Population structure in genetic studies: Confounding factors and mixed models

Old vs. new local ancestry inference in HCHS/SOL: a comparative study

Hispanic/Latino populations are admixed, with genetic contributions from multiple ancestral populations. To uncover genetic associations in these populations, researchers often turn to admixture mapping, which relies on inferred counts of "local" ancestry, i.e. the source ancestral population at a locus. Local ancestries are inferred using external reference panels that represent ancestral populations, making the choice of inference method and reference panel critical. This study used a dataset of Hispanic/Latino individuals from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) to evaluate how updates in local ancestry inference (LAI) affect results, specifically, the 'old' LAI performed using a popular inference method RFMix alongside 'new' inferences performed using Fast Local Ancestry Estimation (FLARE) with an updated reference panel. We compared their performance in terms of global and local ancestry correlations, as well as admixture mapping-based associations. Overall, the old and new inferences produced highly similar global and local ancestry estimates, with FLARE-based results closely matching those from RFMix in admixture mapping analyses. However, in some genomic regions, the old and new local ancestries showed relatively lower correlations (Pearson R < 0.9). Most of these regions (86.42%) were mapped to either ENCODE blacklist regions or gene clusters, compared to 7.67% of randomly-matched regions with high correlations (Pearson R > 0.97). These findings show that old and new inferences largely agree and suggest that regions of lower agreement are mostly due to genomic sequence contexts that lead to less stable inference, rather than due to the LAI software or genotyping technology used.

Beyond the Standard GWAS-A Guide for Plant Biologists

Classic genome-wide association studies (GWAS) look for associations between individual single-nucleotide polymorphisms (SNPs) and phenotypes of interest. With the rapid progress of high-throughput genotyping and phenotyping technologies, GWAS have become increasingly powerful for detecting genetic determinants and their molecular mechanisms underpinning natural phenotypic variation. However, GWAS frequently yield results with neither expected nor promising loci, nor any significant associations. This is often because associations between SNPs and a single phenotype are confounded, for example with the environment, other traits or complex genetic structures. Such confounding can mask true genotype-phenotype associations, or inflate spurious associations. To address these problems, numerous methods have been developed that go beyond the standard model. Such advanced GWAS models are flexible and can offer improved statistical power for understanding the genetics underlying complex traits. Despite this advantage, these models have not been widely adopted and implemented compared to the standard GWAS approach, partly because this literature is diverse and often technical. In this review, our aim is to provide an overview of the application and the benefits of various advanced GWAS models for handling complex traits and genetic structures, targeting plant biologists who wish to carry out GWAS more effectively.

Taurine pangenome uncovers a segmental duplication upstream of KIT associated with depigmentation in white-headed cattle

Cattle have been selectively bred for coat color, spotting, and depigmentation patterns. The assumed autosomal dominant inherited genetic variants underlying the characteristic white head of Fleckvieh, Simmental, and Hereford cattle have not been identified yet, although the contribution of structural variation upstream of the KIT gene has been proposed. Here, we construct a graph pangenome from 24 haplotype assemblies representing seven taurine cattle breeds to identify and characterize the white-head-associated locus for the first time based on long-read sequencing data and pangenome analyses. We introduce a pangenome-wide association mapping approach that examines assembly path similarities within the graph to reveal an association between two most likely serial alleles of a complex structural variant (SV) 66 kb upstream of KIT and facial depigmentation. The complex SV contains a variable number of tandemly duplicated 14.3 kb repeats, consisting of LTRs, LINEs, and other repetitive elements, leading to misleading alignments of short and long reads when using a linear reference. We align 250 short-read sequencing samples spanning 15 cattle breeds to the pangenome graph, further validating that the alleles of the SV segregate with head depigmentation. We estimate an increased count of repeats in Hereford relative to Simmental and other white-headed cattle breeds from the graph alignment coverage, suggesting a large under-assembly in the current Hereford-based cattle reference genome, which had fewer copies. Our work shows that exploiting assembly path similarities within graph pangenomes can reveal trait-associated complex SVs.

Genome-wide association study identifies common variants associated with breast cancer in South African Black women

Genome-wide association studies (GWAS) have characterized the contribution of common variants to breast cancer (BC) risk in populations of European ancestry, however GWAS have not been reported in resident African populations. This GWAS included 2485 resident African BC cases and 1101 population matched controls. Two risk loci were identified, located between UNC13C and RAB27A on chromosome 15 (rs7181788, p = 1.01 × 10-08) and in USP22 on chromosome 17 (rs899342, p = 4.62 × 10-08). Several genome-wide significant signals were also detected in hormone receptor subtype analysis. The novel loci did not replicate in BC GWAS data from populations of West Africa ancestry suggesting genetic heterogeneity in different African populations, but further validation of these findings is needed. A European ancestry derived polygenic risk model for BC explained only 0.79% of variance in our data. Larger studies in pan-African populations are needed to further define the genetic contribution to BC risk.

The Farm Animal Genotype-Tissue Expression (FarmGTEx) Project

Genetic mutation and drift, coupled with natural and human-mediated selection and migration, have produced a wide variety of genotypes and phenotypes in farmed animals. We here introduce the Farm Animal Genotype-Tissue Expression (FarmGTEx) Project, which aims to elucidate the genetic determinants of gene expression across 16 terrestrial and aquatic domestic species under diverse biological and environmental contexts. For each species, we aim to collect multiomics data, particularly genomics and transcriptomics, from 50 tissues of 1,000 healthy adults and 200 additional animals representing a specific context. This Perspective provides an overview of the priorities of FarmGTEx and advocates for coordinated strategies of data analysis and resource-sharing initiatives. FarmGTEx aims to serve as a platform for investigating context-specific regulatory effects, which will deepen our understanding of molecular mechanisms underlying complex phenotypes. The knowledge and insights provided by FarmGTEx will contribute to improving sustainable agriculture-based food systems, comparative biology and eventual human biomedicine.

Genome-wide functional annotation of variants: a systematic review of state-of-the-art tools, techniques and resources

The recent advancement of sequencing technologies marks a significant shift in the character and complexity of the digital genomic data universe, encompassing diverse types of molecular data, screened through manifold technological platforms. As a result, a plethora of fully assembled genomes are generated that span vertically the evolutionary scale. Notwithstanding the tsunami of thriving innovations that accomplish unprecedented, nucleotide-level, structural and functional annotation, an exhaustive, systemic, massive genome-wide functional annotation remains elusive, particularly when the criterion is automation and efficiency in data-agnostic interpretation. The latter is of paramount importance for the elaboration of strategies for sophisticated, data-driven genome-wide annotation, which aim to impart a sustainable and comprehensive systemic approach to addressing whole genome variation. Therefore, it is essential to develop methods and tools that promote systematic functional genomic annotation, with emphasis on mechanistic information exceeding the limits of coding regions, and exploiting the chunks of pertinent information residing in non-coding regions, including promoter and enhancer sequences, non-coding RNAs, DNA methylation sites, transcription factor binding sites, transposable elements and more. This review provides an overview of the current state-of-the-art in genome-wide functional annotation of genetic variation, including existing bioinformatic tools, resources, databases and platforms currently available or reported in the literature. Particular emphasis is placed on the functional annotation of variants that lie outside protein-coding genomic regions (intronic or intergenic), their potential co-localization with regulatory element areas, such as putative non-coding RNA regions, and the assessment of their functional impact on the investigated phenotype. In addition, state-of-the-art tools that leverage data obtained from WGS and GWAS-based analyses are discussed, along with future bioinformatics directions and developments. These future directions emphasize efficient, comprehensive, and largely automated functional annotation of both coding and non-coding genomic variants, as well as their optimal evaluation.

Biases in machine-learning models of human single-cell data

Recent machine-learning (ML)-based advances in single-cell data science have enabled the stratification of human tissue donors at single-cell resolution, promising to provide valuable diagnostic and prognostic insights. However, such insights are susceptible to biases. Here we discuss various biases that emerge along the pipeline of ML-based single-cell analysis, ranging from societal biases affecting whose samples are collected, to clinical and cohort biases that influence the generalizability of single-cell datasets, biases stemming from single-cell sequencing, ML biases specific to (weakly supervised or unsupervised) ML models trained on human single-cell samples and biases during the interpretation of results from ML models. We end by providing methods for single-cell data scientists to assess and mitigate biases, and call for efforts to address the root causes of biases.

