Translational aspects of novel findings in genetics of male infertility-status quo 2021

INTRODUCTION

Male factor infertility concerns 7-10% of men and among these 40-60% remain unexplained.

SOURCES OF DATA

This review is based on recent published literature regarding the genetic causes of male infertility.

AREAS OF AGREEMENT

Screening for karyotype abnormalities, biallelic pathogenic variants in the CFTR gene and Y-chromosomal microdeletions have been routine in andrology practice for >20 years, explaining ~10% of infertility cases. Rare specific conditions, such as congenital hypogonadotropic hypogonadism, disorders of sex development and defects of sperm morphology and motility, are caused by pathogenic variants in recurrently affected genes, which facilitate high diagnostic yield (40-60%) of targeted gene panel-based testing.

AREAS OF CONTROVERSY

Progress in mapping monogenic causes of quantitative spermatogenic failure, the major form of male infertility, has been slower. No 'recurrently' mutated key gene has been identified and worldwide, a few hundred patients in total have been assigned a possible monogenic cause.

GROWING POINTS

Given the high genetic heterogeneity, an optimal approach to screen for heterogenous genetic causes of spermatogenic failure is sequencing exomes or in perspective, genomes. Clinical guidelines developed by multidisciplinary experts are needed for smooth integration of expanded molecular diagnostics in the routine management of infertile men.

AREAS TIMELY FOR DEVELOPING RESEARCH

Di-/oligogenic causes, structural and common variants implicated in multifactorial inheritance may explain the 'hidden' genetic factors. It is also critical to understand how the recently identified diverse genetic factors of infertility link to general male health concerns across lifespan and how the clinical assessment could benefit from this knowledge.

Human Genetics to Identify Therapeutic Targets for NAFLD: Challenges and Opportunities

Non-alcoholic fatty liver disease (NAFLD) is a continuous progression of pathophysiologic stages that is challenging to diagnose due to its inherent heterogeneity and poor standardization across a wide variety of diagnostic measures. NAFLD is heritable, and several loci have been robustly associated with various stages of disease. In the past few years, larger genetic association studies using new methodology have identified novel genes associated with NAFLD, some of which have shown therapeutic promise. This mini-review provides an overview of the heterogeneity in NAFLD phenotypes and diagnostic methods, discusses genetic associations in relation to the specific stages for which they were identified, and offers a perspective on the design of future genetic mapping studies to accelerate therapeutic target identification.

The Counteracting Effects of Demography on Functional Genomic Variation: The Roma Paradigm

Demographic history plays a major role in shaping the distribution of genomic variation. Yet the interaction between different demographic forces and their effects in the genomes is not fully resolved in human populations. Here, we focus on the Roma population, the largest transnational ethnic minority in Europe. They have a South Asian origin and their demographic history is characterized by recent dispersals, multiple founder events, and extensive gene flow from non-Roma groups. Through the analyses of new high-coverage whole exome sequences and genome-wide array data for 89 Iberian Roma individuals together with forward simulations, we show that founder effects have reduced their genetic diversity and proportion of rare variants, gene flow has counteracted the increase in mutational load, runs of homozygosity show ancestry-specific patterns of accumulation of deleterious homozygotes, and selection signals primarily derive from preadmixture adaptation in the Roma population sources. The present study shows how two demographic forces, bottlenecks and admixture, act in opposite directions and have long-term balancing effects on the Roma genomes. Understanding how demography and gene flow shape the genome of an admixed population provides an opportunity to elucidate how genomic variation is modeled in human populations.

A data set of variants derived from 1455 clinical and research exomes is efficient in variant prioritization for early-onset monogenic disorders in Indians

Given the genomic uniqueness, a local data set is most desired for Indians, who are underrepresented in existing public databases. We hypothesize patients with rare monogenic disorders and their family members can provide a reliable source of common variants in the population. Exome sequencing (ES) data from families with rare Mendelian disorders was aggregated from five centers in India. The dataset was refined by excluding related individuals and removing the disease-causing variants (refined cohort). The efficiency of these data sets was assessed in a new set of 50 exomes against gnomAD and GenomeAsia. Our original cohort comprised 1455 individuals from 1203 families. The refined cohort had 836 unrelated individuals that retained 1,251,064 variants with 181,125 population-specific and 489,618 common variants. The allele frequencies from our cohort helped to define 97,609 rare variants in gnomAD and 44,520 rare variants in GenomeAsia as common variants in our population. Our variant dataset provided an additional 1.7% and 0.1% efficiency for prioritizing heterozygous and homozygous variants respectively for rare monogenic disorders. We observed additional 19 genes/human knockouts. We list carrier frequency for 142 recessive disorders. This is a large and useful resource of exonic variants for Indians. Despite limitations, datasets from patients are efficient tools for variant prioritization in a resource-limited setting.

