Genetic Data Analyst (Childhood cancer and blood disorders)

Vacant position for a Genetic Data Analyst focused on analysis of multi-OMICs, functional and clinical data for personalized medicine in sickle cell disease within the group “Rare anemia Disorders/ Cancer and Blood disorders in children”.

Sickle Cell Disease (SCD) is a genetic blood disorder caused by a mutation in the HBB gene, leading to abnormal hemoglobin S (HbS). This causes red blood cells to become sickle-shaped, leading to vascular disfunction and obstruction, hemolysis, pain crises, and organ damage.

Key Challenges:

Limited Curative Options – Stem cell transplantation (available for a minority); gene therapy is promising but costly.

Healthcare Disparities – Many affected populations lack access to advanced treatments.

Pain & Symptom Management – Current drugs help but aren’t universally effective.

Genetic Complexity – Disease severity varies; machine learning (ML) and precision medicine could improve treatment strategies.

Organ Damage & Early Mortality – Long-term complications affect survival and quality of life.

https://pubmed.ncbi.nlm.nih.gov/36844183/

Efforts to improve SCD treatment and management are increasingly leveraging multi-omics approaches and integrated clinical data analysis. By combining insights from genomics, metabolomics, rheology with patient-specific clinical data, researchers can gain a deeper understanding of disease mechanisms and personalize treatment strategies.

Education and qualifications:

• Bachelor’s degree in Biological Sciences, Biomedicine or related fields.
• Master’s degree in Genetics.
• Fluency in Spanish and English (business level).

• Specific/Additional training in clinical genetics, metabolomics.

Experience and knowledge:

• At least 1 year of experience in genetics and genomics research.
• Hands-on experience with next-generation sequencing (NGS), whole-genome/exome sequencing, or other genomic techniques.
• Familiarity with genetic disorders, hereditary diseases, and clinical applications of genetic testing (variant classification following ACMG guidelines).

• Experience analyzing large-scale genetic datasets using bioinformatics tools (e.g., Python, R, Bioconductor, GATK, ANNOVAR).
• Use of statistical methods for association studies (e.g., GWAS, polygenic risk scores).
• Application of AI/ML in variant classification, genotype-phenotype correlations, or disease risk prediction.
• Ability to work with clinicians, bioinformaticians, and researchers in a multidisciplinary setting.
• Experience with patient-derived samples (e.g. blood, bone marrow) and biomarker discovery.
• Experience in the design and writing of scientific papers in JCR-indexed journals.

Main responsibilities and duties:

• Design and conduct experiments related to SCD genetics, pathophysiology, and treatment strategies.
• Generate and analyze large-scale genomic, metabolomic, and proteomic data to study SCD mechanisms and identify biomarkers.
• Perform biostatistical analyses (e.g., GWAS, gene expression profiling, machine learning for genotype-phenotype correlation).
• Investigate the role of genetic modifiers, fetal hemoglobin (HbF) regulation, and pharmacogenomics in SCD.
• Use ML models for genotype-phenotype predictions, such as:
  o Disease severity classification based on genomic and clinical features.
  o Predictive modeling for patient responses to treatments (e.g., hydroxyurea).
  o Deep learning approaches for feature extraction from genomic and metabolomic datasets.

• Design and writing of scientific articles.
• Support in the development of new project applications.
• Participation in scientific divulgation activities.

Labour conditions:

• Full-time position: 40h/week.
• Starting date: immediate.
• Gross annual salary: Remuneration will depend on experience and skills. Salary ranges are consistent with our Collective Agreement pay scale.
• Contract: Technical and scientific activities contract linked to the project activities.

What can we offer?

• Incorporation to Vall d’Hebron Research Institute (VHIR), a public sector institution that promotes and develops the biomedical research, innovation and teaching at Vall d'Hebron University Hospital (HUVH), the biggest hospital of Barcelona and t