Meeting the challenge of interpreting high-resolution single nucleotide polymorphism array data in prenatal diagnosis: does increased diagnostic power outweigh the dilemma of rare variants?

OBJECTIVE: Several studies have already shown the superiority of chromosomal microarray analysis (CMA) compared with conventional karyotyping for prenatal investigation of fetal ultrasound abnormality. This study used very high-resolution single nucleotide polymorphism (SNP) arrays to determine the impact on detection rates of all clinical categories of copy number variations (CNVs), and address the issue of interpreting and communicating findings of uncertain or unknown clinical significance, which are to be expected at higher frequency when using very high-resolution CMA. DESIGN: Prospective validation study. SETTING: Tertiary clinical genetics centre. POPULATION: Women referred for further investigation of fetal ultrasound anomaly. METHODS: We prospectively tested 104 prenatal samples using both conventional karyotyping and high-resolution arrays. MAIN OUTCOME MEASURES: The detection rates for each clinical category of CNV. RESULTS: Unequivocal pathogenic CNVs were found in six cases, including one with uniparental disomy (paternal UPD 14). A further four cases had a 'likely pathogenic' finding. Overall, CMA improved the detection of 'pathogenic' and 'likely pathogenic' abnormalities from 2.9% (3/104) to 9.6% (10/104). CNVs of 'unknown' clinical significance that presented interpretational difficulties beyond results from parental investigations were detected in 6.7% (7/104) of samples. CONCLUSIONS: Increased detection sensitivity appears to be the main benefit of high-resolution CMA. Despite this, in this cohort there was no significant benefit in terms of improving detection of small pathogenic CNVs. A potential disadvantage is the high detection rate of CNVs of 'unknown' clinical significance. These findings emphasise the importance of establishing an evidence-based policy for the interpretation and reporting of CNVs, and the need to provide appropriate pre- and post-test counselling.

Pathogenic aberrations revealed exclusively by single nucleotide polymorphism (SNP) genotyping data in 5000 samples tested by molecular karyotyping.

BACKGROUND: Several recent studies have demonstrated the use of single nucleotide polymorphism (SNP) arrays for the investigation of intellectual disability, developmental delay, autism or congenital abnormalities. In addition to LogR 'copy number' data, these arrays provide SNP genotyping data for gene level autozygosity mapping, estimating low levels of mosaicism, assessing long continuous stretches of homozygosity (LCSH), detection of uniparental disomy, and 'autozygous' regions. However, there remains little specific information on the clinical utility of this genotyping data. METHODS: Molecular karyotyping, using SNP array, was performed on 5000 clinical samples. RESULTS: Clinically significant 'LogR neutral' genotyping abnormalities were detected in 0.5% of cases. Among these were a single case of chimerism, 12 cases with low level chromosome mosaicism, and 11 cases with an LCSH associated with uniparental disomy. In addition, the genotyping data revealed several LCSH associated with clinically relevant 'recessive type' genetic defects. CONCLUSIONS: These results demonstrate the utility of SNP genotyping data for detection of clinically significant abnormalities, including chimerism/mosaicism and recessive Mendelian disorders associated with autozygosity. The incidence of clinically significant low level mosaicism inferred from these cases suggests that this has hitherto been underestimated and chromosome mosaicism frequently occurs in the absence of indicative clinical features. The growing appreciation among clinicians and demand for SNP genotyping data poses significant challenges for the interpretation of LCSH, especially where there is no detailed phenotypic description to direct laboratory analysis. Finally, reporting of unexpected or hidden consanguinity revealed by SNP array analysis raises potential ethical and legal issues.

Detection of cryptic pathogenic copy number variations and constitutional loss of heterozygosity using high resolution SNP microarray analysis in 117 patients referred for cytogenetic analysis and impact on clinical practice.

BACKGROUND: Microarray genome analysis is realising its promise for improving detection of genetic abnormalities in individuals with mental retardation and congenital abnormality. Copy number variations (CNVs) are now readily detectable using a variety of platforms and a major challenge is the distinction of pathogenic from ubiquitous, benign polymorphic CNVs. The aim of this study was to investigate replacement of time consuming, locus specific testing for specific microdeletion and microduplication syndromes with microarray analysis, which theoretically should detect all known syndromes with CNV aetiologies as well as new ones. METHODS: Genome wide copy number analysis was performed on 117 patients using Affymetrix 250K microarrays. RESULTS: 434 CNVs (195 losses and 239 gains) were found, including 18 pathogenic CNVs and 9 identified as "potentially pathogenic". Almost all pathogenic CNVs were larger than 500 kb, significantly larger than the median size of all CNVs detected. Segmental regions of loss of heterozygosity larger than 5 Mb were found in 5 patients. CONCLUSIONS: Genome microarray analysis has improved diagnostic success in this group of patients. Several examples of recently discovered "new syndromes" were found suggesting they are more common than previously suspected and collectively are likely to be a major cause of mental retardation. The findings have several implications for clinical practice. The study revealed the potential to make genetic diagnoses that were not evident in the clinical presentation, with implications for pretest counselling and the consent process. The importance of contributing novel CNVs to high quality databases for genotype-phenotype analysis and review of guidelines for selection of individuals for microarray analysis is emphasised.