Amin Zia

Trait Variation in Yeast Is Defined by Population History

A fundamental goal in biology is to achieve a mechanistic understanding of how and to what extent ecological variation imposes selection for distinct traits and favors the fixation of specific genetic variants. Key to such an understanding is the detailed mapping of the natural genomic and phenomic space and a bridging of the gap that separates these worlds. Here we chart a high-resolution map of natural trait variation in one of the most important genetic model organisms, the budding yeast Saccharomyces cerevisiae, and its closest wild relatives and trace the genetic basis and timing of major phenotype changing events in its recent history. We show that natural trait variation in S. cerevisiae exceeds that of its relatives, despite limited genetic variation, and follows the population history rather than the source environment. In particular, the West African population is phenotypically unique, with an extreme abundance of low-performance alleles, notably a premature translational termination signal in GAL3 that cause inability to utilize galactose. Our observations suggest that many S. cerevisiae traits may be the consequence of genetic drift rather than selection, in line with the assumption that natural yeast lineages are remnants of recent population bottlenecks. Disconcertingly, the universal type strain S288C was found to be highly atypical, highlighting the danger of extrapolating gene-trait connections obtained in mosaic, lab-domesticated lineages to the species as a whole. Overall, this study represents a step towards an in-depth understanding of the causal relationship between co-variation in ecology, selection pressure, natural traits, molecular mechanism, and alleles in a key model organism.

Revealing the genetic structure of a trait by sequencing a population under selection

One approach to understanding the genetic basis of traits is to study their pattern of inheritance among offspring of phenotypically different parents. Previously, such analysis has been limited by low mapping resolution, high labor costs, and large sample size requirements for detecting modest effects. Here, we present a novel approach to map trait loci using artificial selection. First, we generated populations of 10-100 million haploid and diploid segregants by crossing two budding yeast strains of different heat tolerance for up to 12 generations. We then subjected these large segregant pools to heat stress for up to 12 d, enriching for beneficial alleles. Finally, we sequenced total DNA from the pools before and during selection to measure the changes in parental allele frequency. We mapped 21 intervals with significant changes in genetic background in response to selection, which is several times more than found with traditional linkage methods. Nine of these regions contained two or fewer genes, yielding much higher resolution than previous genomic linkage studies. Multiple members of the RAS/cAMP signaling pathway were implicated, along with genes previously not annotated with heat stress response function. Surprisingly, at most selected loci, allele frequencies stopped changing before the end of the selection experiment, but alleles did not become fixed. Furthermore, we were able to detect the same set of trait loci in a population of diploid individuals with similar power and resolution, and observed primarily additive effects, similar to what is seen for complex trait genetics in other diploid organisms such as humans.

A high-definition view of functional genetic variation from natural yeast genomes

The question of how genetic variation in a population influences phenotypic variation and evolution is of major importance in modern biology. Yet much is still unknown about the relative functional importance of different forms of genome variation and how they are shaped by evolutionary processes. Here we address these questions by population level sequencing of 42 strains from the budding yeast Saccharomyces cerevisiae and its closest relative S. paradoxus. We find that genome content variation, in the form of presence or absence as well as copy number of genetic material, is higher within S. cerevisiae than within S. paradoxus, despite genetic distances as measured in single-nucleotide polymorphisms being vastly smaller within the former species. This genome content variation, as well as loss-of-function variation in the form of premature stop codons and frameshifting indels, is heavily enriched in the subtelomeres, strongly reinforcing the relevance of these regions to functional evolution. Genes affected by these likely functional forms of variation are enriched for functions mediating interaction with the external environment (sugar transport and metabolism, flocculation, metal transport, and metabolism). Our results and analyses provide a comprehensive view of genomic diversity in budding yeast and expose surprising and pronounced differences between the variation within S. cerevisiae and that within S. paradoxus. We also believe that the sequence data and de novo assemblies will constitute a useful resource for further evolutionary and population genomics studies.

