Strategies to Identify Genetic Variants Causing Infertility.

There are multiple sources that curate genetic information, with varying emphases on clinical vs basic research data. For those concerned with human reproductive genetics and who are seeking to determine if particular sequence variants are disease related, the ClinVar database ( Box 2 ) lists ~2,000 “pathogenic” infertility variants (predominantly single nucleotide mutations). However, most of these are associated with syndromic disorders such as cystic fibrosis or primary ciliary dyskinesia, and overall, these variants correspond to only ~150 distinct genes. OMIM (Online Mendelian Inheritance in Man) is a more gene: disease oriented resource better suited for, as the name implies, finding genes that, when mutated, cause a particular disease. It currently has 442 listings for infertility genes. On the basic research side, studies of model organisms (e.g., rodents, fruit flies, worms, etc.) have identified many hundreds of genes important for reproduction and fertility. The data from major model organisms (and humans), which includes substantial mechanistic information, can be mined at www.alliancegenome.org , a consortium of bioinformatics groups dedicated to specific organisms. Additionally, each maintains sites dedicated to those individual organisms such as the mouse ( informatics.jax.org ) or fruit flies ( flybase.org ). Existing knowledge of fertility genetics will continue to ramp over the next few years. In terms of basic knowledge, as most major model organisms are being subjected to whole genome functional analysis (see Outstanding questions ). For example, over half of all mouse genes have been knocked out, and a group of international partners (the International Mouse Phenome Consortium) is systematically mutating all remaining genes (protein coding and non-coding) and conducting phenotypic analyses including fertility ( www.mousephenotype.org ). Thus, there will be information on the functions of all genes in next next few years, thus providing useful information for those conserved in humans. This information can be helpful in identifying candidates in regions identified by GWAS in people, or from genomic analysis (generally WES) of individual patients. Aside from GWAS directed at specific traits, increased usage and availability of healthcare system-based biobanks linked to electronic health records (EHRs) is adding to the compendium of human-specific reproductive genetics. For the subset of patients having genomic data associated with their EFIR, this provides new opportunities to discover genetic variants from large patient bases with the same phenotype definitions ( Fig. 2A ). Several initiatives, such as the Clinical Sequencing Exploratory/Evidence-Generating Research (CSER) Consortium, Clinical Genomic Resource (ClinGen), Implementing Genomics in Practice (IGNITE) network and the Electronic Medical Records and Genomics (eMERGE) Network [ 22 – 25 ] evaluate the use of genomic tests. The Geisinger MyCode Community Health Initiative (MyCode®) is being used to generate molecular data, including high-density genotype and exome sequence data, for diagnosis and lifetime patient health management. A recent study exploited EHRs to identify novel variants in 2,995 PCOS patients by GWA [ 26 ]. These patient data were extracted from 3 EHR collections: MyCode® [ 27 ], BioVU at Vanderbilt University [ 28 ], and the eMERGE [ 24 ]. The practice of routine patient sequencing, as exemplified by the individual eMERGE partners, presages the future standard of care for most for developed countries and their populations in the future. Until then, genome wide sequencing (WES or WGS) will remain a niche diagnostic that is performed on a limited basis by caregivers at locations with sufficient expertise, for patients with resources and for whom the diagnostic may be of potential benefit.

