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Pre-implantation genetic diagnosis (PGD) has changed the landscape of clinical genetics by helping families reduce the transmission of monogenic disorders. However, given the high prevalence of embryonic aneuploidy, particularly in patients of advanced reproductive age, unaffected embryos remain at high risk of implantation failure or pregnancy loss due to aneuploidy. 24-chromosome aneuploidy screening has become widely utilized in routine in vitro fertilization (IVF) to pre-select embryos with greater pregnancy potential, but concurrent 24-chromosome aneuploidy screening has not become standard practice in embryos biopsied for PGD. We performed a retrospective cohort study of patients who underwent PGD with or without 24-chromosome aneuploidy screening to explore the value of concurrent screening. Among the PGD + aneuploidy-screened group (n = 355 blastocysts), only 25.6 % of embryos were both Single Gene Disorder (SGD)-negative (or carriers) and euploid; thus the majority of embryos were ineligible for transfer due to the high prevalence of aneuploidy. Despite a young mean age (32.4 ± 5.9y), 49.9 % of Blastocysts were aneuploid. The majority of patients (53.2 %) had ≥1 blastocyst that was Single Gene Disorder (SGD)-unaffected but aneuploid; without screening, these unaffected but aneuploid embryos would likely have been transferred resulting in implantation failure, pregnancy loss, or a pregnancy affected by chromosomal aneuploidy. Despite the transfer of nearly half the number of embryos in the aneuploidy-screened group (1.1 ± 0.3 vs. 1.9 ± 0.6, p < 0.0001), the implantation rate was higher (75 % vs. 53.3 %) and miscarriage rate lower (20 % vs. 40 %) (although not statistically significant). 24-chromosome aneuploidy screening when performed concurrently with PGD provides valuable information for embryo selection, and notably improves single embryo transfer rates.

Objective: To develop a novel and robust protocol for multifactorial preimplantation genetic testing of trophectoderm biopsies using quantitative polymerase chain reaction (qPCR)., Design: Prospective and blinded., Setting: Not applicable., Patient(s): Couples indicated for preimplantation genetic diagnosis (PGD)., Intervention(s): None., Main Outcome Measure(s): Allele dropout (ADO) and failed amplification rate, genotyping consistency, chromosome screening success rate, and clinical outcomes of qPCR-based screening., Result(s): The ADO frequency on a single cell from a fibroblast cell line was 1.64% (18/1,096). When two or more cells were tested, the ADO frequency dropped to 0.02% (1/4,426). The rate of amplification failure was 1.38% (55/4,000) overall, with 2.5% (20/800) for single cells and 1.09% (35/3,200) for samples that had two or more cells. Among 152 embryos tested in 17 cases by qPCR-based PGD and CCS, 100% were successfully given a diagnosis, with 0% ADO or amplification failure. Genotyping consistency with reference laboratory results was >99%. Another 304 embryos from 43 cases were included in the clinical application of qPCR-based PGD and CCS, for which 99.7% (303/304) of the embryos were given a definitive diagnosis, with only 0.3% (1/304) having an inconclusive result owing to recombination. In patients receiving a transfer with follow-up, the pregnancy rate was 82% (27/33)., Conclusion(s): This study demonstrates that the use of qPCR for PGD testing delivers consistent and more reliable results than existing methods and that single gene disorder PGD can be run concurrently with CCS without the need for additional embryo biopsy or whole genome amplification., (Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.)

Tremendous advances in genetics have transformed the field of reproductive endocrinology and infertility over the last few decades. One of the most prominent advances is preimplantation genetic testing (PGT), which allows for the screening of embryos obtained during in vitro fertilization before transfer. Moreover, PGT can be performed for aneuploidy screening, detection of monogenic disorders, or exclusion of structural rearrangements. Refinement of biopsy techniques, such as obtaining samples at the blastocyst rather than the cleavage stage, has helped optimize results from PGT, and technological advances, including next-generation sequencing, have made PGT more efficient and accurate. The continued evolution of the approach to PGT has the potential to further enhance the accuracy of results, expand the application to other conditions, and increase access by reducing cost and improving efficiency. [ABSTRACT FROM AUTHOR]

Background: Automation has been introduced into variant interpretation, but it is not known how automated variant interpretation performs on a stand‐alone basis. The purpose of this study was to evaluate a fully automated computerized approach. Method: We reviewed all variants encountered in a set of carrier screening panels over a 1‐year interval. Observed variants with high‐confidence ClinVar interpretations were included in the analysis; those without high‐confidence ClinVar entries were excluded. Results: Discrepancy rates between automated interpretations and high‐confidence ClinVar entries were analyzed. Of the variants interpreted as positive (likely pathogenic or pathogenic) based on ClinVar information, 22.6% were classified as negative (variants of uncertain significance, likely benign or benign) variants by the automated method. Of the ClinVar negative variants, 1.7% were classified as positive by the automated software. On a per‐case basis, which accounts for variant frequency, 63.4% of cases with a ClinVar high‐confidence positive variant were classified as negative by the automated method. Conclusion: While automation in genetic variant interpretation holds promise, there is still a need for manual review of the output. Additional validation of automated variant interpretation methods should be conducted. [ABSTRACT FROM AUTHOR]

Abstract Background Automation has been introduced into variant interpretation, but it is not known how automated variant interpretation performs on a stand‐alone basis. The purpose of this study was to evaluate a fully automated computerized approach. Method We reviewed all variants encountered in a set of carrier screening panels over a 1‐year interval. Observed variants with high‐confidence ClinVar interpretations were included in the analysis; those without high‐confidence ClinVar entries were excluded. Results Discrepancy rates between automated interpretations and high‐confidence ClinVar entries were analyzed. Of the variants interpreted as positive (likely pathogenic or pathogenic) based on ClinVar information, 22.6% were classified as negative (variants of uncertain significance, likely benign or benign) variants by the automated method. Of the ClinVar negative variants, 1.7% were classified as positive by the automated software. On a per‐case basis, which accounts for variant frequency, 63.4% of cases with a ClinVar high‐confidence positive variant were classified as negative by the automated method. Conclusion While automation in genetic variant interpretation holds promise, there is still a need for manual review of the output. Additional validation of automated variant interpretation methods should be conducted.

