Efficient inference of paternity and sibship inference given known maternity via hierarchical clustering

The promise and deceit of genomic selection component analyses

Selection component analyses (SCA) relate individual genotype to fitness components such as viability, fecundity and mating success. SCA are based on population genetic models and yield selection estimates directly in terms of predicted allele frequency change. This paper explores the statistical properties of gSCA: experiments that apply SCA to genome-wide scoring of SNPs in field sampled individuals. Computer simulations indicate that gSCA involving a few thousand genotyped samples can detect allele frequency changes of the magnitude that has been documented in field experiments on diverse taxa. To detect selection, imprecise genotyping from low-level sequencing of large samples of individuals provides much greater power than precise genotyping of smaller samples. The simulations also demonstrate the efficacy of 'haplotype matching', a method to combine information from a limited collection of whole genome sequence (the reference panel) with the much larger sample of field individuals that are measured for fitness. Pooled sequencing is demonstrated as another way to increase statistical power. Finally, I discuss the interpretation of selection estimates in relation to the Beavis effect, the overestimation of selection intensities at significant loci.

A Semi-Automated SNP-Based Approach for Contaminant Identification in Biparental Polyploid Populations of Tropical Forage Grasses

Artificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs.

Genomic Determination of Reproductive Mode in Facultatively Parthenogenetic Opiliones

Sexual reproduction may pose myriad short-term costs to females. Despite these costs, sexual reproduction is near ubiquitous. Facultative parthenogenesis is theorized to mitigate some of the costs of sex, as individuals can participate in occasional sex to limit costs while obtaining many benefits. However, most theoretical models assume sexual reproduction is fixed following mating, with no possibility of clutches of mixed reproductive ontogeny. Therefore, we asked: if coercive males are present at high frequency in a population of facultative parthenogens, will their clutches be solely sexually produced, or will there be evidence of sexually and asexually-produced offspring? How will their offspring production compare to conspecifics in low-frequency male populations? We addressed our questions by collecting females and egg clutches of the facultatively parthenogenetic Opiliones species Leiobunum manubriatum and L. globosum. In L. manubriatum, females from populations with few males were not significantly more fecund than females from populations with higher male relative frequency, despite the potential release of the former from sexual conflict. We used 3 genotyping methods along with a custom set of DNA capture probes to reveal that offspring of L. manubriatum from these high male populations were primarily produced via asexual reproduction. This is surprising because sex ratios in these southern populations approach equality, increasing the probability for females to encounter mates and produce offspring sexually. We additionally found evidence for reproductive polymorphisms within populations. Rapid and accurate SNP genotyping data will continue to allow us to address broader evolutionary questions regarding the role of facultative reproductive modes in the maintenance of sex.

Genome-wide genotyping estimates mating system parameters and paternity in the island species Tolpis succulenta

PREMISE

The mating system has profound consequences, not only for ecology and evolution, but also for the conservation of threatened or endangered species. Unfortunately, small populations are difficult to study owing to limits on sample size and genetic marker diversity. Here, we estimated mating system parameters in three small populations of an island plant using genomic genotyping. Although self-incompatible (SI) species are known to often set some self-seed, little is known about how "leaky SI" affects selfing rates in nature or the role that multiple paternity plays in small populations.

METHODS

We generalized the BORICE mating system program to determine the siring pattern within maternal families. We applied this algorithm to maternal families from three populations of Tolpis succulenta from Madeira Island and genotyped the progeny using RADseq. We applied BORICE to estimate each individual offspring as outcrossed or selfed, the paternity of each outcrossed offspring, and the level of inbreeding of each maternal plant.

RESULTS

Despite a functional self-incompatibility system, these data establish T. succulenta as a pseudo-self-compatible (PSC) species. Two of 75 offspring were strongly indicated as products of self-fertilization. Despite selfing, all adult maternal plants were fully outbred. There was high differentiation among and low variation within populations, consistent with a history of genetic isolation of these small populations. There were generally multiple sires per maternal family. Twenty-two percent of sib contrasts (between outcrossed offspring within maternal families) shared the same sire.

CONCLUSIONS

Genome-wide genotyping, combined with appropriate analytical methods, enables estimation of mating system and multiple paternity in small populations. These data address questions about the evolution of reproductive traits and the conservation of threatened populations.

The future of parentage analysis: From microsatellites to SNPs and beyond

Parentage analysis is a cornerstone of molecular ecology that has delivered fundamental insights into behaviour, ecology and evolution. Microsatellite markers have long been the king of parentage, their hypervariable nature conferring sufficient power to correctly assign offspring to parents. However, microsatellite markers have seen a sharp decline in use with the rise of next-generation sequencing technologies, especially in the study of population genetics and local adaptation. The time is ripe to review the current state of parentage analysis and see how it stands to be affected by the emergence of next-generation sequencing approaches. We find that single nucleotide polymorphisms (SNPs), the typical next-generation sequencing marker, remain underutilized in parentage analysis but are gaining momentum, with 58 SNP-based parentage analyses published thus far. Many of these papers, particularly the earlier ones, compare the power of SNPs and microsatellites in a parentage context. In virtually every case, SNPs are at least as powerful as microsatellite markers. As few as 100-500 SNPs are sufficient to resolve parentage completely in most situations. We also provide an overview of the analytical programs that are commonly used and compatible with SNP data. As the next-generation parentage enterprise grows, a reliance on likelihood and Bayesian approaches, as opposed to strict exclusion, will become increasingly important. We discuss some of the caveats surrounding the use of next-generation sequencing data for parentage analysis and conclude that the future is bright for this important realm of molecular ecology.