Pathogenic variants in MECP2 commonly lead to Rett syndrome, where MECP2's function as a DNA cytosine methylation reader is believed critical. MECP2 variants are also cataloged in individuals with autism spectrum disorder (ASD), including nine missense variants which had no known clinical significance at the start of this study. To assess these nine variants as risk alleles for ASD, we developed MECP2 variant functional assays using budding yeast and Drosophila. We calibrated these assays with known pathogenic and benign variants. Our data predict that four ASD variants are loss of function and five are functional. Protein destabilization offers insight into the altered function of some of these variants. Notably, yeast and Drosophila lack DNA methylation, yet all Rett pathogenic and ASD variants located in the methyl DNA-binding domain that we analyzed proved to be loss of function, suggesting a clinically relevant role for non-methyl DNA-binding by MECP2.

Genetic intra-tumor heterogeneity is a universal property of all cancers. It emerges from the interplay of cell division, mutation accumulation, and selection with important implications for the evolution of treatment resistance. Theoretical and data-driven approaches extensively studied intra-tumor heterogeneity in ageing somatic tissues or cancers at detection. Yet, the expected patterns of intra-tumor heterogeneity during and after treatment are less well understood. Here, we use stochastic birth–death processes to investigate the expected patterns of intra-tumor heterogeneity across different treatment scenarios. We consider homogeneous treatment response with shrinking, growing, and stable disease, and follow-up investigating heterogeneous treatment response with sensitive and resistant cell types. We derive analytic expressions for the site frequency spectrum, the total mutational burden and the single-cell mutational burden distribution that we validate with computer simulations. We find that the site frequency spectrum after homogeneous treatment response retains its characteristic power-law tail, while emergent resistant clones cause peaks corresponding to their sizes. The frequency of the largest resistant clone is subdominant and independent of the population size at detection, whereas the relative total number of resistant cells increases with detection size. Furthermore, the growth dynamics under treatment determine whether the total mutational burden is dominated by preexisting or newly acquired mutations, suggesting different possible treatment strategies.

In maize, there are 2 meiotic drive systems that target large heterochromatic knobs composed of tandem repeats known as knob180 and TR-1. The first meiotic drive haplotype, abnormal chromosome 10 (Ab10) confers strong meiotic drive (∼75% transmission as a heterozygote) and encodes 2 kinesins: KINDR, which associates with knob180 repeats, and TRKIN, which associates with TR-1 repeats. Prior data show that meiotic drive is conferred primarily by the KINDR/knob180 system while the TRKIN/TR-1 system seems to have little or no role, making it unclear why Trkin has been maintained in Ab10 haplotypes. The second meiotic drive haplotype, K10L2, confers a low level of meiotic drive (∼51-52%) and only encodes the TRKIN/TR-1 system. Here, we used long-read sequencing to assemble the K10L2 haplotype and showed that it has strong homology to an internal portion of the Ab10 haplotype. We also carried out CRISPR mutagenesis to test the role of Trkin on Ab10 and K10L2. The data indicate that the Trkin gene on Ab10 does not improve drive or fitness but instead has a weak deleterious effect when paired with a normal chromosome 10. The deleterious effect is more severe when Ab10 is paired with K10L2: in this context, functional Trkin on either chromosome nearly abolishes Ab10 drive. Mathematical modeling based on the empirical data suggests that Trkin is unlikely to persist on Ab10. We conclude that Trkin either confers an advantage to Ab10 in untested circumstances or that it is in the process of being purged from the Ab10 population.

Three cross-incompatibility loci each control a distinct reproductive barrier in both domesticated maize (Zea mays ssp. mays) and its wild teosinte relatives. These 3 loci, Teosinte crossing barrier1 (Tcb1), Gametophytic factor1 (Ga1), and Ga2, each play a key role in preventing hybridization between incompatible populations and are proposed to maintain the barrier between domesticated and wild subspecies. Each locus encodes both a silk-active and a matching pollen-active pectin methylesterase (PMEs). To investigate the diversity and molecular evolution of these gametophytic factor loci, we identified existing and improved models of the responsible genes in a new genome assembly of maize line P8860 that contains active versions of all 3 loci. We then examined 52 assembled genomes from 17 species to classify haplotype diversity and identify sites under diversifying selection during the evolution of these genes. We show that Ga2, the oldest of these 3 loci, was duplicated to form Ga1 at least 12 million years ago. Tcb1, the youngest locus, arose as a duplicate of Ga1 before or around the time of diversification of the Zea genus. We find evidence of positive selection during evolution of the functional genes at an active site in the pollen-expressed PME and predicted surface sites in both the silk- and pollen-expressed PMEs. The most common allele at the Ga1 locus is a conserved ga1 allele (ga1-Off), which is specific haplotype containing 3 full-length PME gene copies, all of which are noncoding due to conserved stop codons and are between 610 thousand and 1.5 million years old. We show that the ga1-Off allele is associated with and likely generates 24-nt siRNAs in developing pollen-producing tissue, and these siRNAs map to functional Ga1 alleles. In previously published crosses, the ga1-Off allele was associated with reduced function of the typically dominant functional alleles for the Ga1 and Tcb1 barriers. Taken together, this seems to be an example of an allele at a reproductive barrier locus being associated with an as yet undetermined mechanism capable of silencing the reproductive barrier.

