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Results for “founder effect”

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22 records · Page 2Linked to original sources
PubMed2025

Haplotype stacking to improve stability of stripe rust resistance in wheat.

Genotype-by-environment interaction analysis and haplotype-level characterisation provide novel insights into the stability of stripe rust resistance. Breeding selection strategies are proposed to achieve rapid and stable genetic gains across environments. This study investigated stripe/yellow rust (YR) responses in the Vavilov wheat diversity panel evaluated across 11 field experiments conducted in Australia and Ethiopia during 2014-2021. Genotype-by-environment interaction (GEI) was analysed using a factor analytic (FA) model. Genotype-level selection was performed with overall performance (OP) and root-mean-square deviation (RMSD), which reflected average performance and stability of YR resistance across environments, respectively. Genomic estimated breeding values (GEBV) for these traits were calculated and compared with those from a multi-trait GBLUP model with average performance represented by the mean GEBV across environments and stability by the standard deviation of GEBV across environments. The FA-based and multi-trait GBLUP GEBV had high correlations. Haplotypes with large effects on OP and RMSD were identified using the local GEBV method. Favourable haplotypes were then used for stacking in breeding simulations, using the Vavilov collection as a base. Compared to truncation selection, optimal haplotype selection (OHS) using an artificial intelligence (AI)-based algorithm achieved longer-term genetic gains for both OP and RMSD (after many generations) by initially selecting founder parents that maximised favourable haplotypes. Simulations using YR responses from diverse environments that mimicked fluctuating environmental conditions across seasons were conducted to evaluate strategies for selection of YR resistance that is stable across years. Strategies which gave most weight to OP, but some weight to RMSD were optimal in these conditions, and substantially reduced variation of performance across years. This study provides useful information for breeding cultivars with both high YR resistance and high stability of resistance across environments.

Triticum↗
PubMed2026

Cre-loaded integrase-defective lentiviral vectors for targeted cassette exchange in CHO cells.

Genome-modifying enzymes, such as recombinases and CRISPR-associated nucleases, enable targeted gene insertion when delivered transiently to minimize off-target effects. Precise genome engineering requires controlled enzyme activity, as well as efficient donor DNA transfer. Integrase-defective lentiviral vectors (IDLVs) provide a promising platform for transient episomal DNA transfer; however, their integration efficiency depends on complementary genome-targeting strategies. Here, we engineered Cre-loaded IDLVs (Cre-IDLVs) that co-package lentiviral vector genomes together with bioactive Cre recombinase. Cre was inserted into the Gag region of an integrase-defective gag-pol construct, allowing for efficient encapsidation and protease-mediated release during virion maturation without compromising the viral titer. The resulting particles carried donor cassettes flanked by heterospecific loxP sites. When applied to CHO founder cells harboring compatible genomic loxP landing pads, Cre-IDLVs efficiently mediated recombination-mediated cassette exchange, producing the highest number of G418-resistant colonies among the plasmid ratios tested. Genomic PCR and sequencing confirmed precise locus-specific insertion without detectable random integration in the analyzed clones. These findings establish Cre-IDLVs as a streamlined dual-delivery platform that couples transient recombinase activity with episomal donor DNA transfer. This hybrid lentiviral strategy provides a programmable approach for controlled and site-specific genome modification in mammalian cells.

Integrases↗
PubMed2026

Genetic screening of children for familial hypercholesterolaemia: the VRONI study.

BACKGROUND AND AIMS: The role of genetic testing as part of universal screening programmes for familial hypercholesterolaemia (FH) in children is not well defined. Here, a two-step approach to identify children carrying FH-causing variants was investigated. METHODS: In this study from Southern Germany, paediatricians were invited to offer FH screening to all children aged 4.8-14.9 years at routine paediatric examinations. The FH screening programme began in September 2020 in Bavaria and has involved up to 480 paediatricians. It included biochemical and genetic testing using 0.2 mL of blood taken from a fingertip. In case of low-density lipoprotein cholesterol (LDL-C) serum concentration ≥3.36 mmol/L (≥130 mg/dL), FH-causing variants were determined in the same sample with a focused panel covering most frequent variants (n = 48) and sequencing of relevant genes. RESULTS: Out of 25 431 children screened so far, 1689 children had an LDL-C ≥ 3.36 mmol/L (>130 mg/dL), which defined this concentration as the 93rd percentile. Pathogenic variants were identified by the focused panel in 157 and by next-generation sequencing in 283 children, respectively. While 17% (283/1670) of all genetically analysed children tested positive, the fraction of individuals with FH-causing variants increased across the spectrum of LDL-C serum concentrations from 4.7% (23/492) at 3.36-3.49 mmol/L (130-135 mg/dL) to 78.6% (81/103) above 5.17 mmol/L (200 mg/dL). Overall, the prevalence of FH-causing variants was high (1:90). One reason was a founder variant (n = 63) within the LDLR gene, found 40 times more frequent than European average. The analysis of recruitment data revealed significant ascertainment bias, with lower recruitment rate practices exhibiting higher prevalence. After adjustment for the bias using a generalized linear mixed model, the predicted prevalence was 1 in 163 (0.61%), which is highly consistent with large-scale genomic benchmarks as gnomAD (1:165, n = 622 057) and the UK Biobank (1:176, n = 48 741). CONCLUSIONS: The prevalence of FH determined in this study is significantly higher than previously published estimates (∼1:250), highlighting the importance of this condition for public health and supporting calls for a national paediatric screening programme, given the availability of effective treatment options. For children between 5 and 15 years, biochemical screening is an effective way to select patients for genetic testing, with sequencing of candidate genes being superior to variant screening. In summary, the VRONI study demonstrates the feasibility and efficacy of a combined biochemical and genetic screening for FH in children.

Humans↗
PubMed2026

Using deep learning models as a genetic architecture for the simulation of breeding schemes.

In several simulation studies, long-term selection led to the rapid depletion of genetic variance. These outcomes differ from real-life observations that we aim to replicate, thereby highlighting a fundamental limitation of current classical quantitative genetic simulation models. Deep learning (DL) models have demonstrated promising results in capturing complex interactions essential for maintaining genetic variance; thus, we hypothesize that DL-based genetic simulation models may preserve more genetic variance than classical models, because the biological pathways underlying complex traits exhibit interactions that classical models ignore. The primary objective of this study was to introduce alternative DL-based genetic simulation models and compare them with classical genetic simulation models in terms of their retention of additive genetic variance under truncation selection in a simulated full-sib pig breeding scheme using real haplotypes as founders. After 20 generations of directional truncation selection, the classical models (A, ADAA, and ADAAADDD) retained between 55% and 64% of their initial additive genetic variance. In contrast, while the DL_simple model lost all its additive variance, the DL medium retained 92% to 98% of its additive variance, and the DL_complex model's initial additive variance increased by 296% to 314%. This paper introduces DL-based genetic simulation models and concludes that their ability to retain additive genetic variance depends on the models' architectural complexity. When sufficiently complex, DL-based models exhibit greater retention of additive genetic variance because they intrinsically capture epistatic interactions that are converted into additive variance, as selection progresses, thus, affirming the role of non-additive genetic effects in maintaining long-term genetic variation.

Deep Learning↗