The aim of the present article was to study the spermatogenic cycle of Mytilus galloprovincialis collected in the Bay of Naples during a whole year and to acquire new insights into the mechanism of control. Knowledge of the Mytilus cycle in this geographic area is of particular interest as, to the best of our knowledge, the male gonad cycle has been hitherto unexplored. Testis organization was evaluated together with the localization of the enzymes 3β-HSD, 17β-HSD, and P450-aromatase, which are strictly connected to the synthesis of two key hormones involved in the testis activity: testosterone and 17β-estradiol. It was demonstrated that: (1) the spermatogenic cycle starts in late Summer-early Fall and continues until early Winter, when the first spawning occurs; after rapid gonad restoration, several spawning events take place until June, when the testis becomes non-active again; (2) in the testis, true Leydig and Sertoli cells are present; (3) during the reproductive period, Sertoli, Leydig, germ, and adipogranular cells (ADGs) are positive to 3β-HSD and 17β-HSD, while only germ cells are positive to P450 aromatase; by contrast, during the resting period, only ADGs are positive to 3β-HSD and 17β-HSD, and P450-aromatase is no longer recognizable. The presence of a hermaphrodite sample is also described. Anat Rec, 2017. © 2017 Wiley Periodicals, Inc. Anat Rec, 300:1881-1894, 2017. © 2017 Wiley Periodicals, Inc.

Whole-genome data has become significantly more accessible over the last two decades. This can largely be attributed to both reduced sequencing costs and imputation models which make it possible to obtain nearly whole-genome data from less expensive genotyping methods, such as microarray chips. Although there are many different approaches to imputation, the Hidden Markov Model (HMM) remains the most widely used. In this study, we compared the latest versions of the most popular HMM-based tools for phasing and imputation: Beagle5.4, Eagle2.4.1, Shapeit4, Impute5 and Minimac4. We benchmarked them on four input datasets with three levels of chip density. We assessed each imputation software on the basis of accuracy, speed and memory usage, and showed how the choice of imputation accuracy metric can result in different interpretations. The highest average concordance rate was achieved by Beagle5.4, followed by Impute5 and Minimac4, using a reference-based approach during phasing and the highest density chip. IQS and R2 metrics revealed that Impute5 and Minimac4 obtained better results for low frequency markers, while Beagle5.4 remained more accurate for common markers (MAF>5%). Computational load as measured by run time was lower for Beagle5.4 than Minimac4 and Impute5, while Minimac4 utilized the least memory of the imputation tools we compared. ShapeIT4, used the least memory of the phasing tools examined with genotype chip data, while Eagle2.4.1 used the least memory phasing WGS data. Finally, we determined the combination of phasing software, imputation software, and reference panel, best suited for different situations and analysis needs and created an automated pipeline that provides a way for users to create customized chips designed to optimize their imputation results.

Whole-genome data has become significantly more accessible over the last two decades. This can largely be attributed to both reduced sequencing costs and imputation models which make it possible to obtain nearly whole-genome data from less expensive genotyping methods, such as microarray chips. Although there are many different approaches to imputation, the Hidden Markov Model remains the most widely used. In this study, we compared the latest versions of the most popular Hidden Markov Model based tools for phasing and imputation: Beagle 5.2, Eagle 2.4.1, Shapeit 4, Impute 5 and Minimac 4. We benchmarked them on three input datasets with three levels of chip density. We assessed each imputation software on the basis of accuracy, speed and memory usage, and showed how the choice of imputation accuracy metric can result in different interpretations. The highest average concordance rate was achieved by Beagle 5.2, followed by Impute 5 and Minimac 4, using a reference-based approach during phasing and the highest density chip. IQS and R2 metrics revealed that IMPUTE5 obtained better results for low frequency markers, while Beagle 5.2 remained more accurate for common markers (MAF>5%). Computational load as measured by run time was lower for Beagle 5.2 than Impute 5 and Minimac 4, while Minimac utilized the least memory of the imputation tools we compared. ShapeIT 4, used the least memory of the phasing tools examined, even with the highest density chip. Finally, we determined the combination of phasing software, imputation software, and reference panel, best suited for different situations and analysis needs and created an automated pipeline that provides a way for users to create customized chips designed to optimize their imputation results.