Subspecies phylogeny in the human gut revealed by co-evolutionary constraints across the bacterial kingdom

The human gut microbiome contains many bacterial strains of the same species ("strain-level variants") that shape microbiome function. The tremendous scale and molecular resolution at which microbial communities are being interrogated motivates addressing how to describe strain-level variants. We introduce the "Spectral Tree"-an inferred tree of relatedness built from patterns of co-evolutionary constraint between greater than 7,000 diverse bacteria. Using the Spectral Tree to describe over 600 diverse gut commensal strains that we isolated, whole-genome sequenced, and metabolically profiled revealed (1) widespread phylogenetic structure among strain-level variants, (2) the origins of subspecies phylogeny as a shared history of phage infections across humans, and (3) the key role of inter-human strain variation in predicting strain-level metabolic qualities. Overall, our work demonstrates the existence and metabolic importance of structured phylogeny below the level of species for commensal gut bacteria, motivating a redefinition of individual strains according to their evolutionary context. A record of this paper's transparent peer review process is included in the supplemental information.

Old vs. New Local Ancestry Inference in HCHS/SOL: A Comparative Study

Hispanic/Latino populations are admixed, with genetic contributions from multiple ancestral populations. Studies of genetic association in these admixed populations often use methods such as admixture mapping, which relies on inferred counts of "local" ancestry, i.e., of the source ancestral population at a locus. Local ancestries are inferred using external reference panels that represent ancestral populations, making the choice of inference method and reference panel critical. This study used a dataset of Hispanic/Latino individuals from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) to evaluate the "old" local ancestry inference performed using the state-of-the-art inference method, RFMix, alongside "new" inferences performed using Fast Local Ancestry Estimation (FLARE), which also used an updated reference panel. We compared their performance in terms of global and local ancestry correlations, as well as admixture mapping-based associations. Overall, the old RFMix and new FLARE inferences were highly similar for both global and local ancestries, with FLARE-inferred datasets yielding admixture mapping results consistent with those computed from RFMix. However, in some genomic regions the old and new local ancestries have relatively lower correlations (Pearson R < 0.9). Most of these genomic regions (86.42%) were mapped to either ENCODE blacklist regions, or to gene clusters, compared to 7.67% of randomly-matched regions with high correlations (Pearson R > 0.97) between old and new local ancestries.

Characterizing features affecting local ancestry inference performance in admixed populations

In recent years, significant efforts have been made to improve methods for genomic studies of admixed populations using local ancestry inference (LAI). Accurate LAI is crucial to ensure that downstream analyses accurately reflect the genetic ancestry of research participants. Here, we test analytic strategies for LAI to provide guidelines for optimal accuracy, focusing on admixed populations reflective of Latin America's primary continental ancestries-African (AFR), Amerindigenous (AMR), and European (EUR). Simulating linkage-disequilibrium-informed admixed haplotypes under a variety of 2- and 3-way admixture models, we implemented a standard LAI pipeline, testing the impact of reference panel composition, DNA data type, demography, and software parameters to quantify ancestry-specific LAI accuracy. We observe that across all models, AMR tracts have notably reduced LAI accuracy as compared to EUR and AFR tracts, with true positive rate means for AMR ranging from 88% to 94%, EUR from 96% to 99%, and AFR from 98% to 99%. When LAI miscalls occurred, they most frequently erroneously called EUR ancestry in true AMR sites. Concerning reference panel curation, we find that using a reference panel well matched to the target population, even with a smaller sample size, was accurate and the most computationally efficient. Imputation did not harm LAI performance in our tests; rather, we observed that higher variant density improved accuracy. While directly responsive to admixed Latin American cohort compositions, these trends are broadly useful for informing best practices for LAI across admixed populations. Our findings reinforce the need for the inclusion of more underrepresented populations in sequencing efforts to improve reference panels.

Functionally-informed fine-mapping identifies genetic variants linking increased CHD1L expression and HIV restriction in monocytes

Human Immunodeficiency Virus Type 1 (HIV) set-point viral load is a strong predictor of disease progression and transmission risk. A recent genome-wide association study in individuals of African ancestries identified a region on chromosome 1 significantly associated with decreased HIV set-point viral load. Knockout of the closest gene, CHD1L, enhanced HIV replication in vitro in myeloid cells. However, it remains unclear if HIV spVL associated variants are associated with CHD1L gene expression changes. Here we apply a heuristic fine-mapping approach to prioritize combinations of variants that explain the majority of set-point viral load variance and identify variants likely driving the association. We assess the combined impact of these variants on CHD1L regulation using publicly available sequencing studies, and test the relationship between CHD1L expression and set-point viral load using imputed CHD1L expression from monocytes. Taken together, this work characterizes genetically regulated CHD1L expression and further expands our knowledge of CHD1L-mediated HIV restriction in monocytes.

Polygenic scores and their applications in kidney disease

Genome-wide association studies (GWAS) have uncovered thousands of risk variants that individually have small effects on the risk of human diseases, including chronic kidney disease, type 2 diabetes, heart diseases and inflammatory disorders, but cumulatively explain a substantial fraction of disease risk, underscoring the complexity and pervasive polygenicity of common disorders. This complexity poses unique challenges to the clinical translation of GWAS findings. Polygenic scores combine small effects of individual GWAS risk variants across the genome to improve personalized risk prediction. Several polygenic scores have now been developed that exhibit sufficiently large effects to be considered clinically actionable. However, their clinical use is limited by their partial transferability across ancestries and a lack of validated models that combine polygenic, monogenic, family history and clinical risk factors. Moreover, prospective studies are still needed to demonstrate the clinical utility and cost-effectiveness of polygenic scores in clinical practice. Here, we discuss evolving methods for developing polygenic scores, best practices for validating and reporting their performance, and the study designs that will empower their clinical implementation. We specifically focus on the polygenic scores relevant to nephrology and other chronic, complex diseases and review their key limitations, necessary refinements and potential clinical applications.

Twenty-first century mouse genetics is again at an inflection point

The laboratory mouse has been the premier model organism for biomedical research owing to the availability of multiple well-characterized inbred strains, its mammalian physiology and its homozygous genome, and because experiments can be performed under conditions that control environmental variables. Moreover, its genome can be genetically modified to assess the impact of allelic variation on phenotype. Mouse models have been used to discover or test many therapies that are commonly used today. Mouse genetic discoveries are often made using genome-wide association study methods that compare allelic differences in panels of inbred mouse strains with their phenotypic responses. Here we examine changes in the methods used to analyze mouse genetic models of biomedical traits during the twenty-first century. To do this, we first examine where mouse genetics was before the first inflection point, which was just before the revolution in genome sequencing that occurred 20 years ago, and then describe the factors that have accelerated the pace of mouse genetic discovery. We focus on mouse genetic studies that have generated findings that either were translated to humans or could impact clinical medicine or drug development. We next explore how advances in computational capabilities and in DNA sequencing methodology during the past 20 years could enhance the ability of mouse genetics to produce solutions for twenty-first century public-health problems.

Conceptual frameworks for the integration of genetic and social epidemiology in complex diseases

Uncovering the root causes of complex diseases requires complex approaches, yet many studies continue to isolate the effects of genetic and social determinants of disease. Epidemiologic efforts that under-utilize genetic epidemiology methods and findings may lead to incomplete understanding of disease. Meanwhile, genetic epidemiology studies are often conducted without consideration of social and environmental context, limiting the public health impact of genomic discoveries. This divide endures despite shared goals and increases in interdisciplinary data due to a lack of shared theoretical frameworks and differing language. Here, we demonstrate that bridging epidemiological divides does not require entirely new ways of thinking. Existing social epidemiology frameworks including Ecosocial theory and Fundamental Cause Theory, can both be extended to incorporate principles from genetic epidemiology. We show that genetic epidemiology can strengthen, rather than detract from, efforts to understand the impact of social determinants of health. In addition to presenting theoretical synergies, we offer practical examples of how genetics can improve the public health impact of epidemiology studies across the field. Ultimately, we aim to provide a guiding framework for trainees and established epidemiologists to think about diseases and complex systems and foster more fruitful collaboration between genetic and traditional epidemiological disciplines.

