cfDNA Postdoctoral Fellow / Senior Postdoctoral Fellow in statistical genomics

sanger; Hinxton; On-site; GBP 42,000 - 64,000; Full time

Job summary

Wellcome Sanger Institute in Hinxton seeks a Postdoctoral Fellow or Senior Postdoctoral Fellow to advance cfDNA genomics in pregnancy and neonatal research. You will develop statistical genetics methods and contribute to large-scale analyses of sequencing data.

The role emphasizes independence, collaboration, and software development in Python/R, with experience in somatic variant calling, phasing, and imputation highly valued. Applicants contact Hilary Martin for queries.

Qualifications

• PhD in a relevant area (statistics, statistical genetics, genetics, genomics, bioinformatics, machine learning or similar)
• Strong background in statistics or statistical genetics, including the ability to develop, implement and benchmark new statistical methods
• Experience working with raw sequence data (alignments, variant calling, quality control)
• Experience analysing large-scale human genetic or genomic data
• Full working proficiency in Python and/or R, and Unix/Linux
• Experience or enthusiasm for using AI tools to improving productivity in scientific research, statistical methods development and/or software development
• Knowledge and understanding of statistical methods appropriate for large-scale genetic research
• Previous experience writing highly reproducible code with github, Snakemake/Nextflow, RMarkdown or similar
• Experience developing pipelines or software for large-scale analyses of genomic data, including running workflows efficiently on HPC and/or cloud infrastructure
• Creativity and original scientific ideas
• Enthusiasm and commitment
• Attention to detail
• Effective problem-solving skills
• Ability to communicat

Responsibilities

• Develop statistical and deep learning methods for calling fetal and maternal genotypes from high-coverage cfDNA sequence data
• Apply methods to study pregnancy complications in large samples
• Develop biomarkers for pregnancy complications from methylation-aware whole-genome cfDNA data
• Collaborate with groups at external institutions

Skills

Statistics

Genetics

Machine learning

Software development

Education

PhD in statistics / statistical genetics / genetics / genomics / bioinformatics / ML

Tools

Python

R

Unix/Linux

Snakemake/Nextflow

GitHub

Job description

Do you want to help us improve human health and understand life on Earth? Make your mark by shaping the future to enable or deliver life-changing science to solve some of humanity's greatest challenges.

About the group:

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.

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 cell-free DNA (cfDNA) from pregnant women and people to identify monogenic causes of pregnancy loss and identify biomarkers for fetal growth restriction, as well as genetic and multi-omic studies of neonatal conditions using nationwide dried blood spots.

What you'll be doing:

This particular role is focused on cell-free DNA (cfDNA) from pregnant women and people. cfDNA in maternal plasma is a mixture of maternal and fetal (placental) molecules, and offers a route to studying fetal genomes and placental biology at a scale that is impossible with fetal tissue. Possible projects include:

• Developing new statistical and deep learning methods for calling fetal and maternal genotypes from high-coverage sequence data on cfDNA, and for discovering new Mendelian disease genes using the resulting data. These methods will then be applied to study pregnancy complications in large samples.
• Developing biomarkers for pregnancy complications from methylation-aware wholegenome sequence data on cfDNA, including methods development to incorporate both epigenetic and genetic information in the same assay. You will have the opportunity to collaborate with groups at the Norwegian Institute of Public Health (Siri Eldevik Håberg), the University of Helsinki (Simone Rubinacci) and elsewhere.

About you:

We are recruiting at two levels: Postdoctoral Fellow and Senior Postdoctoral Fellow. In both cases, you will have a demonstrable background in statistics or statistical genetics. Experience of working with cfDNA is highly desirable but not essential. The role would particularly suit if you have previously worked on methods for somatic variant calling, or for phasing and imputation from low-coverage sequence data. You would also bring excellent software development skills, since the methods developed here are intended to be applied across several large cohorts and shared with collaborators. The Senior Postdoctoral Fellow role is intended for someone with particularly strong experience in these areas who is ready to work with a high degree of independence, to shape the direction of the methods development, to lead collaborations and to mentor more junior members of the group. You will be considered at the level that best matches your experience, so please apply even if you are unsure which position fits. Prospective candidates are welcome to contact Hilary Martin ( [e-mail] ) with informal queries before deciding whether to apply.

Essential Skills:

• PhD in a relevant area (statistics, statistical genetics, genetics, genomics, bioinformatics, machine learning or similar)
• Strong background in statistics or statistical genetics, including the ability to develop, implement and benchmark new statistical methods
• Experience working with raw sequence data (alignments, variant calling, quality control)
• Experience analysing large-scale human genetic or genomic data
• Full working proficiency in Python and/or R, and Unix/Linux
• Experience or enthusiasm for using AI tools to improving productivity in scientific research, statistical methods development and/or software development
• Knowledge and understanding of statistical methods appropriate for large-scale genetic research
• Previous experience writing highly reproducible code with github, Snakemake/Nextflow, RMarkdown or similar
• Experience developing pipelines or software for large-scale analyses of genomic data, including running workflows efficiently on HPC and/or cloud infrastructure
• Creativity and original scientific ideas
• Enthusiasm and commitment
• Attention to detail
• Effective problem-solving skills
• Ability to communicat