In situations where an “idiopathic” diagnosis is reached following a comprehensive clinical workup of an infertile patient, including fundamental genetic tests such as those for karyotypic abnormalities or co-morbidities (such as cystic fibrosis or Klinefelter Syndrome, the latter of which is a major known genetic cause of NOA), advanced genetic analysis (typically WES) is increasingly becoming the next logical step. While the clinical portion of this “test” is as simple as a blood draw, the most problematic aspect lies in interpreting and understanding the data to draw conclusions as to possible genetic cause(s). At the root of this problem is the issue of genetic variation, whereas every individual carries many thousands of minor alleles (namely SNPs) of uncertain significance, including those in “fertility” genes. Aside from situations in which a patient inherits a known deleterious allele, or a private/rare allele that is obviously deleterious (say, a nonsense mutation), and the patient’s phenotype is congruous with a mutation in that gene, interpretation becomes more difficult and requires expert interpretation beyond the expertise of most clinicians. Of course, the potential character of a variant allele(s) (i.e., recessive, dominant, semidominant) must be considered, and crucially, the phasing (for autosomal genes) if two or more variants are identified in a gene by sequencing. To simplify bioinformatic analysis of patient HTS data in a clinical setting, automated computational pipelines (such as C-PIPE [ 29 ]) have been developed to identify candidate causal variants in a patient and rank them on the bases of various factors such as the predicted consequence upon protein function, conservation of the affected amino acid(s), and frequency in a population [ 30 , 31 ]. ANNOVAR (ANNOtate VARiation) [ 32 ] is a basic and commonly-used tool to prioritize variants, gleaned from a variety of databases ( Box 2 ), regarding probable impact on gene function. More sophisticated tools for in silico prediction of pathogenicity of coding variants include the widely-used SIFT [ 33 ] and PolyPhen2 [ 34 ] algorithms, which focus on particular metrics of analysis such as evolutionary conservation of amino acids or the predicted effects of variants on the physiochemical properties of a protein, and “ensemble” tools such as REVEL [ 35 ] and CADD [ 36 ] that weight predictions from other tools. The genetic Variant Impact Predictor Database (VlPdb) summarizes hundreds of prediction tools on its website ( https://genomeinterpretation.org/vipdb ), underscoring the importance of the issue and demand for solutions [ 37 ]. Finally, the PSAP (population sampling probability) approach prioritizes potentially causative variants obtained from WES data using a model-based likelihood framework, and is designed to address single cases (referred to as the “n = 1” problem) [ 38 ]. Unfortunately, these and other computational tools for in silico predictions are not flawless. For example, a human missense variant of MND1 (Meiotic nuclear divisions 1) that was predicted to be highly deleterious to protein function by several algorithms, did not impact fertility, fecundity or gametogenesis in a mouse model [ 39 ], though caveats with animal modeling are discussed later. The major challenge for a physician, genetic counsellor or researcher is to actually pinpoint, with high confidence, the actual causal lesion. Of course, this assumes that there is indeed a genetic cause, which, in the absence of compelling evidence, may not be the case; alternative etiologies may be related to environmental factors or even epigenetic mechanisms. Unless a suspect variant is a proven infertility allele, experiments are required to validate the candidate variant, which is currently unrealistic in a clinical context. Since this conundrum is representative of the overwhelming majority of cases, how can we move the field forward? Possible solutions are discussed in the following sections.

As for most human diseases of genetic origin, a strong case can be made that comprehensive characterization of VUS would have the broadest impact on infertility genetics, because it would provide a definitive and permanent list of segregating populations variants to be consulted for diagnostic purposes. VUS fall into two broad categories: coding and non-coding. For several reasons, coding variants (predominantly nonsynonymous SNPs, or nsSNPs) are the most tractable to address. First, the universe of known “fertility genes,” as discussed earlier, is already very comprehensive. Second, the majority of mutations or variants known to cause Mendelian traits are in coding regions. Third, it is far simpler to predict consequences of coding than noncoding variants, and most functional prediction programs discussed above focus on coding SNPs and/or can call splice site alterations. Fourth, the consequences of intergenic (non-coding) SNPs are much more difficult to predict, and even if one was to alter function of a regulatory element, typically correspond to QTL as opposed to Mendelian loci [ 20 ]. Also, the genes regulated by elements such as enhancers can be difficult to define. Therefore, we will focus on coding SNPs in this section, but will address regulatory variants later. The first step in defining the universe of potential infertility variants is to select candidate VUS for follow-up study ( Fig. 2B ). Obviously this should involve practical criteria for prioritization, namely that only genes known to be involved in fertility should be considered. This set of genes can then be cross listed with all known nsSNPs. The gnomAD database ( Box 2 ) is a robust source of high-quality human genetic variants from different populations, reflecting over 200,000 whole exome and whole genome sequences [ 1 ]. Next, these SNPs should be evaluated and prioritized by in silico prediction packages as discussed earlier. Sites such as Ensembl and gnomAD have already pre-computed deleteriousness scores from some of these algorithms, simplifying bioinformatic analysis and rankings. Subsequent steps for prioritization can consider factors such as allele frequency. Common variants are unlikely to cause infertility in a Mendelian fashion, because selective forces wouldn’t allow them to persist at high levels in a population. Missense variants can affect a protein in several ways, including tertiary structure, stability, efficiency of protein: macromolecule interaction, or post-translational modification (PTM). For example, two human RABL2A variants were found to destabilize the protein and cause male infertility and ciliopathy [ 40 ], and a SEPT12 variant was found to cause male subfertility by disrupting interaction with SEPT1 [ 41 ]. Aside from the deleteriousness prediction tools discussed earlier there are other in silico approaches are available to predict such outcomes. Some examples are: the neural network-based method DeepDDG, which predicts the consequences of variants/mutations upon protein stability [ 44 ]; SAAMBE-3D a machine learning algorithm that predicts the effects of amino acid substitutions (AAS) on PPIs [ 45 ]; and “Awesome” (A Website Exhibits SNP On Modification Event) that evaluates nsSNPs predicted to alter PTMs [ 46 ]. With time and more data, the universe of genes important for fertility will increase. Similarly, increased sequencing of people from diverse populations around the world will uncover more and rarer population variants. Finally, we can expect that functional assessment algorithms and modeling software will improve in accuracy, enabling better prioritization of VUS that are likely to impact fertility genes. Nevertheless, it remains essential to obtain functional evidence for these variants for eventual clinical utility. This is the focus of the remaining sections.