Predicting venous thromboembolism risk from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges

Genetics play a key role in venous thromboembolism (VTE) risk, however established risk factors in European populations do not translate to individuals of African descent because of the differences in allele frequencies between populations. As part of the fifth iteration of the Critical Assessment of Genome Interpretation, participants were asked to predict VTE status in exome data from African American subjects. Participants were provided with 103 unlabeled exomes from patients treated with warfarin for non-VTE causes or VTE and asked to predict which disease each subject had been treated for. Given the lack of training data, many participants opted to use unsupervised machine learning methods, clustering the exomes by variation in genes known to be associated with VTE. The best performing method using only VTE related genes achieved an area under the ROC curve of 0.65. Here, we discuss the range of methods used in the prediction of VTE from sequence data and explore some of the difficulties of conducting a challenge with known confounders. In addition, we show that an existing genetic risk score for VTE that was developed in European subjects works well in African Americans.

Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges

Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.

Vanno: a visualization-aided variant annotation tool

Next-generation sequencing (NGS) technologies have revolutionized the field of genetics and are trending toward clinical diagnostics. Exome and targeted sequencing in a disease context represent a major NGS clinical application, considering its utility and cost-effectiveness. With the ongoing discovery of disease-associated genes, various gene panels have been launched for both basic research and diagnostic tests. However, the fundamental inconsistencies among the diverse annotation sources, software packages, and data formats have complicated the subsequent analysis. To manage disease-associated NGS data, we developed Vanno, a Web-based application for in-depth analysis and rapid evaluation of disease-causative genome sequence alterations. Vanno integrates information from biomedical databases, functional predictions from available evaluation models, and mutation landscapes from TCGA cancer types. A highly integrated framework that incorporates filtering, sorting, clustering, and visual analytic modules is provided to facilitate exploration of oncogenomics datasets at different levels, such as gene, variant, protein domain, or three-dimensional structure. Such design is crucial for the extraction of knowledge from sequence alterations and translating biological insights into clinical applications. Taken together, Vanno supports almost all disease-associated gene tests and exome sequencing panels designed for NGS, providing a complete solution for targeted and exome sequencing analysis. Vanno is freely available at http://cgts.cgu.edu.tw/vanno.

Low-grade prostate cancer diverges early from high grade and metastatic disease

Understanding the developmental relationship between indolent and aggressive tumors is central to understanding disease progression and making treatment decisions. For example, most men diagnosed with prostate cancer have clinically indolent disease and die from other causes. Overtreatment of prostate cancer remains a concern. Here we use laser microdissection followed by exome sequencing of low- and high-grade prostate cancer foci from four subjects, and metastatic disease from two of those subjects, to evaluate the molecular relationship of coincident cancer foci. Seventy of 79 (87%) high-confidence somatic mutations in low-grade disease were private to low-grade foci. In contrast, high-grade foci and metastases harbored many of the same mutations. In cases in which there was a metastatic focus, 15 of 80 (19%) high-confidence somatic mutations in high-grade foci were private. Seven of the 80 (9%) were shared with low-grade foci and 65 (82%) were shared with metastatic foci. Notably, mutations in cancer-associated genes and the p53 signaling pathway were found exclusively in high-grade foci and metastases. The pattern of mutations is consistent with early divergence between low- and high-grade foci and late divergence between high-grade foci and metastases. These data provide insights into the development of high-grade and metastatic prostate cancer.

Sodium channel SCN8A (Nav1.6): properties and de novo mutations in epileptic encephalopathy and intellectual disability

The sodium channel Nav1.6, encoded by the gene SCN8A, is one of the major voltage-gated channels in human brain. The sequences of sodium channels have been highly conserved during evolution, and minor changes in biophysical properties can have a major impact in vivo. Insight into the role of Nav1.6 has come from analysis of spontaneous and induced mutations of mouse Scn8a during the past 18 years. Only within the past year has the role of SCN8A in human disease become apparent from whole exome and genome sequences of patients with sporadic disease. Unique features of Nav1.6 include its contribution to persistent current, resurgent current, repetitive neuronal firing, and subcellular localization at the axon initial segment (AIS) and nodes of Ranvier. Loss of Nav1.6 activity results in reduced neuronal excitability, while gain-of-function mutations can increase neuronal excitability. Mouse Scn8a (med) mutants exhibit movement disorders including ataxia, tremor and dystonia. Thus far, more than ten human de novo mutations have been identified in patients with two types of disorders, epileptic encephalopathy and intellectual disability. We review these human mutations as well as the unique features of Nav1.6 that contribute to its role in determining neuronal excitability in vivo. A supplemental figure illustrating the positions of amino acid residues within the four domains and 24 transmembrane segments of Nav1.6 is provided to facilitate the location of novel mutations within the channel protein.