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Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

Mutations that affect phenotypes have been identified primarily as those that directly alter amino acid sequences or disrupt splice sites. However, some mutations not located in functionally important sites can also affect phenotypes, such as splice-site-creating mutations (SCMs). To investigate how frequent exon extension/shrinkage events induced by SCMs occur in normal individuals, we used personal genome sequencing data and transcriptome data of the corresponding individuals and identified 371 exon extension/shrinkage events in normal individuals. This number was about three times higher than the number of pseudo-exon activation events identified in the previous study. The average numbers of exon extension and exon shrinkage events in each sample were 3.3 and 11.2, respectively. We also evaluated the impact of exon extension/shrinkage events on the resulting transcripts and their protein products and found that 40.2% of the identified events may have possible functional impacts by either generating premature termination codons in transcripts or affecting protein domains. Our results indicated that a certain fraction of SCMs identified in this study can be pathogenic mutations by creating novel splice sites.

Abstract There are major efforts underway to make genome sequencing a routine part of clinical practice. A critical barrier to these is achieving practical solutions for data ownership and integrity. Blockchain provides solutions to these challenges in other realms, such as finance. However, its use in genomics is stymied due to the difficulty in storing large-scale data on-chain, slow transaction speeds, and limitations on querying. To overcome these roadblocks, we developed a private blockchain network to store genomic variants and reference-aligned reads on-chain. It uses nested database indexing with an accompanying tool suite to rapidly access and analyze the data.

Two major forces have contributed to the fast growth of human genetic data. One from medical research supported by governments and academic institutes; the other from direct-to-consumer (DTC) sequencing companies. While the former benefits from meticulously designed sequencing standards and quality control procedures, the latter comes in various formats and sequencing methods which are subject to changes over time and the particular needs of different companies. Thanks to the general public who shared their DNA data without constraint, here we provide a review for over 7000 genomes made public between 2011 and 2020, and produced by over six DTC sequencing companies. An open source tool-kit to systematically parse, quality check and filter genome files and statistically problematic alleles is provided to prepare consumer DNA datasets for research. The GenomePrep output is available in two common DNA datafile formats to enable further analysis with other tools. We also provide for download the combined output for all OpenSNP array genomes processed in this paper in a single data freeze file.

Human genome gene analysis has made rapid progress. As a result, we are now entering the personal genome era. In particular, pharmacists are able to acquire medical and pharmaceutical knowledge and information on the genomic medicine that satisfy each patient, and provide medication guidance and drug treatment to patients. Therefore, in the present manuscript, following the previous report, we decided to report mainly on "personal genome", "hereditary disease" and "role of pharmacist" as a review. The results shown in this paper are as follows. 1) Already entering the personal genome era in the medical field, 2) Cancer is a personal illness, 3) Gene polymorphisms and mutations, 4) What is a hereditary disease? 5) Genetic abnormalities: mutations and variants, 6) Classification and types of diseases based on genetic factors, 7) Gene-related tests, 8) The role of pharmacists in genetic disorders. From the above results and discussions, it can be concluded that it is important for next-generation pharmacists to contribute to patients by utilizing genomic medicine for drug treatment of hereditary diseases knowledge that pharmacist must understand in the field of genomic medicines in the future. Thus, understanding these items will be useful for next-generation pharmacists to provide drug treatment and medication guidance to patients. [ABSTRACT FROM AUTHOR]

Abstract Genomic global positioning system (GPS) applies the multilateration technique commonly used in the GPS to genomic data. In the framework we present here, investigators calculate genetic distances from their samples to reference samples, which are from data held in the public domain, and share this information with others. This sharing enables certain types of genomic analysis, such as identifying sample overlaps and close relatives, decomposing ancestry, and mapping of geographical origin without disclosing personal genomes. Thus, our method can be seen as a balance between open data sharing and privacy protection.

Causative mutations for human genetic disorders have mainly been identified in exonic regions that code for amino acid sequences. Recently, however, it has been reported that mutations in deep intronic regions can also cause certain human genetic disorders by creating novel splice sites, leading to pseudo-exon activation. To investigate how frequently pseudo-exon activation events occur in normal individuals, we conducted in silico identification of such events using personal genome data and corresponding high-quality transcriptome data. With rather stringent conditions, on average, 2.6 pseudo-exon activation events per individual were identified. More pseudo-exon activation events were found in 5′ donor splice sites than in 3′ acceptor splice sites. Although pseudo-exon activation events have sporadically been reported as causative mutations in genetic disorders, it is revealed in this study that such events can be observed in normal individuals at a certain frequency. We estimate that human genomes typically contain on average at least 10 pseudo-exon activation events. The actual number should be higher than this, because we used stringent criteria to identify pseudo-exon activation events. This suggests that it is worth considering the possibility of pseudo-exon activation when searching for causative mutations of genetic disorders if candidate mutations are not identified in coding regions or RNA splice sites. [ABSTRACT FROM AUTHOR]