Genetic Architecture of Immune Cell DNA Methylation in the Rhesus Macaque

Genetic variation that impacts gene regulation, rather than protein function, can have strong effects on trait variation both within and between species. Epigenetic mechanisms, such as DNA methylation, are often an important intermediate link between genotype and phenotype, yet genetic effects on DNA methylation remain understudied in natural populations. To address this gap, we used reduced representation bisulfite sequencing to measure DNA methylation levels at 555,856 CpGs in peripheral whole blood of 573 samples collected from free-ranging rhesus macaques (Macaca mulatta) living on the island of Cayo Santiago, Puerto Rico. We used allele-specific methods to map cis-methylation quantitative trait loci (meQTL) and tested for effects of 243,389 single nucleotide polymorphisms (SNPs) on local DNA methylation levels. Of 776,092 tested SNP-CpG pairs, we identified 516,213 meQTL, with 69.12% of CpGs having at least one meQTL (FDR < 5%). On average, meQTL explained 21.2% of nearby methylation variance, significantly more than age or sex. meQTL were enriched in genomic compartments where methylation is likely to impact gene expression, for example, promoters, enhancers and binding sites for methylation-sensitive transcription factors. In support, using mRNA-seq data from 172 samples, we confirmed 332 meQTL as whole blood cis-expression QTL (eQTL) in the population, and found meQTL-eQTL genes were enriched for immune response functions, like antigen presentation and inflammation. Overall, our study takes an important step towards understanding the genetic architecture of DNA methylation in natural populations, and more generally points to the biological mechanisms driving phenotypic variation in our close relatives.

Considerations in the search for epistasis

Epistasis refers to changes in the effect on phenotype of a unit of genetic information, such as a single nucleotide polymorphism or a gene, dependent on the context of other genetic units. Such interactions are both biologically plausible and good candidates to explain observations which are not fully explained by an additive heritability model. However, the search for epistasis has so far largely failed to recover this missing heritability. We identify key challenges and propose that future works need to leverage idealized systems, known biology and even previously identified epistatic interactions, in order to guide the search for new interactions.

Rare copy number variant analysis in case-control studies using snp array data: a scalable and automated data analysis pipeline

BACKGROUND

Rare copy number variants (CNVs) significantly influence the human genome and may contribute to disease susceptibility. High-throughput SNP genotyping platforms provide data that can be used for CNV detection, but it requires the complex pipelining of bioinformatic tools. Here, we propose a flexible bioinformatic pipeline for rare CNV analysis from human SNP array data.

RESULTS

The pipeline consists of two major sub-pipelines: (1) Calling and quality control (QC) analysis, and (2) Rare CNV analysis. It is implemented in Snakemake following a rule-based structure that enables automation and scalability while maintaining flexibility.

CONCLUSIONS

Our pipeline automates the detection and analysis of rare CNVs. It implements a rigorous CNV quality control, assesses the frequencies of these rare CNVs in patients versus controls, and evaluates the impact of CNVs on specific genes or pathways. We hence aim to provide an efficient yet flexible bioinformatic framework to investigate rare CNVs in biomedical research.

QTL mapping and genome-wide association analysis reveal genetic loci and candidate gene for resistance to gray leaf spot in tropical and subtropical maize germplasm

KEY MESSAGE

Using QTL mapping and GWAS, two candidate genes (Zm00001d051039 and Zm00001d051147) were consistently identified across the three different environments and BLUP values. GWAS analysis identified the candidate gene, Zm00001d044845. These genes were subsequently validated to exhibit a significant association with maize gray leaf spot (GLS) resistance. Gray leaf spot (GLS) is a major foliar disease of maize (Zea mays L.) that causes significant yield losses worldwide. Understanding the genetic mechanisms underlying gray leaf spot resistance is crucial for breeding high-yielding and disease-resistant varieties. In this study, eight tropical and subtropical germplasms were crossed with the temperate germplasm Ye107 to develop a nested association mapping (NAM) population comprising 1,653 F2:8 RILs, consisting of eight recombinant inbred line (RIL) subpopulations, using the single-seed descent method. The NAM population was evaluated for GLS resistance in three different environments, and genotyping by sequencing of the NAM population generated 593,719 high-quality single-nucleotide polymorphisms (SNPs). Linkage analysis and genome-wide association studies (GWASs) were conducted to identify candidate genes regulating GLS resistance in maize. Both analyses identified 25 QTLs and 149 SNPs that were significantly associated with GLS resistance. Candidate genes were screened 20 Kb upstream and downstream of the significant SNPs, and three novel candidate genes (Zm00001d051039, Zm00001d051147, and Zm00001d044845) were identified. Zm00001d051039 and Zm00001d051147 were located on chromosome 4 and co-localized in both linkage (qGLS4-1 and qGLS4-2) and GWAS analyses. SNP-138,153,206 was located 0.499 kb downstream of the candidate gene Zm00001d051039, which encodes the protein IN2-1 homolog B, a homolog of glutathione S-transferase (GST). GSTs and protein IN2-1 homolog B scavenge reactive oxygen species under various stress conditions, and GSTs are believed to protect plants from a wide range of biotic and abiotic stresses by detoxifying reactive electrophilic compounds. Zm00001d051147 encodes a probable beta-1,4-xylosyltransferase involved in the biosynthesis of xylan in the cell wall, enhancing resistance. SNP-145,813,215 was located 2.69 kb downstream of the candidate gene. SNP-5,043,412 was consistently identified in three different environments and BLUP values and was located 8.788 kb downstream of the candidate gene Zm00001d044845 on chromosome 9. Zm00001d044845 encodes the U-box domain-containing protein 4 (PUB4), which is involved in regulating plant immunity. qRT-PCR analysis showed that the relative expression levels of the three candidate genes were significantly upregulated in the leaves of the TML139 (resistant) parent, indicating that these three candidate genes could be associated with resistance to GLS. The findings of this study are significant for marker-assisted breeding aimed at enhancing resistance to GLS in maize and lay the foundation for further elucidation of the genetic mechanisms underlying resistance to gray leaf spot in maize and breeding of new disease-resistant varieties.

Population Genomic Scans for Natural Selection and Demography

Uncovering the fundamental processes that shape genomic variation in natural populations is a primary objective of population genetics. These processes include demographic effects such as past changes in effective population size or gene flow between structured populations. Furthermore, genomic variation is affected by selection on nonneutral genetic variants, for example, through the adaptation of beneficial alleles or balancing selection that maintains genetic variation. In this article, we discuss the characterization of these processes using population genetic models, and we review methods developed on the basis of these models to unravel the underlying processes from modern population genomic data sets. We briefly discuss the conditions in which these approaches can be used to infer demography or identify specific nonneutral genetic variants and cases in which caution is warranted. Moreover, we summarize the challenges of jointly inferring demography and selective processes that affect neutral variation genome-wide.

GWAS for identification of genomic regions and candidate genes in vegetable crops

Genome-wide association Studies (GWAS), initially developed for human genetics, have been highly effective in plant research, particularly for vegetable crops. GWAS is a robust tool for identifying genes associated with key traits such as yield, nutritional value, disease resistance, adaptability, and bioactive compound biosynthesis. Unlike traditional methods, GWAS does not require prior biological knowledge and can accurately pinpoint loci, minimizing false positives. The process involves developing a diverse panel, rigorous phenotyping and genotyping, and sophisticated statistical analysis using various models and software tools. By scanning the entire genome, GWAS identifies specific loci or single nucleotide polymorphisms (SNPs) linked to target traits. When a causal SNP variant is not directly genotyped, GWAS identifies SNPs in linkage disequilibrium (LD) with the causal variant, mapping the genetic interval. The method begins with careful panel selection, phenotyping, and genotyping, controlling for environmental effects and utilizing Best Linear Unbiased Prediction (BLUP). High-correlation, high-heritability traits are prioritized. Various genotyping methods address confounders like population structure and kinship. Bonferroni correction (BC) prevents false positives, and significant associations are shown in Manhattan plots. Candidate genes are identified through LD analysis and fine mapping, followed by functional validation. GWAS offers critical insights for enhancing vegetable crop breeding efficiency and precision, driving breakthroughs through advanced methods.

