Genetics最新文献

For many problems in population genetics, it is useful to characterize the distribution of fitness effects (DFE) of de novo mutations among a certain class of sites. A DFE is typically estimated by fitting an observed site frequency spectrum (SFS) to an expected SFS given a hypothesized distribution of selection coefficients and demographic history. The development of tools to infer gene trees from haplotype alignments, along with ancient DNA resources, provides us with additional information about the frequency trajectories of segregating mutations. Here, we ask how useful this additional information is for learning about the DFE, using the joint distribution on allele frequency and age to summarize information about the trajectory. To this end, we introduce an accurate and efficient numerical method for computing the density on the age of a segregating variant found at a given sample frequency, given the strength of selection and an arbitrarily complex population size history. We then use this framework to show that the unconditional age distribution of negatively selected alleles is very closely approximated by re-weighting the neutral age distribution in terms of the negatively selected SFS, suggesting that allele ages provide little information about the DFE beyond that already contained in the present day frequency. To confirm this prediction, we extended the standard Poisson Random Field (PRF) method to incorporate the joint distribution of frequency and age in estimating selection coefficients, and test its performance using simulations. We find that when the full SFS is observed and the true allele ages are known, including ages in the estimation provides only small increases in the accuracy of estimated selection coefficients. However, if only sites with frequencies above a certain threshold are observed, then the true ages can provide substantial information about the selection coefficients, especially when the selection coefficient is large. When ages are estimated from haplotype data using state-of-the-art tools, uncertainty about the age abrogates most of the additional information in the fully observed SFS case, while the neutral prior assumed in these tools when estimating ages induces a downward bias in the case of the thresholded SFS.

Natural selection on complex traits is difficult to study in part due to the ascertainment inherent to genome-wide association studies (GWAS). The power to detect a trait-associated variant in GWAS is a function of frequency and effect size - but for traits under selection, the effect size of a variant determines the strength of selection against it, constraining its frequency. Recognizing the biases inherent to GWAS ascertainment, we propose studying the joint distribution of allele frequencies across populations, conditional on the frequencies in the GWAS cohort. Before considering these conditional frequency spectra, we first characterized the impact of selection and non-equilibrium demography on allele frequency dynamics forwards and backwards in time. We then used these results to understand conditional frequency spectra under realistic human demography. Finally, we investigated empirical conditional frequency spectra for GWAS variants associated with 106 complex traits, finding compelling evidence for either stabilizing or purifying selection. Our results provide insight into polygenic score portability and other properties of variants ascertained with GWAS, highlighting the utility of conditional frequency spectra.

Sexual characteristics and reproductive systems are dynamic traits in many taxa, but the developmental modifications that allow change and innovation are largely unknown. A leading model for this process is the evolution of self-fertile hermaphrodites from male/female ancestors. However, these studies require direct analysis of sex-determination in male/female species, as well as in the hermaphroditic species that are related to them. In Caenorhabditis nematodes this has only become possible recently, with the discovery of new species. Here, we use gene editing to characterize major sex-determination genes in C. nigoni, a sister to the widely studied hermaphroditic species C. briggsae. These two species are close enough to mate and form partially fertile hybrids. First, we find that tra-1 functions as the master regulator of sex in C. nigoni, in both the soma and the germ line. Surprisingly, these mutants make only sperm, in contrast to tra-1 mutants in related hermaphroditic species. Moreover, the XX mutants display a unique defect in somatic gonad development that is not seen elsewhere in the genus. Second, the fem-3 gene acts upstream of tra-1 in C. nigoni, and the mutants are females, unlike in the sister species C. briggsae, where they develop as hermaphrodites. This result points to a divergence in the role of fem-3 in the germ line of these species. Third, tra-2 encodes a transmembrane receptor that acts upstream of fem-3 in C. nigoni. Outside of the germ line, tra-2 mutations in all species cause a similar pattern of partial masculinization. However, heterozygosity for tra-2 does not alter germ cell fates in C. nigoni, as it can in sensitized backgrounds of two hermaphroditic species of Caenorhabditis. Finally, the epistatic relationships point to a simple, linear germline pathway in which tra-2 regulates fem-3 which regulates tra-1, unlike the more complex relationships seen in hermaphrodite germ cell development. Taking these results together, the regulation of sex determination is more robust and streamlined in the male/female species C. nigoni than in related species that make self-fertile hermaphrodites, a conclusion supported by studies of interspecies hybrids using sex-determination mutations. Thus, we infer that the origin of self-fertility not only required mutations that activated the spermatogenesis program in XX germ lines, but prior to these there must have been mutations that decanalized the sex-determination process, allowing for subsequent changes to germ cell fates.