High-Resolution Mapping of Complex Traits with a Four-Parent Advanced Intercross Yeast Population

A large fraction of human complex trait heritability is due to a high number of variants with small marginal effects and their interactions with genotype and environment. Such alleles are more easily studied in model organisms, where environment, genetic makeup, and allele frequencies can be controlled. Here, we examine the effect of natural genetic variation on heritable traits in a very large pool of baker’s yeast from a multiparent 12th generation intercross. We selected four representative founder strains to produce the Saccharomyces Genome Resequencing Project (SGRP)-4X mapping population and sequenced 192 segregants to generate an accurate genetic map. Using these individuals, we mapped 25 loci linked to growth traits under heat stress, arsenite, and paraquat, the majority of which were best explained by a diverging phenotype caused by a single allele in one condition. By sequencing pooled DNA from millions of segregants grown under heat stress, we further identified 34 and 39 regions selected in haploid and diploid pools, respectively, with most of the selection against a single allele. While the most parsimonious model for the majority of loci mapped using either approach was the effect of an allele private to one founder, we could validate examples of pleiotropic effects and complex allelic series at a locus. SGRP-4X is a deeply characterized resource that provides a framework for powerful and high-resolution genetic analysis of yeast phenotypes and serves as a test bed for testing avenues to attack human complex traits.

Ranking insertion, deletion and nonsense mutations based on their effect on genetic information

BackgroundGenetic variations contribute to normal phenotypic differences as well as diseases, and new sequencing technologies are greatly increasing the capacity to identify these variations. Given the large number of variations now being discovered, computational methods to prioritize the functional importance of genetic variations are of growing interest. Thus far, the focus of computational tools has been mainly on the prediction of the effects of amino acid changing single nucleotide polymorphisms (SNPs) and little attention has been paid to indels or nonsense SNPs that result in premature stop codons.ResultsWe propose computational methods to rank insertion-deletion mutations in the coding as well as non-coding regions and nonsense mutations. We rank these variations by measuring the extent of their effect on biological function, based on the assumption that evolutionary conservation reflects function. Using sequence data from budding yeast and human, we show that variations which that we predict to have larger effects segregate at significantly lower allele frequencies, and occur less frequently than expected by chance, indicating stronger purifying selection. Furthermore, we find that insertions, deletions and premature stop codons associated with disease in the human have significantly larger predicted effects than those not associated with disease. Interestingly, the large-effect mutations associated with disease show a similar distribution of predicted effects to that expected for completely random mutations.ConclusionsThis demonstrates that the evolutionary conservation context of the sequences that harbour insertions, deletions and nonsense mutations can be used to predict and rank the effects of the mutations.

Sequence to Medical Phenotypes: A Framework for Interpretation of Human Whole Genome DNA Sequence Data

Abstract High throughput sequencing has facilitated a precipitous drop in the cost of genomic sequencing, prompting predictions of a revolution in medicine via genetic personalization of diagnostic and therapeutic strategies. There are significant barriers to realizing this goal that are related to the difficult task of interpreting personal genetic variation. A comprehensive, widely accessible application for interpretation of whole genome sequence data is needed. Here, we present a series of methods for identification of genetic variants and genotypes with clinical associations, phasing genetic data and using Mendelian inheritance for quality control, and providing predictive genetic information about risk for rare disease phenotypes and response to pharmacological therapy in single individuals and father-mother-child trios. We demonstrate application of these methods for disease and drug response prognostication in whole genome sequence data from twelve unrelated adults, and for disease gene discovery in one father-mother-child trio with apparently simplex congenital ventricular arrhythmia. In doing so we identify clinically actionable inherited disease risk and drug response genotypes in pre-symptomatic individuals. We also nominate a new candidate gene in congenital arrhythmia, ATP2B4, and provide experimental evidence of a regulatory role for variants discovered using this framework.

Towards a theoretical understanding of false positives in DNA motif finding

BackgroundDetection of false-positive motifs is one of the main causes of low performance in de novo DNA motif-finding methods. Despite the substantial algorithm development effort in this area, recent comprehensive benchmark studies revealed that the performance of DNA motif-finders leaves room for improvement in realistic scenarios.ResultsUsing large-deviations theory, we derive a remarkably simple relationship that describes the dependence of false positives on dataset size for the one-occurrence per sequence motif-finding problem. As expected, we predict that false-positives can be reduced by decreasing the sequence length or by adding more sequences to the dataset. Interestingly, we find that the false-positive strength depends more strongly on the number of sequences in the dataset than it does on the sequence length, but that the dependence on the number of sequences diminishes, after which adding more sequences does not reduce the false-positive rate significantly. We compare our theoretical predictions by applying four popular motif-finding algorithms that solve the one-occurrence-per-sequence problem (MEME, the Gibbs Sampler, Weeder, and GIMSAN) to simulated data that contain no motifs. We find that the dependence of false positives detected by these softwares on the motif-finding parameters is similar to that predicted by our formula.ConclusionsWe quantify the relationship between the sequence search space and motif-finding false-positives. Based on the simple formula we derive, we provide a number of intuitive rules of thumb that may be used to enhance motif-finding results in practice. Our results provide a theoretical advance in an important problem in computational biology.

