medRxivGenetic & Genomic MedicinePreprint
Structural Variant Imputation in Samoans Using a Population-Specific Reference Panel
Lauren M. SporiD, Emily Liau, Alba Sanchis‐JuaniD, Andrew N. Silva, Hong ChengiD, Take K. NaseriiD, Muagututi‘a Sefuiva ReupenaiD, Satupaitea VialiiD, John Tuitele, Erin E. KershawiD, Ranjan DekaiD, Nicola L. HawleyiD, Stephen T. McGarveyiD, Daniel E. WeeksiD, Jenna C. CarlsoniD, Harrison BrandiD, Ryan L. MinsteriD
Abstract
Structural variants (SVs) are often excluded from genetic research because they are difficult to call, but they can have substantial effects on phenotypic traits. SVs have not previously been characterized in Samoans, an understudied population with a high burden of complex diseases. Using short-read whole genome sequencing data, we called SVs in 1,276 Samoans and created a Samoan-specific imputation panel inclusive of both SVs and single nucleotide variants (SNVs), called the Soifua Manuia-SV panel. Using this panel, we imputed SVs and SNVs in 3,611 Samoans with array data, enabling analysis of SV-phenotype associations in a sample of 4,887 Samoan participants. We evaluated imputation performance in Samoans against two other reference panels: (i) an SNV-only Samoan-specific reference panel, to assess whether SV inclusion impacts SNV imputation, and (ii) an SV and SNV, multi-ancestry reference panel composed of 1000 Genomes participants, which did not include Polynesians, to assess the importance of including the target population in the reference panel. The Soifua Manuia-SV panel substantially outperformed the multi-ancestry SV and SNV panel, yielding 5.5 million more high-quality (r2 [≥] 0.8) variants, including over 8,000 more high-quality SVs. SNV imputation based on the two Samoan-specific panels performed similarly overall, suggesting that SV inclusion does not strongly impact SNV imputation quality. This work highlights the importance of population representation for accurate imputation.
- organism: human
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: imputation
- approach: statistical model
- Citations
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