events or failure events are clustered since implant complication times or failure times within the same patient (cluster) are likely to be correlated. The overall implant survival times and recurrent complication times are both interesting to us. In this paper, a joint modelling approach is proposed for modelling complication events and dental implant survival times simultaneously. The proposed method uses a frailty process to model the correlation within cluster and the correlation within subjects. We use Bayesian methods to obtain estimates of the parameters. Performance of the joint models are shown via simulation studies and data analysis.

Genome-wide study identifies 611 loci associated with risk tolerance and risky behaviors

Humans vary substantially in their willingness to take risks. In a combined sample of over one million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. We identified 611 approximately independent genetic loci associated with at least one of our phenotypes, including 124 with general risk tolerance. We report evidence of substantial shared genetic influences across general risk tolerance and risky behaviors: 72 of the 124 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is moderately to strongly genetically correlated ( to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near general-risk-tolerance-associated SNPs are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We find no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.Humans vary substantially in their willingness to take risks. In a combined sample of over one million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. We identified 611 approximately independent genetic loci associated with at least one of our phenotypes, including 124 with general risk tolerance. We report evidence of substantial shared genetic influences across general risk tolerance and risky behaviors: 72 of the 124 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is moderately to strongly genetically correlated (|rˆ g | ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near general-risk-tolerance-associated SNPs are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We find no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.