Haplomics: A Snakemake pipeline for haplotype-based association analysis in multi-omics studies

medRxiv

Abstract

Abstract Summary Genome-wide association studies (GWAS) generated thousands of loci associated with complex traits and diseases. However, to characterize of the pleiotropic, molecular and population genetic bounds of uncovered loci, investigations are conducted, that remain conceptually and practically separated. Also in the best case, such investigations proceed by one locus at a time. To address these limitations, we introduce an efficient and reproducible Snakemake pipeline for executing haplotype-based association analysis on GWAS-identified genetic loci, which is especially helpful in samples enriched with molecular omics data. Haplomics takes as input the genetic coordinates of each locus along with all available clinical and molecular phenotypes, the necessary covariates, and VCF genotype files, to reconstruct haplotypes and test them for associations with the phenotypes. The reconstructed haplotypes, the annotation of included variants, and association results are graphically displayed in an HTML report. We tested Haplomics in population-based study sample encompassing 391 traits, including 72 clinical markers, 171 serum metabolites, 148 plasma protein concentrations, and whole-exome sequencing (WES) imputed genotypes. We estimated WES-based haplotypes at 11 kidney function genetic loci from a GWAS and conducted association analyses throughout, identifying 19 significant associations after multiple testing correction. Haplomics is a scalable, easy-to-use and fast haplotype reconstruction and association pipeline that makes it possible to jointly conduct molecular and population-genetic characterization of multiple GWAS loci in unified analysis framework. Availability and implementation Haplomics is freely available on GitHub at https://github.com/dariushghasemi/haplomics .

Authors 9

1. Dariush Ghasemi-Semeskandeh corresponding Aachen Department of Human Genetics

   Eurac Research · Leiden University Medical Center · Human Technopole · Institute for Biomedicine

   Affiliation as printed

   Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands

   Institute for Biomedicine, Eurac Research, Bolzano, Italy

   Integrative Data Analysis Unit, Health Data Science Centre, Human Technopole, Milan, Italy

2. Luisa Foco

   Eurac Research · Institute for Biomedicine

   Affiliation as printed

   Eurac Research;

   Institute for Biomedicine, Eurac Research, Bolzano, Italy

3. Ryosuke Fujii

   Fujita Health University

   Affiliation as printed

   Fujita Health University School of Medical Sciences;

   Department of Preventive Medical Sciences, Fujita Health University School of Medical Sciences, Toyoake, Japan

4. Johannes Rainer

   Eurac Research · Institute for Biomedicine

   Affiliation as printed

   Eurac Research;

   Institute for Biomedicine, Eurac Research, Bolzano, Italy

5. Mohadeseh Feli

   University of Mazandaran

   Affiliation as printed

   University of Mazandaran;

   Department of Computer Science, Faculty of Mathematics, University of Mazandaran, Babolsar, Iran

6. Markus Ralser

   Charité - Universitätsmedizin Berlin

   Affiliation as printed

   Charite University

   Institute of Biochemistry, Charité – Universitätsmedizin Berlin, Berlin, Germany

7. Francisco S. Domingues

   Eurac Research · Institute for Biomedicine

   Affiliation as printed

   Eurac Research;

   Institute for Biomedicine, Eurac Research, Bolzano, Italy

8. Peter Paul Pramstaller

   Eurac Research · Institute for Biomedicine

   Affiliation as printed

   Eurac Research;

   Institute for Biomedicine, Eurac Research, Bolzano, Italy

9. Cristian Pattaro corresponding

   Eurac Research · Institute for Biomedicine

   Affiliation as printed

   Eurac Research;

   Institute for Biomedicine, Eurac Research, Bolzano, Italy