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  • Oxford University Press (OUP)  (11)
  • 1
    In: Human Reproduction, Oxford University Press (OUP), Vol. 37, No. 7 ( 2022-06-30), p. 1652-1663
    Abstract: What is the load, distribution and added clinical value of secondary findings (SFs) identified in exome sequencing (ES) of patients with non-obstructive azoospermia (NOA)? SUMMARY ANSWER One in 28 NOA cases carried an identifiable, medically actionable SF. WHAT IS KNOWN ALREADY In addition to molecular diagnostics, ES allows assessment of clinically actionable disease-related gene variants that are not connected to the patient’s primary diagnosis, but the knowledge of which may allow the prevention, delay or amelioration of late-onset monogenic conditions. Data on SFs in specific clinical patient groups, including reproductive failure, are currently limited. STUDY DESIGN, SIZE, DURATION The study group was a retrospective cohort of patients with NOA recruited in 10 clinics across six countries and formed in the framework of the international GEMINI (The GEnetics of Male INfertility Initiative) study. PARTICIPANTS/MATERIALS, SETTING, METHODS ES data of 836 patients with NOA were exploited to analyze SFs in 85 genes recommended by the American College of Medical Genetics and Genomics (ACMG), Geisinger’s MyCode, and Clinical Genome Resource. The identified 6374 exonic variants were annotated with ANNOVAR and filtered for allele frequency, retaining 1381 rare or novel missense and loss-of-function variants. After automatic assessment of pathogenicity with ClinVar and InterVar, 87 variants were manually curated. The final list of confident disease-causing SFs was communicated to the corresponding GEMINI centers. When patient consent had been given, available family health history and non-andrological medical data were retrospectively assessed. MAIN RESULTS AND THE ROLE OF CHANCE We found a 3.6% total frequency of SFs, 3.3% from the 59 ACMG SF v2.0 genes. One in 70 patients carried SFs in genes linked to familial cancer syndromes, whereas 1 in 60 cases was predisposed to congenital heart disease or other cardiovascular conditions. Retrospective assessment confirmed clinico-molecular diagnoses in several cases. Notably, 37% (11/30) of patients with SFs carried variants in genes linked to male infertility in mice, suggesting that some SFs may have a co-contributing role in spermatogenic impairment. Further studies are needed to determine whether these observations represent chance findings or the profile of SFs in NOA patients is indeed different from the general population. LIMITATIONS, REASONS FOR CAUTION One limitation of our cohort was the low proportion of non-Caucasian ethnicities (9%). Additionally, as comprehensive clinical data were not available retrospectively for all men with SFs, we were not able to confirm a clinico-molecular diagnosis and assess the penetrance of the specific variants. WIDER IMPLICATIONS OF THE FINDINGS For the first time, this study analyzed medically actionable SFs in men with spermatogenic failure. With the evolving process to incorporate ES into routine andrology practice for molecular diagnostic purposes, additional assessment of SFs can inform about future significant health concerns for infertility patients. Timely detection of SFs and respective genetic counseling will broaden options for disease prevention and early treatment, as well as inform choices and opportunities regarding family planning. A notable fraction of SFs was detected in genes implicated in maintaining genome integrity, essential in both mitosis and meiosis. Thus, potential genetic pleiotropy may exist between certain adult-onset monogenic diseases and NOA. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the Estonian Research Council grants IUT34-12 and PRG1021 (M.L. and M.P.); National Institutes of Health of the United States of America grant R01HD078641 (D.F.C., K.I.A. and P.N.S.); National Institutes of Health of the United States of America grant P50HD096723 (D.F.C. and P.N.S.); National Health and Medical Research Council of Australia grant APP1120356 (M.K.O’B., D.F.C. and K.I.A.); Fundação para a Ciência e a Tecnologia (FCT)/Ministério da Ciência, Tecnologia e Inovação grant POCI-01-0145-FEDER-007274 (A.M.L., F.C. and J.G.) and FCT: IF/01262/2014 (A.M.L.). J.G. was partially funded by FCT/Ministério da Ciência, Tecnologia e Ensino Superior (MCTES), through the Centre for Toxicogenomics and Human Health—ToxOmics (grants UID/BIM/00009/2016 and UIDB/00009/2020). M.L.E. is a consultant for, and holds stock in, Roman, Sandstone, Dadi, Hannah, Underdog and has received funding from NIH/NICHD. Co-authors L.K., K.L., L.N., K.I.A., P.N.S., J.G., F.C., D.M.-M., K.A., K.A.J., M.K.O’B., A.M.L., D.F.C., M.P. and M.L. declare no conflict of interest. TRIAL REGISTRATION NUMBER N/A.
