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In Brazil, high levels of agricultural activity are reflected in the consumption of enormous amounts of pesticides. The production of grain in Brazil has been estimated at 289.8 million tons in the 2022 harvest, an expansion of 14.7% compared with 2021. These advances are likely associated with a progressive increase in the occupational exposure of a population to pesticides. The Paraoxonase 1 gene (PON1) is involved in liver detoxification; the rs662 variant of this gene modifies the activity of the enzyme. The repair of pesticide-induced genetic damage depends on the protein produced by the X-Ray Repair Cross-Complementing Group 1 gene (XRCC). Its function is impaired due to an rs25487 variant. The present study describes the frequencies of the rs662 and rs25487 and their haplotypes in a sample population from Goiás, Brazil. It compares the frequencies with other populations worldwide to verify the variation in the distribution of these SNPs, with 494 unrelated individuals in the state of Goiás. The A allele of the rs25487 variant had a frequency of 26% in the Goiás population, and the modified rs662 G allele had a frequency of 42.8%. Four haplotypes were recorded for the rs25487 (G > A) and rs662 (A > G) markers, with a frequency of 11.9% being recorded for the A–G haplotype (both modified alleles), 30.8% for the G–G haplotype, 14.3% for the A–A haplotype, and 42.8% for the G–A haplotype (both wild-type alleles). We demonstrated the distribution of important SNPs associated with pesticide exposure in an area with a high agricultural activity level, Central Brazil. [ABSTRACT FROM AUTHOR]

Abstract Background Population variant analysis is of great importance for gathering insights into the links between human genotype and phenotype. The 1000 Genomes Project established a valuable reference for human genetic variation; however, the integrative use of the corresponding data with other datasets within existing repositories and pipelines is not fully supported. Particularly, there is a pressing need for flexible and fast selection of population partitions based on their variant and metadata-related characteristics. Results Here, we target general germline or somatic mutation data sources for their seamless inclusion within an interoperable-format repository, supporting integration among them and with other genomic data, as well as their integrated use within bioinformatic workflows. In addition, we provide VarSum, a data summarization service working on sub-populations of interest selected using filters on population metadata and/or variant characteristics. The service is developed as an optimized computational framework with an Application Programming Interface (API) that can be called from within any existing computing pipeline or programming script. Provided example use cases of biological interest show the relevance, power and ease of use of the API functionalities. Conclusions The proposed data integration pipeline and data set extraction and summarization API pave the way for solid computational infrastructures that quickly process cumbersome variation data, and allow biologists and bioinformaticians to easily perform scalable analysis on user-defined partitions of large cohorts from increasingly available genetic variation studies. With the current tendency to large (cross)nation-wide sequencing and variation initiatives, we expect an ever growing need for the kind of computational support hereby proposed.

Background: Population variant analysis is of great importance for gathering insights into the links between human genotype and phenotype. The 1000 Genomes Project established a valuable reference for human genetic variation; however, the integrative use of the corresponding data with other datasets within existing repositories and pipelines is not fully supported. Particularly, there is a pressing need for flexible and fast selection of population partitions based on their variant and metadata-related characteristics. Results: Here, we target general germline or somatic mutation data sources for their seamless inclusion within an interoperable-format repository, supporting integration among them and with other genomic data, as well as their integrated use within bioinformatic workflows. In addition, we provide VarSum, a data summarization service working on sub-populations of interest selected using filters on population metadata and/or variant characteristics. The service is developed as an optimized computational framework with an Application Programming Interface (API) that can be called from within any existing computing pipeline or programming script. Provided example use cases of biological interest show the relevance, power and ease of use of the API functionalities. Conclusions: The proposed data integration pipeline and data set extraction and summarization API pave the way for solid computational infrastructures that quickly process cumbersome variation data, and allow biologists and bioinformaticians to easily perform scalable analysis on user-defined partitions of large cohorts from increasingly available genetic variation studies. With the current tendency to large (cross)nation-wide sequencing and variation initiatives, we expect an ever growing need for the kind of computational support hereby proposed. [ABSTRACT FROM AUTHOR]

Abstract Background Core promoter controls transcription initiation. However, little is known for core promoter diversity in the human genome and its relationship with diseases. We hypothesized that as a functional important component in the genome, the core promoter in the human genome could be under evolutionary selection, as reflected by its highly diversification in order to adjust gene expression for better adaptation to the different environment. Results Applying the “Exome-based Variant Detection in Core-promoters” method, we analyzed human core-promoter diversity by using the 2682 exome data sets of 25 worldwide human populations sequenced by the 1000 Genome Project. Collectively, we identified 31,996 variants in the core promoter region (− 100 to + 100) of 12,509 human genes ( https://dbhcpd.fhs.um.edu.mo ). Analyzing the rich variation data identified highly ethnic-specific patterns of core promoter variation between different ethnic populations, the genes with highly variable core promoters, the motifs affected by the variants, and their involved functional pathways. eQTL test revealed that 12% of core promoter variants can significantly alter gene expression level. Comparison with GWAS data we located 163 variants as the GWAS identified traits associated with multiple diseases, half of these variants can alter gene expression. Conclusion Data from our study reals the highly diversified nature of core promoter in the human genome, and highlights that core promoter variation could play important roles not only in gene expression regulation but also in disease predisposition.