Study questionCan small genetic variants detected in the whole genome sequencing of spontaneously aborted euploid embryos give insight into possible causes of pregnancy loss?Summary answerBy filtering and prioritizing genetic variants it is possible to identify genomic variants putatively responsible for miscarriage.What is known alreadyMiscarriage is often caused to chromosomal aneuploidies of the gametes but it can also have other genetic causes like small mutations, both de novo or inherited from parents. The analysis of genomic sequences of miscarried embryos has mostly focused on rare variation, and been carried out using criteria and methods that are difficult to reproduce. The role of small mutations has been scantily investigated so far.Study design, size, durationThis is a monocentric observational study. The study includes the data analysis of 46 embryos obtained from women experiencing pregnancy loss recruited by the University of Ferrara from 2017 to 2018. The study was approved by the Ethical committee of Emilia-Romagna (CE/FE 170475).Participants/materials, setting, methodsThe participants are forty-six women, mostly European (87%) diagnosed with first (n=25, av.age 32.7) or recurrent (n=21, av.age 36.5) miscarriage. Embryonic DNA was prepared form chorionic villi and used to select euploid embryos using quantitative PCR, comparative genomic hybridiztion and shallow sequencing of random genomic regions. Euploid embryos were whole-genome sequenced at 30X using Illumina short-reads technology and genomic sequences were used to identify genetic variants. Variants were annotated integrating information from Ensembl100 and literature knowledge on genes associated with embryonic development, miscarriages, lethality, cell cycle. Following annotation, variants were filtered to prioritize putatively detrimental variants in genes that are relevant for embryonic development using a pipeline that we developed. The code is available on gitHub (ezcn/grep).Main results and the role of chanceOur pipeline prioritized 439 putatively causative single nucleotide polymorphisms among 11M variants discovered in ten embryos. By systematic investigation of all coding regions, 47 genes per embryo were selected. Among them STAG2, known in literature for its role in congenital and developmental disorders as well as in cancer, TLE4 a key gene in embryonic development, expressed in both embryonic and extraembryonic tissues in the Wnt and Notch signalling pathways, and FMNL2, involved in cell motility with a major role in driving cell migration. Our analysis is fully reproducible (our code is open-source), and we take measures to increase its robustness to false positives by excluding genes with >5% chance to be selected in a control population.Limitations, reasons for cautionThis pilot study has major limitations in sample size and lack of integration of the parental genomic information. Despite being encouraging, the results need to be interpreted with caution as functional analyses are required to validate the hypotheses that have been generated. Although we have developed a robust and scalable methodology for prioritizing genetic variants, we have not yet extended it beyond the coding regions of the genome.Wider implications of the findingsThis pilot study demonstrate that analysis of genome sequencing can help to clarify the causes of idiopathic miscarriages and provides initial results from the analysis of ten euploid embryos, discovering plausible candidate genes and variants. This study provides guidance for a larger study. Results of this and following wider studies can be used to test genetic predisposition to miscarriages in parents that are planning to conceive or undergoing preimplantation genetic testing. In a wider context, the results of this study might be relevant for genetic counseling and risk management in miscarriagesStudy funding/competing interest(s)A.C. is a full time employee of Igenomix. A.D.M. was employee of Igenomix while working on this project. I.D.B., P.D.A., G.E., S.D.B. are full time employees of the MeriGen Research. All other authors declare that they have no conflicts of interest.

Miscarriage is the spontaneous termination of a pregnancy before 24 weeks of gestation. We studied the genome of euploid miscarried embryos from mothers in the range of healthy adult individuals to understand genetic susceptibility to miscarriage not caused by chromosomal aneuploidies. We developed gp , a pipeline that we used to prioritize 439 unique variants in 399 genes, including genes known to be associated with miscarriages. Among the prioritized genes we found STAG2 coding for the cohesin complex subunit, for which inactivation in mouse is lethal, and TLE4 a target of Notch and Wnt, physically interacting with a region on chromosome 9 associated to miscarriages.