    Type of Medium: Online Resource
    ISSN: 0268-1161 , 1460-2350
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1484864-8
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  • 2
    In: Bioinformatics, Oxford University Press (OUP), Vol. 38, No. 23 ( 2022-11-30), p. 5288-5298
    Abstract: The mammalian testis is a complex organ with a cellular composition that changes smoothly and cyclically in normal adults. While testis histology is already an invaluable tool for identifying and describing developmental differences in evolution and disease, methods for standardized, digital image analysis of testis are needed to expand the utility of this approach. Results We developed SATINN (Software for Analysis of Testis Images with Neural Networks), a multi-level framework for automated analysis of multiplexed immunofluorescence images from mouse testis. This approach uses residual learning to train convolutional neural networks (CNNs) to classify nuclei from seminiferous tubules into seven distinct cell types with an accuracy of 81.7%. These cell classifications are then used in a second-level tubule CNN, which places seminiferous tubules into one of 12 distinct tubule stages with 57.3% direct accuracy and 94.9% within ±1 stage. We further describe numerous cell- and tubule-level statistics that can be derived from wild-type testis. Finally, we demonstrate how the classifiers and derived statistics can be used to rapidly and precisely describe pathology by applying our methods to image data from two mutant mouse lines. Our results demonstrate the feasibility and potential of using computer-assisted analysis for testis histology, an area poised to evolve rapidly on the back of emerging, spatially resolved genomic and proteomic technologies. Availability and implementation The source code to reproduce the results described here and a SATINN standalone application with graphic-user interface are available from http://github.com/conradlab/SATINN. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 3
    In: Bioinformatics, Oxford University Press (OUP), Vol. 39, No. 6 ( 2023-06-01)
    Abstract: Single-cell RNA sequencing (scRNA-seq) data, annotated by cell type, is useful in a variety of downstream biological applications, such as profiling gene expression at the single-cell level. However, manually assigning these annotations with known marker genes is both time-consuming and subjective. Results We present a Graph Convolutional Network (GCN)-based approach to automate the annotation process. Our process builds upon existing labeling approaches, using state-of-the-art tools to find cells with highly confident label assignments through consensus and spreading these confident labels with a semi-supervised GCN. Using simulated data and two scRNA-seq datasets from different tissues, we show that our method improves accuracy over a simple consensus algorithm and the average of the underlying tools. We also compare our method to a nonparametric neighbor majority approach, showing comparable results. We then demonstrate that our GCN method allows for feature interpretation, identifying important genes for cell type classification. We present our completed pipeline, written in PyTorch, as an end-to-end tool for automating and interpreting the classification of scRNA-seq data. Availability and implementation Our code for conducting the experiments in this paper and using our model is available at https://github.com/lewinsohndp/scSHARP.
