Best Paper Selection

Béal J, Montagud A, Traynard P, Barillot E, Calzone L. Personalization of logical models with multi-omics data allows clinical stratification of patients. Front Physiol 24 Jan 2019;9:1965Chen ML, Doddi A, Royer J, Freschi L, Schito M, Ezewudo M, Kohane IS, Beam A, Farhat M. Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction. EBioMedicine 2019 May;43:356–69Kim K, Baik H, Jang CS, Roh JK, Eskin E, Han B. Genomic GPS: using genetic distance from individuals to public data for genomic analysis without disclosing personal genomes. Genome Biol 2019 Dec;20(1):175Marttinen M, Paananen J, Neme A, Vikram M, Takalo M, Natune T, Paldanius KMA, Mäkinen P, Bremang M, Kurki MI, Rauramaa T, Leinonen V, Soininen H, Haapasalo A, Pike I, Hiltunen M. A multiomic approach to characterize the temporal sequence in Alzheimer's disease-related pathology. Neurobiol Dis 2019;124:45468

Korean Genome Project: 1094 Korean personal genomes with clinical information

We present the initial phase of the Korean Genome Project (Korea1K), including 1094 whole genomes (sequenced at an average depth of 31×), along with data of 79 quantitative clinical traits. We identified 39 million single-nucleotide variants and indels of which half were singleton or doubleton and detected Korean-specific patterns based on several types of genomic variations. A genome-wide association study illustrated the power of whole-genome sequences for analyzing clinical traits, identifying nine more significant candidate alleles than previously reported from the same linkage disequilibrium blocks. Also, Korea1K, as a reference, showed better imputation accuracy for Koreans than the 1KGP panel. As proof of utility, germline variants in cancer samples could be filtered out more effectively when the Korea1K variome was used as a panel of normals compared to non-Korean variome sets. Overall, this study shows that Korea1K can be a useful genotypic and phenotypic resource for clinical and ethnogenetic studies.

Opportunities and Challenges in Interpreting and Sharing Personal Genomes

The 2019 “Personal Genomes: Accessing, Sharing and Interpretation” conference (Hinxton, UK, 11–12 April 2019) brought together geneticists, bioinformaticians, clinicians and ethicists to promote openness and ethical sharing of personal genome data while protecting the privacy of individuals. The talks at the conference focused on two main topic areas: (1) Technologies and Applications, with emphasis on personal genomics in the context of healthcare. The issues discussed ranged from new technologies impacting and enabling the field, to the interpretation of personal genomes and their integration with other data types. There was particular emphasis and wide discussion on the use of polygenic risk scores to inform precision medicine. (2) Ethical, Legal, and Social Implications, with emphasis on genetic privacy: How to maintain it, how much privacy is possible, and how much privacy do people want? Talks covered the full range of genomic data visibility, from open access to tight control, and diverse aspects of balancing benefits and risks, data ownership, working with individuals and with populations, and promoting citizen science. Both topic areas were illustrated and informed by reports from a wide variety of ongoing projects, which highlighted the need to diversify global databases by increasing representation of understudied populations.

Analyzing whole genome bisulfite sequencing data from highly divergent genotypes

In the study of DNA methylation, genetic variation between species, strains or individuals can result in CpG sites that are exclusive to a subset of samples, and insertions and deletions can rearrange the spatial distribution of CpGs. How to account for this variation in an analysis of the interplay between sequence variation and DNA methylation is not well understood, especially when the number of CpG differences between samples is large. Here, we use whole-genome bisulfite sequencing data on two highly divergent mouse strains to study this problem. We show that alignment to personal genomes is necessary for valid methylation quantification. We introduce a method for including strain-specific CpGs in differential analysis, and show that this increases power. We apply our method to a human normal-cancer dataset, and show this improves accuracy and power, illustrating the broad applicability of our approach. Our method uses smoothing to impute methylation levels at strain-specific sites, thereby allowing strain-specific CpGs to contribute to the analysis, while accounting for differences in the spatial occurrences of CpGs. Our results have implications for joint analysis of genetic variation and DNA methylation using bisulfite-converted DNA, and unlocks the use of personal genomes for addressing this question.

AluMine: alignment-free method for the discovery of polymorphic Alu element insertions

ABSTRACTBackgroundRecently, alignment-free sequence analysis methods have gained popularity in the field of personal genomics. These methods are based on counting frequencies of short k-mer sequences, thus allowing faster and more robust analysis compared to traditional alignment-based methods.ResultsWe have created a fast alignment-free method, AluMine, to analyze polymorphic insertions of Alu elements in the human genome. We tested the method on 2,241 individuals from the Estonian Genome Project and identified 28,962 potential polymorphic Alu element insertions. Each tested individual had on average 1,574 Alu element insertions that were different from those in the reference genome. In addition, we propose an alignment-free genotyping method that uses the frequency of insertion/deletion-specific 32-mer pairs to call the genotype directly from raw sequencing reads. Using this method, the concordance between the predicted and experimentally observed genotypes was 98.7%. The running time of the discovery pipeline is approximately 2 hours per individual. The genotyping of potential polymorphic insertions takes between 0.4 and 4 hours per individual, depending on the hardware configuration.ConclusionsAluMine provides tools that allow discovery of novel Alu element insertions and/or genotyping of known Alu element insertions from personal genomes within few hours.

VSIM: Visualization and simulation of variants in personal genomes with an application to premarital testing

BackgroundInterpretation of personal genomics data, for example in genetic counseling, is challenging due to the complexity of the data and the amount of background knowledge required for its interpretation. This background knowledge is distributed across several databases. Further information about genomic features can also be predicted through machine learning methods. Making this information accessible more easily has the potential to improve interpretation of variants in personal genomes.ResultsWe have developed VSIM, a web application for the interpretation and visualization of variants in personal genome sequences. VSIM identifies disease variants related to Mendelian, complex, and digenic disease as well as pharmacogenomic variants in personal genomes and visualizes them using a webserver. VSIM can further be used to simulate populations of children based on two parent genomes, and can be applied to support premarital genetic counseling. We make VSIM available as source code as well as through a container that can be installed easily in network environments in which genomic data is specially protected. VSIM and related documentation is freely available athttps://github.com/bio-ontology-research-group/VSIM.ConclusionsVSIM is a software that provides a web-based interface to variant interpretation in genetic counseling. VSIM can also be used for premarital genetic screening by simulating a population of children and analyze the disorder they might be carrying.