Abstract Background A range of rare and common genetic variants have been discovered to be potentially associated with mental diseases, but many more have not been uncovered. Powerful integrative methods are needed to systematically prioritize both variants and genes that confer susceptibility to mental diseases in personal genomes of individual patients and to facilitate the development of personalized treatment or therapeutic approaches. Methods Leveraging deep neural network on the TensorFlow framework, we developed a computational tool, integrated Mental-disorder GEnome Score (iMEGES), for analyzing whole genome/exome sequencing data on personal genomes. iMEGES takes as input genetic mutations and phenotypic information from a patient with mental disorders, and outputs the rank of whole genome susceptibility variants and the prioritized disease-specific genes for mental disorders by integrating contributions from coding and non-coding variants, structural variants (SVs), known brain expression quantitative trait loci (eQTLs), and epigenetic information from PsychENCODE. Results iMEGES was evaluated on multiple datasets of mental disorders, and it achieved improved performance than competing approaches when large training dataset is available. Conclusion iMEGES can be used in population studies to help the prioritization of novel genes or variants that might be associated with the susceptibility to mental disorders, and also on individual patients to help the identification of genes or variants related to mental diseases.

A person's genome typically contains millions of variants which represent the differences between this personal genome and the reference human genome. The interpretation of these variants, i.e., the assessment of their potential impact on a person's phenotype, is currently of great interest in human genetics and medicine. We have developed a prioritization tool called OpenCausal which takes as inputs 1) a personal genome and 2) a reference contextspecific TF expression profile and returns a list of noncoding variants prioritized according to their impact on chromatin accessibility for any given genomic region of interest. We applied OpenCausal to 6,430 samples across 18 tissues derived from the GTEx project and found that the variants prioritized by OpenCausal are highly enriched for eQTLs and caQTLs. We further propose a strategy to integrate the predicted open scores with genome-wide association studies (GWAS) data to prioritize putative causal variants and regulatory elements for a given risk locus (i.e., fine-mapping analysis). As an initial example, we applied this method to a GWAS dataset of human height and found that the prioritized putative variants and elements are correlated with the phenotype (i.e., heights of individuals) better than others. [ABSTRACT FROM AUTHOR]

Although genome sequencing has become increasingly popular, the simulation of individual genomes is still important. This is because sequencing a large number of individual genomes is costly and genome data with extreme and boundary conditions, such as fatal genetic defects, are difficult to obtain. Privacy and legal barriers also prevent many applications of real data. Large sequencing projects in recent years have provided a deeper understanding of the human genome. However, there is a lack of tools to leverage known data to simulate personal genomes as real as possible. Here, we designed and developed PGsim, a comprehensive and highly customizable individual genome simulator, that fully uses existing knowledge, such as variant allele frequencies in global or world main populations, mutation probability differences between protein-coding regions and non-coding regions, transition/transversion (Ti/Tv) ratios, Indel incidence, Indel length distribution, structural variation sites, and pathogenic mutation sites. Users can flexibly control the proportion and quantity of known variants, common variants, novel variants in both coding and non-coding regions, and special variants through detailed parameter settings. To ensure that the simulated personal genome has sufficient randomness, PGsim makes the generated variants more real and reliable in terms of variant distribution, proportion, and population characteristics. PGsim is able to employ a huge volume database as background data to simulate personal genomes and does not require SQL database support. Users can easily change the variant databases used as needed. As a Perl script, there is no obstacle to running PGsim on any version of the MAC OS or Linux systems, and no libraries, packages, interpreters, compilers, or other dependencies need to be installed in advance. The PGsim tool is publicly available at https://github.com/lrjuan/PGsim.

Background: A range of rare and common genetic variants have been discovered to be potentially associated with mental diseases, but many more have not been uncovered. Powerful integrative methods are needed to systematically prioritize both variants and genes that confer susceptibility to mental diseases in personal genomes of individual patients and to facilitate the development of personalized treatment or therapeutic approaches. Methods: Leveraging deep neural network on the TensorFlow framework, we developed a computational tool, integrated Mental-disorder GEnome Score (iMEGES), for analyzing whole genome/exome sequencing data on personal genomes. iMEGES takes as input genetic mutations and phenotypic information from a patient with mental disorders, and outputs the rank of whole genome susceptibility variants and the prioritized disease-specific genes for mental disorders by integrating contributions from coding and non-coding variants, structural variants (SVs), known brain expression quantitative trait loci (eQTLs), and epigenetic information from PsychENCODE. Results: iMEGES was evaluated on multiple datasets of mental disorders, and it achieved improved performance than competing approaches when large training dataset is available. Conclusion: iMEGES can be used in population studies to help the prioritization of novel genes or variants that might be associated with the susceptibility to mental disorders, and also on individual patients to help the identification of genes or variants related to mental diseases. [ABSTRACT FROM AUTHOR]

