Identification of Mislabeled Samples and Sample Mix-ups in Genotype Data Using Barcode Genotypes

Genetics of Plasma Bilirubin and Associations between Bilirubin and Cardiometabolic Risk Profiles in Danish Children and Adolescents

“…Outliers in a principal component analysis of ancestral markers as well as the individual having the lowest call rate of first-degree relationships were removed from the analysis. Potential sex discrepancies and sample swaps were addressed using Wunderbar [ 44 ]. We removed markers with a call rate < 95%, a minor allele frequency (MAF) < 1%, and markers with a significant deviation from Hardy–Weinberg equilibrium (HWE) ( p < 0.001).…”

Who are you? A framework to identify and report genetic sample mix‐ups

“…An additional step when investigating sample mix-ups is to determine where in the data processing pipeline the mix-up may have occurred in order to prevent them from happening in the future. This can turn out to be a difficult task if the study system is lacking additional information such as a verified pedigree and phenotypic information or if the data handling procedures are not well documented and the genotyping technology unexplored (Have et al, 2014). Sampling errors can happen from the moment the sample was taken in the field, during any stage of transportation and storage, in any step of the wet lab procedures and up to the moment when the bioinformatics processing commences (Figure 1).…”

“…This is, to our knowledge, because there is no sample‐verification guideline available, neither for individual based ecological genomic data, nor in other applications such as eDNA or human studies where sample mix‐ups have been reported (Have et al, 2014; Nicholson et al, 2020). Further, it does not appear to be standard practice to systematically report the validation of genetic, genomic or transcriptomic data and sample‐ID assignments in ecological studies.…”

“…At the same time, standardized laboratory handling protocols and quality checks should ensure sample identification – in theory. However, even the most experienced laboratory is not safe from the occasional sample mix‐up, often resulting in the affected sample being discarded if it is detected (Have et al, 2014; Wang et al, 2019). Sample mix‐ups can happen at any stage in a research project: during data collection, labelling, transport and storage, or handling and processing in wet and dry laboratories (Figure 1), with laboratory mix‐ups appearing to be particularly common (McClure et al, 2018).…”

“…However, less than 10% of the examined studies documented at least one individual sample-ID check in their main manuscript and none of the studies reported following a standardized protocol (Supporting Information 1). This is, to our knowledge, because there is no sample-verification guideline available, neither for individual based ecological genomic data, nor in other applications such as eDNA or human studies where sample mix-ups have been reported (Have et al, 2014;Nicholson et al, 2020). Further, it does not appear to be standard practice to systematically report the validation of genetic, genomic or transcriptomic data and sample-ID assignments in ecological studies.…”