Chromosomal abnormality, laboratory techniques, tools and databases in molecular Cytogenetics

Prevalence and patterns of chromosomal abnormalities in patients suspected of genetic disorders: insights from a study in Ouagadougou, Burkina Faso

BACKGROUND

Chromosomal abnormalities contribute significantly to human morbidity and mortality, leading to various pathologies. This study aimed to assess the prevalence of chromosomal abnormalities among patients suspected of genetic disorders in Ouagadougou, Burkina Faso.

METHODS AND RESULTS

A descriptive cross-sectional study was conducted from January 1, 2018, to July 16, 2021, involving patients from different university hospitals in Ouagadougou. Blood samples were collected at Hôpital Saint Camille de Ouagadougou (HOSCO) and sent to the Cerba laboratory in France for cytogenetic analysis. A total of 61 cases with suspected genetic disorders were referred for cytogenetic examination. The average age of the patients was 26.81 years ± 18.92, ranging from 1 month to 68 years. Among the cases, 37 (60.65%) exhibited chromosomal abnormalities. Structural abnormalities were the most prevalent (78.38%), while number anomalies accounted for 21.62% of the cases. Chronic myeloid leukemia was detected in 59.45% of cases, followed by free and homogeneous trisomy 21 (18.91%) and sexual inversion (8.10%). Additionally, one case each of Turner syndrome and Klinefelter syndrome were identified.

CONCLUSION

This this study revealed a high frequency of chromosomal abnormalities, with a predominance of structural abnormalities, among patients suspected of genetic disorders in Ouagadougou. The findings emphasize the significance of genetic evaluation and counseling services in the region, particularly for autosomal abnormalities.

Chromosome classification via deep learning and its application to patients with structural abnormalities of chromosomes

BACKGROUND AND OBJECTIVE

Karyotyping is an important technique in cytogenetic practice for the early diagnosis of genetic diseases. Clinical karyotyping is tedious, time-consuming, and error-prone. The objective of our study was to develop a single-stage deep convolutional neural networks (DCNN)-based model to automatically classify normal and abnormal chromosomes in an end-to-end manner.

METHODS

We analyzed 2,424 normal chromosomes and 544 abnormal chromosomes. A preliminary support vector machine (SVM) model was developed to evaluate the basic recognition performance on the dataset. A DCNN-based model was then proposed to process the same dataset.

RESULTS

By utilizing the SVM model, the classification accuracy of 24 normal chromosomes was 86.01 %. The 32 types of normal and abnormal chromosomes got an accuracy of 85.37 %. The accuracy of the DCNN-based model performing the 24 normal chromosomal classification was 91.75 %. The accuracy of the 32 type classification was 87.76 %. To differentiate eight common structural abnormalities, we obtained accuracies that ranged from 90.84 % to 100 %, and the values of the AUC ranged from 91.81 % to 100 %.

CONCLUSIONS

Our proposed DCNN-based model effectively performed the karyotype classification in an end-to-end manner. It had the competence to be used as a prediction tool for abnormal karyotype detection and screening in genetic diagnosis without initial feature extraction. We believe our work is meaningful for genetic triage management to lower the cost in clinical practice.

Detection rates of abnormalities in over 10,000 amniotic fluid samples at a single laboratory

BACKGROUND

A growing number of cytogenetic techniques have been used for prenatal diagnosis. This study aimed to demonstrate the usefulness of karyotyping, BACs-on-Beads (BoBs) assay and single nucleotide polymorphism (SNP) array in prenatal diagnosis during the second trimester based on our laboratory experience.

METHODS

A total of 10,580 pregnant women with a variety of indications for amniocentesis were enrolled in this retrospective study between January 2015 and December 2020, of whom amniotic fluid samples were analysed in 10,320 women. The main technical indicators of participants in the three different technologies were summarized, and cases of chromosome abnormalities were further evaluated.

RESULTS

The overall abnormality detection rate of karyotyping among all the amniotic fluid samples was 15.4%, and trisomy 21 was the most common abnormality (20.9%). The total abnormality detection rate of the BoBs assay was 5.6%, and the diagnosis rate of microdeletion/microduplication syndromes that were not identified by karyotyping was 0.2%. The detection results of the BoBs assay were 100.0% concordant with karyotyping analysis in common aneuploidies. Seventy (87.5%) cases of structural abnormalities were missed by BoBs assay. The total abnormality detection rate of the SNP array was 21.6%. The detection results of common aneuploidies were exactly the same between SNP array and karyotyping. Overall, 60.1% of structural abnormalities were missed by SNP array. The further detection rate of pathogenic significant copy number variations (CNVs) by SNP was 1.4%.

CONCLUSIONS

Karyotyping analysis combined with BoBs assay or SNP array for prenatal diagnosis could provide quick and accurate results. Combined use of the technologies, especially with SNP array, improved the diagnostic yield and interpretation of the results, which contributes to genetic counselling. BoBs assay or SNP array could be a useful supplement to karyotyping.

Copy number variation characterization and possible candidate genes in miscarriage and stillbirth by next-generation sequencing analysis

Background

The present study aimed to explore the etiological relationship between miscarriage and stillbirth and copy number variations (CNVs), as well as provide useful genetic guidance for high‐risk pregnancy.

Methods

In total, 659 fetal samples were recruited and subjected to DNA extraction and CNV sequencing (CNV‐seq), relevant medical records were collected.

Results

There were 322 cases (48.86%) with chromosomal abnormalities, including 230 with numerical abnormalities and 92 with structural abnormalities. Chromosomal monosomy variations mainly occurred on sex chromosomes and trisomy variations mainly occurred on chromosomes 16, 22, 21, 18, 13 and 15. In total, 41 pathogenic CNVs (23 microdeletions and 18 microduplications) were detected in 27 fetal tissues. The rates of numerical chromosomal abnormalities were 29.30% (109/372), 32.39% (57/176) and 57.66% (64/111) in < 30‐year‐old, 30–34‐year‐old and ≥ 35‐year‐old age pregnant women, respectively, and increased with an increasing age (p < 0.001). There was statistically significant difference (χ² = 7.595, p = 0.022) in the rates of structural chromosomal abnormalities in these groups (13.71%, 18.75% and 7.21%, respectively). The rates of numerical chromosomal abnormalities were 45.44% (219/482), 7.80% (11/141) and 0% (0/36) in the ≤ 13 gestational weeks, 14–27 weeks and ≥ 28 weeks groups, respectively, and decreased with respect to the increasing gestational age of the fetuses (p < 0.001).

Conclusions

The present study has obtained useful and accurate genetic etiology information that will provide useful genetic guidance for high‐risk pregnancies.