Batch effects in a multiyear sequencing study: False biological trends due to changes in read lengths

• Published in: Molecular ecology resources 2018-07, Vol.18 (4), p.778-788
• Main Authors: Leigh, D. M., Lischer, H. E. L., Grossen, C., Keller, L. F.
• Format: Article
• Language: English
• Subjects: Alleles; Alpine environments; Animals; Biological effects; Case studies; Gene Frequency; genotyping error; Goats - genetics; GWAS; High-Throughput Nucleotide Sequencing; long‐term data; Next-generation sequencing; outlier; Polymorphism, Single Nucleotide; Population genetics; Populations; RADseq; Randomization; Selection, Genetic; sequencing error; Statistical analysis
• ISSN: 1755-098X; 1755-0998
• DOI: 10.1111/1755-0998.12779

High‐throughput sequencing is a powerful tool, but suffers biases and errors that must be accounted for to prevent false biological conclusions. Such errors include batch effects; technical errors only present in subsets of data due to procedural changes within a study. If overlooked and multiple batches of data are combined, spurious biological signals can arise, particularly if batches of data are correlated with biological variables. Batch effects can be minimized through randomization of sample groups across batches. However, in long‐term or multiyear studies where data are added incrementally, full randomization is impossible, and batch effects may be a common feature. Here, we present a case study where false signals of selection were detected due to a batch effect in a multiyear study of Alpine ibex (Capra ibex). The batch effect arose because sequencing read length changed over the course of the project and populations were added incrementally to the study, resulting in nonrandom distributions of populations across read lengths. The differences in read length caused small misalignments in a subset of the data, leading to false variant alleles and thus false SNPs. Pronounced allele frequency differences between populations arose at these SNPs because of the correlation between read length and population. This created highly statistically significant, but biologically spurious, signals of selection and false associations between allele frequencies and the environment. We highlight the risk of batch effects and discuss strategies to reduce the impacts of batch effects in multiyear high‐throughput sequencing studies.