Implicit bias in diagnosing mosaicism amongst preimplantation genetic testing providers: results from a multicenter study of 36 395 blastocysts.

STUDY QUESTION: Does the diagnosis of mosaicism affect ploidy rates across different providers offering preimplantation genetic testing for aneuploidies (PGT-A)? SUMMARY ANSWER: Our analysis of 36 395 blastocyst biopsies across eight genetic testing laboratories revealed that euploidy rates were significantly higher in providers reporting low rates of mosaicism. WHAT IS KNOWN ALREADY: Diagnoses consistent with chromosomal mosaicism have emerged as a third category of possible embryo ploidy outcomes following PGT-A. However, in the era of mosaicism, embryo selection has become increasingly complex. Biological, technical, analytical, and clinical complexities in interpreting such results have led to substantial variability in mosaicism rates across PGT-A providers and clinics. Critically, it remains unknown whether these differences impact the number of euploid embryos available for transfer. Ultimately, this may significantly affect clinical outcomes, with important implications for PGT-A patients. STUDY DESIGN, SIZE, DURATION: In this international, multicenter cohort study, we reviewed 36 395 consecutive PGT-A results, obtained from 10 035 patients across 11 867 treatment cycles, conducted between October 2015 and October 2021. A total of 17 IVF centers, across eight PGT-A providers, five countries and three continents participated in the study. All blastocysts were tested using trophectoderm biopsy and next-generation sequencing. Both autologous and donation cycles were assessed. Cycles using preimplantation genetic testing for structural rearrangements were excluded from the analysis. PARTICIPANTS/MATERIALS, SETTING, METHODS: The PGT-A providers were randomly categorized (A to H). Providers B, C, D, E, F, G, and H all reported mosaicism, whereas Provider A reported embryos as either euploid or aneuploid. Ploidy rates were analyzed using multilevel mixed linear regression. Analyses were adjusted for maternal age, paternal age, oocyte source, number of embryos biopsied, day of biopsy, and PGT-A provider, as appropriate. We compared associations between genetic testing providers and PGT-A outcomes, including the number of chromosomally normal (euploid) embryos determined to be suitable for transfer. MAIN RESULTS AND THE ROLE OF CHANCE: The mean maternal age (±SD) across all providers was 36.2 (±5.2). Our findings reveal a strong association between PGT-A provider and the diagnosis of euploidy and mosaicism. Amongst the seven providers that reported mosaicism, the rates varied from 3.1% to 25.0%. After adjusting for confounders, we observed a significant difference in the likelihood of diagnosing mosaicism across providers (P < 0.001), ranging from 6.5% (95% CI: 5.2-7.4%) for Provider B to 35.6% (95% CI: 32.6-38.7%) for Provider E. Notably, adjusted euploidy rates were highest for providers that reported the lowest rates of mosaicism (Provider B: euploidy, 55.7% (95% CI: 54.1-57.4%), mosaicism, 6.5% (95% CI: 5.2-7.4%); Provider H: euploidy, 44.5% (95% CI: 43.6-45.4%), mosaicism, 9.9% (95% CI: 9.2-10.6%)); and Provider D: euploidy, 43.8% (95% CI: 39.2-48.4%), mosaicism, 11.0% (95% CI: 7.5-14.5%)). Moreover, the overall chance of having at least one euploid blastocyst available for transfer was significantly higher when mosaicism was not reported, when we compared Provider A to all other providers (OR = 1.30, 95% CI: 1.13-1.50). Differences in diagnosing and interpreting mosaic results across PGT-A laboratories raise further concerns regarding the accuracy and relevance of mosaicism predictions. While we confirmed equivalent clinical outcomes following the transfer of mosaic and euploid blastocysts, we found that a significant proportion of mosaic embryos are not used for IVF treatment. LIMITATIONS, REASONS FOR CAUTION: Due to the retrospective nature of the study, associations can be ascertained, however, causality cannot be established. Certain parameters such as blastocyst grade were not available in the dataset. Furthermore, certain platform-related and clinic-specific factors may not be readily quantifiable or explicitly captured in our dataset. As such, a full elucidation of all potential confounders accounting for variability may not be possible. WIDER IMPLICATIONS OF THE FINDINGS: Our findings highlight the strong need for standardization and quality assurance in the industry. The decision not to transfer mosaic embryos may ultimately reduce the chance of success of a PGT-A cycle by limiting the pool of available embryos. Until we can be certain that mosaic diagnoses accurately reflect biological variability, reporting mosaicism warrants utmost caution. A prudent approach is imperative, as it may determine the difference between success or failure for some patients. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by the Torres Quevedo Grant, awarded to M.P. (PTQ2019-010494) by the Spanish State Research Agency, Ministry of Science and Innovation, Spain. M.P., L.B., A.R.L., A.L.R.d.C.L., N.P.P., M.P., D.S., F.A., A.P., B.M., L.D., F.V.M., D.S., M.R., E.P.d.l.B., A.R., and R.V. have no competing interests to declare. B.L., R.M., and J.A.O. are full time employees of IB Biotech, the genetics company of the Instituto Bernabeu group, which performs preimplantation genetic testing. M.G. is a full time employee of Novagen, the genetics company of Cegyr, which performs preimplantation genetic testing. TRIAL REGISTRATION NUMBER: N/A.