Infertility affects approximately 15% of couples worldwide. About half of cases are estimated to be attributable to female factors alone, 20-30% to male factors alone, and the remaining 20-30% from a combination of male and female factors. Clinically, infertility is a highly heterogeneous disease with complex etiologies ( Box 1 ), and genetic causes account for about half of cases. Several known causative genetic aberrations are used for clinical diagnosis of male infertility, such as Klinefelter Syndrome (XXY) and Y chromosome microdeletions. However, a large proportion of cases are grouped as idiopathic infertility, having unknown genetic or biological causes. The genetic causes of idiopathic infertility can be rooted in de novo mutations, rare (including pedigree-specific), or segregating population variants. Variants can take the form of small deletions or insertions (indels), larger scale structural variants, copy number alterations, and single nucleotide polymorphisms (SNPs ; see Glossary ). In human populations, SNPs account for approximately 90% of genetic variants and contribute a major source of genetic heterogeneity. However, proving causality of a SNP can be exceedingly difficult. Advances in high-throughput sequencing (HTS) and bioinformatic methods are having a major impact with respect to characterizing variants present in human populations [ 1 ]. However, the vast majority are classified as “Variants of Uncertain Significance (VUS)” due to the lack of functional evidence to determine whether they are pathogenic or benign. Identifying the genetic causes of idiopathic infertility and interpreting VUS in essential fertility genes is challenging but profoundly important for clinical management and genetic counseling. HTS has been making a large impact on the clinical identification of causative mutations responsible for rare diseases, because these are predominantly Mendelian in nature, and, since most are identified at an early age, parents and siblings are often available for genotyping [ 2 ]. More recently, the same methods are being applied towards infertility patients, but identification of causative mutations or variants faces several hurdles: it is often not certain that infertility in a patient is genetic in nature; the disease is often detected later in life; infertility is highly heterogeneous, likely reflecting the large number of genes known to be required for fertility; some cases may have complex genetic origins; and the affected patient is often the only family member manifesting the disease (referred to as the “n = 1” problem). Therefore, modalities beyond HTS are crucial for determining, with high confidence, the actual genetic causes of infertility in a given patient (see Clinician’s Corner ). This review addresses genetic and genomic strategies to identify candidate infertility variants, and to validate them.

Breathtaking advances in genome biology are enabling and revolutionizing advances in many areas of medicine such as cancer and regenerative medicine. Translation to the field of reproductive biology has lagged relative to genomic efforts applied to life-threatening diseases, and the extreme biological and genetic complexity of reproduction, including lack of effective and efficient in vitro systems that are so widely applied in the genomics arena, underlies this dichotomy. Nevertheless, there is much reason for optimism. We can reasonably expect that, due to the radical decline in HTS costs and the increased number of individuals being trained in genome sciences and bioinformatics, that WES or WGS will become standard of care in the near future, possibly for newborns (see Clinician’s Corner and Highlights ). With increasing functional information and intense focus on understanding human genetic variation, precision medicine will become more routine and practical, including for infertility patients. Sequencing at birth can predict genetic predisposition to potential fertility problems, potentially enabling earlier and more efficacious interventions for maladies such as POI, where steps might be taken to preserve oocytes or prevent loss prior to manifestation of the phenotype. Regarding improvements to prediction or diagnosis of genetically-driven infertility phenotypes, a strong argument can be made that improvements to in vitro systems would be crucial for enabling a high-throughput platform not only for evaluating functions of VUS segregating in populations, but also as an advanced clinical diagnostic for individual patients. With certainty about the genetic etiology of a patient’s infertility, the next challenge lies with perfecting platforms (e.g., SSCs or iPSCs) for genetic correction or in vivo rescues, and addressing ethical boundaries. Finally, as with all other areas of medicine, the professionals in the reproductive medicine community must keep abreast of these developing technologies to provide patients with the most modern options.

