Parkinson’s disease (PD) is a progressive neurological disorder and appears to have gender-specific symptoms. Studies have observed a higher frequency for development of PD in male than in female. In the current study, we evaluated the gender-based changes in cortical thickness and structural connectivity in PD patients. With informed consent, 64 PD (43 males and 21 females) patients, and 46 (12 males and 34 females) age-matched controls underwent clinical assessment including MiniMental State Examination (MMSE) and magnetic resonance imaging on a 1.5 Tesla clinical MR scanner. Whole brain high-resolution T1-weighted images were acquired from all subjects and used to measure cortical thickness and structural network connectivity. No significant difference in MMSE score was observed between male and female both in control and PD subjects. Male PD patients showed significantly reduced cortical thickness in multiple brain regions including frontal, parietal, temporal, and occipital lobes as compared with those in female PD patients. The graph theory-based network analysis depicted lower connection strengths, lower clustering coefficients, and altered network hubs in PD male than in PD female. Male-specific cortical thickness changes and altered connectivity in PD patients may derive from behavioral, physiological, environmental, and genetical differences between male and female, and may have significant implications in diagnosing and treating PD among genders.

Sustainable food production in the context of climate change necessitates diversification of agriculture and a more efficient utilization of plant genetic resources. Fonio millet (Digitaria exilis) is an orphan African cereal crop with a great potential for dryland agriculture. Here, we establish high-quality genomic resources to facilitate fonio improvement through molecular breeding. These include a chromosome-scale reference assembly and deep re-sequencing of 183 cultivated and wild Digitaria accessions, enabling insights into genetic diversity, population structure, and domestication. Fonio diversity is shaped by climatic, geographic, and ethnolinguistic factors. Two genes associated with seed size and shattering showed signatures of selection. Most known domestication genes from other cereal models however have not experienced strong selection in fonio, providing direct targets to rapidly improve this crop for agriculture in hot and dry environments.

33Sustainable food production in the context of climate change necessitates diversification of 34 agriculture and a more efficient utilization of plant genetic resources. Fonio millet (Digitaria 35 exilis) is an orphan African cereal crop with a great potential for dryland agriculture. Here, we 36 established high-quality genomic resources to facilitate fonio improvement through molecular 37 breeding. These include a chromosome-scale reference assembly and deep re-sequencing of 183 38 cultivated and wild Digitaria accessions, enabling insights into genetic diversity, population 39 structure, and domestication. Fonio diversity is shaped by climatic, geographic, and ethnolinguistic 40 factors. Two genes associated with seed size and shattering showed signatures of selection. Most 41 known domestication genes from other cereal models however have not experienced strong 42 selection in fonio, providing direct targets to rapidly improve this crop for agriculture in hot and 43 dry environments. 44 45 agriculture 1-3 . The Food and Agriculture Organization of the United Nations (FAO) stated that arid 50 and semi-arid regions are the most vulnerable environments to increasing uncertainties in regional 51 and global food production 4 . In most countries of Africa and the Middle East, agricultural 52 productivity will decline in the near future 4 , because of climate change, land degradation, and 53 groundwater depletion 5 . Agricultural selection, from the early steps of domestication to modern-54 day crop breeding, has resulted in a marked decrease in agrobiodiversity 6,7 . Today, three cereal 55 4 crops alone, bread wheat (Triticum aestivum), maize (Zea mays), and rice (Oryza sativa) account 56 for more than half of the globally consumed calories 8 . 57Many of today's major cereal crops, including rice and maize, originated in relatively humid 58 tropical and sub-tropical regions 9,10 . Although plant breeding has adapted the major cereal crops 59 to a wide range of climates and cultivation practices, there is limited genetic diversity within these 60 few plant species for cultivation in the most extreme environments. On the other hand, crop wild 61 relatives and orphan crops are often adapted to extreme environments and their utility to unlock 62 marginal lands for agriculture has recently regained interest 2,6,[11][12][13][14] . Current technological advances 63 in genomics and genome editing provide an opportunity to rapidly domesticate wild relatives and 64 to improve orphan crops 15,16 . De novo domestication of wild species or rapid improvement of semi-65 domesticated crops can be achieved in less than a decade by targeting a few key genes 6 . 66White fonio (Digitaria exilis (Kippist) Stapf) (Fig. 1) is an indigenous African millet species with 67 a great potential for agriculture in marginal environments 17,18 . Fonio is cultivated under a large 68 range of environmental conditions, from a tropical monsoon climate in western Guinea to a hot, 69 arid desert climate (BWh) in the Sahel zone. Some extra-early matur...

