Biology subjects

Jonas Warringer

Publications and source records attributed to Jonas Warringer.

5 recordsLinked to original sources

bioRxiv2016

Contrasting genome dynamics between domesticated and wild yeasts

Structural rearrangements have long been recognized as an important source of genetic variation with implications in phenotypic diversity and disease, yet their evolutionary dynamics are difficult to characterize with short-read sequencing. Here, we report long-read sequencing for 12 strains representing major subpopulations of the partially domesticated yeast Saccharomyces cerevisiae and its wild relative Saccharomyces paradoxus. Complete genome assemblies and annotations generate population-level reference genomes and allow for the first explicit definition of chromosome partitioning into cores, subtelomeres and chromosome-ends. High-resolution view of structural dynamics uncovers that, in chromosomal cores, S. paradoxus exhibits higher accumulation rate of balanced structural rearrangements (inversions, translocations and transpositions) whereas S. cerevisiae accumulates unbalanced rearrangements (large insertions, deletions and duplications) more rapidly. In subtelomeres, recurrent interchromosomal reshuffling was found in both species, with higher rate in S. cerevisiae. Such striking contrasts between wild and domesticated yeasts reveal the influence of human activities on structural genome evolution.

Genomics↗

bioRxiv2016

Background-dependent effects of selection on subclonal heterogeneity

In BriefVazquez-Garcia et al. examine the role of clonal heterogeneity in the acquisition of antimicrobial resistance. They report that pre-existing and de novo genetic variation jointly contribute to clonal evolution. By building a library of adaptive mutations in multiple genetic backgrounds, they resolve the fitness effects of mutations in a clonal lineage.\n\nHighlightsO_LIClonal heterogeneity influences the acquisition of antimicrobial resistance\nC_LIO_LIJoint role of pre-existing and de novo genetic variation in clonal evolution\nC_LIO_LIClonal dynamics are shaped by background-dependent fitness effects of mutations\nC_LIO_LILoss of clonal heterogeneity is balanced by genomic instability and diversification\nC_LI\n\nSummaryThe joint contribution of pre-existing and de novo genetic variation to clonal adaptation is poorly understood, but essential to design successful antimicrobial or cancer therapies. To address this, we evolve genetically diverse populations of budding yeast, S. cerevisiae, consisting of diploid cells with unique haplotype combinations. We study the asexual evolution of these populations under selective inhibition with chemotherapeutic drugs by time-resolved whole-genome sequencing and phenotyping. All populations undergo clonal expansions driven by de novo mutations, but remain genetically and phenotypically diverse. The clones exhibit widespread genomic instability, rendering recessive de novo mutations homozygous and refining pre-existing variation. Finally, we decompose the fitness contributions of pre-existing and de novo mutations by creating a large recombinant library of adaptive mutations in an ensemble of genetic backgrounds. Both pre-existing and de novo mutations substantially contribute to fitness, and the relative fitness of pre-existing variants sets a selective threshold for new adaptive mutations.

Genetics↗

bioRxiv2016

Powerful decomposition of complex traits in a diploid model using Phased Outbred Lines

Explaining trait differences between individuals is a core but challenging aim of life sciences. Here, we introduce a powerful framework for complete decomposition of trait variation into its underlying genetic causes in diploid model organisms. We intercross two natural genomes over many sexual generations, sequence and systematically pair the recombinant gametes into a large array of diploid hybrids with fully assembled and phased genomes, termed Phased Outbred Lines (POLs). We demonstrate the capacity of the framework by partitioning fitness traits of 7310 yeast POLs across many environments, achieving near complete trait heritability (mean H2 = 91%) and precisely estimating additive (74%), dominance (8%), second (9%) and third (1.8%) order epistasis components. We found nonadditive quantitative trait loci (QTLs) to outnumber (3:1) but to be weaker than additive loci; dominant contributions to heterosis to outnumber overdominant (3:1); and pleiotropy to be the rule rather than the exception. The POL approach presented here offers the most complete decomposition of diploid traits to date and can be adapted to most model organisms.

Genetics↗

bioRxiv2015

Scan-o-matic: high-resolution microbial phenomics at a massive scale

The capacity to map traits over large cohorts of individuals - phenomics - lags far behind the explosive development in genomics. For microbes the estimation of growth is the key phenotype. We introduce an automated microbial phenomics framework that delivers accurate and highly resolved growth phenotypes at an unprecedented scale. Advancements were achieved through introduction of transmissive scanning hardware and software technology, frequent acquisition of precise colony population size measurements, extraction of population growth rates from growth curves and removal of spatial bias by reference-surface normalization. Our prototype arrangement automatically records and analyses 100,000 experiments in parallel. We demonstrate the power of the approach by extending and nuancing the known salt defence biology in bakers yeast. The introduced framework will have a transformative impact by providing high-quality microbial phenomics data for extensive cohorts of individuals and generating well-populated and standardized phenomics databases.

Microbiology↗

bioRxiv2015

Predicting quantitative traits from genome and phenome with near perfect accuracy

In spite of decades of linkage and association studies and its potential impact on human health1, reliable prediction of an individual's risk for heritable disease remains difficult2-4. Large numbers of mapped loci do not explain substantial fractions of the heritable variation, leaving an open question of whether accurate complex trait predictions can be achieved in practice5,6. Here, we use a full genome sequenced population of 7396 yeast strains of varying relatedness, and predict growth traits from family information, effects of segregating genetic variants, and growth measurements in other environments with an average coefficient of determination R2 of 0.91. This accuracy exceeds narrow-sense heritability, approaches limits imposed by measurement repeatability, and is higher than achieved with a single replicate assay in the lab. We find that both relatedness and variant-based predictions are greatly aided by availability of closer relatives, while information from a large number of more distant relatives does not improve predictive performance when close relatives can be used. Our results prove that very accurate prediction of heritable traits is possible, and recommend prioritizing collection of deeper family-based data over large reference cohorts.

Genetics↗