Two PhD positions in Statistical Genomics​/Computational Genomics

This is a unique opportunity for two doctoral students to participate in an international collaboration between two research institutes in Switzerland and Belgium. The Animal Genomics group at the Institute of Agricultural Sciences at ETH Zurich and the Quantitative Genetics & Genomics group of Dr. Tom Druet from the University of Liège, Belgium, investigate DNA variation in individual animal genomes and at the population scale.

Our groups apply state‑of‑the‑art technologies to sequence the genomes and transcriptomes of farm animals with long and short reads, and apply bioinformatics and statistical genomics approaches to characterise trait‑associated sequence variation. We offer two PhD positions at the interface of computational and statistical genomics, and bioinformatics.

The project “Pangenomi

X - Assessing impacts of sex chromosomal structural variants on reproduction- and meiosis-related traits in cattle through pangenomes and advanced imputation and association method” is a joint project co‑developed by Dr. Tom Druet and Prof. Hubert Pausch. Pangenomi

X was recently funded as a Weave‑project by the Fonds de la recherche scientifique (F.R.S.

-FNRS) and the Swiss National Science Foundation.

Pangenomi

X aims to study how structural variants (SV) on the sex chromosomes contribute to genetic variation in complex traits, particularly those related to reproduction and meiosis. To this end, a new cattle pangenome that includes near complete assemblies of the sex chromosomes will be generated and statistical methods will be developed to transfer information from the pangenome variation panel to large mapping populations through imputation so that association testing between complex traits and SVs will eventually be possible.

This 4‑years project builds upon previous research conducted by the Animal Genomics group and the Quantitative Genetics & Genomics group
. We have collected large amounts of long read sequencing data (Pac Bio HiFi) to build genome assemblies and integrate them into pangenomes. This allowed us to investigate the distribution of structural variants in cattle and related species, construct different pangenome graphs, and identify trait‑associated structural variants. Moreover, we have developed imputation methods that provide accurate genotypes in pedigreed populations, and haplotype‑based association testing approaches, including some that had specifically been designed for the sex chromosomes.

Pangenomi

X will exploit large‑scale long and short read sequencing data from two cattle populations to characterise structural variant diversity on the sex chromosomes, and investigate how these types of variants influence male fertility and recombination rates.

We are looking for two enthusiastic and highly‑motivated candidates to work on the project.

• The first candidate will build pangenomes from long read sequencing data collected from two cattle breeds. Existing Pac Bio HiFi sequencing data will be complemented with ultra‑long sequencing using ONT to build near complete assemblies for the sex chromosomes. This sub‑project will be closely supervised by Prof. Hubert Pausch at ETH Zurich.
• The second candidate will focus on imputation and association testing. Approaches to impute structural variants into large mapping cohorts that had array‑or short read sequencing‑derived genotypes will be developed. The resulting genotypes will be tested for association with complex traits using methods that allow to account for the multi‑allelic nature of the SVs. This sub‑project will be closely supervised by Dr.
  
  Tom Druet at the University of Liège.

Close collaboration of both doctoral students is expected. Research exchanges between both groups are anticipated.

Prior experience with genomic data analysis on a high‑performance computing cluster, along with strong communication skills, is desirable.

• Research interest in statistical genomics
  , computational biology
  , computational genomics
  , or animal genomics
  .
• Experience with a programming language (e.g., python, R) and basic working knowledge with high‑performance computing clusters is required.
• A MSc degree in genetics,…