Insights into variation in meiosis from 31,228 human sperm genomes.

Meiosis, although essential for reproduction, is also variable and error-prone: rates of chromosome crossover vary among gametes, between the sexes, and among humans of the same sex, and chromosome missegregation leads to abnormal chromosome numbers (aneuploidy)1-8. To study diverse meiotic outcomes and how they covary across chromosomes, gametes and humans, we developed Sperm-seq, a way of simultaneously analysing the genomes of thousands of individual sperm. Here we analyse the genomes of 31,228 human gametes from 20 sperm donors, identifying 813,122 crossovers and 787 aneuploid chromosomes. Sperm donors had aneuploidy rates ranging from 0.01 to 0.05 aneuploidies per gamete; crossovers partially protected chromosomes from nondisjunction at the meiosis I cell division. Some chromosomes and donors underwent more-frequent nondisjunction during meiosis I, and others showed more meiosis II segregation failures. Sperm genomes also manifested many genomic anomalies that could not be explained by simple nondisjunction. Diverse recombination phenotypes-from crossover rates to crossover location and separation, a measure of crossover interference-covaried strongly across individuals and cells. Our results can be incorporated with earlier observations into a unified model in which a core mechanism, the variable physical compaction of meiotic chromosomes, generates interindividual and cell-to-cell variation in diverse meiotic phenotypes.

Single cell analysis of DNA in more than 10,000 individual sperm from men with abnormal reproductive outcomes.

PURPOSE: This pilot study sought to (1) validate the use of a novel technology for single-sperm-cell genome sequencing (Sperm-seq) in infertile men who may not have optimal quantity or quality of sperm for genomic analysis and (2) compare these results to fertile donors. METHODS: Infertile men undergoing IVF with female partners with a previous history of failed fertilization with ICSI (FF) or poor blastulation of embryos (PB) were recruited from a large IVF center. Sperm-seq was used to analyze thousands of individual sperm and was carried out at an affiliated university research institute. Global aneuploidy rate, crossover locations, and crossover frequencies were assessed in the infertile population, and compared with a control group of 20 fertile donors, which were analyzed previously at the same laboratory. RESULTS: Eight patients were initially included, but 3 samples did not yield high-quality genomic data for analysis. A total of 10,042 sperm were analyzed from 5 patients, 2 in the FF group, and 3 in the PB group. The global aneuploidy rate among the samples was 2-4%, similar to the control group. Likewise, crossover locations and frequencies were similar. CONCLUSION: Sperm-seq provides a robust analysis but may not be applicable to all male infertility cases due to technical limitations. This group of male infertility patients did not have higher rates of aneuploidy or abnormal crossover patterns compared to a fertile donor population. Our data may suggest that FF and PB phenotypes may not be related to sperm aneuploidy or meiotic errors but rather to other intrinsic nuclear anomalies.

A web application for gene-based queries of CaeNDR RNA-seq data.

Variation in gene expression is a feature of all living systems and has recently been characterized extensively among wild strains of the model organism Caenorhabditis elegans. To enable researchers to query gene expression and gene expression variation at any gene of interest, we have created a user-friendly web application that shares RNA-seq transcription data for 208 wild C. elegans strains generated by the Caenorhabditis Natural Diversity Resource (CaeNDR). Here, we describe the features of the web application and the details of the data and data processing underlying it. We hope that this website, wildworm.biosci.gatech.edu/cendrexp/ , will help C. elegans researchers better understand their favorite genes and strains.

Diversification of small RNA pathways underlies germline RNA interference incompetence in wild Caenorhabditis elegans strains.

