The use of high-resolution SNP arrays to detect congenital cardiac defects

OBJECTIVE

Copy number variations (CNVs) detected by high-resolution single nucleotide polymorphism microarrays (SNP arrays) have been associated with congenital heart defects (CHDs). The genetic mechanism underlying the development of CHDs remains unclear.

METHODS

High-resolution SNP arrays were used to detect CNVs and traditional chromosomal analyses, respectively, were carried out on 60 and 249 fetuses from gestational 12-37 weeks old, having isolated or complex CHDs that were diagnosed using prenatal ultrasound.

RESULTS

Twenty of the 60 fetuses (33.5%) had abnormalities, of which 23 CNVs (12 pathogenic, five probable pathogenic and six of undetermined significance) were detected by SNP arrays, and two distinct CNVs were present in three of these fetuses. In addition, in 39 patients with isolated congenital heart disease who had normal karyotypes, abnormal CNVs were present in 28.2% (11/39), and in patients with complex coronary artery disease, 19.0% (4/21) had abnormal karyotypes and 42.9% (9/21) had abnormal CNVs. In patients with complex coronary artery disease, 19.0% (4/21) had abnormal karyotypes and 42.9% (9/21) had abnormal CNVs.

CONCLUSIONS

In conclusion, genome-wide high-resolution SNP array can improve the diagnostic rate and uncover additional pathogenic CNVs. The submicroscopic deletions and duplications of Online Mendelian Inheritance in Man (OMIM) genes found in this study have haploinsufficient (deletion) or triplosensitive (duplication) traits, which further clarify the etiology and inheritance of CHDs.

Application of Single Nucleotide Polymorphism Microarray in Prenatal Diagnosis of Fetuses with Central Nervous System Abnormalities

Background

The current gold standard of karyotype analysis for prenatal diagnosis of fetuses with central nervous system (CNS) abnormalities has some limitations. Here, we assessed the value of single nucleotide polymorphism (SNP) arrays as a diagnostic tool.

Methods

The results of prenatal diagnosis of 344 fetuses with CNS abnormalities as determined by ultrasonographic screening were retrospectively analyzed. All fetuses underwent chromosomal karyotype analysis and genome-wide SNP array analysis simultaneously. The resultant rates and frequencies of genomic abnormalities were compared.

Results

Karyotype analysis found 45 (13.2%) abnormal CNS cases, while SNP array found 60 (17.4%) cases. SNP array detected 23 (6.7%) cases of submicroscopic abnormalities that karyotype analysis did not find. The detection rate of karyotype analysis was 8.1% in the group with isolated CNS anomalies, but 16.5% in the group with CNS abnormalities plus extra ultrasound anomalies. Detection rates of SNP array were 12.4% and 20.8% in these two groups, respectively. Statistical analysis showed that the detection rates of both methods were significantly higher in the group with CNS malformations and other ultrasound anomalies than in the group with isolated CNS anomalies. Abnormal chromosomes were detected most frequently in fetuses with holoprosencephaly.

Conclusion

Genome-wide SNP array technology can significantly improve the positive detection rate of fetuses with CNS abnormalities. Combining karyotype analysis and SNP array technology is recommended for detecting the development of fetuses with abnormal CNS.

Detection of segmental aneuploidy and mosaicism in the human preimplantation embryo: technical considerations and limitations

Whole-chromosome aneuploidy screening has become a common practice to improve outcomes and decrease embryonic transfer order in patients undergoing treatment for infertility through in vitro fertilization. Despite implementation of this powerful technology, a significant percentage of euploid embryos fail to result in successful deliveries. As technology has evolved, detection of subchromosomal imbalances and embryonic mosaicism has become possible, and these serve as potential explanations for euploid embryo transfer failures. Cases involving a parent with a balanced translocation provide a unique opportunity to characterize the capabilities and limitations of detecting segmental imbalances with a variety chromosome screening platforms. Adaptation of these methods to de novo imbalances now represent an ongoing challenge in the field of preimplantation genetic screening as additional factors including mosaicism, clinical predictive value, and distinguishing true imbalances from technical artifacts must be more carefully considered.