# Seeking a summer research intern (EXPIRED)

I am posting a summer research project that may be suitable for Cambridge Part II students (or senior undergraduate students from other universities). Cambridge students can potentially be supported by Research in the CMS. This internship will be co-supervised by me and Dr Jingshu Wang at University of Chicago. Please email me if you are interested.

## Project title

Mendelian randomisation using genome-wide association studies

## Project dates

8-10 weeks, starting around 1st July.

## Brief description

Mendelian randomisation (MR) is a popular research design in epidemiology and genetics to examine the causal effect of a risk exposure on diseases. From a statistical perspective, MR is an instance of the instrumental variables method with genetic variants acting as an instrument for the exposure of interest. Most of the current MR studies utilize summary statistics obtained in large-scale genome-wide association studies (GWAS).

This internship will involve three steps:

1. Read the literature on Mendelian randomisation [1-3] and get familiar with the current practice. (2 weeks)
2. Try to improve the spike-and-slab empirical Bayes shrinkage in [3] using Tweedie’s formula or the more flexible $g$-modelling [4]. (2-3 weeks)
3. Develop an R package to streamline several analyses developed by the supervisor’s team [5]. Integrate it with the MR-Base platform and write a markdown tutorial. (4-5 weeks)

## Keywords

Statistics; Causal Inference; Instrumental Variables; Empirical Bayes; R Software Development.

## References

[1] George Davey Smith, Shah Ebrahim, ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?, International Journal of Epidemiology, 32(1):1–22. 2003. doi:10.1093/ije/dyg070

[2] Didelez V, Sheehan N. Mendelian randomization as an instrumental variable approach to causal inference. Statistical Methods in Medical Research. 16(4):309-330. 2007. doi:10.1177/0962280206077743

[3] Qingyuan Zhao, Yang Chen, Jingshu Wang, Dylan S Small, Powerful three-sample genome-wide design and robust statistical inference in summary-data Mendelian randomization, International Journal of Epidemiology, 48(5):1478–1492. 2019. doi:10.1093/ije/dyz142

[4] Efron, Bradley. Two modeling strategies for empirical Bayes estimation. Statistical Science 29(2):285-301. 2014. doi:10.1214/13-STS455.

## Skills required

Part II Statistical Modelling or equivalent.

## Skills desired

Experience or interest in statistical computing.