Workflow optimization of whole genome amplification and targeted panel sequencing for CTC mutation detection

Genomic characterization of circulating tumor cells (CTCs) may prove useful as a surrogate for conventional tissue biopsies. This is particularly important as studies have shown different mutational profiles between CTCs and ctDNA in some tumor subtypes. However, isolating rare CTCs from whole blood has significant hurdles. Very limited DNA quantities often can’t meet NGS requirements without whole genome amplification (WGA). Moreover, white blood cells (WBC) germline contamination may confound CTC somatic mutation analyses. Thus, a good CTC enrichment platform with an efficient WGA and NGS workflow are needed. Here, Vortex label-free CTC enrichment platform was used to capture CTCs. DNA extraction was optimized, WGA evaluated and targeted NGS tested. We used metastatic colorectal cancer (CRC) as the clinical target, HCT116 as the corresponding cell line, GenomePlex® and REPLI-g as the WGA methods, GeneRead DNAseq Human CRC Panel as the 38 gene panel. The workflow was further validated on metastatic CRC patient samples, assaying both tumor and CTCs. WBCs from the same patients were included to eliminate germline contaminations. The described workflow performed well on samples with sufficient DNA, but showed bias for rare cells with limited DNA input. REPLI-g provided an unbiased amplification on fresh rare cells, enabling an accurate variant calling using the targeted NGS. Somatic variants were detected in patient CTCs and not found in age matched healthy donors. This demonstrates the feasibility of a simple workflow for clinically relevant monitoring of tumor genetics in real time and over the course of a patient’s therapy using CTCs.Liquid biopsy: Simple workflow allows DNA analysis of circulating tumor cellsA microfluidic device that isolates cancer cells circulating in a blood sample allows for real-time genetic monitoring. A team led by Elodie Sollier-Christen of Vortex Biosciences, a cancer diagnostics company in Menlo Park, California, USA, in collaboration with Professor Stefanie Jeffrey at Stanford University School of Medicine, developed a simple workflow for analyzing the genomes of rare circulating tumor cells (CTCs) found in the bloodstream after they’ve been collected through a proprietary microfluidic system. They optimized rare cell DNA extraction, compared different whole genome amplification methods, and then tested the workflow on blood samples from patients with metastatic colorectal cancer. The analysis also included white blood cells from the same patients to parse cancer-causing mutations from inherited ones. The method could aid in the translation of liquid biopsies for the clinical care of cancer patients.

Mutations in the Kinetochore Gene KNSTRN in Basal Cell Carcinoma

Institute Innovation Award. Theresa H.M. Keegan, Susan M. Swetter, Li Tao, John B. Sunwoo and Christina A. Clarke Department of Research, Cancer Prevention Institute of California, Fremont, California, USA; Division of Epidemiology, Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA; Department of Dermatology, Pigmented Lesion and Melanoma Program, Stanford University Medical Center and Cancer Institute, and Dermatology Service, VA Palo Alto Health Care System, Palo Alto, California, USA and Division of Head and Neck Surgery, Department of Otolaryngology, Stanford University School of Medicine and Cancer Institute, Palo Alto, California, USA E-mail:tkeegan@ucdavis.edu

WISP3 mutation associated with pseudorheumatoid dysplasia

Progressive pseudorheumatoid dysplasia (PPD) is a skeletal dysplasia characterized by predominant involvement of articular cartilage with progressive joint stiffness. Here we report genetic characterization of a consanguineous family segregating an uncharacterized from of skeletal dysplasia. Whole-exome sequencing of four affected siblings and their parents identified a loss-of-function homozygous mutation in the WISP3 gene, leading to diagnosis of PPD in the affected individuals. The identified variant (Chr6: 112382301; WISP3:c.156C>A p.Cys52*) is rare and predicted to cause premature termination of the WISP3 protein.