ABSTRACT
The number of publications on research of male infertility is increasing. Technologies used in research of male infertility generate complex results and various types of data that need to be appropriately managed, arranged, and made available to other researchers for further use. In our previous study, we collected over 800 candidate loci for male fertility in seven mammalian species. However, the continuation of the work towards a comprehensive database of candidate genes associated with different types of idiopathic human male infertility is challenging due to fragmented information, obtained from a variety of technologies and various omics approaches. Results are published in different forms and usually need to be excavated from the text, which hinders the gathering of information. Standardized reporting of genetic anomalies as well as causative and risk factors of male infertility therefore presents an important issue. The aim of the study was to collect examples of diverse genomic loci published in association with human male infertility and to propose a standardized format for reporting genetic causes of male infertility. From the currently available data we have selected 75 studies reporting 186 representative genomic loci which have been proposed as genetic risk factors for male infertility. Based on collected and formatted data, we suggested a first step towards unification of reporting the genetics of male infertility in original and review studies. The proposed initiative consists of five relevant data types: 1) genetic locus, 2) race/ethnicity, number of participants (infertile/controls), 3) methodology, 4) phenotype (clinical data, disease ontology, and disease comorbidity), and 5) reference. The proposed form for standardized reporting presents a baseline for further optimization with additional genetic and clinical information. This data standardization initiative will enable faster multi-omics data integration, database development and sharing, establishing more targeted hypotheses, and facilitating biomarker discovery.
Introduction
Male infertility accounts for about half of all infertility cases and affects one out of seven European couples. The term idiopathic is used only if defined causes of male infertility can be excluded, such as congenital factors – anorchia, cryptorchidism, congenital absence of vas deferens, genetic abnormalities related to specific syndromes – or acquired factors – testis trauma, testicular torsion, post-inflammatory forms, obstruction, subobstruction of proximal and/or distal urogenital tract, recurrent urogenital infections, prostatitis, prostatovesciculitis, exogenous factors, systemic diseases, varicocele, surgeries that can damage vascularization of the testes, erectile, ejaculatory dysfunction, acquired hypogonadotrophic hypogonadism, or endocrine factors. The condition is phenotypically expressed in different forms: oligozoospermia (sperm concentration <15×106/ml; total sperm number <39×106/ml), asthenozoospermia (<32% progressively motile spermatozoa), teratozoospermia (<4% morphologically normal spermatozoa), oligoasthenoteratozoospermia (disturbance of all three parameters), azoospermia (no spermatozoa in the ejaculate), aspermia (no ejaculate), and leukocytospermia (>1×106 ml leucocytes in the ejaculate) [Krausz Citation2011].
Genetic abnormalities leading to male infertility are responsible for 15 to 30% of cases. Physiological processes including hormonal homeostasis, spermatogenesis, and sperm quality are disturbed by molecular defects and genetic alterations. The most frequently reported genetic causes of male infertility are chromosomal abnormalities, single gene point mutations, polygenic or multifactorial genetic defects including Y chromosome deletions or microdeletions, X chromosome, mitochondrial DNA (mtDNA) mutations, and endocrine disorders of genetic origin [Reijo et al. Citation1995; Meschede and Horst Citation1997; Shamsi et al. Citation2011; Peterlin et al. Citation2002; Walsh et al. Citation2009]. It has also been discussed that spermatogenic defects might be just one aspect of a more systemic problem associated with increased genomic instability [Aston and Carrell Citation2012].
Recent advances in assisted-reproductive technologies such as in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) have enabled men once considered to be irreparably sterile to have children. However, if genetic anomalies are the cause of their infertility, there is an increased risk of transmitting genetic defects to future generations [Tahmasbpour et al. Citation2014; Johnson Citation1998]. Moreover, in some cases, male infertility presents just one of the symptoms in the clinical picture and is co-present with other clinical signs, such as in Deafness-Infertility Syndrome (DIS) [Zhang et al. Citation2007].
