Sex differences in the extent of acute axonal pathologies after experimental concussion.

Although human females appear be at a higher risk of concussion and suffer worse outcomes than males, underlying mechanisms remain unclear. With increasing recognition that damage to white matter axons is a key pathologic substrate of concussion, we used a clinically relevant swine model of concussion to explore potential sex differences in the extent of axonal pathologies. At 24 h post-injury, female swine displayed a greater number of swollen axonal profiles and more widespread loss of axonal sodium channels than males. Axon degeneration for both sexes appeared to be related to individual axon architecture, reflected by a selective loss of small caliber axons after concussion. However, female brains had a higher percentage of small caliber axons, leading to more extensive axon loss after injury compared to males. Accordingly, sexual dimorphism in axonal size is associated with more extensive axonal pathology in females after concussion, which may contribute to worse outcomes.

Linkage disequilibrium based genotype calling from low-coverage shotgun sequencing reads.

BACKGROUND: Recent technology advances have enabled sequencing of individual genomes, promising to revolutionize biomedical research. However, deep sequencing remains more expensive than microarrays for performing whole-genome SNP genotyping. RESULTS: In this paper we introduce a new multi-locus statistical model and computationally efficient genotype calling algorithms that integrate shotgun sequencing data with linkage disequilibrium (LD) information extracted from reference population panels such as Hapmap or the 1000 genomes project. Experiments on publicly available 454, Illumina, and ABI SOLiD sequencing datasets suggest that integration of LD information results in genotype calling accuracy comparable to that of microarray platforms from sequencing data of low-coverage. A software package implementing our algorithm, released under the GNU General Public License, is available at http://dna.engr.uconn.edu/software/GeneSeq/. CONCLUSIONS: Integration of LD information leads to significant improvements in genotype calling accuracy compared to prior LD-oblivious methods, rendering low-coverage sequencing as a viable alternative to microarrays for conducting large-scale genome-wide association studies.

Genotype error detection using Hidden Markov Models of haplotype diversity.

The presence of genotyping errors can invalidate statistical tests for linkage and disease association, particularly for methods based on haplotype analysis. Becker et al. have recently proposed a simple likelihood ratio approach for detecting errors in trio genotype data. Under this approach, a SNP genotype is flagged as a potential error if the likelihood associated with the original trio genotype data increases by a multiplicative factor exceeding a user selected threshold when the SNP genotype under test is deleted. In this article we give improved error detection methods using the likelihood ratio test approach in conjunction with likelihood functions that can be efficiently computed based on a Hidden Markov Model of haplotype diversity in the population under study. Experimental results on both simulated and real datasets show that proposed methods have highly scalable running time and achieve significantly improved detection accuracy compared to previous methods.