Genome-wide association study of salt tolerance at the seed germination stage in lettuce

Developing lettuce varieties with salt tolerance at the seed germination stage is essential since lettuce seeds are planted half an inch deep in soil where salt levels are often highest in the salinity-affected growing regions. Greater knowledge of genetics and genomics of salt tolerance in lettuce will facilitate breeding of improved lettuce varieties with salt tolerance. Accordingly, we conducted a genome-wide association study (GWAS) in lettuce to identify marker-trait association for salt tolerance at the seed germination stage. The study involved 445 diverse lettuce accessions and 56,820 single nucleotide polymorphism (SNP) markers obtained through genotype-by-sequencing technology using lettuce reference genome version v8. GWAS using two single-locus and three multi-locus models for germination rate (GR) under salinity stress, 5 days post seeding (GR5d_S) and a salinity susceptibility index (SSI) based on GR under salinity stress and control conditions, 5 days post seeding (SSI_GR5d) revealed 10 significant SNPs on lettuce chromosomes 2, 4, and 7. The 10 SNPs were associated with five novel QTLs for salt tolerance in lettuce, explaining phenotyping variations of 5.85%, 4.38%, 4.26%, 3.77%, and 1.80%, indicating the quantitative nature of these two salt tolerance-related traits. Using the basic local alignment search tool (BLAST) within 100 Kb upstream and downstream of each of the 10 SNPs, we identified 25 salt tolerance-related putative candidate genes including four genes encoding for major transcription factors. The 10 significant salt tolerance-related SNPs and the 25 candidate genes identified in the current study will be a valuable resource for molecular marker development and marker-assisted selection for breeding lettuce varieties with improved salt tolerance at the seed germination stage.

BLUPmrMLM: A Fast mrMLM Algorithm in Genome-wide Association Studies

Multilocus genome-wide association study has become the state-of-the-art tool for dissecting the genetic architecture of complex and multiomic traits. However, most existing multilocus methods require relatively long computational time when analyzing large datasets. To address this issue, in this study, we proposed a fast mrMLM method, namely, best linear unbiased prediction multilocus random-SNP-effect mixed linear model (BLUPmrMLM). First, genome-wide single-marker scanning in mrMLM was replaced by vectorized Wald tests based on the best linear unbiased prediction (BLUP) values of marker effects and their variances in BLUPmrMLM. Then, adaptive best subset selection (ABESS) was used to identify potentially associated markers on each chromosome to reduce computational time when estimating marker effects via empirical Bayes. Finally, shared memory and parallel computing schemes were used to reduce the computational time. In simulation studies, BLUPmrMLM outperformed GEMMA, EMMAX, mrMLM, and FarmCPU as well as the control method (BLUPmrMLM with ABESS removed), in terms of computational time, power, accuracy for estimating quantitative trait nucleotide positions and effects, false positive rate, false discovery rate, false negative rate, and F1 score. In the reanalysis of two large rice datasets, BLUPmrMLM significantly reduced the computational time and identified more previously reported genes, compared with the aforementioned methods. This study provides an excellent multilocus model method for the analysis of large-scale and multiomic datasets. The software mrMLM v5.1 is available at BioCode (https://ngdc.cncb.ac.cn/biocode/tool/BT007388) or GitHub (https://github.com/YuanmingZhang65/mrMLM).

The Fate of a Polygenic Phenotype Within the Genomic Landscapes of Introgression in the European Seabass Hybrid Zone

Unraveling the evolutionary mechanisms and consequences of hybridization is a major concern in biology. Many studies have documented the interplay between recombination and selection in modulating the genomic landscape of introgression, but few have considered how associations with phenotype may affect this landscape. Here, we use the European seabass (Dicentrarchus labrax), a key species in marine aquaculture that undergoes natural hybridization, to determine how selection on phenotype modulates the introgression landscape between Atlantic and Mediterranean lineages. We use a high-density single nucleotide polymorphism array to assess individual local ancestry along the genome and improve the mapping of muscle fat content, a polygenic trait that is divergent between lineages. Taking into account variation in recombination rates, we reveal a purging of Atlantic ancestry in the admixed Mediterranean populations. While Atlantic individuals had higher muscle fat content, we observed that genomic regions associated with this trait in Mediterranean populations displayed reduced introgression of Atlantic ancestry. These results emphasize how selection against maladapted alleles shapes the genomic landscape of introgression.

Genomic Profiling of Antimicrobial Resistance Genes in Clinical Salmonella Isolates from Cattle in the Texas Panhandle, USA

Rising antimicrobial resistance (AMR) in Salmonella serotypes host-adapted to cattle is of increasing concern to the beef and dairy industry. The bulk of the existing literature focuses on AMR post-slaughter. In comparison, the understanding of AMR in Salmonella among pre-harvest cattle is still limited, particularly in Texas, which ranks top five in beef and dairy exports in the United States; inherently, the health of Texas cattle has nationwide implications for the health of the United States beef and dairy industry. In this study, long-read whole genome sequencing and bioinformatic methods were utilized to analyze antimicrobial resistance genes (ARGs) in 98 isolates from beef and dairy cattle in the Texas Panhandle. Fisher exact tests and elastic net models accounting for population structure were used to infer associations between genomic ARG profiles and antimicrobial phenotypic profiles and metadata. Gene mapping was also performed to assess the role of mobile genetic elements in harboring ARGs. Antimicrobial resistance genes were found to be statistically different between the type of cattle operation and Salmonella serotypes. Beef operations were statistically significantly associated with more ARGs compared to dairy operations. Salmonella Heidelberg, followed by Salmonella Dublin isolates, were associated with the most ARGs. Additionally, specific classes of ARGs were only present within mobile genetic elements.

Genome Sequencing of Lentinula edodes Revealed a Genomic Variant Block Associated with a Thermo-Tolerant Trait in Fruit Body Formation

The formation of multicellular fruiting bodies in basidiomycete mushrooms is a crucial developmental process for sexual reproduction and subsequent spore development. Temperature is one of the most critical factors influencing the phase transition for mushroom reproduction. During the domestication of mushrooms, traits related to fruiting bodies have significantly impacted agricultural adaptation and human preferences. Recent research has demonstrated that chromosomal variations, such as structural variants (SVs) and variant blocks (VBs), play crucial roles in agronomic traits and evolutionary processes. However, the lack of high-quality genomic information and important trait data have hindered comprehensive identification and characterization in Lentinula edodes breeding processes. In this study, the genomes of two monokaryotic L. edodes strains, characterized by thermo-tolerance and thermo-sensitivity during fruiting body formation, were reassembled at the chromosomal level. Comparative genomic studies of four thermo-tolerant and thermo-sensitive monokaryotic L. edodes strains identified a 0.56 Mbp variant block on chromosome 9. Genes associated with DNA repair or cellular response to DNA damage stimulus were enriched in this variant block. Finally, we developed eight CAPS markers from the variant block to discriminate the thermo-tolerant traits in L. edodes cultivars. Our findings show that the identified variant block is highly correlated with the thermo-tolerant trait for fruiting body formation and that alleles present in this block may have been artificially selected during L. edodes domestication.