A hallmark of the genomics era is high-throughput biology. Strategies to assay roles of all human genes in a certain process can be performed in a single experiment via knockout, knockdown, or upregulation. Such experiments typically require cell culture platform, as well as an appropriate assay such as cell growth, expression of diagnostic markers, or even perturbations to the transcriptome of single cells [ 47 – 50 ]. Unfortunately, we have yet to develop such massively parallel reporter assays ( MPRAs ) relevant to fertility, because tenable platforms are not yet available. In particular, despite advances with in vitro germ cell development, there are currently no feasible ways to conduct MPRAs on effects of alleles – whether knockouts or VUS – in a manner that accurately recapitulates spermatogenesis or oogenesis. Until these culture systems are substantially improved in terms of developmental accuracy and efficiency, there are nevertheless alternative methods, albeit less efficient, that can be very informative. A few examples of such alternatives are discussed here. There are several experimental methods for functional testing of variants in vitro and in vivo. One is biochemical, that is, to test if some activity or property of a protein is altered by a nsSNP. An advantage is that a relevant biological framework isn’t required. For proteins with a known activity, for example, kinases, sequence-specific DNA binding, or DNA repair function, or interaction with other proteins, relevant biochemical or in vitro assays in cells can be performed. For example, one study of patients with maturation arrest of oocytes found variants in TRIP13 , encoding a gene important for recombination during meiosis [ 51 , 52 ], that affected protein abundance and binding to a partner protein using in transfection studies [ 53 ]. Pathogenic variants in Anti-Mullerian Flormone (AMFI), causing polycystic ovarian syndrome (PCOS) were validated in one study using luciferase assays detecting robustness of signaling to a BMP-response element target [ 54 ]. A third example used reporter assays to test a missense variant in the transcription factor NR5A1 (nuclear receptor subfamily 5 group A member 1) present in some POI patients. The variant reduce ability to stimulate transcription of target gene promoters [ 55 ]. Disease causing mutations in people preferentially map to the interfaces of protein-protein interactions (PPI), presumably disrupting association with partners [ 56 ]. For example, two variants of KLFIL10 (Kelch like family member 10) found in oligozoospermic patients impair homodimerization with the wide-type protein [ 57 ]. A high-throughput yeast two-hybrid screen for nsSNPs that disrupt PPIs, in conjunction with structural predictions and human cell assays, allowed identification of a deleterious allele of SEPT12 that proved to disrupt sperm function in CRISPR-edited mice [ 41 ]. For genes that also function in cell types outside of the reproductive system or germline, testing of variants is more accessible to high throughput assays relevant to that gene. As an example, variants in the BRCA2 gene, which functions in homologous recombination repair, is intensely studied due to its role in breast cancer. This gene also function in meiosis [ 58 ], and mutations in it may cause POI [ 59 ]. A recent report describes a high throughput assay that enabled rapid screening of nearly 200 VUS in BRCA2 for their impact on DNA repair function [ 60 ]. While in vitro systems yield useful information on the impact of variants on protein function, and thus help prioritize or classify them with greater confidence than in silico predictions, the impact of such functional variants on reproductive organs or phenotype is lacking. The following sections discusses in vivo systems that can be used to address this issue. In vivo functional validation can utilize appropriate non-mammalian experimental models. For example, because the flagellar 9+2 microtubule doublet axoneme structure is widely conserved across species, variants identified in patients suffering from MMAF and PCD ( Box1 ) have been tested in the flagellated protist Trypanosoma brucei [ 61 ]. Since many key genes in meiosis are conserved across eukaryotes, yeast can be used to screen functional consequences of nsSNPs, as demonstrated in the case of a putative NOA allele of ENTPD6 [ 62 ], and for alleles of mismatch repair genes that may contribute to Lynch Syndrome [ 63 ]. The fruit fly Drosophila melanogaster is another workhorse genetic model, and a strategy was developed using in vivo RNA interference and genome editing for functional