We developed PyLAE, a new tool for determining local ancestry along a genome using whole-genome sequencing data or high-density genotyping experiments. PyLAE can process an arbitrarily large number of ancestral populations (with or without an informative prior). Since PyLAE does not involve estimating many parameters, it can process thousands of genomes within a day. PyLAE can run on phased or unphased genomic data. We have shown how PyLAE can be applied to the identification of differentially enriched pathways between populations. The local ancestry approach results in higher enrichment scores compared to whole-genome approaches. We benchmarked PyLAE using the 1000 Genomes dataset, comparing the aggregated predictions with the global admixture results and the current gold standard program RFMix. Computational efficiency, minimal requirements for data pre-processing, straightforward presentation of results, and ease of installation make PyLAE a valuable tool to study admixed populations. [ABSTRACT FROM AUTHOR]

Summary We developed PyLAE, a new tool for determining local ancestry along a genome using whole-genome sequencing data or high-density genotyping experiments. PyLAE can process an arbitrarily large number of ancestral populations (with or without an informative prior). Since PyLAE does not involve estimating many parameters, it can process thousands of genomes within a day. PyLAE can run on phased or unphased genomic data. We have shown how PyLAE can be applied to the identification of differentially enriched pathways between populations. The local ancestry approach results in higher enrichment scores compared to whole-genome approaches. We benchmarked PyLAE using the 1000 Genomes dataset, comparing the aggregated predictions with the global admixture results and the current gold standard program RFMix. Computational efficiency, minimal requirements for data pre-processing, straightforward presentation of results, and ease of installation make PyLAE a valuable tool to study admixed populations. Availability and implementation The source code and installation manual are available at https://github.com/smetam/pylae.

Abstract 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.

Abstract Background The aim of this study is to analyze the worldwide distribution of SNP rs4870723 in COL14A1 gene to check if there are significant genetic differences among different populations and to test if the gene is a trait under selection. Methods Genomic DNA was extracted from 69 unrelated individuals from Sardinia and genotyped for SNP rs4870723. Data were compared with 26 different populations, clustered in 5 super‐populations, from the public 1000 genomes database. Allele frequency and heterozygosity were calculated with Genepop. The Hardy–Weinberg equilibrium and pairwise population differentiation through analysis of molecular variance (AMOVA FST) were determined with Arlequin. Results Allele frequencies of COL14A1 rs4870723 were compared in 27 populations clustered in 5 super‐populations. All populations were in the Hardy–Weinberg equilibrium. In almost all populations, allele C was the most frequent allele, reaching the highest values in East Asia. The 27 populations showed an appreciable structure, with significant differences observed between European, African, and Asian populations. Conclusion Significant differences were observed in the rs4870723 SNP distribution among the populations studied. However, we found no evidence for a selective pressure. Rather, the differentiation among the populations is likely the result of founder effect, genetic drift, and cultural factors, all events known to establish and maintain genetic diversity between populations.

Background: The aim of this study is to analyze the worldwide distribution of SNP rs4870723 in COL14A1 gene to check if there are significant genetic differences among different populations and to test if the gene is a trait under selection. Methods: Genomic DNA was extracted from 69 unrelated individuals from Sardinia and genotyped for SNP rs4870723. Data were compared with 26 different populations, clustered in 5 super‐populations, from the public 1000 genomes database. Allele frequency and heterozygosity were calculated with Genepop. The Hardy–Weinberg equilibrium and pairwise population differentiation through analysis of molecular variance (AMOVA FST) were determined with Arlequin. Results: Allele frequencies of COL14A1 rs4870723 were compared in 27 populations clustered in 5 super‐populations. All populations were in the Hardy–Weinberg equilibrium. In almost all populations, allele C was the most frequent allele, reaching the highest values in East Asia. The 27 populations showed an appreciable structure, with significant differences observed between European, African, and Asian populations. Conclusion: Significant differences were observed in the rs4870723 SNP distribution among the populations studied. However, we found no evidence for a selective pressure. Rather, the differentiation among the populations is likely the result of founder effect, genetic drift, and cultural factors, all events known to establish and maintain genetic diversity between populations. [ABSTRACT FROM AUTHOR]

Abstract Background Simulation of genetic variants data is frequently required for the evaluation of statistical methods in the fields of human and animal genetics. Although a number of high-quality genetic simulators have been developed, many of them require advanced knowledge in population genetics or in computation to be used effectively. In addition, generating simulated data in the context of family-based studies demands sophisticated methods and advanced computer programming. Results To address these issues, we propose a new user-friendly and integrated R package, sim1000G, which simulates variants in genomic regions among unrelated individuals or among families. The only input needed is a raw phased Variant Call Format (VCF) file. Haplotypes are extracted to compute linkage disequilibrium (LD) in the simulated genomic regions and for the generation of new genotype data among unrelated individuals. The covariance across variants is used to preserve the LD structure of the original population. Pedigrees of arbitrary sizes are generated by modeling recombination events with sim1000G. To illustrate the application of sim1000G, various scenarios are presented assuming unrelated individuals from a single population or two distinct populations, or alternatively for three-generation pedigree data. Sim1000G can capture allele frequency diversity, short and long-range linkage disequilibrium (LD) patterns and subtle population differences in LD structure without the need of any tuning parameters. Conclusion Sim1000G fills a gap in the vast area of genetic variants simulators by its simplicity and independence from external tools. Currently, it is one of the few simulation packages completely integrated into R and able to simulate multiple genetic variants among unrelated individuals and within families. Its implementation will facilitate the application and development of computational methods for association studies with both rare and common variants.

Abstract Background The advent of next-generation sequencing (NGS) has made whole-genome sequencing of cohorts of individuals a reality. Primary datasets of raw or aligned reads of this sort can get very large. For scientific questions where curated called variants are not sufficient, the sheer size of the datasets makes analysis prohibitively expensive. In order to make re-analysis of such data feasible without the need to have access to a large-scale computing facility, we have developed a highly scalable, storage-agnostic framework, an associated API and an easy-to-use web user interface to execute custom filters on large genomic datasets. Results We present BAMSI, a Software as-a Service (SaaS) solution for filtering of the 1000 Genomes phase 3 set of aligned reads, with the possibility of extension and customization to other sets of files. Unique to our solution is the capability of simultaneously utilizing many different mirrors of the data to increase the speed of the analysis. In particular, if the data is available in private or public clouds – an increasingly common scenario for both academic and commercial cloud providers – our framework allows for seamless deployment of filtering workers close to data. We show results indicating that such a setup improves the horizontal scalability of the system, and present a possible use case of the framework by performing an analysis of structural variation in the 1000 Genomes data set. Conclusions BAMSI constitutes a framework for efficient filtering of large genomic data sets that is flexible in the use of compute as well as storage resources. The data resulting from the filter is assumed to be greatly reduced in size, and can easily be downloaded or routed into e.g. a Hadoop cluster for subsequent interactive analysis using Hive, Spark or similar tools. In this respect, our framework also suggests a general model for making very large datasets of high scientific value more accessible by offering the possibility for organizations to share the cost of hosting data on hot storage, without compromising the scalability of downstream analysis.

