Analysis of the batch effect Due to sequencing center in population statistics quantifying rare events in the 1000 Genomes Project

Maceda Porto, Iago, 1986-; Lao Grueso, Oscar, 1976-

doi:http://dx.doi.org/10.3390/genes13010044

Analysis of the batch effect Due to sequencing center in population statistics quantifying rare events in the 1000 Genomes Project

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Citació

Maceda I, Lao O. Analysis of the batch effect Due to sequencing center in population statistics quantifying rare events in the 1000 Genomes Project. Genes (Basel). 2021 Dec 24;13(1):44. DOI: 10.3390/genes13010044

Resum

The 1000 Genomes Project (1000G) is one of the most popular whole genome sequencing datasets used in different genomics fields and has boosting our knowledge in medical and population genomics, among other fields. Recent studies have reported the presence of ghost mutation signals in the 1000G. Furthermore, studies have shown that these mutations can influence the outcomes of follow-up studies based on the genetic variation of 1000G, such as single nucleotide variants (SNV) imputation. While the overall effect of these ghost mutations can be considered negligible for common genetic variants in many populations, the potential bias remains unclear when studying low frequency genetic variants in the population. In this study, we analyze the effect of the sequencing center in predicted loss of function (LoF) alleles, the number of singletons, and the patterns of archaic introgression in the 1000G. Our results support previous studies showing that the sequencing center is associated with LoF and singletons independent of the population that is considered. Furthermore, we observed that patterns of archaic introgression were distorted for some populations depending on the sequencing center. When analyzing the frequency of SNPs showing extreme patterns of genotype differentiation among centers for CEU, YRI, CHB, and JPT, we observed that the magnitude of the sequencing batch effect was stronger at MAF < 0.2 and showed different profiles between CHB and the other populations. All these results suggest that data from 1000G must be interpreted with caution when considering statistics using variants at low frequency.