Identifying the physiological traits associated with DNA marker using genome wide association in wheat under heat stress

Heat stress poses a significant environmental challenge that profoundly impacts wheat productivity. It disrupts vital physiological processes such as photosynthesis, by impeding the functionality of the photosynthetic apparatus and compromising plasma membrane stability, thereby detrimentally affecting grain development in wheat. The scarcity of identified marker trait associations pertinent to thermotolerance presents a formidable obstacle in the development of marker-assisted selection strategies against heat stress. To address this, wheat accessions were systematically exposed to both normal and heat stress conditions and phenotypic data were collected on physiological traits including proline content, canopy temperature depression, cell membrane injury, photosynthetic rate, transpiration rate (at vegetative and reproductive stage and 'stay-green'. Principal component analysis elucidated the most significant contributors being proline content, transpiration rate, and canopy temperature depression, which exhibited a synergistic relationship with grain yield. Remarkably, cluster analysis delineated the wheat accessions into four discrete groups based on physiological attributes. Moreover, to explore the relationship between physiological traits and DNA markers, 158 wheat accessions were genotyped with 186 SSRs. Allelic frequency and polymorphic information content value were found to be highest on genome A (4.94 and 0.688), chromosome 1A (5.00 and 0.712), and marker Xgwm44 (13.0 and 0.916). Population structure, principal coordinate analysis and cluster analysis also partitioned the wheat accessions into four subpopulations based on genotypic data, highlighting their genetic homogeneity. Population diversity and presence of linkage disequilibrium established the suitability of population for association mapping. Additionally, linkage disequilibrium decay was most pronounced within a 15-20 cM region on chromosome 1A. Association mapping revealed highly significant marker trait associations at Bonferroni correction P < 0.00027. Markers Xwmc418 (located on chromosome 3D) and Xgwm233 (chromosome 7A) demonstrated associations with transpiration rate, while marker Xgwm494 (chromosome 3A) exhibited an association with photosynthetic rates at both vegetative and reproductive stages under heat stress conditions. Additionally, markers Xwmc201 (chromosome 6A) and Xcfa2129 (chromosome 1A) displayed robust associations with canopy temperature depression, while markers Xbarc163 (chromosome 4B) and Xbarc49 (chromosome 5A) were strongly associated with cell membrane injury at both stages. Notably, marker Xbarc49 (chromosome 5A) exhibited a significant association with the 'stay-green' trait under heat stress conditions. These results offers the potential utility in marker-assisted selection, gene pyramiding and genomic selection models to predict performance of wheat accession under heat stress conditions.

Characterizing features affecting local ancestry inference performance in admixed populations

In recent years, significant efforts have been made to improve methods for genomic studies of admixed populations using Local Ancestry Inference (LAI). Accurate LAI is crucial to ensure downstream analyses reflect the genetic ancestry of research participants accurately. Here, we test analytic strategies for LAI to provide guidelines for optimal accuracy, focusing on admixed populations reflective of Latin America's primary continental ancestries - African (AFR), Amerindigenous (AMR), and European (EUR). Simulating LD-informed admixed haplotypes under a variety of 2 and 3-way admixture models, we implemented a standard LAI pipeline, testing three reference panel compositions to quantify their overall and ancestry-specific accuracy. We examined LAI miscall frequencies and true positive rates (TPR) across simulation models and continental ancestries. AMR tracts have notably reduced LAI accuracy as compared to EUR and AFR tracts in all comparisons, with TPR means for AMR ranging from 88-94%, EUR from 96-99% and AFR 98-99%. When LAI miscalls occurred, they most frequently erroneously called European ancestry in true Amerindigenous sites. Using a reference panel well-matched to the target population, even with a lower sample size, LAI produced true-positive estimates that were not statistically different from a high sample size but mismatched reference, while being more computationally efficient. While directly responsive to admixed Latin American cohort compositions, these trends are broadly useful for informing best practices for LAI across other admixed populations. Our findings reinforce the need for inclusion of more underrepresented populations in sequencing efforts to improve reference panels.

Genome-Wide Methylation Analysis Reveals a KCNK3-Prominent Causal Cascade on Hypertension

BACKGROUND

Despite advances in understanding hypertension's genetic structure, how noncoding genetic variants influence it remains unclear. Studying their interaction with DNA methylation is crucial to deciphering this complex disease's genetic mechanisms.

METHODS

We investigated the genetic and epigenetic interplay in hypertension using whole-genome bisulfite sequencing. Methylation profiling in 918 males revealed allele-specific methylation and methylation quantitative trait loci. We engineered rs1275988T/C mutant mice using CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 (CRISPR-associated protein 9), bred them for homozygosity, and subjected them to a high-salt diet. Telemetry captured their cardiovascular metrics. Protein-DNA interactions were elucidated using DNA pull-downs, mass spectrometry, and Western blots. A wire myograph assessed vascular function, and analysis of the Kcnk3 gene methylation highlighted the mutation's role in hypertension.

RESULTS

We discovered that DNA methylation-associated genetic effects, especially in non-cytosine-phosphate-guanine (non-CpG) island and noncoding distal regulatory regions, significantly contribute to hypertension predisposition. We identified distinct methylation quantitative trait locus patterns in the hypertensive population and observed that the onset of hypertension is influenced by the transmission of genetic effects through the demethylation process. By evidence-driven prioritization and in vivo experiments, we unearthed rs1275988 in a cell type-specific enhancer as a notable hypertension causal variant, intensifying hypertension through the modulation of local DNA methylation and consequential alterations in Kcnk3 gene expression and vascular remodeling. When exposed to a high-salt diet, mice with the rs1275988C/C genotype exhibited exacerbated hypertension and significant vascular remodeling, underscored by increased aortic wall thickness. The C allele of rs1275988 was associated with elevated DNA methylation levels, driving down the expression of the Kcnk3 gene by attenuating Nr2f2 (nuclear receptor subfamily 2 group F member 2) binding at the enhancer locus.

CONCLUSIONS

Our research reveals new insights into the complex interplay between genetic variations and DNA methylation in hypertension. We underscore hypomethylation's potential in hypertension onset and identify rs1275988 as a causal variant in vascular remodeling. This work advances our understanding of hypertension's molecular mechanisms and encourages personalized health care strategies.

Genome-wide association study and expression of candidate genes for Fe and Zn concentration in sorghum grains

Sorghum germplasm showed grain Fe and Zn genetic variability, but a few varieties were biofortified with these minerals. This work contributes to narrowing this gap. Fe and Zn concentrations along with 55,068 high-quality GBS SNP data from 140 sorghum accessions were used in this study. Both micronutrients exhibited good variability with respective ranges of 22.09-52.55 ppm and 17.92-43.16 ppm. Significant marker-trait associations were identified on chromosomes 1, 3, and 5. Two major effect SNPs (S01_72265728 and S05_58213541) explained 35% and 32% of Fe and Zn phenotypic variance, respectively. The SNP S01_72265728 was identified in the cytochrome P450 gene and showed a positive effect on Fe accumulation in the kernel, while S05_58213541 was intergenic near Sobic.005G134800 (zinc-binding ribosomal protein) and showed negative effect on Zn. Tissue-specific in silico expression analysis resulted in higher levels of Sobic.003G350800 gene product in several tissues such as leaf, root, flower, panicle, and stem. Sobic.005G188300 and Sobic.001G463800 were expressed moderately at grain maturity and anthesis in leaf, root, panicle, and seed tissues. The candidate genes expressed in leaves, stems, and grains will be targeted to improve grain and stover quality. The haplotypes identified will be useful in forward genetics breeding.

Latin American Trans-ancestry INitiative for OCD genomics (LATINO): Study protocol

Obsessive-compulsive disorder (OCD) is a debilitating psychiatric disorder. Worldwide, its prevalence is ~2% and its etiology is mostly unknown. Identifying biological factors contributing to OCD will elucidate underlying mechanisms and might contribute to improved treatment outcomes. Genomic studies of OCD are beginning to reveal long-sought risk loci, but >95% of the cases currently in analysis are of homogenous European ancestry. If not addressed, this Eurocentric bias will result in OCD genomic findings being more accurate for individuals of European ancestry than other ancestries, thereby contributing to health disparities in potential future applications of genomics. In this study protocol paper, we describe the Latin American Trans-ancestry INitiative for OCD genomics (LATINO, https://www.latinostudy.org). LATINO is a new network of investigators from across Latin America, the United States, and Canada who have begun to collect DNA and clinical data from 5000 richly phenotyped OCD cases of Latin American ancestry in a culturally sensitive and ethical manner. In this project, we will utilize trans-ancestry genomic analyses to accelerate the identification of OCD risk loci, fine-map putative causal variants, and improve the performance of polygenic risk scores in diverse populations. We will also capitalize on rich clinical data to examine the genetics of treatment response, biologically plausible OCD subtypes, and symptom dimensions. Additionally, LATINO will help elucidate the diversity of the clinical presentations of OCD across cultures through various trainings developed and offered in collaboration with Latin American investigators. We believe this study will advance the important goal of global mental health discovery and equity.