Potato is a key food crop with a complex, polyploid genome. Advancements in sequencing technologies coupled with improvements in genome assembly algorithms have enabled generation of phased, chromosome-scale genome assemblies for cultivated tetraploid potato. The SpudDB database houses potato genome sequence and annotation, with the doubled monoploid DM 1-3 516 R44 (hereafter DM) genome serving as the reference genome and haplotype. Diverse annotation data types for DM genes are provided through a suite of Gene Report Pages including gene expression profiles across 438 potato samples. To further annotate potato genes based on expression, 65 gene co-expression modules were constructed that permit identification of tightly co-regulated genes within DM across development and responses to wounding, abiotic stress, and biotic stress. Genome browser views of DM and 28 other potato genomes are provided along with a download page for genome sequence and annotation. To link syntenic genes within and between haplotypes, syntelogs were identified across 25 cultivated potato genomes. Through access to potato genome sequences and associated annotations, SpudDB can enable potato biologists, geneticists and breeders to continue to improve this key food crop.

For decades, it has been repeatedly claimed that the potent bacterial helicase-nuclease RecBCD (exonuclease V) destroys foreign (non-self) DNA, such as that of phages, but repairs and recombines cellular (self) DNA. While this would constitute a strong host-survival mechanism, no phage destroyed by RecBCD is ever specified in those claims. To determine which phages are destroyed by RecBCD, we searched for phage isolates that grow on Escherichia coli ΔrecBCD but not on recBCD+. In contrast to the prevailing claim, we found none among >80 new isolates from nature and >80 from previous collections. Based on these and previous observations, we conclude that RecBCD repairs broken DNA that can recombine but destroys DNA that cannot recombine and recycles the nucleotides.

The glucose-6-phosphate dehydrogenase (G6PD) enzyme protects red blood cells against oxidative damage. Individuals with G6PD-impairing polymorphisms are at risk of hemolytic anemia from oxidative stressors. Prevention of G6PD deficiency-related hemolytic anemia is achievable by identifying affected individuals through G6PD genetic testing. However, accurately predicting the clinical consequence of G6PD variants is limited by over 800 G6PD variants which remain of uncertain significance (VUS). There also remains inconsistency in which deficiency-causing variants are included in genetic testing arrays: many institutions only test c.202G > A, though dozens of other variants can cause G6PD deficiency. Here, we improve G6PD genotype interpretations using the All of Us Research Program data and a yeast functional assay. We confirm that G6PD coding variants are the main contributor to decreased G6PD activity and that 13% of individuals in the All of Us data with deficiency-causing variants would be missed by only genotyping for c.202G > A. We expand clinical interpretation for G6PD VUS, reporting that c.595A > G ("Dagua" or "Açores") and the novel variant c.430C > G reduce activity sufficiently to lead to G6PD deficiency. We also provide evidence that 5 missense VUS are unlikely to lead to G6PD deficiency, and we applied the new World Health Organization (WHO) guidelines to recommend classifying 2 synonymous variants as WHO Class C. In total, we provide new or updated clinical interpretations for 9 G6PD variants. We anticipate these results will improve the accuracy, and prompt increased use, of G6PD genetic tests through a more complete clinical interpretation of G6PD variants.