    Type of Medium: Online Resource
    ISSN: 1367-4811
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1468345-3
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  • 4
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2017
    In:  Bioinformatics Vol. 33, No. 15 ( 2017-08-01), p. 2322-2329
    In: Bioinformatics, Oxford University Press (OUP), Vol. 33, No. 15 ( 2017-08-01), p. 2322-2329
    Abstract: Accurate identification of genotypes is an essential part of the analysis of genomic data, including in identification of sequence polymorphisms, linking mutations with disease and determining mutation rates. Biological and technical processes that adversely affect genotyping include copy-number-variation, paralogous sequences, library preparation, sequencing error and reference-mapping biases, among others. Results We modeled the read depth for all data as a mixture of Dirichlet-multinomial distributions, resulting in significant improvements over previously used models. In most cases the best model was comprised of two distributions. The major-component distribution is similar to a binomial distribution with low error and low reference bias. The minor-component distribution is overdispersed with higher error and reference bias. We also found that sites fitting the minor component are enriched for copy number variants and low complexity regions, which can produce erroneous genotype calls. By removing sites that do not fit the major component, we can improve the accuracy of genotype calls. Availability and Implementation Methods and data files are available at https://github.com/CartwrightLab/WuEtAl2017/ (doi:10.5281/zenodo.256858). Supplementary information Supplementary data is available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 5
    In: Biology of Reproduction, Oxford University Press (OUP), Vol. 93, No. 3 ( 2015-09-01)
    Type of Medium: Online Resource
    ISSN: 0006-3363 , 1529-7268
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2015
    detail.hit.zdb_id: 1469812-2
    SSG: 12
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  • 6
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2009
    In:  Human Molecular Genetics Vol. 18, No. 12 ( 2009-6-15), p. 2241-2256
    In: Human Molecular Genetics, Oxford University Press (OUP), Vol. 18, No. 12 ( 2009-6-15), p. 2241-2256
    Type of Medium: Online Resource
    ISSN: 1460-2083 , 0964-6906
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2009
    detail.hit.zdb_id: 1474816-2
    SSG: 12
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  • 7
    In: Genetics, Oxford University Press (OUP), Vol. 183, No. 3 ( 2009-11-01), p. 1065-1077
    Abstract: We have evaluated the extent to which SNPs identified by genomewide surveys as showing unusually high levels of population differentiation in humans have experienced recent positive selection, starting from a set of 32 nonsynonymous SNPs in 27 genes highlighted by the HapMap1 project. These SNPs were genotyped again in the HapMap samples and in the Human Genome Diversity Project–Centre d'Etude du Polymorphisme Humain (HGDP–CEPH) panel of 52 populations representing worldwide diversity; extended haplotype homozygosity was investigated around all of them, and full resequence data were examined for 9 genes (5 from public sources and 4 from new data sets). For 7 of the genes, genotyping errors were responsible for an artifactual signal of high population differentiation and for 2, the population differentiation did not exceed our significance threshold. For the 18 genes with confirmed high population differentiation, 3 showed evidence of positive selection as measured by unusually extended haplotypes within a population, and 7 more did in between-population analyses. The 9 genes with resequence data included 7 with high population differentiation, and 5 showed evidence of positive selection on the haplotype carrying the nonsynonymous SNP from skewed allele frequency spectra; in addition, 2 showed evidence of positive selection on unrelated haplotypes. Thus, in humans, high population differentiation is (apart from technical artifacts) an effective way of enriching for recently selected genes, but is not an infallible pointer to recent positive selection supported by other lines of evidence.
    Type of Medium: Online Resource
    ISSN: 1943-2631
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2009
    detail.hit.zdb_id: 1477228-0
    SSG: 12
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  • 8
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2017
    In:  G3: Genes|Genomes|Genetics Vol. 7, No. 1 ( 2017-01), p. 247-255
    In: G3: Genes|Genomes|Genetics, Oxford University Press (OUP), Vol. 7, No. 1 ( 2017-01), p. 247-255
    Type of Medium: Online Resource
    ISSN: 2160-1836
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
    detail.hit.zdb_id: 2629978-1
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  • 9
    In: Human Molecular Genetics, Oxford University Press (OUP), Vol. 24, No. 19 ( 2015-10-01), p. 5628-5636
    Type of Medium: Online Resource
    ISSN: 0964-6906 , 1460-2083
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2015
    detail.hit.zdb_id: 1474816-2
    SSG: 12
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  • 10
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2017
    In:  Biology of Reproduction Vol. 97, No. 1 ( 2017-07), p. 2-4
    In: Biology of Reproduction, Oxford University Press (OUP), Vol. 97, No. 1 ( 2017-07), p. 2-4
    Type of Medium: Online Resource
    ISSN: 0006-3363 , 1529-7268
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
    detail.hit.zdb_id: 1469812-2
    SSG: 12
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