screening of gene variants associated with NOA in Chinese men [ 64 , 65 ]. Of course, this strategy is limited to highly conserved genes, and caveats about phenotype translatability apply. While non-mammalian systems can help identify and validate infertility-causing mutations, the most relevant experimental model is the mouse, where human alleles can be modeled in orthologous genes. This approach has been possible for many years using homologous recombination technology to “knock in” specific changes into a mouse gene in embryonic stem cells (ESCs), such as was done to validate an infertility-causing MIWI allele [ 66 ]. However, this methodology requires building complex targeting vectors and screening hundreds of ESC clones for proper genome modification, before making chimeric mice. Fortunately, the advent of CRISPR/Cas9 genome editing technology, which enables the precise replacement of mouse genomic sequences with orthologous human sequences directly in single cell embryos, has become routine. Introduction of subtle changes (such as a single nucleotide) is especially efficient, taking advantage of homology-directed repair (HDR) of a Cas9-induced double strand break (DSB) with a synthetic template DNA molecule (most commonly a single-stranded oligodeoxynucleotide, or ssODN) homologous to the DNA sequence flanking the DSB ( Fig. 3A ). There are now numerous examples in the literature where such modeling has been used to confirm or reject potential human infertility-causing mutations or variants, some of which are cited here [ 39 – 41 , 43 , 67 – 71 ]. The efficiency of CRISPR/Cas9 editing in mice has opened the door to larger, systematic identification of infertility alleles in humans. Because over half of mouse genes have already been mutated and the phenotypes described, there is substantial knowledge of the “reproductive genome” (reviewed in [ 3 ]). Therefore, nsSNPs in genes known to be required for gametogenesis and fertility, and which are selected as being potentially deleterious by computational predictions or other evidence, can be modeled in mice [ 43 ] and subjected to systematic phenotyping [ 72 ]. The authors and collaborators maintain a website ( www.infertilitygenetics.org ) overviewing progress on this ongoing project. Although the generation and analysis of mutant mice can be time consuming due to the reproductive life cycle of the animal, several developments have made the process of making edited animals simpler and more efficient. Recently, promising alternatives to DSB-induced HDR have been developed that mitigate the non-homologous end-joining (NHEJ) -induced indels that occur when DSBs are introduced in the genome. One class includes base editors (BE) ( Fig. 3B ), enabling A·T>G·C and C·G>T·A changes [ 73 , 74 ]. However, recent studies found that the former type has DNA and RNA off-target effects [ 75 – 77 ]. Another promising strategy is “search-and replace” technology called prime editing (PE) [ 78 ] ( Fig. 3C ). Both systems were reported to function in mouse embryos [ 79 , 80 ]. However, more data on efficiency and fidelity is keenly anticipated. The ability to model mutations in mice with reasonably high throughput is a powerful and biologically relevant approach. However, there are specific and general caveats. Most problematic are variants affecting amino acids that are not conserved in mice. In this case it might be possible to replace the entire mouse gene (or part of it) with the human counterpart, or to add the human allele as a transgene in the context of a mouse null for the orthologous allele, but this assumes that a divergent gene (and the relevant amino acids) is functionally interchangeable. Even for genes conserved at the sequence level, it isn’t a guarantee that deleterious variants in humans will actually be deleterious in mice (or vice versa). Non-human primates would be a more relevant system for modeling human variants via gene editing [ 81 ], though there are more logistical and regulatory obstacles in this regard. The discussion until this point has focused on variants affecting proteins, but most genetic variation lies within noncoding regions of the genome – the portion that contains regulatory sequences. Understanding how the genome is regulated has been a major emphasis of large-scale genomic efforts such as ENCODE project [ 20 ]. ENCODE’s focus on characterizing epigenetic features of regulatory sequences led to the realization that most QTL from GWAS studies map to regions containing regulatory features such as enhancers. However, identifying variants that may impact regulatory sequences in the germline