The hepatic lipase plays a central role in the lipid metabolism, catalyzing the hydrolysis of phospholipids, monoglycerides, diglycerides, and triglycerides, and acyl-CoA. It is also implied in the conversion of very low-density lipoprotein and intermediate density lipoprotein to low density lipoproteins. As a consequence, the gene encoding the hepatic lipase (LIPC) is associated with several diseases derived from the imbalance of lipids that are in general derived from the interaction between life styles and genetic architecture. Therefore, it is interesting to understand more about the characteristics of the microevolutionary processes affecting genes that, like LIPC, have a role in nutrition and lipid metabolism in human populations. We explored the selection signatures on LIPC in 26 populations, detecting three regions under recent positive selection. [ABSTRACT FROM AUTHOR]

Currently, the genetic variants strongly associated with risk for Multiple Sclerosis (MS) are located in the Major Histocompatibility Complex. This includes DRB1*15:01 and DRB1*15:03 alleles at the HLA-DRB1 locus, the latter restricted to African populations; the DQB1*06:02 allele at the HLA-DQB1 locus which is in high linkage disequilibrium (LD) with DRB1*15:01 ; and protective allele A*02:01 at the HLA-A locus. HLA allele identification is facilitated by co-inherited ('tag') single nucleotide polymorphisms (SNPs); however, SNP validation is not typically done outside of the discovery population. We examined 19 SNPs reported to be in high LD with these alleles in 2,502 healthy subjects included in the 1000 Genomes panel having typed HLA data. Examination of 3 indices (LD R2 values, sensitivity and specificity, minor allele frequency) revealed few SNPs with high tagging performance. All SNPs examined that tag DRB1*15:01 were in perfect LD in the British population; three showed high tagging performance in 4 of the 5 European, and 2 of the 4 American populations. For DQB1*06:02 , with no previously validated tag SNPs, we show that rs3135388 has high tagging performance in one South Asian, one American, and one European population. We identify for the first time that rs2844821 has high tagging performance for A*02:01 in 5 of 7 African populations including African Americans, and 4 of the 5 European populations. These results provide a basis for selecting SNPs with high tagging performance to assess HLA alleles across diverse populations, for MS risk as well as for other diseases and conditions. [ABSTRACT FROM AUTHOR]

Despite critical roles in chromosome segregation and disease, the repetitive structure and vast size of centromeres and their surrounding heterochromatic regions impede studies of genomic variation. Here we report the identification of large-scale haplotypes (cenhaps) in humans that span the centromere-proximal regions of all metacentric chromosomes, including the arrays of highly repeated α-satellites on which centromeres form. Cenhaps reveal deep diversity, including entire introgressed Neanderthal centromeres and equally ancient lineages among Africans. These centromere-spanning haplotypes contain variants, including large differences in α-satellite DNA content, which may influence the fidelity and bias of chromosome transmission. The discovery of cenhaps creates new opportunities to investigate their contribution to phenotypic variation, especially in meiosis and mitosis, as well as to more incisively model the unexpectedly rich evolution of these challenging genomic regions.

Highlights • Most human DNA variation is constrained by purifying selection and gene conversion. • Most neutral variation lacks exonic function, but ∼35% of it falls at introns.. • Demographies based on 'non-neutral' variation can be biased. • 'Non-neutral' variation can be used for estimation of biogeographical ancestry. Abstract Most of the variation in the human genome (∼95%) is constrained, directly or indirectly, by purifying selection and GC-biased gene conversion, according to a recent article by Pouyet et al. (2018). The use of 'non-neutral' variation to infer human demographies can lead to undesirable biases; for example, in estimation of the time of the most recent common ancestor. Further examination of 'neutral' variation in entire human genomes from The 1000 Genomes Project reveals that ∼99% of this variation lacks exonic function, but ∼35% of it falls in introns. In addition, estimates of biogeographical ancestry using 'non-neutral' SNPs differ very marginally from inferences obtained from 'neutral' variation. Additional investigations should be carried out before establishing the roadmap for future human population and forensic genetic studies. [ABSTRACT FROM AUTHOR]

Genotype imputation is a powerful tool for increasing statistical power in an association analysis. Meta-analysis of multiple study datasets also requires a substantial overlap of SNPs for a successful association analysis, which can be achieved by imputation. Quality of imputed datasets is largely dependent on the software used, as well as the reference populations chosen. The accuracy of imputation of available reference populations has not been tested for the five-way admixed South African Colored (SAC) population. In this study, imputation results obtained using three freely-accessible methods were evaluated for accuracy and quality. We show that the African Genome Resource is the best reference panel for imputation of missing genotypes in samples from the SAC population, implemented via the freely accessible Sanger Imputation Server.