Genome-wide association studies reveal novel loci controlling tuber flesh color and oxidative browning in Dioscorea alata

BACKGROUND

Consumers' preferences for food crops are guided by quality attributes. This study aimed at deciphering the genetic basis of quality traits, especially tuber flesh color (FC) and oxidative browning (OB) in Dioscorea alata, based on the genome-wide association studies (GWAS) approach. The D. alata panel was planted at two locations in Guadeloupe. At harvest, the FC was scored visually as white, cream, or purple on longitudinally sliced mature tubers. The OB was scored visually as the presence or absence of browning after 15 min of exposure of the sliced samples to ambient air.

RESULTS

Phenotypic characterization for FC and OB of a diverse panel of D. alata genotypes highlighted significant variation within the panel and across two locations. The genotypes within the panel displayed a weak structure and could be classified into three subpopulations. GWAS identified 14 and 4 significant associations for tuber FC and OB, respectively, with phenotypic variance, explained values ranging from 7.18% to 18.04%. Allele segregation analysis at the significantly associated loci highlighted the favorable alleles for the desired traits, i.e., white FC and no OB. A total of 24 putative candidate genes were identified around the significant signals. A comparative analysis with previously reported quantitative trait loci indicated that numerous genomic regions control these traits in D. alata.

CONCLUSION

Our study provides important insights into the genetic control of tuber FC and OB in D. alata. The major and stable loci can be further utilized to improve selection in breeding programs for developing new cultivars with enhanced tuber quality. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Genetic Dissection of Diverse Seed Coat Patterns in Cowpea through a Comprehensive GWAS Approach

This study investigates the genetic determinants of seed coat color and pattern variations in cowpea (Vigna unguiculata), employing a genome-wide association approach. Analyzing a mapping panel of 296 cowpea varieties with 110,000 single nucleotide polymorphisms (SNPs), we focused on eight unique coat patterns: (1) Red and (2) Cream seed; (3) White and (4) Brown/Tan seed coat; (5) Pink, (6) Black, (7) Browneye and (8) Red/Brown Holstein. Across six GWAS models (GLM, SRM, MLM, MLMM, FarmCPU from GAPIT3, and TASSEL5), 13 significant SNP markers were identified and led to the discovery of 23 candidate genes. Among these, four specific genes may play a direct role in determining seed coat pigment. These findings lay a foundational basis for future breeding programs aimed at creating cowpea varieties aligned with consumer preferences and market requirements.

Genetically correlated leaf tensile and morphological traits are driven by growing season length in a widespread perennial grass

PREMISE

Leaf tensile resistance, a leaf's ability to withstand pulling forces, is an important determinant of plant ecological strategies. One potential driver of leaf tensile resistance is growing season length. When growing seasons are long, strong leaves, which often require more time and resources to construct than weak leaves, may be more advantageous than when growing seasons are short. Growing season length and other ecological conditions may also impact the morphological traits that underlie leaf tensile resistance.

METHODS

To understand variation in leaf tensile resistance, we measured size-dependent leaf strength and size-independent leaf toughness in diverse genotypes of the widespread perennial grass Panicum virgatum (switchgrass) in a common garden. We then used quantitative genetic approaches to estimate the heritability of leaf tensile resistance and whether there were genetic correlations between leaf tensile resistance and other morphological traits.

RESULTS

Leaf tensile resistance was positively associated with aboveground biomass (a proxy for fitness). Moreover, both measures of leaf tensile resistance exhibited high heritability and were positively genetically correlated with leaf lamina thickness and leaf mass per area (LMA). Leaf tensile resistance also increased with the growing season length in the habitat of origin, and this effect was mediated by both LMA and leaf thickness.

CONCLUSIONS

Differences in growing season length may promote selection for different leaf lifespans and may explain existing variation in leaf tensile resistance in P. virgatum. In addition, the high heritability of leaf tensile resistance suggests that P. virgatum will be able to respond to climate change as growing seasons lengthen.

The role of interaction between vitamin D and VDR FokI gene polymorphism (rs2228570) in sleep quality of adults

To evaluate association of vitamin D with sleep quality in adults and the influence of VDR-gene polymorphism FokI (rs2228570;A > G). Cross-sectional population-based study in adults, conducted in Brazil. The outcome was sleep-quality, evaluated by the Pittsburgh Sleep Quality Index. Vitamin D was determined by indirect electrochemiluminescence and classified as deficiency (VDD), 25(OH)D < 20 ng/mL in a healthy population or 25(OH)D < 30 ng/mL for groups at risk for VDD. FokI polymorphism in the VDR-gene was genotyped by qPCR and classified as homozygous wild (FF or AA), heterozygous (Ff or AG), or homozygous mutant (ff or GG). Multivariate logistic analysis was used to estimate the association between vitamin D and FokI polymorphism with sleep-quality. In a total of 1674 individuals evaluated, 53.6% had poor-sleep-quality, 31.5% had VDD, and the genotype frequency of the FokI polymorphism was 9.9% FF, 44.6% Ff, and 45.5% ff. In multivariate analysis, individuals with VDD had 1.51 times the chance of poor-sleep-quality, and individuals with the ff genotype had 1.49 times the chance of poor-sleep-quality (OR:1.49;95%CI:1.05-2.12) when compared to individuals with the FF or Ff genotype. In the combined analysis, individuals with VDD and ff genotype had more chance of poor-sleep-quality than individuals with sufficient vitamin D and genotype Ff or FF (OR:2.19;95%CI:1.27-3.76). Our data suggest that VDD and VDR FokI gene polymorphism are associated with poor-sleep-quality, and combining the two factors increases the chance of poor-sleep-quality compared to separate groups.

Gene-based association tests in family samples using GWAS summary statistics

Genome-wide association studies (GWAS) have led to rapid growth in detecting genetic variants associated with various phenotypes. Owing to a great number of publicly accessible GWAS summary statistics, and the difficulty in obtaining individual-level genotype data, many existing gene-based association tests have been adapted to require only GWAS summary statistics rather than individual-level data. However, these association tests are restricted to unrelated individuals and thus do not apply to family samples directly. Moreover, due to its flexibility and effectiveness, the linear mixed model has been increasingly utilized in GWAS to handle correlated data, such as family samples. However, it remains unknown how to perform gene-based association tests in family samples using the GWAS summary statistics estimated from the linear mixed model. In this study, we show that, when family size is negligible compared to the total sample size, the diagonal block structure of the kinship matrix makes it possible to approximate the correlation matrix of marginal Z scores by linkage disequilibrium matrix. Based on this result, current methods utilizing summary statistics for unrelated individuals can be directly applied to family data without any modifications. Our simulation results demonstrate that this proposed strategy controls the type 1 error rate well in various situations. Finally, we exemplify the usefulness of the proposed approach with a dental caries GWAS data set.

The relationship between extreme inter-individual variation in macrophage gene expression and genetic susceptibility to inflammatory bowel disease

The differentiation of resident intestinal macrophages from blood monocytes depends upon signals from the macrophage colony-stimulating factor receptor (CSF1R). Analysis of genome-wide association studies (GWAS) indicates that dysregulation of macrophage differentiation and response to microorganisms contributes to susceptibility to chronic inflammatory bowel disease (IBD). Here, we analyzed transcriptomic variation in monocyte-derived macrophages (MDM) from affected and unaffected sib pairs/trios from 22 IBD families and 6 healthy controls. Transcriptional network analysis of the data revealed no overall or inter-sib distinction between affected and unaffected individuals in basal gene expression or the temporal response to lipopolysaccharide (LPS). However, the basal or LPS-inducible expression of individual genes varied independently by as much as 100-fold between subjects. Extreme independent variation in the expression of pairs of HLA-associated transcripts (HLA-B/C, HLA-A/F and HLA-DRB1/DRB5) in macrophages was associated with HLA genotype. Correlation analysis indicated the downstream impacts of variation in the immediate early response to LPS. For example, variation in early expression of IL1B was significantly associated with local SNV genotype and with subsequent peak expression of target genes including IL23A, CXCL1, CXCL3, CXCL8 and NLRP3. Similarly, variation in early IFNB1 expression was correlated with subsequent expression of IFN target genes. Our results support the view that gene-specific dysregulation in macrophage adaptation to the intestinal milieu is associated with genetic susceptibility to IBD.