is difficult both from the computational prediction and experimental angles. Modeling in non-primates is problematic because intergenic regulatory regions are typically not highly conserved. An ideal solution would be to perfect in vitro human gametogenesis. This would enable introduction of edits into regulatory sequences of stem cells, then measuring transcription of linked genes following differentiation to the desired cell state. As mentioned earlier, such methods suffer from low efficiency and scalability [ 82 ], but it remains very promising to test molecular endpoints such as gene expression. Alternatives for latter stages of germ cell development are spermatogonial stem cell (SSC) [ 83 ] and oogonial stem cell [ 84 , 85 ] lines, although there is controversy over the latter. For intergenic variants that may alter regulation of key reproductive genes, the next steps might involve testing the impact of an individual SNP by mining eQTL (expression QTL) data, or, if biopsies from relevant tissues/cells can be obtained or modeled, testing alterations to chromatin state surrounding the variant (e.g., via ChIP-seq of ATAC-seq). The ultimate aim of identifying causative variants is to help clinicians diagnose and treat infertile patients, particularly for cases in which current ART (assisted reproductive technology) technologies such as IVF, ICSI, and microTESE (microscopic testicular sperm extraction) do not suffice. Opportunities for intervention with genomic-based technologies, such as gene therapy, will depend greatly upon the genes involved, the patient situation, and the stage of germ cell development being affected. Options for permanently effective genetic interventions are most optimistic for men, due to the ongoing nature of spermatogenesis enabled by an SSC pool. The ability to isolate SSCs, then transfer them into the testis where they can proliferate and reestablish spermatogenesis, was first developed in mice, and later in primates [ 86 , 87 ]. Coupled with ongoing improvements to human SSC culture systems [ 88 ], there is potential for using gene therapy approaches in a patient’s SSCs, followed by autologous transfer of cell clones or cultures having the desired modification(s) or gene correction. Proof-in-principle for such an approach has been demonstrated in mice. In one experiment, SSCs isolated from a testis of a sterile c-Kit mutant mice were cultured in vitro , the mutation was corrected by CRISPR/Cas9-mediated FIDR, then the repaired cells were transferred back into the remaining testis restore spermatogenesis [ 89 ]. However, genetic manipulations to the germline are controversial, and there are legitimate safety concerns concerning off-target effects that may occur during genome editing, and which can be difficult to monitor unless spermatogonial clones can be readily edited, propagated, and deep sequenced to ensure fidelity of the genome. It is also conceivable that the long culture process may impart epigenetic alterations on SSCs that can affect resulting offspring. Nevertheless, assuming these technical and ethical concerns will be surmounted, the power and efficiency of genome editing can be applied conceivably not only for Mendelian traits, but also to situations where two or more variants, including those in multiple genes, are contributing to a trait. Multi-locus genome editing in mice was demonstrated several years ago [ 90 ]. There are also circumstances in which gene therapy can be performed without changing the genome. In particular, genetic defects in oocytes or early embryos can be compensated by introducing exogenous protein or synthetic mRNA (sometimes called complementary RNA, or cRNA). For example, defects in egg activation due to mutations in the sperm-borne protein phospholipase C zeta 1 ( PLCZ1 ) can be rescued by recombinant protein or cRNA [ 91 – 93 ]. Proof-in-principle for the oocyte cRNA correction approach has also been reported for mutations in WEE2, CDC20 and TRIP13 [ 53 , 94 , 95 ]. However, for mutations in genes that are required for establishment of the ovarian reserve, which requires successful completion of key steps of Meiosis I including meiotic recombination, there are fewer options for intervention. One possibility is to derive patient-specific induced pluripotent stem cells (iPSCs), correct genetic lesions by genome editing, then differentiate them in vitro into oocytes. It may also be possible to reconstruct ovaries as has been done in rodents [ 96 ]. Promising results for implantation of in vitro -developed primordial germ cell like cells (PGCLCs) cells has been presented in rhesus macaques [ 97 ].