Genetic Analysis Algorithm for the Study of Patients with Multiple Congenital Anomalies and Isolated Congenital Heart Disease.

Congenital anomalies (CA) affect 3-5% of newborns, representing the second-leading cause of infant mortality in Argentina. Multiple congenital anomalies (MCA) have a prevalence of 2.26/1000 births in newborns, while congenital heart diseases (CHD) are the most frequent CA with a prevalence of 4.06/1000 births. The aim of this study was to identify the genetic causes in Argentinian patients with MCA and isolated CHD. We recruited 366 patients (172 with MCA and 194 with isolated CHD) born between June 2015 and August 2019 at public hospitals. DNA from peripheral blood was obtained from all patients, while karyotyping was performed in patients with MCA. Samples from patients presenting conotruncal CHD or DiGeorge phenotype (n = 137) were studied using MLPA. Ninety-three samples were studied by array-CGH and 18 by targeted or exome next-generation sequencing (NGS). A total of 240 patients were successfully studied using at least one technique. Cytogenetic abnormalities were observed in 13 patients, while 18 had clinically relevant imbalances detected by array-CGH. After MLPA, 26 patients presented 22q11 deletions or duplications and one presented a TBX1 gene deletion. Following NGS analysis, 12 patients presented pathogenic or likely pathogenic genetic variants, five of them, found in KAT6B, SHH, MYH11, MYH7 and EP300 genes, are novel. Using an algorithm that combines molecular techniques with clinical and genetic assessment, we determined the genetic contribution in 27.5% of the analyzed patients.

First custom next-generation sequencing infertility panel in Latin America: design and first results.

OBJECTIVE: To present the development of the first custom gene panel for the diagnosis of male and female infertility in Latin America. METHODS: We developed a next-generation sequencing (NGS) panel that assesses genes associated with infertility. The panel targeted exons and their flanking regions. Selected introns in the CFTR gene were also included. The FMR1 gene and Y chromosome microdeletions were analyzed with other recommended methodologies. An in-house developed bioinformatic pipeline was applied for the interpretation of the results. Clear infertility phenotypes, idiopathic infertility, and samples with known pathogenic variants were evaluated. RESULTS: A total of 75 genes were selected based on female (primary ovarian insufficiency, risk of ovarian hyperstimulation syndrome, recurrent pregnancy loss, oocyte maturation defects, and embryo development arrest) and male conditions (azoospermia, severe oligospermia, asthenozoospermia, and teratozoospermia). The panel designed was used to assess 25 DNA samples. Two of the variants found were classified as pathogenic and enable the diagnosis of a woman with secondary amenorrhea and a man with oligoasthenoteratozoospermia. Targeted NGS assay metrics resulted in a mean of 180X coverage, with more than 98% of the bases covered ≥20X. CONCLUSION: Our custom gene sequencing panel designed for the diagnosis of male and female infertility caused by genetic defects revealed the underlying genetic cause of some cases of infertility. The panel will allow us to develop more precise approaches in assisted reproduction.