Background: Analysis of variants in distant regulatory elements could improve the current 25–50% yield of genetic testing for monogenic diseases. However, the vast size of the regulome, great number of variants, and the difficulty in predicting their phenotypic impact make searching for pathogenic variants in the regulatory genome challenging. New tools for the identification of regulatory variants based on their relevance to the phenotype are needed. Methods: We used tissue-specific regulatory loci mapped by ENCODE and FANTOM, together with miRNA–gene interactions from miRTarBase and miRWalk, to develop Remus, a web application for the identification of tissue-specific regulatory regions. Remus searches for regulatory features linked to the known disease-associated genes and filters them using activity status in the target tissues relevant for the studied disorder. For user convenience, Remus provides a web interface and facilitates in-browser filtering of variant files suitable for sensitive patient data. Results: To evaluate our approach, we used a set of 146 regulatory mutations reported causative for 68 distinct monogenic disorders and a manually curated a list of tissues affected by these disorders. In 89.7% of cases, Remus identified the regulator containing the pathogenic mutation. The tissue-specific search limited the number of considered variants by 82.5% as compared to a tissue-agnostic search. Conclusion: Remus facilitates the identification of regulatory regions potentially associated with a monogenic disease and can supplement classical analysis of coding variations with the aim of improving the diagnostic yield in whole-genome sequencing experiments. [ABSTRACT FROM AUTHOR]

Preimplantation genetic testing (PGT) is a significant challenge and a widely established reproductive alternative for couples with high-risk of transmitting an inherited monogenic disorder. In this study, we report single centre PGT experience at the Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico of Milan, Italy, from 2015 to 2018 after the ruling of the Italian Constitutional Court (96/2015) which legalized embryo genetic analysis in couples with high genetic risk for the first time in Italy. Four diseases were firstly considered: cystic fibrosis and beta-thalassaemia, as autosomal recessive disorders, and hemophilia A and B, as X-linked disorders. Pre-clinical PGT workup was performed for 124 couples after genetic counselling. Thirty-one underwent PGD cycles for cystic fibrosis, 41 for beta-thalassemia and 3 couples for hemophilia A and 1 for hemophilia B. In vitro fertilization (IVF) procedures were used to generate embryos in vitro by intracytoplasmic sperm injection (ICSI). Blastocysts at day 5-7 were biopsied and vitrified post biopsy. Informative short tandem repeat (STR) markers were used for linkage analysis alone or in combination with family gene mutation detection by multiplex PCR. Following diagnosis, embryos with no gene mutation were transferred. Two hundred and forty-seven embryos were obtained. Blastocyst biopsy was performed on 236 day 5-7 embryos. Successful genetic results were obtained in 195 embryos (83%), 121 of those were diagnosed unaffected (62%) and genetically transferable. No conclusive diagnosis was obtained for 41 biopsies and 15 embryos were rebiopsied. Transfer of 84 cryopreserved embryos resulted in 46 pregnancy with an implantation rate of 55% per embryo transfer (ET). Pregnancies result in 26 live births and 8 miscarriages. Thirty-six embryos still remain cryopreserved. Fourteen couples underwent prenatal diagnosis and results were consistent with PGT genetic analysis. Parameters such as allele drop-out, contamination and recombination were considered to evaluate diagnostic accuracy and reliability of genetic testing. Our data showed an allele drop-out rate of 5%, amplification failure of 10% and recombination rate of 6%. Our four years' experience on PGT demonstrates that couples with high risks of transmitting genetic mutations that cause severe medical conditions can really benefit from PGT technique, as it represents a robust reproductive option preventing abortion of an affected pregnancy with great suffering to the pair. Our PGT positive experience serves as an incentive for the use of this procedure in other genetic diseases in our centre. [ABSTRACT FROM AUTHOR]

Preimplantation genetic testing (PGT) has been successfully applied to reduce the risk of miscarriage, improve IVF success rates, and prevent inheritance of monogenic disease and unbalanced translocations. The present study provides the first method capable of simultaneous testing of aneuploidy (PGT-A), structural rearrangements (PGT-SR), and monogenic (PGT-M) disorders using a single platform. Using positive controls to establish performance characteristics, accuracies of 97 to >99% for each type of testing were observed. In addition, this study expands PGT to include predicting the risk of polygenic disorders (PGT-P) for the first time. Performance was established for two common diseases, hypothyroidism and type 1 diabetes, based upon availability of positive control samples from commercially available repositories. Data from the UK Biobank, eMERGE, and T1DBASE were used to establish and validate SNP-based predictors of each disease (7,311 SNPs for hypothyroidism and 82 for type 1 diabetes). Area under the curve of disease status prediction from genotypes alone were 0.71 for hypothyroidism and 0.68 for type 1 diabetes. The availability of expanded PGT to evaluate the risk of polygenic disorders in the preimplantation embryo has the potential to lower the prevalence of common genetic disease in humans. [ABSTRACT FROM AUTHOR]