Postdoctoral Fellow

The Martin group works on the genetic basis of rare and common disease, using large-scale human genetic datasets with rich linkage to electronic health records and questionnaires. Over the last few years, we have had a particular emphasis on the genetics of neurodevelopmental disorders and related traits, the genetics of complex disease in populations with high levels of consanguinity, and factors influencing germline mutation rates.

Recent work from the group has appeared in Nature, Cell, Nature Genetics, and Nature Human Behaviour. A full list of our recent papers can be found here:  The group is now opening new research directions involving genomics and multi-omics in very early life. Most conditions in the earliest stages of life remain untouched by large-scale genomics, and molecular predictors that could guide clinical management are urgently needed.

We are building a programme spanning a clinical continuum from pregnancy loss, through fetal growth restriction, to complications of premature birth. This includes large-scale sequencing of birth cohorts to study the genetic basis of perinatal and early-life traits and conditions, genetic and multi-omic studies of neonatal conditions using nationwide Scandinavian dried blood spots, and large-scale sequencing of cell-free DNA (cfDNA) from pregnant mothers.

We are seeking a highly motivated postdoc to work on one or more of the following projects:

• Analysis of large-scale proteomic data from nationwide Scandinavian dried blood spots, in order to develop biomarkers for understudied neonatal conditions that are common in premature babies. These samples are collected before disease onset and linked to rich registry data, which opens up questions about molecular subtypes, prediction and aetiology that have not previously been tractable. There may also be the opportunity to analyse large scale genetic datasets from these same blood spots, to carry out the first well-powered genetic studies of these neonatal conditions.
• Analysis of new sequence and genotype data from over 110,000 individuals across four birth cohorts, including the Norwegian Mother, Father and Child Cohort Study (MoBa), to explore genetic influences on neurodevelopmental and perinatal traits. This is one of the world's largest family-based sequence datasets.

Questions here could include:

• Disentangling direct from indirect genetic effects using new methods. Exploring time-varying effects of genetics using longitudinal phenotype data.
• Exploring the factors influencing penetrance of variants associated with rare neurodevelopmental conditions.
• Studying how genetic architecture of different traits varies across the phenotype distribution.
• Developing biomarkers for pregnancy complications from methylation-aware whole genome sequence data on cell-free DNA (cfDNA) from pregnant women, including methods development to incorporate both epigenetic and genetic information in the same assay.

This project is also listed in the parallel advert for our cfDNA-focused postdoctoral position, and could be taken forward from either post. The project can be tailored around the postdoc's interests and expertise. The work will be done in collaboration with groups in the UK, Scandinavia and elsewhere, as well as with other groups at the Sanger Institute.

This role would suit someone with experience in statistical analyses of large-scale human genetic or other omic datasets, particularly on DNA sequence data, methylation data and/or with high-throughput proteomic data. You will be based at the Wellcome Sanger Institute on the Wellcome Genome Campus near Cambridge, with access to world-leading sequencing capacity and computational infrastructure, and to a large community of statistical and computational geneticists.

You will join the Postdoctoral Fellow community and be able to take part in training, networking opportunities. Prospective candidates are welcome to contact  with informal queries before deciding whether to apply.

• PhD in a relevant area (statistics, statistical genetics, genetics, genomics, proteomics, bioinformatics or similar)
• Experience analysing large-scale high-throughput human genetic and/or epigenetic and/or proteomic data, including quality control and normalization where relevant
• Knowledge of statistical methods appropriate for large-scale omics research
• Full working proficiency in Python and/or R, and Unix/Linux
• Experience or enthusiasm for using AI tools to improve…