Disorders of the cardiac rhythm are quite prevalent in clinical practice. Though the variability in drug response between individuals has been extensively studied, this information has not been widely used in clinical practice. Rapid advances in the field of pharmacogenomics have provided us with crucial insights on inter-individual genetic variability and its impact on drug metabolism and action. Technologies for faster and cheaper genetic testing and even personal genome sequencing would enable clinicians to optimize prescription based on the genetic makeup of the individual, which would open up new avenues in the area of personalized medicine. We have systematically looked at literature evidence on pharmacogenomics markers for anti-arrhythmic agents from the OpenPGx consortium collection and reason the applicability of genetics in the management of arrhythmia. We also discuss potential issues that need to be resolved before personalized pharmacogenomics becomes a reality in regular clinical practice.

Even though each of us shares more than 99% of the DNA sequences in our genome, there are millions of sequence codes or structure in small regions that differ between individuals, giving us different characteristics of appearance or responsiveness to medical treatments. Currently, genetic variants in diseased tissues, such as tumors, are uncovered by exploring the differences between the reference genome and the sequences detected in the diseased tissue. However, the public reference genome was derived with the DNA from multiple individuals. As a result of this, the reference genome is incomplete and may misrepresent the sequence variants of the general population. The more reliable solution is to compare sequences of diseased tissue with its own genome sequence derived from tissue in a normal state. As the price to sequence the human genome has dropped dramatically to around $1000, it shows a promising future of documenting the personal genome for every individual. However, de novo assembly of individual genomes at an affordable cost is still challenging. Thus, till now, only a few human genomes have been fully assembled. In this review, we introduce the history of human genome sequencing and the evolution of sequencing platforms, from Sanger sequencing to emerging "third generation sequencing" technologies. We present the currently available de novo assembly and post-assembly software packages for human genome assembly and their requirements for computational infrastructures. We recommend that a combined hybrid assembly with long and short reads would be a promising way to generate good quality human genome assemblies and specify parameters for the quality assessment of assembly outcomes. We provide a perspective view of the benefit of using personal genomes as references and suggestions for obtaining a quality personal genome. Finally, we discuss the usage of the personal genome in aiding vaccine design and development, monitoring host immune-response, tailoring drug therapy and detecting tumors. We believe the precision medicine would largely benefit from bioinformatics solutions, particularly for personal genome assembly. [ABSTRACT FROM AUTHOR]

Background: The 1000 Genome project paved the way for sequencing diverse human populations. New genome projects are being established to sequence underrepresented populations helping in understanding human genetic diversity. The Kuwait Genome Project an initiative to sequence individual genomes from the three subgroups of Kuwaiti population namely, Saudi Arabian tribe; “tent-dwelling” Bedouin; and Persian, attributing their ancestry to different regions in Arabian Peninsula and to modern-day Iran (West Asia). These subgroups were in line with settlement history and are confirmed by genetic studies. In this work, we report whole genome sequence of a Kuwaiti native from Persian subgroup at >37X coverage. Results: We document 3,573,824 SNPs, 404,090 insertions/deletions, and 11,138 structural variations. Out of the reported SNPs and indels, 85,939 are novel. We identify 295 ‘loss-of-function’ and 2,314 ’deleterious’ coding variants, some of which carry homozygous genotypes in the sequenced genome; the associated phenotypes include pharmacogenomic traits such as greater triglyceride lowering ability with fenofibrate treatment, and requirement of high warfarin dosage to elicit anticoagulation response. 6,328 non-coding SNPs associate with 811 phenotype traits: in congruence with medical history of the participant for Type 2 diabetes and β-Thalassemia, and of participant’s family for migraine, 72 (of 159 known) Type 2 diabetes, 3 (of 4) β-Thalassemia, and 76 (of 169) migraine variants are seen in the genome. Intergenome comparisons based on shared disease-causing variants, positions the sequenced genome between Asian and European genomes in congruence with geographical location of the region. On comparison, bead arrays perform better than sequencing platforms in correctly calling genotypes in low-coverage sequenced genome regions however in the event of novel SNP or indel near genotype calling position can lead to false calls using bead arrays. Conclusions: We report, for the first time, reference genome resource for the population of Persian ancestry. The resource provides a starting point for designing large-scale genetic studies in Peninsula including Kuwait, and Persian population. Such efforts on populations under-represented in global genome variation surveys help augment current knowledge on human genome diversity. [ABSTRACT FROM AUTHOR]