In our previous literature review, we collected more than 800 candidate genes associated with male reproduction in seven mammalian species [Ogorevc et al. Citation2011]. The collected data included loci from different omics approaches, including: genomics (DNA level), transcriptomics, proteomics, epigenomics, and miRNomics. We reviewed papers describing copy number variations (CNV), DNA mutations and polymorphisms, RNA expression patterns, small-noncoding RNAs (microRNA (miRNAs), piRNAs), protein expression patterns, and epigenetic factors. Additionally, we also included syndromes and disorders in which male infertility is present as one of the clinical features [Ogorevc et al. Citation2011]. As for male infertility in humans, the results obtained up to 2007 were summarized in the meta-analysis by Tüttelmann et al. [Citation2007]. Since then, a number of factors have been discovered that were thoroughly described in the review article by Krausz et al. [Citation2015]. However, there is no database available that would cover data on all aspects of male infertility in mammals, but its existence would be very helpful for the development of the field. Considering risks of transmitting an abnormal paternal genotype to the descendant, an appropriate database would enable the quantification of such risks. Similarly, we have developed catalogs of genomics data in other fields, for instance molecular mechanisms involved in cryptorchidism development [Cannistraci et al. Citation2013]. Standardization of reporting may be necessary when combining data sets from several manuscripts. In our studies, we are proposing initiatives to unify the format of research and review papers, because it is becoming evident that making sense of rapidly evolving evidence on genetic associations is very important for advances in genomics and the eventual integration of this information in the practice of medicine. For example, in our previous study we proposed an initiative for reporting standardization of the results of next generation sequencing (NGS) [Pipan and Kunej Citation2015] and molecular mechanisms of cryptorchidism development [Urh and Kunej Citation2016]. The STrengthening the REporting of Genetic Association studies (STREGA) initiative, for example, proposes a minimum checklist of items for reporting genetic association studies [Little et al. Citation2009]. However, most study fields, including male reproduction, need additional, specific directions for standardization of reporting of published data. On the basis of the genes collected by Ogorevc et al. [Citation2011], we began to rearrange the information and formulate a set of typical genetic loci proposed as genetic risk factors for human male infertility. In view of the findings, we also developed the first initiative for standardization of the results format in original and review studies.
Results
In the present study, we collected a set of published representative genomics loci proposed as risk factors for male reproduction. The study consisted of three main steps: 1) collecting available genomics and clinical data from the publications, 2) complementing the data with relevant information and editing according to the gene and disease ontology and IDs, and 3) development of the initiative for standardization of result presentation in publications reporting genetic causes of male infertility.
Development of the set of representative genomics loci potentially associated with male infertility
With the aim to standardize the format of published data, we first collected examples of diverse genomic loci reported as risk factors for male infertility using different omics approaches. The set of loci contains the essential information related with the locus and phenotype. The information that we obtained from the published literature was collected into an Excel table (Supplementary ). The current version of the set consists of data extracted from 75 studies, describing 186 genetic causes potentially associated with male infertility.
Table 1. Minimal checklist of essential information proposed to be included in the studies reporting genetics of male infertility, source databases, and examples.
The collected data is heterologous, composed of a variety of cases, including two repeat polymorphisms, five miRNA genes, 17 copy number variations (CNVs), four chromosome mutations, and ten protein-coding genes. Out of those ten genes, three have been found to be dysregulated in association with male infertility, six have had abnormal epigenetic patterns, and the remaining genes had both abnormal epigenetic patterns and expression. The collected loci also include 144 single-nucleotide polymorphisms (SNPs) associated with male infertility using genome-wide association studies (GWAS) and single loci association studies. Most of those SNPs (n=103) are located within regions encoding for protein-coding genes. Polymorphisms include missense, stop-codon gained, synonymous, intronic, upstream, downstream, and 3’UTR variants. Most of the collected SNPs (n=127) have reference SNP ID numbers (rs#) available; however, in older studies, rs# IDs are not provided. The published literature included diverse types of loci, from protein-coding genes and non-coding RNA genes including miRNA genes to SNPs, repeat polymorphisms, chromosome mutations, and CNVs. Two proteins were also included. The collected loci have been proposed to be associated with male infertility using various study approaches, including association studies and mutation screening (single locus association studies and GWAS), RNA or protein expression analyses, molecular cytogenetics, and epigenetics. We also included genes associated with syndromes, which include male infertility in the clinical picture, for instance, BBS7 gene, associated with Bardet-Biedl Syndrome. Due to the various study approaches, very diverse genomics data have been reported, depending on the methodology used. In contrast, some of the genetic loci have been associated with male infertility in more than one study, such as polymorphism rs1801133 in MTHFR gene. The latter has been subjected to a meta-analysis that has shown significant association with male infertility [Tüttelmann et al. Citation2007].
In the next step, we supplemented the collected data with additional relevant information. Gene names were unified according to the Human Genome Variation Society and HGNC databases. We added Entrez gene ID, Ensembl genomic coordinates of the polymorphism, and PubMed identifier (PMID) of the publication. We also added race/ethnicity of the investigated patients or the country where the study was conducted, study approach, and the corresponding Disease Ontology term and ID if available in the database. Loci were sorted and supplemented according to locus biotype. Additional data, specific for a study approach, was also included. If the information was available, we added polymorphism ID (rs#), polymorphism biotype, minor allele frequency (MAF), P-value, odds ratio (OR), and methodology details (platform name). For repeat polymorphisms, we extracted repeat sequence and number of repeats.
