Fast inference of genetic recombination rates in biobank scale data.

Although rates of recombination events across the genome (genetic maps) are fundamental to genetic research, the majority of current studies only use one standard map. There is evidence suggesting population differences in genetic maps, and thus estimating population-specific maps, are of interest. Although the recent availability of biobank-scale data offers such opportunities, current methods are not efficient at leveraging very large sample sizes. The most accurate methods are still linkage disequilibrium (LD)-based methods that are only tractable for a few hundred samples. In this work, we propose a fast and memory-efficient method for estimating genetic maps from population genotyping data. Our method, FastRecomb, leverages the efficient positional Burrows-Wheeler transform (PBWT) data structure for counting IBD segment boundaries as potential recombination events. We used PBWT blocks to avoid redundant counting of pairwise matches. Moreover, we used a panel-smoothing technique to reduce the noise from errors and recent mutations. Using simulation, we found that FastRecomb achieves state-of-the-art performance at 10-kb resolution, in terms of correlation coefficients between the estimated map and the ground truth. This is mainly because FastRecomb can effectively take advantage of large panels comprising more than hundreds of thousands of haplotypes. At the same time, other methods lack the efficiency to handle such data. We believe further refinement of FastRecomb would deliver more accurate genetic maps for the genetics community.

FastRecomb: Fast inference of genetic recombination rates in biobank scale data.

While rates of recombination events across the genome (genetic maps) are fundamental to genetic research, the majority of current studies only use one standard map. There is evidence suggesting population differences in genetic maps, and thus estimating population-specific maps are of interest. While the recent availability of biobank-scale data offers such opportunities, current methods are not efficient at leveraging very large sample sizes. The most accurate methods are still linkage-disequilibrium (LD)-based methods that are only tractable for a few hundred samples. In this work, we propose a fast and memory-efficient method for estimating genetic maps from population genotyping data. Our method, FastRecomb, leverages the efficient positional Burrows-Wheeler transform (PBWT) data structure for counting IBD segment boundaries as potential recombination events. We used PBWT blocks to avoid redundant counting of pairwise matches. Moreover, we used a panel smoothing technique to reduce the noise from errors and recent mutations. Using simulation, we found that FastRecomb achieves state-of-the-art performance at 10k resolution, in terms of correlation coefficients between the estimated map and the ground truth. This is mainly due to the fact that FastRecomb can effectively take advantage of large panels comprising more than hundreds of thousands of haplotypes. At the same time, other methods lack the efficiency to handle such data. We believe further refinement of FastRecomb would deliver more accurate genetic maps for the genetics community.

P-smoother: efficient PBWT smoothing of large haplotype panels.

Motivation: As large haplotype panels become increasingly available, efficient string matching algorithms such as positional Burrows-Wheeler transformation (PBWT) are promising for identifying shared haplotypes. However, recent mutations and genotyping errors create occasional mismatches, presenting challenges for exact haplotype matching. Previous solutions are based on probabilistic models or seed-and-extension algorithms that passively tolerate mismatches. Results: Here, we propose a PBWT-based smoothing algorithm, P-smoother, to actively 'correct' these mismatches and thus 'smooth' the panel. P-smoother runs a bidirectional PBWT-based panel scanning that flips mismatching alleles based on the overall haplotype matching context, which we call the IBD (identical-by-descent) prior. In a simulated panel with 4000 haplotypes and a 0.2% error rate, we show it can reliably correct 85% of errors. As a result, PBWT algorithms running over the smoothed panel can identify more pairwise IBD segments than that over the unsmoothed panel. Most strikingly, a PBWT-cluster algorithm running over the smoothed panel, which we call PS-cluster, achieves state-of-the-art performance for identifying multiway IBD segments, a challenging problem in the computational community for years. We also showed that PS-cluster is adequately efficient for UK Biobank data. Therefore, P-smoother opens up new possibilities for efficient error-tolerating algorithms for biobank-scale haplotype panels. Availability and implementation: Source code is available at github.com/ZhiGroup/P-smoother.

Chronic presence of blood circulating anti-NMDAR1 autoantibodies impairs cognitive function in mice.

High titers of anti-NMDAR1 autoantibodies in brain cause anti-NMDAR1 encephalitis that displays psychiatric symptoms of schizophrenia and/or other psychiatric disorders in addition to neurological symptoms. Low titers of anti-NMDAR1 autoantibodies are reported in the blood of a subset of the general human population and psychiatric patients. Since ~0.1-0.2% of blood circulating antibodies cross the blood-brain barriers and antibodies can persist for months and years in human blood, it is important to investigate whether chronic presence of these blood circulating anti-NMDAR1 autoantibodies may impair human cognitive functions and contribute to the development of psychiatric symptoms. Here, we generated mice carrying low titers of anti-NMDAR1 autoantibodies in blood against a single antigenic epitope of mouse NMDAR1. Mice carrying the anti-NMDAR1 autoantibodies are healthy and display no differences in locomotion, sensorimotor gating, and contextual memory compared to controls. Chronic presence of the blood circulating anti-NMDAR1 autoantibodies, however, is sufficient to impair T-maze spontaneous alternation in the integrity of blood-brain barriers across all 3 independent mouse cohorts, indicating a robust cognitive deficit in spatial working memory and/or novelty detection. Our studies implicate that chronic presence of low titers of blood circulating anti-NMDAR1 autoantibodies may impair cognitive functions in both the general healthy human population and psychiatric patients.