Role Overview

You will lead the design, analysis, and interpretation of population genomics studies using large-scale datasets (WES/WGS/eQTL/pQTL), contributing directly to disease research and therapeutic development.

Key Responsibilities

- Design and execute population genomics studies for rare and complex disease research leveraging multi-modal integrative analyses
- Well versed with NGS data analyses- WGS/ WES data, variant annotation and interpretation, QTL analyses (eQTL, pQTL, mQTL, sQTL)
- Genetic association analyses (GWAS) and downstream analyses (fine-mapping, LD mapping, causal gene-phenotype correlation, Mendelian randomization)
- Solid statistical background- regression, glm, MDR, PCA prediction modelling, Bayesian statistics, SVM
- Prior experience in tools like PLINK, GCTA, ANNOVAR, samtools, bcftools, GATK
- Build and apply pipelines for:
- Variant calling and quality control
- Genotype imputation and fst statistics




- Haplotyping and linkage disequilibrium analysis
- Variant annotation and gene-aggregate testing (SKAT, burden tests, etc.)
- Polygenic risk score (PRS) modelling- PRSice, LDPred2
- Utilize and interpret data from genomic and transcriptomic resources such as gnomAD, ClinVar, GWAS Catalog, and PRS Catalog

Required Qualifications

- Ph.D. in Genomics, Statistical Genetics, Bioinformatics, or a related discipline (or equivalent industry experience)
- Strong understanding of population genetics, statistical modeling, and genomic data interpretation

Preferred Qualifications

- Programming experience in Python and/or R
- Experience working in HPC environments or cloud platforms (AWS, GCP, Azure)
- Prior exposure to complex and rare disease genomics or building optimised pipelines for translational research workflows

📌 Scientist (Statistical Genomics) (Hyderabad)
🏢 Aganitha Cognitive Solutions
📍 Hyderabad