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Population-scale genetic studies can identify drug targets and allow disease risk to be predicted with resulting benefit for management of individual health risks and system-wide allocation of health care delivery resources. Although population-scale projects are underway in many parts of the world, genetic variation between population groups means that additional projects are warranted. South Asia has a population whose genetics is the least characterized of any of the world’s major populations. Here we describe GenomeAsia studies that characterize population structure in South Asia and that create tools for economical and accurate genotyping at population-scale. Prior work on population structure characterized isolated population groups, the relevance of which to large-scale studies of disease genetics is unclear. For our studies we used whole genome sequence information from 4,807 individuals recruited in the health care delivery systems of Pakistan, India and Bangladesh to ensure relevance to population-scale studies of disease genetics. We combined this with WGS data from 927 individuals from isolated South Asian population groups, and developed a custom SNP array (called SARGAM) that is optimized for future human genetic studies in South Asia. We find evidence for high rates of reproductive isolation, endogamy and consanguinity that vary across the subcontinent and that lead to levels of homozygosity that approach 100 times that seen in outbred populations. We describe founder effects that increase the power to associate functional variants with disease processes and that make South Asia a uniquely powerful place for population-scale genetic studies.

Personalized medical care focuses on prediction of disease risk and response to medications. To build the risk models, access to both large-scale genomic resources and human genetic studies is required. The Taiwan Biobank (TWB) has generated high-coverage, whole-genome sequencing data from 1492 individuals and genome-wide SNP data from 103,106 individuals of Han Chinese ancestry using custom SNP arrays. Principal components analysis of the genotyping data showed that the full range of Han Chinese genetic variation was found in the cohort. The arrays also include thousands of known functional variants, allowing for simultaneous ascertainment of Mendelian disease-causing mutations and variants that affect drug metabolism. We found that 21.2% of the population are mutation carriers of autosomal recessive diseases, 3.1% have mutations in cancer-predisposing genes, and 87.3% carry variants that affect drug response. We highlight how TWB data provide insight into both population history and disease burden, while showing how widespread genetic testing can be used to improve clinical care.

Background Data from the 1000 Genomes project is quite often used as a reference for human genomic analysis. However, its accuracy needs to be assessed to understand the quality of predictions made using this reference. We present here an assessment of the genotyping, phasing, and imputation accuracy data in the 1000 Genomes project. We compare the phased haplotype calls from the 1000 Genomes project to experimentally phased haplotypes for 28 of the same individuals sequenced using the 10X Genomics platform. Results We observe that phasing and imputation for rare variants are unreliable, which likely reflects the limited sample size of the 1000 Genomes project data. Further, it appears that using a population specific reference panel does not improve the accuracy of imputation over using the entire 1000 Genomes data set as a reference panel. We also note that the error rates and trends depend on the choice of definition of error, and hence any error reporting needs to take these definitions into account. Conclusions The quality of the 1000 Genomes data needs to be considered while using this database for further studies. This work presents an analysis that can be used for these assessments. Electronic supplementary material The online version of this article (10.1186/s12864-019-5957-x) contains supplementary material, which is available to authorized users.

© 2017 the Owner Societies. We have used the AMOEBA model to simulate the THz spectra of two zwitterionic amino acids in aqueous solution, which is compared to the results on these same systems using ab initio molecular dynamics (AIMD) simulations. Overall we find that the polarizable force field shows promising agreement with AIMD data for both glycine and valine in water. This includes the THz spectral assignments and the mode-specific spectral decomposition into intramolecular solute motions as well as distinct solute-water cross-correlation modes some of which cannot be captured by non-polarizable force fields that rely on fixed partial charges. This bodes well for future studies for simulating and decomposing the THz spectra for larger solutes such as proteins or polymers for which AIMD studies are presently intractable. Furthermore, we believe that the current study on rather simple aqueous solutions offers a way to systematically investigate the importance of charge transfer, nuclear quantum effects, and the validity of computationally practical density functionals, all of which are needed to fully quantitatively capture complex dynamical motions in the condensed phase.