Background Personal genome assembly is a critical process when studying tumor genomes and other highly divergent sequences. The accuracy of downstream analyses, such as RNA-seq and ChIP-seq, can be greatly enhanced by using personal genomic sequences rather than standard references. Unfortunately, reads sequenced from these types of samples often have a heterogeneous mix of various subpopulations with different variants, making assembly extremely difficult using existing assembly tools. To address these challenges, we developed SHEAR (Sample Heterogeneity Estimation and Assembly by Reference; http://vk.cs.umn.edu/SHEAR), a tool that predicts SVs, accounts for heterogeneous variants by estimating their representative percentages, and generates personal genomic sequences to be used for downstream analysis. Results By making use of structural variant detection algorithms, SHEAR offers improved performance in the form of a stronger ability to handle difficult structural variant types and better computational efficiency. We compare against the lead competing approach using a variety of simulated scenarios as well as real tumor cell line data with known heterogeneous variants. SHEAR is shown to successfully estimate heterogeneity percentages in both cases, and demonstrates an improved efficiency and better ability to handle tandem duplications. Conclusion SHEAR allows for accurate and efficient SV detection and personal genomic sequence generation. It is also able to account for heterogeneous sequencing samples, such as from tumor tissue, by estimating the subpopulation percentage for each heterogeneous variant. [ABSTRACT FROM AUTHOR]

Direct-to-consumer (DTC) DNA testing has grown from contentious beginnings into a global industry, by providing a wide range of personal genomic information directly to its clients. These companies, typified by the well-established 23andMe, generally carry out a gene-chip analysis of single nucleotide polymorphisms (SNPs) using DNA extracted from a saliva sample. These genetic data are then assimilated and provided direct to the client, with varying degrees of interpretation. Although much debate has focused on the limitations and ethical aspects of providing genotypes for disease risk alleles, the provision of pharmacogenetic results by DTC companies is less studied. We set out to evaluate current DTC pharmacogenetics offerings, and then to consider how these services might best evolve and adapt in order to play a potentially useful future role in delivery of personalized medicine.

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• We reported and analyzed the whole genomes of nine Egyptian females from different regions. • A total 12 SNPs were shared in most of subjects. • mtDNA mutation A4282G was common in all subjects. • Both mtDNA and admixture analyses showed that Egyptians sharing genetic signature and metabolic phenotypes with other Mediterranean nations. Egyptians are at a crossroad between Africa and Eurasia, providing useful genomic resources for analyzing both genetic and environmental factors for future personalized medicine. Two personal Egyptian whole genomes have been published previously by us and here nine female whole genome sequences with clinical information have been added to expand the genomic resource of Egyptian personal genomes. Here we report the analysis of whole genomes of nine Egyptian females from different regions using Illumina short-read sequencers. At 30x sequencing coverage, we identified 12 SNPs that were shared in most of the subjects associated with obesity which are concordant with their clinical diagnosis. Also, we found mtDNA mutation A4282G is common in all the samples and this is associated with chronic progressive external ophthalmoplegia (CPEO). Haplogroup and Admixture analyses revealed that most Egyptian samples are close to the other north Mediterranean, Middle Eastern, and European, respectively, possibly reflecting the into-Africa influx of human migration. In conclusion, we present whole-genome sequences of nine Egyptian females with personal clinical information that cover the diverse regions of Egypt. Although limited in sample size, the whole genomes data provides possible geno-phenotype candidate markers that are relevant to the region's diseases. [ABSTRACT FROM AUTHOR]

A key aspect of genomic medicine is to make individualized clinical decisions from personal genomes. We developed a machine-learning framework to integrate personal genomes and electronic health record (EHR) data and used this framework to study abdominal aortic aneurysm (AAA), a prevalent irreversible cardiovascular disease with unclear etiology. Performing whole-genome sequencing on AAA patients and controls, we demonstrated its predictive precision solely from personal genomes. By modeling personal genomes with EHRs, this framework quantitatively assessed the effectiveness of adjusting personal lifestyles given personal genome baselines, demonstrating its utility as a personal health management tool. We showed that this new framework agnostically identified genetic components involved in AAA, which were subsequently validated in human aortic tissues and in murine models. Our study presents a new framework for disease genome analysis, which can be used for both health management and understanding the biological architecture of complex diseases. VIDEO ABSTRACT., (Copyright © 2018. Published by Elsevier Inc.)

The sequencing and genotyping of personal genomes by commercial services outside traditional clinical settings may help to shape the expectations of research subjects and patients regarding control of and responsibility for the information contained in their DNA. A greater sense of individual ownership of personal genomic information could replace overly complex and paternalistic institutional proxies for the protection of personal genotype and sequence data, and also could encourage research participants and patients to become better educated regarding genetic contributors to disease.