SNP-based imputation approaches for human leukocyte antigen (HLA) typing take advantage of the haplotype structure within the major histocompatibility complex (MHC) region. These methods predict HLA classical alleles using dense SNP genotypes, commonly found on array-based platforms used in genome-wide association studies (GWAS). The analysis of HLA classical alleles can be conducted on current SNP datasets at no additional cost. Here, we describe the workflow of HIBAG, an imputation method with attribute bagging, to infer a sample's HLA classical alleles using SNP data. Two examples are offered to demonstrate the functionality using public HLA and SNP data from the latest release of the 1000 Genomes project: genotype imputation using pre-built classifiers in a GWAS, and model training to create a new prediction model. The GPU implementation facilitates model building, making it hundreds of times faster compared to the single-threaded implementation., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Genotype imputation is a powerful tool for increasing statistical power in an association analysis. Meta-analysis of multiple study datasets also requires a substantial overlap of SNPs for a successful association analysis, which can be achieved by imputation. Quality of imputed datasets is largely dependent on the software used, as well as the reference populations chosen. The accuracy of imputation of available reference populations has not been tested for the five-way admixed South African Colored (SAC) population. In this study, imputation results obtained using three freely-accessible methods were evaluated for accuracy and quality. We show that the African Genome Resource is the best reference panel for imputation of missing genotypes in samples from the SAC population, implemented via the freely accessible Sanger Imputation Server. [ABSTRACT FROM AUTHOR]

Background: Simulation of genetic variants data is frequently required for the evaluation of statistical methods in the fields of human and animal genetics. Although a number of high-quality genetic simulators have been developed, many of them require advanced knowledge in population genetics or in computation to be used effectively. In addition, generating simulated data in the context of family-based studies demands sophisticated methods and advanced computer programming. Results: To address these issues, we propose a new user-friendly and integrated R package, sim1000G, which simulates variants in genomic regions among unrelated individuals or among families. The only input needed is a raw phased Variant Call Format (VCF) file. Haplotypes are extracted to compute linkage disequilibrium (LD) in the simulated genomic regions and for the generation of new genotype data among unrelated individuals. The covariance across variants is used to preserve the LD structure of the original population. Pedigrees of arbitrary sizes are generated by modeling recombination events with sim1000G. To illustrate the application of sim1000G, various scenarios are presented assuming unrelated individuals from a single population or two distinct populations, or alternatively for three-generation pedigree data. Sim1000G can capture allele frequency diversity, short and long-range linkage disequilibrium (LD) patterns and subtle population differences in LD structure without the need of any tuning parameters. Conclusion: Sim1000G fills a gap in the vast area of genetic variants simulators by its simplicity and independence from external tools. Currently, it is one of the few simulation packages completely integrated into R and able to simulate multiple genetic variants among unrelated individuals and within families. Its implementation will facilitate the application and development of computational methods for association studies with both rare and common variants. [ABSTRACT FROM AUTHOR]

Background: The advent of next-generation sequencing (NGS) has made whole-genome sequencing of cohorts of individuals a reality. Primary datasets of raw or aligned reads of this sort can get very large. For scientific questions where curated called variants are not sufficient, the sheer size of the datasets makes analysis prohibitively expensive. In order to make re-analysis of such data feasible without the need to have access to a large-scale computing facility, we have developed a highly scalable, storage-agnostic framework, an associated API and an easy-to-use web user interface to execute custom filters on large genomic datasets. Results: We present BAMSI, a Software as-a Service (SaaS) solution for filtering of the 1000 Genomes phase 3 set of aligned reads, with the possibility of extension and customization to other sets of files. Unique to our solution is the capability of simultaneously utilizing many different mirrors of the data to increase the speed of the analysis. In particular, if the data is available in private or public clouds – an increasingly common scenario for both academic and commercial cloud providers – our framework allows for seamless deployment of filtering workers close to data. We show results indicating that such a setup improves the horizontal scalability of the system, and present a possible use case of the framework by performing an analysis of structural variation in the 1000 Genomes data set. Conclusions: BAMSI constitutes a framework for efficient filtering of large genomic data sets that is flexible in the use of compute as well as storage resources. The data resulting from the filter is assumed to be greatly reduced in size, and can easily be downloaded or routed into e.g. a Hadoop cluster for subsequent interactive analysis using Hive, Spark or similar tools. In this respect, our framework also suggests a general model for making very large datasets of high scientific value more accessible by offering the possibility for organizations to share the cost of hosting data on hot storage, without compromising the scalability of downstream analysis. [ABSTRACT FROM AUTHOR]

Empirical assessments of human accelerated noncoding DNA frgaments have delineated presence of many cis-regulatory elements. Enhancers make up an important category of such accelerated cis-regulatory elements that efficiently control the spatiotemporal expression of many developmental genes. Establishing plausible reasons for accelerated enhancer sequence divergence in Homo sapiens has been termed significant invarious previously publishedstudies. This accelerationby including closely relatedprimates and archaic human data has the potential to open up evolutionary avenues for deducing present-day brain structure. This study relied on empirically confirmed brain exclusive enhancers to avoid any misjudgments about their regulatory status and categorized among thema subset of enhancers with an exceptionally accelerated rate of lineage specific divergence in humans. In this assorted set, 13 distinct transcription factorbinding siteswere located that possessed unique existence inhumans. Three of 13 such sites belonging to transcription factors SOX2, RUNX1/3, and FOS/JUND possessed single nucleotide variants that made them unique to H. sapiens upon comparisons with Neandertal and Denisovan orthologous sequences. These variants modifying the binding sites inmodern human lineage were further substantiated as single nucleotide polymorphisms via exploiting 1000 Genomes Project Phase3 data. Long range haplotype based tests laid out evidence of positive selection to be governing in African population on two of the modern human motif modifying alleles with strongest results for SOX2 binding site. In sum, our study acknowledges acceleration in noncoding regulatory landscape of the genome and highlights functional parts within it to have undergone accelerated divergence in present-day human population. [ABSTRACT FROM AUTHOR]