Initiative for standardization of reporting genetics of male infertility
Based on the collected and complemented data, we then proposed an initiative for standardized reporting genetics of male infertility. The procedure revealed basic data types that are common to most study approaches. The minimal checklist of essential information that is recommended to be reported in scientific studies reporting male infertility is listed in . Additional information depends on locus biotype and study approach and is listed in . The collected data was sorted into five categories: 1) information related with genetic loci, 2) ethnicity and number of participants (infertility/controls), 3) methodology, 4) clinical data, disease comorbidity, and 5) the reference (in review papers). Twelve examples of candidate genetic loci associated with male fertility presented according to our proposed standardized format are in and [Aston and Carrell Citation2009; Mashayekhi and Hadiyan Citation2012; Lai et al. Citation2009; Lian et al. Citation2009; Tüttelmann et al. Citation2011; Escoffier et al. Citation2015; Shen et al. Citation2013; Lu et al. Citation2013; Ramasamy et al. Citation2014; Yakut et al. Citation2013; He et al. Citation2014; Luo et al. Citation2014; Krausz et al. Citation2012]. The presented results enable insight into a large range of published data. The proposed format is suggested to be used in original as well as review papers.
Table 2. Proposed format of additional information in studies reporting genetics of male infertility.*
Table 3. Examples of genomics loci proposed to be associated with male infertility, presented according to the suggested format for standardization of reporting of genetics of male infertility.
Table 4. Continuation of : Proposed format for additional details in studies reporting genetics of male infertility.
Discussion
Our literature review and data extraction revealed great diversity and inconsistency in published data related with genetics of male infertility. Most research groups do not pay attention to the format of results presentation and data standardization, which is an important aspect for the data being available to other researchers. Technologies used in studies vary greatly, which is understandable given the wide area of research. The data associated with male infertility are inconsistent, which makes gathering information from publications very challenging. In some cases, information is in the main text, while in others it is in supplementary materials and they are often insufficiently supplemented with basic genetic information, which has to be extracted or verified in other resources. For example, genomic coordinates of genetic loci have to be updated according to the latest genome assemblies in genomic browsers, like Ensembl. In some cases, the results of association studies between genomic abnormality and phenotype are not conclusive, and the data are therefore difficult to interpret and use for further bioinformatics studies. Researchers use different terminology for describing the same methods, and the description of methodology is often imprecise, including in GWAS studies that lack a description of the platform used. Each piece of information has to be extracted manually, which is very time-consuming. It is recommended that the authors present genomics data in a tabular form, including genotype and phenotype data, in order to avoid misinterpretation of the results by the reader, who is not necessarily an expert in the particular field, and to facilitate the work of database curators. The results formatted in a tabular format and supplemented with gene ID, taxonomy ID, and other essential information would enable direct transfer of data from the publication to the databases. Our study also revealed that reproduction related traits are not complete in disease ontology databases and need to be complemented in the future. Disease Ontology is currently the only database where the phenotypes of male infertility are defined in greater detail. Male infertility (DOID: 12336) includes the terms: Sertoli cell-only syndrome (DOID: 0050457), azoospermia (DOID: 14227), oligospermia (DOID: 14228), and infertility due to extratesticular cause (DOID: 14096). The International Classification of Diseases (ICD) database includes only the term ‘male infertility’ (ICD ID: N46) and the Vertebrate Trait Ontology includes the term ‘male fertility trait’ (VTO ID: 0001922). The hierarchy of other terms is currently not available in ICD and VTO databases. A lot of effort will be required to extract the data from previous publications; especially earlier studies, as polymorphisms do not have ID numbers available. Inconsistent reporting of the closest gene to the researched SNP is also frequently observed, which means that the data in publications does not correspond to data in genomic databases (dbSNP). Additionally, previous gene symbols and synonyms used in several publications and genomic locations are not consistent with the current genome assemblies. Several studies reported genomic locations in relation to the NCBI accession number, which is an appropriate way to publish results, however the results refer to different accession numbers, and therefore comparison between studies is time consuming. We are of the opinion that genomic locations of genetic loci should be reported according to the main genomic databases, for example Ensembl, UCSC genomic browser, or NCBI Map Viewer, formatted as chromosome number: start-end (for example 14:83247846-84577905). This approach would enable easier identification of overlapping genomics elements and also the performance of comparative genomics studies through analysis of the syntenic regions.