The discovery that experimental delivery of dsRNA can induce gene silencing at target genes revolutionized genetics research, by both uncovering essential biological processes and creating new tools for developmental geneticists. However, the efficacy of exogenous RNA interference (RNAi) varies dramatically within the Caenorhabditis elegans natural population, raising questions about our understanding of RNAi in the lab relative to its activity and significance in nature. Here, we investigate why some wild strains fail to mount a robust RNAi response to germline targets. We observe diversity in mechanism: in some strains, the response is stochastic, either on or off among individuals, while in others, the response is consistent but delayed. Increased activity of the Argonaute PPW-1, which is required for germline RNAi in the laboratory strain N2, rescues the response in some strains but dampens it further in others. Among wild strains, genes known to mediate RNAi exhibited very high expression variation relative to other genes in the genome as well as allelic divergence and strain-specific instances of pseudogenization at the sequence level. Our results demonstrate functional diversification in the small RNA pathways in C. elegans and suggest that RNAi processes are evolving rapidly and dynamically in nature.

Beyond the reference: gene expression variation and transcriptional response to RNAi in C. elegans.

A universal feature of living systems is that natural variation in genotype underpins variation in phenotype. Yet, research in model organisms is often constrained to a single genetic background, the reference strain. Further, genomic studies that do evaluate wild strains typically rely on the reference strain genome for read alignment, leading to the possibility of biased inferences based on incomplete or inaccurate mapping; the extent of reference bias can be difficult to quantify. As an intermediary between genome and organismal traits, gene expression is well positioned to describe natural variability across genotypes generally and in the context of environmental responses, which can represent complex adaptive phenotypes. C. elegans sits at the forefront of investigation into small-RNA gene regulatory mechanisms, or RNA interference (RNAi), and wild strains exhibit natural variation in RNAi competency following environmental triggers. Here, we examine how genetic differences among five wild strains affect the C. elegans transcriptome in general and after inducing RNAi responses to two germline target genes. Approximately 34% of genes were differentially expressed across strains; 411 genes were not expressed at all in at least one strain despite robust expression in others, including 49 genes not expressed in reference strain N2. Despite the presence of hyper-diverse hotspots throughout the C. elegans genome, reference mapping bias was of limited concern: over 92% of variably expressed genes were robust to mapping issues. Overall, the transcriptional response to RNAi was strongly strain-specific and highly specific to the target gene, and the laboratory strain N2 was not representative of the other strains. Moreover, the transcriptional response to RNAi was not correlated with RNAi phenotypic penetrance; the two germline RNAi incompetent strains exhibited substantial differential gene expression following RNAi treatment, indicating an RNAi response despite failure to reduce expression of the target gene. We conclude that gene expression, both generally and in response to RNAi, differs across C. elegans strains such that choice of strain may meaningfully influence scientific conclusions. To provide a public, easily accessible resource for querying gene expression variation in this dataset, we introduce an interactive website at https://wildworm.biosci.gatech.edu/rnai/ .

Beyond the reference: gene expression variation and transcriptional response to RNA interference in Caenorhabditis elegans.

Though natural systems harbor genetic and phenotypic variation, research in model organisms is often restricted to a reference strain. Focusing on a reference strain yields a great depth of knowledge but potentially at the cost of breadth of understanding. Furthermore, tools developed in the reference context may introduce bias when applied to other strains, posing challenges to defining the scope of variation within model systems. Here, we evaluate how genetic differences among 5 wild Caenorhabditis elegans strains affect gene expression and its quantification, in general and after induction of the RNA interference (RNAi) response. Across strains, 34% of genes were differentially expressed in the control condition, including 411 genes that were not expressed at all in at least 1 strain; 49 of these were unexpressed in reference strain N2. Reference genome mapping bias caused limited concern: despite hyperdiverse hotspots throughout the genome, 92% of variably expressed genes were robust to mapping issues. The transcriptional response to RNAi was highly strain- and target-gene-specific and did not correlate with RNAi efficiency, as the 2 RNAi-insensitive strains showed more differentially expressed genes following RNAi treatment than the RNAi-sensitive reference strain. We conclude that gene expression, generally and in response to RNAi, differs across C. elegans strains such that the choice of strain may meaningfully influence scientific inferences. Finally, we introduce a resource for querying gene expression variation in this dataset at https://wildworm.biosci.gatech.edu/rnai/.

A method for low-coverage single-gamete sequence analysis demonstrates adherence to Mendel's first law across a large sample of human sperm.

