Biomedical subjects

Jean-Luc Jannink

Publications and source records attributed to Jean-Luc Jannink.

8 recordsLinked to original sources

PubMed2026

Translating functional molecular knowledge into crop-breeding success.

Historical plant breeding, which optimizes phenotypes through selective crossing guided by phenotypic evaluation and molecular markers, is limited by evolutionary constraints that hinder rapid crop improvement. A new paradigm, precision breeding, circumvents these limitations by targeting genetic variants through functional molecular knowledge. To generate this knowledge at scale, sequence-based deep learning leverages high-quality genome sequence data to predict variant effects at base-pair resolution. When linked to agronomically important traits, these predictions enable breeders to prioritize variants for precision selection or editing. Although it is still in the early stages of development, we foresee three key applications for this approach: introgressing genes from distant breeding pools, purging deleterious mutations and designing new plant ideotypes. Looking ahead, refined computational models will facilitate targeted editing and the systematic redesign of complex physiological processes to address emerging breeding goals under shifting environmental conditions.

Crops, Agricultural↗

PubMed2025

Global genomic population structure of wild and cultivated oat reveals signatures of chromosome rearrangements.

The genus Avena consists of approximately 30 wild and cultivated oat species. Cultivated oat is an important food crop, yet the broader genetic diversity within the Avena gene pool remains underexplored and underexploited. Here, we characterize over 9000 wild and cultivated hexaploid oat accessions of global origin using genotyping-by-sequencing and explore population structure using multidimensional scaling and population-based clustering methods. We also conduct analyses to reveal chromosome regions associated with local adaptation, sometimes resulting from large-scale chromosome rearrangements. We report four distinct genetic populations within the wild species A. sterilis, a distinct population of cultivated A. byzantina, and multiple populations within cultivated A. sativa. Some chromosome regions associated with local adaptation are also associated with confirmed structural rearrangements on chromosomes 1A, 1C, 3C, 4C, and 7D. This work provides evidence suggesting multiple polyploid origins, multiple domestications, and/or reproductive barriers amongst Avena populations caused by differential chromosome structure.

Avena↗

PubMed2006

Identifying quantitative trait locus by genetic background interactions in association studies.

Association studies are designed to identify main effects of alleles across a potentially wide range of genetic backgrounds. To control for spurious associations, effects of the genetic background itself are often incorporated into the linear model, either in the form of subpopulation effects in the case of structure or in the form of genetic relationship matrices in the case of complex pedigrees. In this context epistatic interactions between loci can be captured as an interaction effect between the associated locus and the genetic background. In this study I developed genetic and statistical models to tie the locus by genetic background interaction idea back to more standard concepts of epistasis when genetic background is modeled using an additive relationship matrix. I also simulated epistatic interactions in four-generation randomly mating pedigrees and evaluated the ability of the statistical models to identify when a biallelic associated locus was epistatic to other loci. Under additive-by-additive epistasis, when interaction effects of the associated locus were quite large (explaining 20% of the phenotypic variance), epistasis was detected in 79% of pedigrees containing 320 individuals. The epistatic model also predicted the genotypic value of progeny better than a standard additive model in 78% of simulations. When interaction effects were smaller (although still fairly large, explaining 5% of the phenotypic variance), epistasis was detected in only 9% of pedigrees containing 320 individuals and the epistatic and additive models were equally effective at predicting the genotypic values of progeny. Epistasis was detected with the same power whether the overall epistatic effect was the result of a single pairwise interaction or the sum of nine pairwise interactions, each generating one ninth of the epistatic variance. The power to detect epistasis was highest (94%) at low QTL minor allele frequency, fell to a minimum (60%) at minor allele frequency of about 0.2, and then plateaued at about 80% as alleles reached intermediate frequencies. The power to detect epistasis declined when the linkage disequilibrium between the DNA marker and the functional polymorphism was not complete.

Animals↗

PubMed2006

In vitro bile acid binding activity within flour fractions from oat lines with typical and high beta-glucan amounts.

Whole flours from four oat lines with different amounts of beta-glucan (4.8-8.1%) were examined for their antioxidant activity and total phenolic and lignin concentrations. These data, along with the beta-glucan percentages, were compared with bile acid (BA) binding. Only the lignin concentrations of the flours significantly (P < 0.01) correlated with the BA binding values. The oat flours also were fractionated into bran, protein concentrate, starch, layer above starch, and soluble beta-glucan (SBG)-free flour, and their BA binding capacities were evaluated. The bran fractions were the only fractions that bound greater BA than did the whole oat flours on dry matter basis. Extraction of the soluble beta-glucan to create the SBG-free flour significantly (P < 0.01) decreased the BA binding of the remaining flour. These data suggest that BA binding of the oat flours involves the synergistic interactions of the oat components, with beta-glucan and lignin (insoluble fiber) having a great impact.

