Evaluating the accuracy of variant calling methods using the frequency of parent-offspring genotype mismatch

Are microhaplotypes derived from the 1000 Genomes Project reliable for forensic purposes?

Microhaplotypes (MHs) have emerged as an important genetic marker in forensic genetics. However, most studies have overlooked the potential for phasing errors within microhaplotypes based on the 1000 Genome Project (1kGP), which may impact the evaluation of various forensic parameters and lead to misleading results. In this study, we constructed a dense and extensive set of MHs across the human genome, using the expanded 1000 Genomes Project data aligned to GRCh38 reference genome. We applied three different SNP minor allele frequency (MAF) thresholds (0, 0.01, and 0.05) to evaluate the reliability of these markers. Utilizing pedigree data from 18 populations, which included a total of 602 trios, we scanned for and confirmed suspected phasing error events at these MH loci. We also sequenced 50 MHs for one trio of the Southern Han Chinese (CHS) population to further investigate these discrepancies. The results revealed the presence of phasing errors in MHs from 1kGP when analyzed using targeted enrichment and next-generation sequencing. The probability of suspected phasing error events was strongly and positively correlated with the effective number of alleles (Ae) and informativeness (In) of the markers. Additionally, these mismatch probabilities varied significantly across different continental populations. Additionally, when selecting loci, applying MAF filtering and avoiding regions such as the MHC can reduce the occurrence of such events to some extent. Based on these findings, we suggest that relying solely on sequencing data of the 1kGP for forensic purpose may be risky. A thorough investigation of the true forensic parameters of MHs is essential to ensure their reliability in forensic applications.

Same trait, different genes: pelvic spine loss in three brook stickleback populations in Alberta, Canada

The genetic basis of phenotypic or adaptive parallelism can reveal much about constraints on evolution. This study investigated the genetic basis of a canonically parallel trait: pelvic spine reduction in sticklebacks. Pelvic reduction has a highly parallel genetic basis in threespine stickleback in populations around the world, always involving a deletion of the pel1 enhancer of Pitx1. We conducted a genome-wide association study to investigate the genetic basis of pelvic spine reduction in 3 populations of brook stickleback in Alberta, Canada. Pelvic reduction did not involve Pitx1 in any of the 3 populations. Instead, pelvic reduction in 1 population involved a mutation in an exon of Tbx4, and it involved a mutation in an intron of Lmbr1 in the other two populations. Hence, the parallel phenotypic evolution of pelvic spine reduction across stickleback genera, and among brook stickleback populations, has a nonparallel genetic basis. This suggests that there may be redundancy in the genetic basis of this adaptive polymorphism, but it is not clear whether a lack of parallelism indicates a lack of constraint on the evolution of this adaptive trait. Whether different pleiotropic effects of different mutations have different fitness consequences or whether certain pelvic reduction mutations confer specific benefits in certain environments remains to be determined.

Repeated global adaptation across plant species

Global adaptation occurs when all populations of a species undergo selection toward a common optimum. This can occur by a hard selective sweep with the emergence of a new globally advantageous allele that spreads throughout a species' natural range until reaching fixation. This evolutionary process leaves a temporary trace in the region affected, which is detectable using population genomic methods. While selective sweeps have been identified in many species, there have been few comparative and systematic studies of the genes involved in global adaptation. Building upon recent findings showing repeated genetic basis of local adaptation across independent populations and species, we asked whether certain genes play a more significant role in driving global adaptation across plant species. To address this question, we scanned the genomes of 17 plant species to identify signals of repeated global selective sweeps. Despite the substantial evolutionary distance between the species analyzed, we identified several gene families with strong evidence of repeated positive selection. These gene families tend to be enriched for reduced pleiotropy, consistent with predictions from Fisher's evolutionary model and the cost of complexity hypothesis. We also found that genes with repeated sweeps exhibit elevated levels of gene duplication. Our findings contrast with recent observations of increased pleiotropy in genes driving local adaptation, consistent with predictions based on the theory of migration-selection balance.