A crucial resources for identifying the genetic bases of infertility (and indeed, any disease) is to define the universe of genes that can cause it. The compendium of genes known to be vital for mammalian reproduction and fertility have been populated primarily by mutagenesis studies in mice [ 3 ]. This information is instrumental for informing the methods used most commonly by contemporary human geneticists for identifying infertility alleles or mutations in people, delineated below. The methods employed are determined by the goal of the study, whether it be to identify the cause of infertility in an individual patient, or to characterize variants or genes in the population that are responsible for a substantial fraction of infertility cases. By virtue of ever-increasing deployment of such methods, the number of identified infertility-causing variants has been rising dramatically, as reflected in the ClinVar database ( Fig. 1 ). A dated but still common method for identifying genes that drive infertility, either in a given patient or in the general population, is candidate gene re-sequencing. This approach is applicable for addressing a specific subtype of infertility, such as nonobstructive azoospermia (NOA), or when an investigator is particularly interested in the role of a specific gene or class of genes known to cause that phenotype. Using the NOA example, there are some reports (e.g. [ 4 , 5 ]) of studies that involve re-sequencing of essential meiosis genes such as the meiosis-specific stromal antigen 3 ( STAG3 ). Resequencing is readily accomplished by Sanger sequencing of PCR products. The experimental designs typically involve assessing whether there is an increased incidence of putatively deleterious variants (see below) in cases vs controls. This method is largely obsolete, since it is no longer cheaper or less labor intensive than some whole exome sequencing (WES), discussed below. WES is presently the method of choice for rapid and cost-effective (currently costing under $500) genomic analysis as applied to diverse purposes in clinical and basic research. Regarding the former, it has gained increasing utilization for screening of individual patients, or larger cohorts of patients, that are infertile as a potential consequence of a Mendelian disorder. The exome contains ~85% of known disease variants [ 6 ], and compared to WGS, WES is cheaper, enabling higher read depth, and is simpler to analyze. This approach is being/has been used by various groups to identify mutations or population variants that may underlie primary/premature ovarian insufficiency (POI) [ 7 – 9 ] and NOA [ 4 , 10 – 12 ]. As with candidate gene resequencing, studies involving a substantial number of individuals utilize a case:control design to identify genes preferentially altered, and evidence for those candidate genes can be supported across multiple studies and by dint of large cohorts. Finally, Genome-Wide Association (GWA), a mainstay approach in human genetics for identifying the locations of segregating variants (not de novo mutations) in the genome that affect a phenotype or predispose to a disease, is being used to address infertility genetics. Of course, this is not a diagnostic tool, and it can only identify phenotype-contributing alleles in haplotypes that are not very rare. However, once association to such loci are established, inferences to new patients can be drawn. Crucial to GWA study ( GWAS ) is defining the phenotype being studied quantitatively and qualitatively, and choosing a suitable study population [ 13 ]. The subjects are genotyped, then statistical analyses are performed to identify phenotype-associated genomic regions. For large cohorts, SNP arrays are a cost-effective genotyping platform, although WGS or WES provides data that essentially doubles as a SNP genotyping platform, in addition to providing information at non-polymorphic sites. GWAS have been performed to identify infertility-causing loci with varying success ( Fig. 2A ). Variants associated with PCOS [ 14 ], early menopause and POI [ 15 ], endometriosis [ 16 ], azoospermia, and oligozoospermia [ 17 – 19 ] have been identified. SNPs most significantly associated with a trait by GWA are rarely causal or functional; they are simply marking genetic proximity, i.e., high linkage disequilibrium (LD) , with the measured phenotype. Typically, GWA “hits” map predominantly to non-coding or intergenic regions [ 20 ], and the underlying causal sequence variants are more difficult to pinpoint and validate as compared to coding variants. Sequencing of the haplotypes, and finer mapping through larger cohorts and application of advanced statistical methods [ 21 ], can refine candidate genes to a manageable number, but proof of causality and mechanism remains elusive. By nature, GWAS are designed not to identify Mendelian mutations/variants, but rather quantitative trait loci (QTL), i.e., collections of alleles that on their own may not cause infertility, but rather have additive, opposing, or synergistic effects on a phenotype. Relevant to fertility, examples would be sperm count/quality, size of ovarian reserve, age to menopause, etc. Even if the precise identity, or mechanistic effects of, specific QTL may not be known, the identification of haplotypes impacting a fertility parameter will ultimately be useful for predictive medicine and for making conclusions as to the likelihood of genetic causes underlying a patient’s condition. In aggregate, these various methods for both identifying fertility genes and population variants are constantly increasing our understanding of genetic architectures of infertility, thereby providing resources for basic and translational benefit. Nevertheless, there is a long way to go before we have a comprehensive understanding of how common, rare, or unique variants contribute to human infertility. The following sections address this problem, beginning with resources being developed for the overall community, followed by overview of methods for increasing the knowledgebase and for enabling genetic diagnosis of individual patients.