Background and Objectives: ABO remains the clinically most important blood group system, but despite earlier extensive research, significant findings are still being made. The vast majority of catalogued ABO null alleles are based on the c.261delG polymorphism. Apart from c.802G>A, other mechanisms for O alleles are rare. While analysing the data set from the 1000 Genomes (1000G) project, we encountered two previously uncharacterized deletions, which needed further exploration. Materials and Methods: The Erythrogene database, complemented with bioinformatics software, was used to analyse ABO in 2504 individuals from 1000G. DNA samples from selected 1000G donors and African blood donors were examined by allele‐specific PCR and Sanger sequencing to characterize predicted deletions. Results: A 5821‐bp deletion encompassing exons 5‐7 was called in twenty 1000G individuals, predominantly Africans. This allele was confirmed and its exact deletion point defined by bioinformatic analyses and in vitro experiments. A PCR assay was developed, and screening of African samples revealed three donors heterozygous for this deletion, which was thereby phenotypically established as an O allele. Analysis of upstream genetic markers indicated an ancestral origin from ABO*O.01.02. We estimate this deletion as the 3rd most common mechanism behind O alleles. A 24‐bp deletion was called in nine individuals and showed greater diversity regarding ethnic distribution and allelic background. It could neither be confirmed by in silico nor in vitro experiments. Conclusion: A previously uncharacterized ABO deletion among Africans was comprehensively mapped and a genotyping strategy devised. The false prediction of another deletion emphasizes the need for cautious interpretation of NGS data and calls for strict validation routines. [ABSTRACT FROM AUTHOR]

Fifteen years after the publication of the full sequence of the human genome which revolutionized medicine and biotechnology, profound elucidation of the molecular mechanisms of genetic disorders remains a challenge. National and international institutions conduct a number of research projects in genomics. Some of them are focused on the characterization of functional elements of the genome (e.g., the Genome Browser database by the ENCODE consortium), some gather information on polymorphisms (HapMap, The 1000 Genomes Project) and mutations (The Human Gene Mutation Database), while other are specifically dedicated to the genomic characterization of cancer (The Cancer Genome Atlas, The Pediatric Cancer Genome Project). Even though the projects are conducted independently, juxtapositions of the constantly updated project data may be performed, leading to interesting results. The genome-wide association studies (GWAS) allowed the identification of millions of SNPs and short insertions/deletions, as well as thousands of structural variants of polymorphic gene products. Further data-mining studies allowed the distinction between synonymous and nonsynonymous SNPs, which became the basis for the epidemiological studies of various types of genetic disorders. The results of the sequencing of entire genomes and transcriptomes may be useful in the identification of novel prognostic and predictive markers. High-throughput technologies are emerging methods in molecular diagnostics, furthermore the correlation of DNA methylation patterns and gene expression profiles may also provide useful results in cancer diagnostics. [ABSTRACT FROM AUTHOR]

The VISAGE Enhanced Tool for Appearance and Ancestry (ET) has been designed to combine markers for the prediction of bio-geographical ancestry plus a range of externally visible characteristics into a single massively parallel sequencing (MPS) assay. We describe the development of the ancestry panel markers used in ET, and the enhanced analyses they provide compared to previous MPS-based forensic ancestry assays. As well as established autosomal single nucleotide polymorphisms (SNPs) that differentiate sub-Saharan African, European, East Asian, South Asian, Native American, and Oceanian populations, ET includes autosomal SNPs able to efficiently differentiate populations from Middle East regions. The ability of the ET autosomal ancestry SNPs to distinguish Middle East populations from other continentally defined population groups is such that characteristic patterns for this region can be discerned in genetic cluster analysis using STRUCTURE. Joint cluster membership estimates showing individual co-ancestry that signals North African or East African origins were detected, or cluster patterns were seen that indicate origins from central and Eastern regions of the Middle East. In addition to an augmented panel of autosomal SNPs, ET includes panels of 85 Y-SNPs, 16 X-SNPs and 21 autosomal Microhaplotypes. The Y- and X-SNPs provide a distinct method for obtaining extra detail about co-ancestry patterns identified in males with admixed backgrounds. This study used the 1000 Genomes admixed African and admixed American sample sets to fully explore these enhancements to the analysis of individual co-ancestry. Samples from urban and rural Brazil with contrasting distributions of African, European, and Native American co-ancestry were also studied to gauge the efficiency of combining Y- and X-SNP data for this purpose. The small panel of Microhaplotypes incorporated in ET were selected because they showed the highest levels of haplotype diversity amongst the seven population groups we sought to differentiate. Microhaplotype data was not formally combined with single-site SNP genotypes to analyse ancestry. However, the haplotype sequence reads obtained with ET from these loci creates an effective system for de-convoluting two-contributor mixed DNA. We made simple mixture experiments to demonstrate that when the contributors have different ancestries and the mixture ratios are imbalanced (i.e., not 1:1 mixtures) the ET Microhaplotype panel is an informative system to infer ancestry when this differs between the contributors. • 226 ancestry (BGA) markers compiled for VISAGE Enhanced Tool (ET) combined with 184 appearance markers in one MPS assay. • Autosomal BGA SNP number in ET reduced to allow inclusion of 85 Y-SNPs, 16 X-SNPs and 21 Microhaplotypes (MHs). • Extra BGA markers give enhanced detail of co-ancestry patterns in admixed males and MH loci allow ancestry-based mixed DNA. • Comprehensive reference population datasets and analyses of global distribution of variation in the ET BGA markers outlined. • Expanded Middle East-informative SNPs enhance differentiation of these populations particularly when combined with nested K:5 STRUCTURE runs. [ABSTRACT FROM AUTHOR]

Polyadenylation is a step of mRNA processing which is crucial for its expression and stability. Themajor polyadenylation signal (PAS) represents a nucleotide hexamer that adheres to the AATAAA consensus sequence. Over a half of human genes have multiple cleavage and polyadenylation sites, resulting in a great diversity of transcripts differing in function, stability, and translational activity. Here,weuse availablewhole-genomehumanpolymorphismdata together withdataoninterspeciesdivergence tostudy thepatterns of selectionactingonPAShexamers.CommonvariantsofPAShexamers aredepletedof single nucleotidepolymorphisms (SNPs),and SNPs within PAS hexamers have a reduced derived allele frequency (DAF) and increased conservation, indicating prevalent negative selection; at the same time, the SNPs that "improve" the PAS (i.e., those leading to higher cleavage efficiency) have increased DAF, compared to those that "impair" it. SNPs are rarer at PAS of "unique" polyadenylation sites (one site per gene); among alternative polyadenylation sites, at the distal PAS and at exonic PAS. Similar trends were observed in DAFs and divergence between species of placental mammals. Thus, selection permits PAS mutations mainly at redundant and/or weakly functional PAS. Nevertheless, a fractionof the SNPs at PAShexamers likely affect gene functions; in particular, someof the observed SNPs are associatedwithdisease. [ABSTRACT FROM AUTHOR]