Whole chloroplast genome sequence and phylogenetic analysis of Calanthe discolor (Orchidaceae)

The orchid Calanthe discolor, which has high ornamental and medicinal value, is mainly distributed in Zhejiang, Jiangsu, and southeast Hubei Provinces of China, as well as in Japan and the southern Korean peninsula. In this study, the whole chloroplast genome sequence of C. discolor was first assembled using high-throughput Illumina paired-end technology, providing data to evaluate the evolution of this species. The C. discolor chloroplast genome was158,286 bp long, including a large single-copy region of 87,095 bp, a small single-copy region of 18,407 bp, and two copies of a repeat region (26,392-bp each). The overall G + C content was 41.2%. A total of 133 genes were predicted from the genome, including 87 protein-coding genes, eight ribosomal RNAs, 38 transfer RNAs. Phylogenetic analysis indicated a close relationship between C. discolor and C. bicolor.

The genetic architecture of repeated local adaptation to climate in distantly related plants

Closely related species often use the same genes to adapt to similar environments. However, we know little about why such genes possess increased adaptive potential and whether this is conserved across deeper evolutionary lineages. Adaptation to climate presents a natural laboratory to test these ideas, as even distantly related species must contend with similar stresses. Here, we re-analyse genomic data from thousands of individuals from 25 plant species as diverged as lodgepole pine and Arabidopsis (~300 Myr). We test for genetic repeatability based on within-species associations between allele frequencies in genes and variation in 21 climate variables. Our results demonstrate significant statistical evidence for genetic repeatability across deep time that is not expected under randomness, identifying a suite of 108 gene families (orthogroups) and gene functions that repeatedly drive local adaptation to climate. This set includes many orthogroups with well-known functions in abiotic stress response. Using gene co-expression networks to quantify pleiotropy, we find that orthogroups with stronger evidence for repeatability exhibit greater network centrality and broader expression across tissues (higher pleiotropy), contrary to the 'cost of complexity' theory. These gene families may be important in helping wild and crop species cope with future climate change, representing important candidates for future study.

Exploring the impact of sequence context on errors in SNP genotype calling with whole genome sequencing data using AI-based autoencoder approach

A critical step in the analysis of whole genome sequencing data is variant calling. Despite its importance, variant calling is prone to errors. Our study investigated the association between incorrect single nucleotide polymorphism (SNP) calls and variant quality metrics and nucleotide context. In our study, incorrect SNPs were defined in 20 Holstein-Friesian cows by comparing their SNPs genotypes identified by whole genome sequencing with the IlluminaNovaSeq6000 and the EuroGMD50K genotyping microarray. The dataset was divided into the correct SNP set (666 333 SNPs) and the incorrect SNP set (4 557 SNPs). The training dataset consisted of only the correct SNPs, while the test dataset contained a balanced mix of all the incorrectly and correctly called SNPs. An autoencoder was constructed to identify systematically incorrect SNPs that were marked as outliers by a one-class support vector machine and isolation forest algorithms. The results showed that 59.53% (±0.39%) of the incorrect SNPs had systematic patterns, with the remainder being random errors. The frequent occurrence of the CGC 3-mer was due to mislabelling a call for C. Incorrect T instead of A call was associated with the presence of T in the neighbouring downstream position. These errors may arise due to the fluorescence patterns of nucleotide labelling.

Kuura-An automated workflow for analyzing WES and WGS data

The advent of high-throughput sequencing technologies has revolutionized the field of genomic sciences by cutting down the cost and time associated with standard sequencing methods. This advancement has not only provided the research community with an abundance of data but has also presented the challenge of analyzing it. The paramount challenge in analyzing the copious amount of data is in using the optimal resources in terms of available tools. To address this research gap, we propose "Kuura-An automated workflow for analyzing WES and WGS data", which is optimized for both whole exome and whole genome sequencing data. This workflow is based on the nextflow pipeline scripting language and uses docker to manage and deploy the workflow. The workflow consists of four analysis stages-quality control, mapping to reference genome & quality score recalibration, variant calling & variant recalibration and variant consensus & annotation. An important feature of the DNA-seq workflow is that it uses the combination of multiple variant callers (GATK Haplotypecaller, DeepVariant, VarScan2, Freebayes and Strelka2), generating a list of high-confidence variants in a consensus call file. The workflow is flexible as it integrates the fragmented tools and can be easily extended by adding or updating tools or amending the parameters list. The use of a single parameters file enhances reproducibility of the results. The ease of deployment and usage of the workflow further increases computational reproducibility providing researchers with a standardized tool for the variant calling step in different projects. The source code, instructions for installation and use of the tool are publicly available at our github repository https://github.com/dhanaprakashj/kuura_pipeline.