Recently published single-cell sequencing data from individual human sperm (n=41,189; 969-3377 cells from each of 25 donors) offer an opportunity to investigate questions of inheritance with improved statistical power, but require new methods tailored to these extremely low-coverage data (∼0.01× per cell). To this end, we developed a method, named rhapsodi, that leverages sparse gamete genotype data to phase the diploid genomes of the donor individuals, impute missing gamete genotypes, and discover meiotic recombination breakpoints, benchmarking its performance across a wide range of study designs. We then applied rhapsodi to the sperm sequencing data to investigate adherence to Mendel's Law of Segregation, which states that the offspring of a diploid, heterozygous parent will inherit either allele with equal probability. While the vast majority of loci adhere to this rule, research in model and non-model organisms has uncovered numerous exceptions whereby 'selfish' alleles are disproportionately transmitted to the next generation. Evidence of such 'transmission distortion' (TD) in humans remains equivocal in part because scans of human pedigrees have been under-powered to detect small effects. After applying rhapsodi to the sperm data and scanning for evidence of TD, our results exhibited close concordance with binomial expectations under balanced transmission. Together, our work demonstrates that rhapsodi can facilitate novel uses of inferred genotype data and meiotic recombination events, while offering a powerful quantitative framework for testing for TD in other cohorts and study systems.

Many species on Earth can carry up to two different versions of a given gene, with each of these 'alleles' having only a 50/50 chance of being transmitted to the next generation via sexual reproduction. Certain 'selfish' sequences, however, can hijack this process and increase their probability of being passed on to an offspring. Known as transmission distortion, this phenomenon may result in alleles spreading through the population even if they are detrimental to fertility. Transmission distortion has been detected in many species such as flies, mice and some plants. It can take place at various stages during reproduction; for example, the selfish alleles may become overrepresented among eggs or sperm. However, scientists need to study a large number of offspring or reproductive cells to be able to detect whether an allele is inherited more often than expected. This has made it difficult to determine whether transmission distortion also happens in humans, and research so far has resulted in conflicting conclusions. A recently published dataset of human sperm from 25 donors offered Carioscia, Weaver et al. the opportunity to examine this question. Every volunteer had produced between 969 and 3377 sperm cells, each with about 1% of their genome sequenced. Carioscia, Weaver et al. developed a computational method, which they named rhapsodi, that allowed them to 'fill in the gaps' and infer missing regions of the genome for each cell. To do so, they relied on the fact that sperm cells from a given individual are highly related to one another. With this more complete data at hand, it became possible to look for evidence of transmission distortion by searching for alleles that were overrepresented in sperm from a given donor. No selfish sequence could be detected in any of the 25 individuals, suggesting that human sperm may not be subject to pervasive transmission distortion. Signatures of selfish alleles detected in previous human studies may have therefore not resulted from this mechanism taking place at the sperm level. Instead, transmission distortion in humans could primarily target eggs or happen at later stages (for instance, if embryos carrying the selfish allele have better chances of survival). The 'rhapsodi' method developed by Carioscia, Weaver et al. should allow other scientists to work with datasets for which large portions of the genetic information is missing. It may therefore become easier for researchers to track selfish alleles which are difficult to detect, and to examine bigger, more diverse samples which also include individuals with known fertility challenges.

Analyzing Copy Number Variation with Droplet Digital PCR.

Many genomic segments vary in copy number among individuals of the same species, or between cancer and normal cells within the same person. Correctly measuring this copy number variation is critical for studying its genetic properties, its distribution in populations and its relationship to phenotypes. Droplet digital PCR (ddPCR) enables accurate measurement of copy number by partitioning a PCR reaction into thousands of nanoliter-scale droplets, so that a genomic sequence of interest-whose presence or absence in a droplet is determined by end-point fluorescence-can be digitally counted. Here, we describe how we analyze copy number variants using ddPCR and review the design of effective assays, the performance of ddPCR with those assays, the optimization of reactions, and the interpretation of data.