Heterozygous inversion breakpoints inhibit the formation of chiasmata in their vicinity, and it has been suggested that they do so through the same mechanism that also causes interference between chiasmata. In this paper, I therefore extend my earlier model of chiasma interference to account for interference between breakpoints and chiasmata in pericentric inversion heterokaryotypes. Using this model to analyze recombination and sterility datasets for Drosophila melanogaster, I find support for the hypothesis that inversion breakpoints interfere with chiasmata in the same way and to the same degree that other chiasmata do. I also find that breakpoints, like chiasmata, appear to show negative interference in the pericentromeric region, and positive interference elsewhere. I discuss the implications of these findings in light of the recent HEI10 coarsening interference hypothesis, and conclude with some remarks about the evolutionary origin of chiasma interference.

We introduce a framework for using contrastive learning for dimensionality reduction on genetic datasets to create principal component analysis (PCA)-like population visualizations. Contrastive learning is a self-supervised deep learning method that uses similarities between samples to train the neural network to discriminate between samples. Many of the advances in these types of models have been made for computer vision, but some common methodology does not translate well from image to genetic data. We define a loss function that outperforms loss functions commonly used in contrastive learning, and a data augmentation scheme tailored specifically towards SNP genotype datasets. We compare the performance of our method to PCA and contemporary nonlinear methods with respect to how well they preserve local and global structure, and how well they generalize to new data. Our method displays good preservation of global structure and has improved generalization properties over t-distributed stochastic neighbor embedding, Uniform Manifold Approximation and Projection, and popvae, while preserving relative distances between individuals to a high extent. A strength of the deep learning framework is the possibility of projecting new samples and fine-tuning to new datasets using a pretrained model without access to the original training data, and the ability to incorporate more domain-specific information in the model. We show examples of population classification on two datasets of dog and human genotypes.

qpAdm is a statistical tool that is often used for testing large sets of alternative admixture models for a target population. Despite its popularity, qpAdm remains untested on 2D stepping stone landscapes and in situations with low prestudy odds (low ratio of true to false models). We tested high-throughput qpAdm protocols with typical properties such as number of source combinations per target, model complexity, model feasibility criteria, etc. Those protocols were applied to admixture graph-shaped and stepping stone simulated histories sampled randomly or systematically. We demonstrate that false discovery rates of high-throughput qpAdm protocols exceed 50% for many parameter combinations since: (1) prestudy odds are low and fall rapidly with increasing model complexity; (2) complex migration networks violate the assumptions of the method; hence, there is poor correlation between qpAdm P-values and model optimality, contributing to low but nonzero false-positive rate and low power; and (3) although admixture fraction estimates between 0 and 1 are largely restricted to symmetric configurations of sources around a target, a small fraction of asymmetric highly nonoptimal models have estimates in the same interval, contributing to the false-positive rate. We also reinterpret large sets of qpAdm models from 2 studies in terms of source-target distance and symmetry and suggest improvements to qpAdm protocols: (1) temporal stratification of targets and proxy sources in the case of admixture graph-shaped histories, (2) focused exploration of few models for increasing prestudy odds; and (3) dense landscape sampling for increasing power and stringent conditions on estimated admixture fractions for decreasing the false-positive rate.

Data obtained from high throughput experiments often exhibit complex dependencies among features. These dependencies arise from various sources, including genetic correlation, batch effects, technical replicates, and shared biological pathways. Ignoring these dependencies can lead to inflated false discovery rate (FDR), reduced statistical power, and biased biological interpretations. Properly accounting for these dependencies is crucial for accurate detection of biological signals. We propose a new method called Analysis of Correlated Expressions (ACE) to compare the mean expression of features between two groups. ACE is based on a factor analytic model that accounts for dependence among features and also incorporates heterogeneity of variances between groups, a common feature of high throughput data. Furthermore, ACE does not require the data to be normally distributed. It is scalable and free of any unknown tuning parameters. Extensive simulation studies indicate that it is more powerful than many existing methods while controlling the FDR. Application of ACE to a microRNA dataset, a neuroblastoma gene expression dataset, and a Huntington's disease dataset resulted in some novel findings that were missed by existing methods.