Background: Genome-wide association studies (GWASs) are powerful in identifying genetic loci which cause complex traits of common diseases. However, it is well known that inappropriately accounting for pedigree or population structure leads to spurious associations. GWASs have often encountered increased type I error rates due to the correlated genotypes of cryptically related individuals or subgroups. Therefore, accurate pedigree information is crucial for successful GWASs. Results: We propose a distance-based method KIND to estimate kinship coefficients among individuals. Our method utilizes the spatial distribution of SNPs in the genome that represents how far each minor-allele variant is located from its neighboring minor-allele variants. The SNP distribution of each individual was presented in a feature vector in Euclidean space, and then the kinship coefficient was inferred from the two vectors of each individual pair. We demonstrate that the distance information can measure the similarity of genetic variants of individuals accurately and efficiently. We applied our method to a synthetic data set and two real data sets (i.e. the HapMap phase III and the 1000 genomes data). We investigated the estimation accuracy of kinship coefficients not only within homogeneous populations but also for a population with extreme stratification. Conclusions: Our method KIND usually produces more accurate and more robust kinship coefficient estimates than existing methods especially for populations with extreme stratification. It can serve as an important and very efficient tool for GWASs. [ABSTRACT FROM AUTHOR]

To characterize natural selection, various analytical methods for detecting candidate genomic regions have been developed. We propose to perform genome-wide scans of natural selection using principal component analysis (PCA). We show that the common FST index of genetic differentiation between populations can be viewed as the proportion of variance explained by the principal components. Considering the correlations between genetic variants and each principal component provides a conceptual framework to detect genetic variants involved in local adaptation without any prior definition of populations. To validate the PCA-based approach, we consider the 1000 Genomes data (phase 1) considering 850 individuals coming from Africa, Asia, and Europe. The number of genetic variants is of the order of 36 millions obtained with a low-coverage sequencing depth (3×). The correlations between genetic variation and each principal component provide well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and noncoding RNAs. In addition to identifying genes involved in biological adaptation, we identify two biological pathways involved in polygenic adaptation that are related to the innate immune system (beta defensins) and to lipid metabolism (fatty acid omega oxidation). An additional analysis of European data shows that a genome scan based on PCA retrieves classical examples of local adaptation even when there are no well-defined populations. PCA-based statistics, implemented in the PCAdapt R package and the PCAdapt fast open-source software, retrieve well-known signals of human adaptation, which is encouraging for future whole-genome sequencing project, especially when defining populations is difficult. [ABSTRACT FROM AUTHOR]

Ancestry informative markers (AIMs) can be used to detect and adjust for population stratification and predict the ancestry of the source of an evidence sample. Autosomal single nucleotide polymorphisms (SNPs) are the best candidates for AIMs. It is essential to identify the most informative AIM SNPs across relevant populations. Several informativeness measures for ancestry estimation have been used for AIMs selection: absolute allele frequency differences ( δ), F statistics ( F), and informativeness for assignment measure (In). However, their efficacy has not been compared objectively, particularly for determining affiliations of major US populations. In this study, these three measures were directly compared for AIMs selection among four major US populations, i.e., African American, Caucasian, East Asian, and Hispanic American. The results showed that the F panel performed slightly better for population resolution based on principal component analysis (PCA) clustering than did the δ panel and both performed better than the In panel. Therefore, the 23 AIMs selected by the F measure were used to characterize the four major American populations. Genotype data of nine sample populations were used to evaluate the efficiency of the 23-AIMs panel. The results indicated that individuals could be correctly assigned to the major population categories. Our AIMs panel could contribute to the candidate pool of AIMs for potential forensic identification purposes. [ABSTRACT FROM AUTHOR]

Methods to impute HLA alleles based on dense single nucleotide polymorphism (SNP) data provide a valuable resource to association studies and evolutionary investigation of the MHC region. The availability of appropriate training sets is critical to the accuracy of HLA imputation, and the inclusion of samples with various ancestries is an important pre-requisite in studies of admixed populations. We assess the accuracy of HLA imputation using 1000 Genomes Project data as a training set, applying it to a highly admixed Brazilian population, the Quilombos from the state of São Paulo. To assess accuracy, we compared imputed and experimentally determined genotypes for 146 samples at 4 HLA classical loci. We found imputation accuracies of 82.9%, 81.8%, 94.8% and 86.6% for HLA - A , - B , -C and - DRB1 respectively (two-field resolution). Accuracies were improved when we included a subset of Quilombo individuals in the training set. We conclude that the 1000 Genomes data is a valuable resource for construction of training sets due to the diversity of ancestries and the potential for a large overlap of SNPs with the target population. We also show that tailoring training sets to features of the target population substantially enhances imputation accuracy. [ABSTRACT FROM AUTHOR]

Recent advances in high-throughout sequencing technologies have made it possible to accurately assign copy number (CN) at CN variable loci. However, current analytic methods often perform poorly in regions in which complex CN variation is observed. Here we report the development of a read depth-based approach, CNVrd2, for investigation of CN variation using high-throughput sequencing data. This methodology was developed using data from the 1000 Genomes Project from the CCL3L1 locus, and tested using data from the DEFB103A locus. In both cases, samples were selected for which paralogue ratio test data were also available for comparison. The CNVrd2 method first uses observed read-count ratios to refine segmentation results in one population. Then a linear regression model is applied to adjust the results across multiple populations, in combination with a Bayesian normal mixture model to cluster segmentation scores into groups for individual CN counts. The performance of CNVrd2 was compared to that of two other read depth-based methods (CNVnator, cn.mops) at the CCL3L1 and DEFB103A loci, and the highest concordance with the paralog ratio test method was observed for CNVrd2 (77.8%/90.4% for CNVrd2, 36.7%/4.8% for cn.mops and 7.2%/1% for CNVnator at CCL3L1 and DEF103A). CNVrd2 is available as an R package as part of the Bioconductor project: http://www.bioconductor.org/packages/release/bioc/html/CNVrd2.html.