Antioxidants↗

PubMed2005

In vitro bile acid binding of flours from oat lines varying in percentage and molecular weight distribution of beta-glucan.

Two experimental high beta-glucan oat (Avena sativa) lines (7.64 and 8.05%) and two traditional lines (4.77 and 5.26% beta-glucan) were used to evaluate the effect of beta-glucan quantity and molecular weight on bile acid (BA) binding. The oat flour samples were digested by an in vitro system that simulated human digestion. No significant differences among oat type were found in the overall beta-glucan, starch, and pentosan digestibilities. Considering the standard, cholestyramine, as 100% bound, the relative BA binding for the oat flour samples on a dry matter basis was in the range of 7.5-14.8%, which is higher than the values determined for some other grains and plant materials in the literature. Although the high beta-glucan flours bound a high amount of BA, no significant correlations were found between beta-glucan content in the flours and BA binding. Significant correlations were found between BA binding and insoluble dietary fiber content. Partial hydrolysis with lichenase of the beta-glucan molecules did not affect the BA binding. A summary of all data suggested that BA binding is a multicomponent-dependent process.

Avena↗

PubMed2004

Optimal sampling of a population to determine QTL location, variance, and allelic number.

In a population intended for breeding and selection, questions of interest relative to a specific segregating QTL are the variance it generates in the population, and the number and effects of its alleles. One approach to address these questions is to extract several inbreds from the population and use them to generate multiple mapping families. Given random sampling of parents, sampling strategy may be an important factor determining the power of the analysis and its accuracy in estimating QTL variance and allelic number. We describe appropriate multiple-family QTL mapping methodology and apply it to simulated data sets to determine optimal sampling strategies in terms of family number versus family size. Genomes were simulated with seven chromosomes, on which 107 markers and six QTL were distributed. The total heritability was 0.60. Two to ten alleles were segregating at each QTL. Sampling strategies ranged from sampling two inbreds and generating a single family of 600 progeny to sampling 40 inbreds and generating 40 families of 15 progeny each. Strategies involving only one to five families were subject to variation due to the sampling of inbred parents. For QTL where more than two alleles were segregating, these strategies did not sample QTL alleles representative of the original population. Conversely, strategies involving 30 or more parents were subject to variation due to sampling of QTL genotypes within the small families obtained. Given these constraints, greatest QTL detection power was obtained for strategies involving five to ten mapping families. The most accurate estimation of the variance generated by the QTL, however, was obtained with strategies involving 20 or more families. Finally, strategies with an intermediate number of families best estimated the number of QTL alleles. We conclude that no overall optimal sampling strategy exists but that the strategy adopted must depend on the objective.

Alleles↗

PubMed2004

On the Metropolis-Hastings acceptance probability to add or drop a quantitative trait locus in Markov chain Monte Carlo-based Bayesian analyses.

The Metropolis-Hastings algorithm used in analyses that estimate the number of QTL segregating in a mapping population requires the calculation of an acceptance probability to add or drop a QTL from the model. Expressions for this acceptance probability need to recognize that sets of QTL are unordered such that the number of equivalent sets increases with the factorial of the QTL number. Here, we show how accounting for this fact affects the acceptance probability and review expressions found in the literature.

Algorithms↗

PubMed2003

Estimating allelic number and identity in state of QTLs in interconnected families.

When multiple related families derived from inbred lines are jointly analysed to detect quantitative trait loci (QTLs), the analysis should estimate allelic effects as accurately as possible and estimate the probability that different parents carry alleles that are identical in state. Analyses exist that assume that all parents carry unique alleles or that all parents but one carry the same allele. In practice, many configurations are possible that group different parents according to their identity-in-state condition at a putative QTL allele. Here, we propose a variable model Bayesian analysis that selects among possible identity-in-state configurations and jointly estimates the allelic effects of identical-in-state parents. We contrast this analysis with a fixed model analysis that estimates unique allelic effects for all parents. We analyse two simulated mating designs: an experimental design in which three inbred parents were crossed to generate two families of 150 doubled haploid lines; and a breeding design in which 20 inbred parents were crossed to generate 60 families of 20 doubled haploid lines, with each parent contributing to six families. In all cases where some parents were simulated to carry alleles of identical effect (that is, they were identical in state), the variable analysis estimated allelic effects with lower mean-squared error than the fixed analysis. The variable analysis showed that, unless each family contains many individuals (more than 100), there is insufficient information in DNA-marker and phenotypic data to determine with high probability the QTL allelic number.

Bayes Theorem↗