The proposed initiative for reporting standardization is important for clinical studies, since it is designed to integrate the scattered data into a more holistic view. Our aim is to raise awareness of researchers to start considering reporting standardization in the field of male infertility. At this point, our initiative presents only a first step towards a standardized format for reporting genomics of male infertility, as it will need to be extended and optimized in cooperation with clinicians, which will contribute their expertise regarding phenotype information, for example age at the diagnosis, hormones, and environmental effects. The proposed format should be used in original papers as well in the review papers. The whole scientific community will need to collaborate in order to edit, reformat, complement, and reanalyze previously published data, and we are aware that it would take a very long time and a lot of effort before most journals would unify reporting. A lot of data is derived from single studies or from multiple studies, but with controversial outcomes; even those that have been replicated in at least two independent study populations are of questionable clinical relevance due to the small sample size. Systematics reviews using our suggested format could contribute substantially to the development of the field.
Our collection could represent a template and an example for other researchers in order to make the increasing amount of data more transparent and easily available. Standardization of the format for data presentation will facilitate further development of the database as well as enable other researchers to retrieve published data.
In conclusion, the collected set of candidate loci and initiative for reporting standardization will further facilitate development of systems approaches in the field of male infertility. The number of published studies is expected to grow and the number of causative genomics loci will continue to increase due to the large mass of data produced using GWAS and NGS technologies. Our goal is to create an organized and comprehensive database, joining male infertility-associated information regardless of the study approach. Unified results would enable development of the database, multi-omics data integration, and further bioinformatics and experimental studies. It would be useful to researchers involved in genomics, proteomics, metabolomics, bioinformatics, molecular and biochemical systems approaches, development of biomarkers, design of targeted resequencing panels, and prioritization of genomic loci for further functional studies or genomic overlap analysis and biopathways.
Materials and methods
The publications have been extracted from the PubMed (http://www.ncbi.nlm.nih.gov/pubmed), Web of Science (https://webofknowledge.com/), and OMIM (http://www.ncbi.nlm.nih.gov/omim) databases using key words such as: genetics, gene candidates, male infertility, microarray, risk factor, association, non-coding RNA (ncRNA), miRNA, epigenetic, reproduction, syndromes, and assisted reproduction up to 05/2016.
We gathered relevant information on detected genome variations: gene name, number of participants (infertile/controls), clinical data, methodology, study approach, locus type, and additional details depending on the technology used. We also extracted data regarding the race/ethnicity or country where the study was conducted if race/ethnicity was not specified. The collected data were complemented with additional information: gene names have been unified with HUGO Gene Nomenclature Committee (HGNC) (http://www.genenames.org/) [den Dunnen and Antonarakis Citation2000; Gray et al. Citation2015], Gene ID was obtained from the Entrez database (www.ncbi.nlm.nih.gov), terminology for miRNA was unified in accordance with the Sanger miRBase (http://www.mirbase.org/), PubMed identifier (PMID) from PubMed (http://www.ncbi.nlm.nih.gov/pubmed), and Taxonomy browser (http://www.ncbi.nlm.nih.gov/taxonomy) was used for obtaining Taxonomy ID. Genomic location and minor allele frequency (MAF) were extracted from the Ensembl genomics browser (http://www.ensembl.org), release 84, which is based on the genome assembly GRCh38.p5. Polymorphism ID and biotype were extracted from the dbSNP database (http://www.ncbi.nlm.nih.gov/SNP/). Information for the genomic coordinates of chromosome breakpoints was extracted from the UCSC Genome Bioinformatics Site (https://genome.ucsc.edu/). Disease Ontology database (http://disease-ontology.org/), an open source ontology for the integration of biomedical data that is associated with human disease [Kibbe et al. Citation2015], was used for extraction of ontology terms and identification numbers (DOID). Furthermore, the availability of ontology terms was also checked in The International Classification of Diseases (ICD) (http://www.who.int/classifications/icd/en/) and The Vertebrate Trait Ontology (https://bioportal.bioontology.org/ontologies/VT) (VTO) databases.
Declaration of interest
This work was supported by the Slovenian Research Agency (ARRS) through the research program Comparative Genomics and Genome Biodiversity (grant number P4-0220). The authors report no conflicts of interest.
IAAN_1250181_Supplementary_File.zip
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We thank the editors and anonymous reviewers for their constructive comments, which helped us to improve the manuscript.
Supplemental Material
Supplemental data for this article can be accessed on the publisher’s website.
Additional information
Notes on contributors
Eva Traven
Collected the data from publications, developed the database, and wrote the draft: ET, AO; Designed the study and edited the final text: TK.
Ana Ogrinc
Collected the data from publications, developed the database, and wrote the draft: ET, AO; Designed the study and edited the final text: TK.
Tanja Kunej
Collected the data from publications, developed the database, and wrote the draft: ET, AO; Designed the study and edited the final text: TK.
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