Multiple-allele single nucleotide polymorphisms (SNPs) are potentially useful for forensic DNA analysis as they can provide more discrimination power than normal binary SNPs. In addition, the presence in a profile of more than two alleles per marker provides a clearer indication of mixed DNA than assessments of imbalanced signals in the peak pairs of binary SNPs. Using the 1000 Genomes Phase III human variant data release of 2014 as the starting point, this study collated 961 tetra-allelic SNPs that pass minimum sequence quality thresholds and where four separate nucleotide substitution alleles were detected. Although most of these loci had three of the four alleles in combined frequencies of 2% or less, 160 had high heterozygosities with 50 exceeding those of ‘ideal’ 0.5:0.5 binary SNPs. From this set of most polymorphic tetra-allelic SNPs, we identified markers most informative for forensic purposes and explored these loci in detail. Subsets of the most polymorphic tetra-allelic SNPs will make useful additions to current panels of forensic identification SNPs and ancestry-informative SNPs. The 24 most discriminatory tetra-allelic SNPs were estimated to detect more than two alleles in at least one marker per profile in 99.9% of mixtures of African contributors. In European contributor mixtures 99.4% of profiles would show multiple allele patterns, but this drops to 92.6% of East Asian contributor mixtures due to reduced levels of polymorphism for the 24 SNPs in this population group. [ABSTRACT FROM AUTHOR]

Background: Interlocus gene conversion (IGC) is a recombination-based mechanism that results in the unidirectional transfer of short stretches of sequence between paralogous loci. Although IGC is a well-established mechanism of human disease, the extent to which this mutagenic process has shaped overall patterns of segregating variation in multi-copy regions of the human genome remains unknown. One expected manifestation of IGC in population genomic data is the presence of one-to-one paralogous SNPs that segregate identical alleles. Results: Here, I use SNP genotype calls from the low-coverage phase 3 release of the 1000 Genomes Project to identify 15,790 parallel, shared SNPs in duplicated regions of the human genome. My approach for identifying these sites accounts for the potential redundancy of short read mapping in multi-copy genomic regions, thereby effectively eliminating false positive SNP calls arising from paralogous sequence variation. I demonstrate that independent mutation events to identical nucleotides at paralogous sites are not a significant source of shared polymorphisms in the human genome, consistent with the interpretation that these sites are the outcome of historical IGC events. These putative signals of IGC are enriched in genomic contexts previously associated with non-allelic homologous recombination, including clear signals in gene families that form tandem intra-chromosomal clusters. Conclusions: Taken together, my analyses implicate IGC, not point mutation, as the mechanism generating at least 2.7% of single nucleotide variants in duplicated regions of the human genome. [ABSTRACT FROM AUTHOR]

Ever since its introduction, the haplotype copy model has proven to be one of the most successful approaches for modeling genetic variation in human populations, with applications ranging from ancestry inference to genotype phasing and imputation. Motivated by coalescent theory, this approach assumes that any chromosome (haplotype) can be modeled as a mosaic of segments copied from a set of chromosomes sampled from the same population. At the core of the model is the assumption that any chromosome from the sample is equally likely to contribute a priori to the copying process. Motivated by recent works that model genetic variation in a geographic continuum, we propose a new spatial-aware haplotype copy model that jointly models geography and the haplotype copying process. We extend hidden Markov models of haplotype diversity such that at any given location, haplotypes that are closest in the genetic-geographic continuum map are a priori more likely to contribute to the copying process than distant ones. Through simulations starting from the 1000 Genomes data, we show that our model achieves superior accuracy in genotype imputation over the standard spatial-unaware haplotype copy model. In addition, we show the utility of our model in selecting a small personalized reference panel for imputation that leads to both improved accuracy as well as to a lower computational runtime than the standard approach. Finally, we show our proposed model can be used to localize individuals on the genetic-geographical map on the basis of their genotype data. [ABSTRACT FROM AUTHOR]

To compile a new South Asian-informative panel of forensic ancestry SNPs, we changed the strategy for selecting the most powerful markers for this purpose by targeting polymorphisms with near absolute specificity – when the South Asian-informative allele identified is absent from all other populations or present at frequencies below 0.001 (one in a thousand). More than 120 candidate SNPs were identified from 1000 Genomes datasets satisfying an allele frequency screen of ≥ 0.1 (10 % or more) allele frequency in South Asians, and ≤ 0.001 (0.1 % or less) in African, East Asian, and European populations. From the candidate pool of markers, a final panel of 36 SNPs, widely distributed across most autosomes, were selected that had allele frequencies in the five 1000 Genomes South Asian populations ranging from 0.4 to 0.15. Slightly lower average allele frequencies, but consistent patterns of informativeness were observed in gnomAD South Asian datasets used to validate the 1000 Genomes variant annotations. We named the panel of 36 South Asian-specific SNPs Eurasiaplex-2 , and the informativeness of the panel was evaluated by compiling worldwide population data from 4097 samples in four genome variation databases that largely complement the global sampling of 1000 Genomes. Consistent patterns of allele frequency distribution, which were specific to South Asia, were observed in all populations in, or closely sited to, the Indian sub-continent. Pakistani populations from the HGDP-CEPH panel had markedly lower allele frequencies, highlighting the need to develop a statistical system to evaluate the ancestry inference value of counting the number of population-specific alleles present in an individual. • New South Asian-informative forensic ancestry marker panel of 36 SNPs compiled called Eurasiaplex-2. • SNPs selected to have zero or near zero South Asian-specific allele frequencies in all other populations located outside Indian sub-continent. • Survey of 4097 worldwide samples show average 11–14 South Asian-specific genotypes in South Asians vs. 0.2 in all other population samples. • Forensic ancestry markers with near absolute specificity like the SNPs of Eurasiaplex-2 offer potential for highly informative panels differentiating worldwide populations. [ABSTRACT FROM AUTHOR]

