Results of the UK study confirm for clinical laboratory professionals the importance of fully understanding the design and function of SNP chips they may be using in their labs

Here is another example of a long-established clinical laboratory test that—upon new evidence—turns out to be not as accurate as once thought. According to research conducted at the University of Exeter in Devon, UK, Single-nucleotide polymorphism (SNP) chips (aka, SNP microarrays)—technology commonly used in commercial genetic testing—is inadequate at detecting rare gene variants that can increase breast cancer risk.  

A news release announcing the results of the large-scale study states, “A technology that is widely used by commercial genetic testing companies is ‘extremely unreliable’ in detecting very rare variants, meaning results suggesting individuals carry rare disease-causing genetic variants are usually wrong.”

Why is this a significant finding for clinical laboratories? Because medical laboratories performing genetic tests that use SNP chips should be aware that rare genetic variants—which are clinically relevant to a patient’s case—may not be detected and/or reported by the tests they are running.

UK Researchers Find ‘Shockingly High False Positives’

The objective of the Exeter study published in British Medical Journal (BMJ), titled, “Use of SNP Chips to Detect Rare Pathogenic Variants: Retrospective, Population Based Diagnostic Evaluation,” was “To determine whether the sensitivity and specificity of SNP chips are adequate for detecting rare pathogenic variants in a clinically unselected population.”

The conclusion reached by the Exeter researchers, the BMJ study states, is that “SNP chips are extremely unreliable for genotyping very rare pathogenic variants and should not be used to guide health decisions without validation.”  

Leigh Jackson, PhD, Lecturer in Genomic Medicine at University of Exeter and co-author of the BMJ study, said in the news release, “The number of false positives on rare genetic variants produced by SNP chips was shockingly high. To be clear: a very rare, disease-causing variant detected using [an] SNP chip is more likely to be wrong than right.” 

Caroline Wright, PhD, Professor in Genomic Medicine at the University of Exeter Medical School
In the news release, Caroline Wright, PhD (above), Professor in Genomic Medicine at the University of Exeter Medical School and senior author of the BMJ study, said, “SNP chips are fantastic at detecting common genetic variants, yet we have to recognize that tests that perform well in one scenario are not necessarily applicable to others.” She added, “We’ve confirmed that SNP chips are extremely poor at detecting very rare disease-causing genetic variants, often giving false positive results that can have profound clinical impact. These false results had been used to schedule invasive medical procedures that were both unnecessary and unwarranted.” (Photo copyright: University of Exeter.)

Large-Scale Study Taps UK Biobank Data

The Exeter researchers were concerned about cases of unnecessary invasive medical procedures being scheduled by women after learning of rare genetic variations in BRCA1 (breast cancer type 1) and BRCA2 (breast cancer 2) tests.

“The inherent technical limitation of SNP chips for correctly detecting rare genetic variants is further exacerbated when the variants themselves are linked to very rare diseases. As with any diagnostic test, the positive predictive value for low prevalence conditions will necessarily be low in most individuals. For pathogenic BRCA variants in the UK Biobank, the SNP chips had an extremely low positive predictive value (1-17%) when compared with sequencing. Were these results to be fed back to individuals, the clinical implications would be profound. Women with a positive BRCA result face a lifetime of additional screening and potentially prophylactic surgery that is unwarranted in the case of a false positive result,” they wrote.

Using UK Biobank data from 49,908 participants (55% were female), the researchers compared next-generation sequencing (NGS) to SNP chip genotyping. They found that SNP chips—which test genetic variation at hundreds-of-thousands of specific locations across the genome—performed well when compared to NGS for common variants, such as those related to type 2 diabetes and ancestry assessment, the study noted.

“Because SNP chips are such a widely used and high-performing assay for common genetic variants, we were also surprised that the differing performance of SNP chips for detecting rare variants was not well appreciated in the wider research or medical communities. Luckily, we had recently received both SNP chip and genome-wide DNA sequencing data on 50,000 individuals through the UK Biobank—a population cohort of adult volunteers from across the UK. This large dataset allowed us to systematically investigate the performance of SNP chips across millions of genetic variants with a wide range of frequencies, down to those present in fewer than 1 in 50,000 individuals,” wrote Wright and Associate Professor of Bioinformatics and Human Genetics at Exeter, Michael Weedon, PhD, in a BMJ blog post.

The Exeter researchers also analyzed data from a small group of people in the Personal Genome Project who had both SNP genotyping and sequencing information available. They focused their analysis on rare pathogenic variants in BRCA1 and BRCA2 genes.

The researchers found:

  • The rarer the variant, the less reliable the test result. For example, for “very rare variants” in less than one in 100,000 people, 84% found by SNP chips were false positives.
  • Low positive predictive values of about 16% for very rare variants in the UK Biobank.
  • Nearly all (20 of 21) customers of commercial genetic testing had at least one false positive rare disease-causing variant incorrectly genotyped.
  • SNP chips detect common genetic variants “extremely well.”

Advantages and Capabilities of SNP Chips

Compared to next-gen genetic sequencing, SNP chips are less costly. The chips use “grids of hundreds of thousands of beads that react to specific gene variants by glowing in different colors,” New Scientist explained.

Common variants of BRCA1 and BRCA2 can be found using SNP chips with 99% accuracy, New Scientist reported based on study data.

However, when the task is to find thousands of rare variants in BRCA1 and BRCA2 genes, SNP chips do not fare so well.

“It is just not the right technology for the job when it comes to rare variants. They’re excellent for the common variants that are present in lots of people. But the rarer the variant is, the less likely they are to be able to correctly detect it,” Wright told CNN.

SNP chips can’t detect all variants because they struggle to cluster needed data, the Exeter researchers explained.

“SNP chips perform poorly for genotyping rare genetic variants owing to their reliance on data clustering. Clustering data from multiple individuals with similar genotypes works very well when variants are common,” the researchers wrote. “Clustering becomes more difficult as the number of people with a particular genotype decreases.”

Clinical laboratories Using SNP Chips

The researchers at Exeter unveiled important information that pathologists and medical laboratory professionals will want to understand and monitor. Cancer patients with rare genetic variants may not be diagnosed accurately because SNP chips were not designed to identify specific genetic variants. Those patients may need additional testing to validate diagnoses and prevent harm.

—Donna Marie Pocius

Related Information:

Large-scale Study Finds Genetic Testing Technology Falsely Detects Very Rare Variants

Use of SNP Chips to Detect Rare Pathogenic Variants: Retrospective, Population-Based Diagnostic Evaluation

The Home DNA Kits “Falsely Warning of High Risk of Cancer”: DIY Genetic Tests are “Extremely Unreliable” at Detecting Rare Genetic Variants, Major New Study Warns

SNP Chips Perform Poorly for Detecting Rare Genetic Variants

Chip-based DNA Testing Wrong More than Right for Very Rare Variants

Common Genetic Tests Often Wrong When Identifying Rare Disease-Causing Variants Such as BRCA1and BRCA2, Study Says