Understanding and exploiting genetic diversity is a key factor for the productive and stable production of rice. Here, we utilize 73 high-quality genomes that encompass the subpopulation structure of Asian rice (Oryza sativa), plus the genomes of two wild relatives (O. rufipogon and O. punctata), to build a pan-genome inversion index of 1769 non-redundant inversions that span an average of ~29% of the O. sativa cv. Nipponbare reference genome sequence. Using this index, we estimate an inversion rate of ~700 inversions per million years in Asian rice, which is 16 to 50 times higher than previously estimated for plants. Detailed analyses of these inversions show evidence of their effects on gene expression, recombination rate, and linkage disequilibrium. Our study uncovers the prevalence and scale of large inversions (≥100 bp) across the pan-genome of Asian rice and hints at their largely unexplored role in functional biology and crop performance.

Understanding and exploiting genetic diversity is a key factor for the productive and stable production of rice. Utilizing 16 high-quality genomes that represent the subpopulation structure of Asian rice (O. sativa), plus the genomes of two close relatives (O. rufipogon and O. punctata), we built a pan-genome inversion index of 1,054 non-redundant inversions that span an average of ~ 14% of the O. sativa cv. Nipponbare reference genome sequence. Using this index we estimated an inversion rate of 1,100 inversions per million years in Asian rice, which is 37 to 73 times higher than previously estimated for plants. Detailed analyses of these inversions showed evidence of their effects on gene regulation, recombination rate, linkage disequilibrium and agronomic trait performance. Our study uncovers the prevalence and scale of large inversions (≥ 100 bp) across the pan-genome of Asian rice, and hints at their largely unexplored role in functional biology and crop performance.

Next generation sequencing (NGS) data analysis is highly compute intensive. In-memory computing, vectorization, bulk data transfer, CPU frequency scaling are some of the hardware features in the modern computing architectures. To get the best execution time and utilize these hardware features, it is necessary to tune the system level parameters before running the application. We studied the GATK-HaplotypeCaller which is part of common NGS workflows, that consume more than 43% of the total execution time. Multiple GATK 3.x versions were benchmarked and the execution time of HaplotypeCaller was optimized by various system level parameters which included: (i) tuning the parallel garbage collection and kernel shared memory to simulate in-memory computing, (ii) architecture-specific tuning in the PairHMM library for vectorization, (iii) including Java 1.8 features through GATK source code compilation and building a runtime environment for parallel sorting and bulk data transfer (iv) the default ’on-demand’ mode of CPU frequency is over-clocked by using ’performance-mode’ to accelerate the Java multi-threads. As a result, the HaplotypeCaller execution time was reduced by 82.66% in GATK 3.3 and 42.61% in GATK 3.7. Overall, the execution time of NGS pipeline was reduced to 70.60% and 34.14% for GATK 3.3 and GATK 3.7 respectively.

No abstract

Motivation Structural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex and may affect coding regions of multiple genes, or affect the functions of genomic regions in different ways from single nucleotide variants. Interpreting the phenotypic consequences of structural variants relies on information about gene functions, haploinsufficiency or triplosensitivity, and other genomic features. Phenotype-based methods to identifying variants that are involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been applied successfully to single nucleotide variants as well as short insertions and deletions, the complexity of structural variants makes it more challenging to link them to phenotypes. Furthermore, structural variants can affect a large number of coding regions, and phenotype information may not be available for all of them. Results We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. We incorporate phenotypes linked to genes, functions of gene products, gene expression in individual celltypes, and anatomical sites of expression, and systematically relate them to their phenotypic consequences through ontologies and machine learning. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families. Availability https://github.com/bio-ontology-research-group/DeepSVP