Interaction models matter: an efficient, flexible computational framework for model-specific investigation of epistasis

PURPOSE

Epistasis, the interaction between two or more genes, is integral to the study of genetics and is present throughout nature. Yet, it is seldom fully explored as most approaches primarily focus on single-locus effects, partly because analyzing all pairwise and higher-order interactions requires significant computational resources. Furthermore, existing methods for epistasis detection only consider a Cartesian (multiplicative) model for interaction terms. This is likely limiting as epistatic interactions can evolve to produce varied relationships between genetic loci, some complex and not linearly separable.

METHODS

We present new algorithms for the interaction coefficients for standard regression models for epistasis that permit many varied models for the interaction terms for loci and efficient memory usage. The algorithms are given for two-way and three-way epistasis and may be generalized to higher order epistasis. Statistical tests for the interaction coefficients are also provided. We also present an efficient matrix based algorithm for permutation testing for two-way epistasis. We offer a proof and experimental evidence that methods that look for epistasis only at loci that have main effects may not be justified. Given the computational efficiency of the algorithm, we applied the method to a rat data set and mouse data set, with at least 10,000 loci and 1,000 samples each, using the standard Cartesian model and the XOR model to explore body mass index.

RESULTS

This study reveals that although many of the loci found to exhibit significant statistical epistasis overlap between models in rats, the pairs are mostly distinct. Further, the XOR model found greater evidence for statistical epistasis in many more pairs of loci in both data sets with almost all significant epistasis in mice identified using XOR. In the rat data set, loci involved in epistasis under the XOR model are enriched for biologically relevant pathways.

CONCLUSION

Our results in both species show that many biologically relevant epistatic relationships would have been undetected if only one interaction model was applied, providing evidence that varied interaction models should be implemented to explore epistatic interactions that occur in living systems.

Sex-dependent placental methylation quantitative trait loci provide insight into the prenatal origins of childhood onset traits and conditions

Molecular quantitative trait loci (QTLs) allow us to understand the biology captured in genome-wide association studies (GWASs). The placenta regulates fetal development and shows sex differences in DNA methylation. We therefore hypothesized that placental methylation QTL (mQTL) explain variation in genetic risk for childhood onset traits, and that effects differ by sex. We analyzed 411 term placentas from two studies and found 49,252 methylation (CpG) sites with mQTL and 2,489 CpG sites with sex-dependent mQTL. All mQTL were enriched in regions that typically affect gene expression in prenatal tissues. All mQTL were also enriched in GWAS results for growth- and immune-related traits, but male- and female-specific mQTL were more enriched than cross-sex mQTL. mQTL colocalized with trait loci at 777 CpG sites, with 216 (28%) specific to males or females. Overall, mQTL specific to male and female placenta capture otherwise overlooked variation in childhood traits.

IL-10 (-1082 G/A) polymorphism in Bataknese with schizophrenia

Objectives

Three biallelic polymorphisms at the promoter region of the interleukin-10 (IL-10) gene have been associated with susceptibility to schizophrenia. The aim of this case-control study was to investigate the association between IL-10 (-1082) G/A gene polymorphisms and schizophrenia among Bataknese, a native tribe inhabiting the North Sumatera province in Indonesia.

Methods

A total of 194 unrelated participants (n = 97 for each case and control groups) participated in this study. Polymerase chain reaction restriction fragment length polymorphism molecular genotyping was conducted to assess the genotype and allele distribution of IL-10 (-1082 G/A).

Results

Allele variations indicated that the dominant allele in the Batak tribe was allele A, whereas homozygous GG genotypes were not found in either group. The A allele and AA genotype were found to be risk factors for developing schizophrenia (OR = 2.26, 95% CI = 1.1825-4.3559 and OR = 2.56, 95% CI = 1.280-5.152, respectively).

Conclusion

Only the A allele and AA genotype of the IL-10 gene polymorphism at -1082 G/A contribute to schizophrenia susceptibility in Bataknese.

Genetic analyses and prediction for lodging‑related traits in a diverse Iranian hexaploid wheat collection

Lodging is one of the most important limiting environmental factors for achieving the maximum yield and quality of grains in cereals, including wheat. However, little is known about the genetic foundation underlying lodging resistance (LR) in wheat. In this study, 208 landraces and 90 cultivars were phenotyped in two cropping seasons (2018-2019 and 2019-2020) for 19 LR-related traits. A genome-wide association study (GWAS) and genomics prediction were carried out to dissect the genomic regions of LR. The number of significant marker pairs (MPs) was highest for genome B in both landraces (427,017) and cultivars (37,359). The strongest linkage disequilibrium (LD) between marker pairs was found on chromosome 4A (0.318). For stem lodging-related traits, 465, 497, and 478 marker-trait associations (MTAs) and 45 candidate genes were identified in year 1, year 2, and pooled. Gene ontology exhibited genomic region on Chr. 2B, 6B, and 7B control lodging. Most of these genes have key roles in defense response, calcium ion transmembrane transport, carbohydrate metabolic process, nitrogen compound metabolic process, and some genes harbor unknown functions that, all together may respond to lodging as a complex network. The module associated with starch and sucrose biosynthesis was highlighted. Regarding genomic prediction, the GBLUP model performed better than BRR and RRBLUP. This suggests that GBLUP would be a good tool for wheat genome selection. As a result of these findings, it has been possible to identify pivotal QTLs and genes that could be used to improve stem lodging resistance in Triticum aestivum L.

kGWASflow: a modular, flexible, and reproducible Snakemake workflow for k-mers-based GWAS

Genome-wide association studies (GWAS) have been widely used to identify genetic variation associated with complex traits. Despite its success and popularity, the traditional GWAS approach comes with a variety of limitations. For this reason, newer methods for GWAS have been developed, including the use of pan-genomes instead of a reference genome and the utilization of markers beyond single-nucleotide polymorphisms, such as structural variations and k-mers. The k-mers-based GWAS approach has especially gained attention from researchers in recent years. However, these new methodologies can be complicated and challenging to implement. Here, we present kGWASflow, a modular, user-friendly, and scalable workflow to perform GWAS using k-mers. We adopted an existing kmersGWAS method into an easier and more accessible workflow using management tools like Snakemake and Conda and eliminated the challenges caused by missing dependencies and version conflicts. kGWASflow increases the reproducibility of the kmersGWAS method by automating each step with Snakemake and using containerization tools like Docker. The workflow encompasses supplemental components such as quality control, read-trimming procedures, and generating summary statistics. kGWASflow also offers post-GWAS analysis options to identify the genomic location and context of trait-associated k-mers. kGWASflow can be applied to any organism and requires minimal programming skills. kGWASflow is freely available on GitHub (https://github.com/akcorut/kGWASflow) and Bioconda (https://anaconda.org/bioconda/kgwasflow).

An evolution-based framework for describing human gut bacteria

The human gut microbiome contains many bacterial strains of the same species ('strain-level variants'). Describing strains in a biologically meaningful way rather than purely taxonomically is an important goal but challenging due to the genetic complexity of strain-level variation. Here, we measured patterns of co-evolution across >7,000 strains spanning the bacterial tree-of-life. Using these patterns as a prior for studying hundreds of gut commensal strains that we isolated, sequenced, and metabolically profiled revealed widespread structure beneath the phylogenetic level of species. Defining strains by their co-evolutionary signatures enabled predicting their metabolic phenotypes and engineering consortia from strain genome content alone. Our findings demonstrate a biologically relevant organization to strain-level variation and motivate a new schema for describing bacterial strains based on their evolutionary history.

Genome-wide association studies and haplotype sharing analysis targeting the growth traits in Yandang partridge chickens

The body size of a chicken is an economically important trait as it directly influences the benefits of the poultry industry, but the relevant genetic mechanisms have not yet been elucidated. In this study, we measured eight growth traits for 94 Yandang partridge chickens, then undertook genome-wide association studies (GWAS) for those traits in using a linear mixed model based on 10× whole genomic sequencing data to better understand the knowledge of the genetic architecture of growth traits. Ninety-four individuals and 7647883 SNPs remained after quality control and removal of the sex chromosomes, and these data were used to carry out a GWAS analysis. The result showed that only one significant single-nucleotide polymorphisms (SNP) locates at 14852873 bp on SSC13 surpassed the genome-wide significance level for Keel length (KL). Through linkage disequilibrium analysis and haplotype sharing analysis, we identified one haplotype underlying the SSC13 significantly associated with KL, which could be selected as a potential candidate haplotype that is used in molecular breeding of Yandang partridge chicken. On the other hand, we have learned from a method called bootstrap testing to verify the reliability of GWAS with small experimental samples, which users can access at https://github.com/xuwenwu24/Bootstrap-test.