Figure S1. Representative distribution of INFO scores for chromosome 1 in HG00250 in (a) experimental SNPs (b) all 1000G SNPs. Figure S2. Imputation error in experimental SNPs after filtering low INFO score (

Baboons (genus Papio) are broadly studied in the wild and in captivity. They are widely used as a nonhuman primate model for biomedical studies, and the Southwest National Primate Research Center (SNPRC) at Texas Biomedical Research Institute has maintained a large captive baboon colony for more than 50 yr. Unlike other model organisms, however, the genomic resources for baboons are severely lacking. This has hindered the progress of studies using baboons as a model for basic biology or human disease. Here, we describe a data set of 100 high-coverage whole-genome sequences obtained from the mixed colony of olive (P. anubis) and yellow (P. cynocephalus) baboons housed at the SNPRC. These data provide a comprehensive catalog of common genetic variation in baboons, as well as a fine-scale genetic map. We show how the data can be used to learn about ancestry and admixture and to correct errors in the colony records. Finally, we investigated the consequences of inbreeding within the SNPRC colony and found clear evidence for increased rates of infant mortality and increased homozygosity of putatively deleterious alleles in inbred individuals.

© 2017 American Chemical Society. We analyze the role of solvation for enzymatic catalysis in two distinct, artificially designed Kemp Eliminases, KE07 and KE70, and mutated variants that were optimized by laboratory directed evolution. Using a spatially resolved analysis of hydration patterns, intermolecular vibrations, and local solvent entropies, we identify distinct classes of hydration water and follow their changes upon substrate binding and transition state formation for the designed KE07 and KE70 enzymes and their evolved variants. We observe that differences in hydration of the enzymatic systems are concentrated in the active site and undergo significant changes during substrate recruitment. For KE07, directed evolution reduces variations in the hydration of the polar catalytic center upon substrate binding, preserving strong protein-water interactions, while the evolved enzyme variant of KE70 features a more hydrophobic reaction center for which the expulsion of low-entropy water molecules upon substrate binding is substantially enhanced. While our analysis indicates a system-dependent role of solvation for the substrate binding process, we identify more subtle changes in solvation for the transition state formation, which are less affected by directed evolution.

Interactions with solvent have been shown to affect the dynamics of small biomolecules. While bulk properties of solvation can be studied via experiments like spectral measurements, understanding the precise impact of solvent interactions on specific motions of biomolecules, and the spatial extent of impact of the biomolecules on the solvent are accessible through simulations. We have used the polarizable AMOEBA molecular mechanics force field to simulate terahertz (THz) spectra of two zwitterionic peptides, glycine and valine in aqueous solution. Ab initio molecular dynamics (AIMD) simulations have been previously used to study these spectra. An analysis method has been previously developed by Marx and co-workers (Sun et. al. JACS 2014) to decompose the THz spectrum into the component motion modes for specific molecules, as well as modes arising from intermolecular interactions, for AIMD simulations. We present here an approach whereby classical MD simulations performed via the AMOEBA forcefield can be analyzed with that mode decomposition method to obtain dynamic modes for the zwitterions. Based on this decomposition, overall we find very good agreement of the AMOEBA classical MD simulations with AIMD. THz spectral assignments and the spectral decomposition of the total spectrum into intramolecular peptide motions and peptide-water cross-correlation modes shows substantial agreement between AMOEBA and AIMD. The unique feature of this approach comes from the good agreement of the peptide-water cross-correlation modes, which involve distortions of the electron density as a result of polarization and hence cannot be captured by fixed charge force fields. This is a promising first step towards future studies for simulating and decomposing the THz observable for larger solutes such as polymers or proteins where AIMD studies are currently intractable.

Directed evolution is an experimental technique used to improve catalytic activity of low performing enzymes. However, the underlying principles behind directed evolution are not well understood. In addition to the structure of an enzyme, recent studies have shown the dynamics of the enzyme to affect the catalytic activity. We are analyzing the impact of protein dynamics on the catalytic activity of an enzyme through certain dynamic metrics such as distance and velocity time correlation functions. These metrics can give an insight into the coupling of motions of various residues with the active site. Recent efforts in directed evolution have shown very promising results for artificially designed Kemp Eliminases, in the form of a 200 fold increase in activity over seven rounds of directed evolution. Dynamic metrics can be used to rationalize the mutations that occurred over the rounds of directed evolution. These metrics can then be used as predictive tools, to predict new potential targets for mutation, thus reducing the search space for potential mutations by many orders of magnitude.