Extending the use of GWAS data by combining data from different genetic platforms.

Area of Science:

• Genetics
• Bioinformatics
• Statistical Genetics

Background:

• Genome-wide Association Studies (GWAS) identify associations between single nucleotide polymorphisms (SNPs) and phenotypes.
• Imputation methods enhance GWAS by inferring ungenotyped variants and combining data from different genotyping platforms.

Purpose of the Study:

• To evaluate and compare two distinct strategies for combining GWAS data from multiple genotyping platforms.
• To determine whether imputing data before or after merging platform-specific datasets impacts imputation quality and efficiency.

Main Methods:

• Genotyped 979 individuals on three platforms: MetaboChip, Human CVD BeadChip, and HumanExome chip.
• Utilized Minimac and MaCH for imputation against the 1,000 Genomes reference panel after pre-imputation quality control.
• Applied post-imputation quality control, excluding markers with an r2 value <0.3.

Main Results:

• Imputing individual datasets before merging resulted in a larger number of available SNPs (4,117,036) compared to merging before imputation (3,933,494).
• Careful matching of datasets on strand, SNP ID, and genomic coordinates was performed.
• The study involved 979 unique individuals and 258,925 unique markers after merging.

Conclusions:

• Imputing datasets from different genotyping platforms prior to merging generates a greater number of single nucleotide polymorphisms (SNPs).
• This pre-merging imputation strategy is more efficient for combining GWAS data from diverse platforms.