Variable selection and large-scale hypothesis testing are techniques commonly used to analyze high-dimensional genomic data. Despite recent advances in theory and methodology, variable selection and inference with highly collinear features remain challenging. For instance, collinearity poses a great challenge in genome-wide association studies involving millions of variants, many of which may be in high linkage disequilibrium. In such settings, collinearity can significantly reduce the power of variable selection methods to identify individual variants associated with an outcome. To address such challenges, we developed a Bayesian hierarchical hypothesis testing (BHHT)-a novel multiresolution testing procedure that offers high power with adequate error control and fine-mapping resolution. We demonstrate through simulations that the proposed methodology has a power-FDR performance that is competitive with (and in many scenarios better than) state-of-the-art methods. Finally, we demonstrate the feasibility of using BHHT with large sample size (n∼ 300,000) and ultra dimensional genotypes (∼ 15 million single-nucleotide polymorphisms or SNPs) by applying it to eight complex traits using data from the UK-Biobank. Our results show that the proposed methodology leads to many more discoveries than those obtained using traditional SNP-centered inference procedures. The article is accompanied by open-source software that implements the methods described in this study using algorithms that scale to biobank-size ultra-high-dimensional data.

Budding yeast (Saccharomyces cerevisiae) is the most extensively characterized eukaryotic model organism and has long been used to gain insight into the fundamentals of genetics, cellular biology, and the functions of specific genes and proteins. The Saccharomyces Genome Database (SGD) is a scientific resource that provides information about the genome and biology of S. cerevisiae. For more than 30 years, SGD has maintained the genetic nomenclature, chromosome maps, and functional annotation for budding yeast along with search and analysis tools to explore these data. Here we describe recent updates at SGD, including the two most recent reference genome annotation updates, expanded biochemical pathways representation, changes to SGD search and data files, and other enhancements to the SGD website and user interface. These activities are part of our continuing effort to promote insights gained from yeast to enable the discovery of functional relationships between sequence and gene products in fungi and higher eukaryotes.

Patterson's f-statistics are among the most heavily utilized tools for analyzing genome-wide allele frequency data for demographic inference. Beyond studying admixture, f3- and f4-statistics are also used for clustering populations to identify groups with similar histories. However, previous studies have noted an unexpected behavior of f-statistics: multiple populations from a certain region systematically show higher genetic affinity to a more distant population than to their neighbors, a pattern that is mismatched with alternative measures of genetic similarity. We call this counter-intuitive pattern "sister repulsion". We first present a novel instance of sister repulsion, where genomes from Bronze Age East Anatolian sites show higher affinity toward Bronze Age Greece rather than each other. This is observed both using f3- and f4-statistics, contrasts with archaeological/historical expectation, and also contradicts genetic affinity patterns captured using principal components analysis or multidimensional scaling on genetic distances. We then propose a simple demographic model to explain this pattern, where sister populations receive gene flow from a genetically distant source. We calculate f3- and f4-statistics using simulated genetic data with varying population genetic parameters, confirming that low-level gene flow from an external source into populations from 1 region can create sister repulsion in f-statistics. Unidirectional gene flow between the studied regions (without an external source) can likewise create repulsion. Meanwhile, similar to our empirical observations, multidimensional scaling analyses of genetic distances still cluster sister populations together. Overall, our results highlight the impact of low-level admixture events when inferring demographic history using f-statistics.

We consider how the genetic architecture underlying locally adaptive traits determines the strength of a barrier to gene flow in a mainland-island model. Assuming a general life cycle, we derive an expression for the effective migration rate when local adaptation is due to genetic variation at many loci under directional selection on the island, allowing for arbitrary fitness and dominance effects across loci. We show how the effective migration rate can be combined with classical single-locus diffusion theory to accurately predict multilocus differentiation between the mainland and island at migration-selection-drift equilibrium and determine the migration rate beyond which local adaptation collapses, while accounting for genetic drift and weak linkage. Using our efficient numerical tools, we then present a detailed study of the effects of dominance on barriers to gene flow, showing that when total selection is sufficiently strong, more recessive local adaptation generates stronger barriers to gene flow. We then study how heterogeneous genetic architectures of local adaptation affect barriers to gene flow, characterizing adaptive differentiation at migration-selection balance for different distributions of fitness effects. We find that a more heterogeneous genetic architecture generally yields a stronger genome-wide barrier to gene flow and that the detailed genetic architecture underlying locally adaptive traits can have an important effect on observable differentiation when divergence is not too large. Lastly, we study the limits of our approach as loci become more tightly linked, showing that our predictions remain accurate over a large biologically relevant domain.