Effect of heterogeneity in recombination rate on variation in realised relationship

Potential and pitfalls of using identity-by-descent for malaria genomic surveillance

The ability to genotype malaria parasites on an epidemiological scale is crucial for genomic surveillance as it aids in understanding malaria transmission dynamics and parasite demography changes in response to antimalarial interventions. Identity-by-descent (IBD)-based methods have demonstrated potential in various aspects of malaria genomic surveillance. However, there is a need for validation of existing approaches and development of new techniques to address challenges posed by the parasites' unique evolutionary dynamics and complex biological characteristics, which differ markedly from organisms like humans. This review examines current IBD use cases, identifies limitations of IBD-based methods, and explores promising future directions to enhance malaria genomic surveillance.

Diversity and determinants of recombination landscapes in flowering plants

During meiosis, crossover rates are not randomly distributed along the chromosome and their location may have a strong impact on the functioning and evolution of the genome. To date, the broad diversity of recombination landscapes among plants has rarely been investigated and a formal comparative genomic approach is still needed to characterize and assess the determinants of recombination landscapes among species and chromosomes. We gathered genetic maps and genomes for 57 flowering plant species, corresponding to 665 chromosomes, for which we estimated large-scale recombination landscapes. We found that the number of crossover per chromosome spans a limited range (between one to five/six) whatever the genome size, and that there is no single relationship across species between genetic map length and chromosome size. Instead, we found a general relationship between the relative size of chromosomes and recombination rate, while the absolute length constrains the basal recombination rate for each species. At the chromosome level, we identified two main patterns (with a few exceptions) and we proposed a conceptual model explaining the broad-scale distribution of crossovers where both telomeres and centromeres play a role. These patterns correspond globally to the underlying gene distribution, which affects how efficiently genes are shuffled at meiosis. These results raised new questions not only on the evolution of recombination rates but also on their distribution along chromosomes.

Genomic Predictions With Nonadditive Effects Improved Estimates of Additive Effects and Predictions of Total Genetic Values in Pinus sylvestris

Genomic selection study (GS) focusing on nonadditive genetic effects of dominance and the first order of epistatic effects, in a full-sib family population of 695 Scots pine (Pinus sylvestris L.) trees, was undertaken for growth and wood quality traits, using 6,344 single nucleotide polymorphism markers (SNPs) generated by genotyping-by-sequencing (GBS). Genomic marker-based relationship matrices offer more effective modeling of nonadditive genetic effects than pedigree-based models, thus increasing the knowledge on the relevance of dominance and epistatic variation in forest tree breeding. Genomic marker-based models were compared with pedigree-based models showing a considerable dominance and epistatic variation for growth traits. Nonadditive genetic variation of epistatic nature (additive × additive) was detected for growth traits, wood density (DEN), and modulus of elasticity (MOEd) representing between 2.27 and 34.5% of the total phenotypic variance. Including dominance variance in pedigree-based Best Linear Unbiased Prediction (PBLUP) and epistatic variance in genomic-based Best Linear Unbiased Prediction (GBLUP) resulted in decreased narrow-sense heritability and increased broad-sense heritability for growth traits, DEN and MOEd. Higher genetic gains were reached with early GS based on total genetic values, than with conventional pedigree selection for a selection intensity of 1%. This study indicates that nonadditive genetic variance may have a significant role in the variation of selection traits of Scots pine, thus clonal deployment could be an attractive alternative for the species. Additionally, confidence in the role of nonadditive genetic effects in this breeding program should be pursued in the future, using GS.

Genomic characterization of selfing in the cyclic parthenogen Daphnia magna

Inbreeding refers to the fusion of related individuals' gametes, with self-fertilization (selfing) being an extreme form of inbreeding-involving gametes produced by the same individual. Selfing is expected to reduce heterozygosity by an average of 50% in one generation; however, little is known about the empirical variation on a genome level surrounding this figure and the factors that affect variation. We selfed genotypes of the cyclic parthenogen Daphnia magna and analysed whole genomes of mothers and selfed offspring, observing the predicted 50% heterozygosity reduction on average. We also saw substantial variation around this value and significant differences among mother-offspring pairs. Crossover analysis confirmed the known trend of recombination occurring more often towards the telomeres. This effect was shown, through simulations, to increase the variance of heterozygosity reduction compared to when a uniform distribution of crossovers was used. Similarly, we simulated inbred line production after several generations of selfing and we observed higher variance in achieved homozygosity when we consider a higher recombination rate towards the telomeres. Our empirical and simulation study highlights that the expected mean values of heterozygosity reduction show remarkable variation, which can help understand, for example, differences among inbred individuals.

Variation in Genetic Relatedness Is Determined by the Aggregate Recombination Process

The genomic proportion that two relatives share identically by descent-their genetic relatedness-can vary depending on the history of recombination and segregation in their pedigree. Previous calculations of the variance of genetic relatedness have defined genetic relatedness as the proportion of total genetic map length (cM) shared by relatives, and have neglected crossover interference and sex differences in recombination. Here, we consider genetic relatedness as the proportion of the total physical genome (bp) shared by relatives, and calculate its variance for general pedigree relationships, making no assumptions about the recombination process. For the relationships of grandparent-grandoffspring and siblings, the variance of genetic relatedness is a simple decreasing function of [Formula: see text], the average proportion of locus pairs that recombine in meiosis. For general pedigree relationships, the variance of genetic relatedness is a function of metrics analogous to [Formula: see text] Therefore, features of the aggregate recombination process that affect [Formula: see text] and analogs also affect variance in genetic relatedness. Such features include the number of chromosomes and heterogeneity in their size, the number of crossovers and their spatial organization along chromosomes, and sex differences in recombination. Our calculations help to explain several recent observations about variance in genetic relatedness, including that it is reduced by crossover interference (which is known to increase [Formula: see text]). Our methods further allow us to calculate the neutral variance of ancestry among F2s in a hybrid cross, enabling precise statistical inference in F2-based tests for various kinds of selection.

Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine

BACKGROUND

Genomic selection (GS) or genomic prediction is a promising approach for tree breeding to obtain higher genetic gains by shortening time of progeny testing in breeding programs. As proof-of-concept for Scots pine (Pinus sylvestris L.), a genomic prediction study was conducted with 694 individuals representing 183 full-sib families that were genotyped with genotyping-by-sequencing (GBS) and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic with pedigree prediction models. Additionally, four prediction efficiency methods were used to evaluate the impact of genomic breeding value estimations by assigning diverse ratios of training and validation sets, as well as several subsets of SNP markers.

RESULTS

Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed slightly higher prediction efficiencies than Pedigree Best Linear Unbiased Prediction (PBLUP) and Bayesian LASSO, with some exceptions. A subset of approximately 6000 SNP markers, was enough to provide similar prediction efficiencies as the full set of 8719 markers. Additionally, prediction efficiencies of genomic models were enough to achieve a higher selection response, that varied between 50-143% higher than the traditional pedigree-based selection.

CONCLUSIONS

Although prediction efficiencies were similar for genomic and pedigree models, the relative selection response was doubled for genomic models by assuming that earlier selections can be done at the seedling stage, reducing the progeny testing time, thus shortening the breeding cycle length roughly by 50%.