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Genomic background selection to reduce the mutation load after random mutagenesis.

ABSTRACT: Random mutagenesis is a standard procedure to increase allelic variation in a crop species, especially in countries where the use of genetically modified crops is limited due to legal constraints. The chemical mutagen EMS is used in many species to induce random mutations throughout the genome with high mutation density. The major drawback for functional analysis is a high background mutation load in a single plant that must be eliminated by subsequent backcrossing, a time and resource-intensive activity. Here, we demonstrate that genomic background selection combined with marker-assisted selection is an efficient way to select individuals with reduced background mutations within a short period. We identified BC₁ plants with a significantly higher share of the recurrent parent genome, thus saving one backcross generation. Furthermore, spring rapeseed as the recurrent parent in a backcrossing program could accelerate breeding by reducing the generation cycle. Our study depicts the potential for reducing the background mutation load while accelerating the generation cycle in EMS-induced winter oilseed rape populations by integrating genomic background selection.

SUBMITTER: Karunarathna NL

PROVIDER: S-EPMC8484577 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Genomic background selection to reduce the mutation load after random mutagenesis.

Karunarathna Nirosha L NL Patiranage Dilan S R DSR Harloff Hans-Joachim HJ Sashidhar Niharika N Jung Christian C

Scientific reports 20210930 1

Random mutagenesis is a standard procedure to increase allelic variation in a crop species, especially in countries where the use of genetically modified crops is limited due to legal constraints. The chemical mutagen EMS is used in many species to induce random mutations throughout the genome with high mutation density. The major drawback for functional analysis is a high background mutation load in a single plant that must be eliminated by subsequent backcrossing, a time and resource-intensive ...[more]

PMID: 34593904

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Project description:Key messageThe integration of known and latent environmental covariates within a single-stage genomic selection approach provides breeders with an informative and practical framework to utilise genotype by environment interaction for prediction into current and future environments. This paper develops a single-stage genomic selection approach which integrates known and latent environmental covariates within a special factor analytic framework. The factor analytic linear mixed model of Smith et al. (2001) is an effective method for analysing multi-environment trial (MET) datasets, but has limited practicality since the underlying factors are latent so the modelled genotype by environment interaction (GEI) is observable, rather than predictable. The advantage of using random regressions on known environmental covariates, such as soil moisture and daily temperature, is that the modelled GEI becomes predictable. The integrated factor analytic linear mixed model (IFA-LMM) developed in this paper includes a model for predictable and observable GEI in terms of a joint set of known and latent environmental covariates. The IFA-LMM is demonstrated on a late-stage cotton breeding MET dataset from Bayer CropScience. The results show that the known covariates predominately capture crossover GEI and explain 34.4% of the overall genetic variance. The most notable covariates are maximum downward solar radiation (10.1%), average cloud cover (4.5%) and maximum temperature (4.0%). The latent covariates predominately capture non-crossover GEI and explain 40.5% of the overall genetic variance. The results also show that the average prediction accuracy of the IFA-LMM is [Formula: see text] higher than conventional random regression models for current environments and [Formula: see text] higher for future environments. The IFA-LMM is therefore an effective method for analysing MET datasets which also utilises crossover and non-crossover GEI for genomic prediction into current and future environments. This is becoming increasingly important with the emergence of rapidly changing environments and climate change.

Project description:BackgroundRegions of the genome that are under evolutionary constraint across multiple species have previously been used to identify functional sequences in the human genome. Furthermore, it is known that there is an inverse relationship between evolutionary constraint and the allele frequency of a mutation segregating in human populations, implying a direct relationship between interspecies divergence and fitness in humans. Here we utilise this relationship to test differences in the accumulation of putatively deleterious mutations both between populations and on the individual level.ResultsUsing whole genome and exome sequencing data from Phase 1 of the 1000 Genome Project for 1,092 individuals from 14 worldwide populations we show that minor allele frequency (MAF) varies as a function of constraint around both coding regions and non-coding sites genome-wide, implying that negative, rather than positive, selection primarily drives the distribution of alleles among individuals via background selection. We find a strong relationship between effective population size and the depth of depression in MAF around the most conserved genes, suggesting that populations with smaller effective size are carrying more deleterious mutations, which also translates into higher genetic load when considering the number of putatively deleterious alleles segregating within each population. Finally, given the extreme richness of the data, we are now able to classify individual genomes by the accumulation of mutations at functional sites using high coverage 1000 Genomes data. Using this approach we detect differences between 'healthy' individuals within populations for the distributions of putatively deleterious rare alleles they are carrying.ConclusionsThese findings demonstrate the extent of background selection in the human genome and highlight the role of population history in shaping patterns of diversity between human individuals. Furthermore, we provide a framework for the utility of personal genomic data for the study of genetic fitness and diseases.

Project description:Despite the reasonably long half-life of immunoglogulin G (IgGs), market pressure for higher patient convenience while conserving efficacy continues to drive IgG half-life improvement. IgG half-life is dependent on the neonatal Fc receptor (FcRn), which among other functions, protects IgG from catabolism. FcRn binds the Fc domain of IgG at an acidic pH ensuring that endocytosed IgG will not be degraded in lysosomal compartments and will then be released into the bloodstream. Consistent with this mechanism of action, several Fc-engineered IgG with increased FcRn affinity and conserved pH dependency were designed and resulted in longer half-life in vivo in human FcRn-transgenic mice (hFcRn), cynomolgus monkeys, and recently in healthy humans. These IgG variants were usually obtained by in silico approaches or directed mutagenesis in the FcRn-binding site. Using random mutagenesis, combined with a pH-dependent phage display selection process, we isolated IgG variants with improved FcRn-binding, which exhibited longer in vivo half-life in hFcRn mice. Interestingly, many mutations enhancing Fc/FcRn interaction were located at a distance from the FcRn-binding site validating our random molecular approach. Directed mutagenesis was then applied to generate new variants to further characterize our IgG variants and the effect of the mutations selected. Since these mutations are distributed over the whole Fc sequence, binding to other Fc effectors, such as complement C1q and FcγRs, was dramatically modified, even by mutations distant from these effectors' binding sites. Hence, we obtained numerous IgG variants with increased FcRn-binding and different binding patterns to other Fc effectors, including variants without any effector function, providing distinct "fit-for-purpose" Fc molecules. We therefore provide evidence that half-life and effector functions should be optimized simultaneously as mutations can have unexpected effects on all Fc receptors that are critical for IgG therapeutic efficacy.

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Genomic background selection to reduce the mutation load after random mutagenesis.

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Genomic background selection to reduce the mutation load after random mutagenesis.

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