Recent advances in high-throughout sequencing technologies have made it possible to accurately assign copy number (CN) at CN variable loci. However, current analytic methods often perform poorly in regions in which complex CN variation is observed. Here we report the development of a read depth-based approach, CNVrd2, for investigation of CN variation using high-throughput sequencing data. This methodology was developed using data from the 1000 Genomes Project from the CCL3L1 locus, and tested using data from the DEFB103A locus. In both cases, samples were selected for which paralog ratio test data were also available for comparison. The CNVrd2 method first uses observed read-count ratios to refine segmentation results in one population. Then a linear regression model is applied to adjust the results across multiple populations, in combination with a Bayesian normal mixture model to cluster segmentation scores into groups for individual CN counts. The performance of CNVrd2 was compared to that of two other read depth-based methods (CNVnator, cn.mops) at the CCL3L1 and DEFB103A loci. The highest concordance with the paralog ratio test method was observed for CNVrd2 (77.8/90.4% for CNVrd2, 36.7/4.8% for cn.mops and 7.2/1% for CNVnator at CCL3L1 and DEF103A). CNVrd2 is available as an R package as part of the Bioconductor project: http://www.bioconductor.org/packages/release/bioc/html/CNVrd2.html. [ABSTRACT FROM AUTHOR]

Abstract: Emerging next-generation sequencing technologies will enable DNA analyses to add pigmentation predictive and ancestry informative (AIM) SNPs to the range of markers detectable from a single PCR test. This prompted us to re-appraise current forensic and genomics AIM-SNPs and from the best sets, to identify the most divergent markers for a five population group differentiation of Africans, Europeans, East Asians, Native Americans and Oceanians by using our own online genome variation browsers. We prioritized careful balancing of population differentiation across the five group comparisons in order to minimize bias when estimating co-ancestry proportions in individuals with admixed ancestries. The differentiation of European from Middle East or South Asian ancestries was not chosen as a characteristic in order to concentrate on introducing Oceanian differentiation for the first time in a forensic AIM set. We describe a complete set of 128 AIM-SNPs that have near identical population-specific divergence across five continentally defined population groups. The full set can be systematically reduced in size, while preserving the most informative markers and the balance of population-specific divergence in at least four groups. We describe subsets of 88, 55, 28, 20 and 12 AIMs, enabling both new and existing SNP genotyping technologies to exploit the best markers identified for forensic ancestry analysis. [Copyright &y& Elsevier]

ABSTRACT The potential pathogenicity of genetic variants identified in disease-based resequencing studies is often overlooked where variants have previously been reported in db SNP, the 1000 genomes project, or the National Heart, Lung and Blood Institute Exome Sequencing Project ( ESP). In this work, we estimate that collectively, these databases capture ∼52% of mutations (db SNP 50.4%; 1000 genomes 4.8%; and ESP 10.2%) reported as disease causing within phenotype-based locus-specific databases ( LSDBs). To investigate whether these mutations may simply represent benign population variants, we evaluated whether the carrier frequencies associated with mutations implicated in amyotrophic lateral sclerosis were higher than what could be accounted for by high-penetrance disease models. In doing so, we have questioned the veracity of 51 mutations, but also demonstrated that each of the three databases included credible disease variants. Our results demonstrate the benefits of using databases such as db SNP, the 1000 genomes project, and the ESP to evaluate the pathogenicity of putative disease variants, and suggest that many disease mutations reported across LSDBs may not actually be pathogenic. However, they also demonstrate that even in the context of rare Mendelian disorders, the potential pathogenicity of variants reported by these databases should not be overlooked without proper evaluation. [ABSTRACT FROM AUTHOR]

We set out to test the hypothesis that pharmacometabolomic data could be efficiently merged with pharmacogenomic data by single-nucleotide polymorphism (SNP) imputation of metabolomic-derived pathway data on a ‘scaffolding’ of genome-wide association (GWAS) SNP data to broaden and accelerate ‘pharmacometabolomics-informed pharmacogenomic’ studies by eliminating the need for initial genotyping and by making broader SNP association testing possible.We previously genotyped 131 tag SNPs for six genes encoding enzymes in the glycine synthesis and degradation pathway using DNA from 529 depressed patients treated with citalopram/escitalopram to pursue a glycine metabolomics ‘signal’ associated with selective serotonine reuptake inhibitor response. We identified a significant SNP in the glycine dehydrogenase gene. Subsequently, GWAS SNP data were generated for the same patients. In this study, we compared SNP imputation within 200 kb of these same six genes with the results of the previous tag SNP strategy as a rapid strategy for merging pharmacometabolomic and pharmacogenomic data.Imputed genotype data provided greater coverage and higher resolution than did tag SNP genotyping, with a higher average genotype concordance between genotyped and imputed SNP data for ‘1000 Genomes’ (96.4%) than HapMap 2 (93.2%) imputation. Many low P-value SNPs with novel locations within genes were observed for imputed compared with tag SNPs, thus altering the focus for subsequent functional genomic studies.These results indicate that the use of GWAS data to impute SNPs for genes in pathways identified by other ‘omics’ approaches makes it possible to rapidly and cost efficiently identify SNP markers to ‘broaden’ and accelerate pharmacogenomic studies. [ABSTRACT FROM AUTHOR]