Genome-Wide Association Study of Fiber Diameter in Alpacas

The aim of this study was the identification of candidate genomic regions associated with fiber diameter in alpacas. DNA samples were collected from 1011 female Huacaya alpacas from two geographical Andean regions in Peru (Pasco and Puno), and three alpaca farms within each region. The samples were genotyped using an Affymetrix Custom Alpaca genotyping array containing 76,508 SNPs. After the quality controls, 960 samples and 51,742 SNPs were retained. Three association study methodologies were performed. The GWAS based on a linear model allowed us to identify 11 and 35 SNPs (-log10(p-values) > 4) using information on all alpacas and alpacas with extreme values of fiber diameter, respectively. The haplotype and marker analysis method allowed us to identify nine haplotypes with standardized haplotype heritability higher than six standard deviations. The selection signatures based on cross-population extended haplotype homozygosity (XP-EHH) allowed us to identify 180 SNPs with XP-EHH values greater than |3|. Four candidate regions with adjacent SNPs identified via two association methods of analysis are located on VPA6, VPA9, VPA29 and one chromosomally unassigned scaffold. This study represents the first analysis of alpaca whole genome association with fiber diameter, using a recently assembled alpaca SNP microarray.

Demographic modeling of admixed Latin American populations from whole genomes

Demographic models of Latin American populations often fail to fully capture their complex evolutionary history, which has been shaped by both recent admixture and deeper-in-time demographic events. To address this gap, we used high-coverage whole-genome data from Indigenous American ancestries in present-day Mexico and existing genomes from across Latin America to infer multiple demographic models that capture the impact of different timescales on genetic diversity. Our approach, which combines analyses of allele frequencies and ancestry tract length distributions, represents a significant improvement over current models in predicting patterns of genetic variation in admixed Latin American populations. We jointly modeled the contribution of European, African, East Asian, and Indigenous American ancestries into present-day Latin American populations. We infer that the ancestors of Indigenous Americans and East Asians diverged ∼30 thousand years ago, and we characterize genetic contributions of recent migrations from East and Southeast Asia to Peru and Mexico. Our inferred demographic histories are consistent across different genomic regions and annotations, suggesting that our inferences are robust to the potential effects of linked selection. In conjunction with published distributions of fitness effects for new nonsynonymous mutations in humans, we show in large-scale simulations that our models recover important features of both neutral and deleterious variation. By providing a more realistic framework for understanding the evolutionary history of Latin American populations, our models can help address the historical under-representation of admixed groups in genomics research and can be a valuable resource for future studies of populations with complex admixture and demographic histories.

Strategies for the Genomic Analysis of Admixed Populations

Admixed populations constitute a large portion of global human genetic diversity, yet they are often left out of genomics analyses. This exclusion is problematic, as it leads to disparities in the understanding of the genetic structure and history of diverse cohorts and the performance of genomic medicine across populations. Admixed populations have particular statistical challenges, as they inherit genomic segments from multiple source populations-the primary reason they have historically been excluded from genetic studies. In recent years, however, an increasing number of statistical methods and software tools have been developed to account for and leverage admixture in the context of genomics analyses. Here, we provide a survey of such computational strategies for the informed consideration of admixture to allow for the well-calibrated inclusion of mixed ancestry populations in large-scale genomics studies, and we detail persisting gaps in existing tools.

The design of mapping populations: Impacts of geographic scale on genetic architecture and mapping efficacy for defense and immunity

Genome-wide association studies (GWAS) have yielded tremendous insight into the genetic architecture of trait variation. However, the collections of loci they uncover are far from exhaustive. As many of the complicating factors that confound or limit the efficacy of GWAS are exaggerated over broad geographic scales, a shift toward more analyses using mapping panels sampled from narrow geographic localities ("local" populations) could provide novel, complementary insights. Here, we present an overview of the major complicating factors, review mounting evidence from genomic analyses that these factors are pervasive, and synthesize theoretical and empirical evidence for the power of GWAS in local populations.

Molecular characterization of doubled haploid lines derived from different cycles of the Iowa Stiff Stalk Synthetic (BSSS) maize population

Molecular characterization of a given set of maize germplasm could be useful for understanding the use of the assembled germplasm for further improvement in a breeding program, such as analyzing genetic diversity, selecting a parental line, assigning heterotic groups, creating a core set of germplasm and/or performing association analysis for traits of interest. In this study, we used single nucleotide polymorphism (SNP) markers to assess the genetic variability in a set of doubled haploid (DH) lines derived from the unselected Iowa Stiff Stalk Synthetic (BSSS) maize population, denoted as C0 (BSSS(R)C0), the seventeenth cycle of reciprocal recurrent selection in BSSS (BSSS(R)C17), denoted as C17 and the cross between BSSS(R)C0 and BSSS(R)C17 denoted as C0/C17. With the aim to explore if we have potentially lost diversity from C0 to C17 derived DH lines and observe whether useful genetic variation in C0 was left behind during the selection process since C0 could be a reservoir of genetic diversity that could be untapped using DH technology. Additionally, we quantify the contribution of the BSSS progenitors in each set of DH lines. The molecular characterization analysis confirmed the apparent separation and the loss of genetic variability from C0 to C17 through the recurrent selection process. Which was observed by the degree of differentiation between the C0_DHL versus C17_DHL groups by Wright's F-statistics (FST). Similarly for the population structure based on principal component analysis (PCA) revealed a clear separation among groups of DH lines. Some of the progenitors had a higher genetic contribution in C0 compared with C0/C17 and C17 derived DH lines. Although genetic drift can explain most of the genetic structure genome-wide, phenotypic data provide evidence that selection has altered favorable allele frequencies in the BSSS maize population through the reciprocal recurrent selection program.

The STROMICS genome study: deep whole-genome sequencing and analysis of 10K Chinese patients with ischemic stroke reveal complex genetic and phenotypic interplay

Ischemic stroke is a leading cause of global mortality and long-term disability. However, there is a paucity of whole-genome sequencing studies on ischemic stroke, resulting in limited knowledge of the interplay between genomic and phenotypic variations among affected patients. Here, we outline the STROMICS design and present the first whole-genome analysis on ischemic stroke by deeply sequencing and analyzing 10,241 stroke patients from China. We identified 135.59 million variants, > 42% of which were novel. Notable disparities in allele frequency were observed between Chinese and other populations for 89 variants associated with stroke risk and 10 variants linked to response to stroke medications. We investigated the population structure of the participants, generating a map of genetic selection consisting of 31 adaptive signals. The adaption of the MTHFR rs1801133-G allele, which links to genetically evaluated VB9 (folate acid) in southern Chinese patients, suggests a gene-specific folate supplement strategy. Through genome-wide association analysis of 18 stroke-related traits, we discovered 10 novel genetic-phenotypic associations and extensive cross-trait pleiotropy at 6 lipid-trait loci of therapeutic relevance. Additionally, we found that the set of loss-of-function and cysteine-altering variants present in the causal gene NOTCH3 for the autosomal dominant stroke disorder CADASIL displayed a broad neuro-imaging spectrum. These findings deepen our understanding of the relationship between the population and individual genetic layout and clinical phenotype among stroke patients, and provide a foundation for future efforts to utilize human genetic knowledge to investigate mechanisms underlying ischemic stroke outcomes, discover novel therapeutic targets, and advance precision medicine.

Admixture mapping implicates 13q33.3 as ancestry-of-origin locus for Alzheimer disease in Hispanic and Latino populations

Alzheimer disease (AD) is the most common form of senile dementia, with high incidence late in life in many populations including Caribbean Hispanic (CH) populations. Such admixed populations, descended from more than one ancestral population, can present challenges for genetic studies, including limited sample sizes and unique analytical constraints. Therefore, CH populations and other admixed populations have not been well represented in studies of AD, and much of the genetic variation contributing to AD risk in these populations remains unknown. Here, we conduct genome-wide analysis of AD in multiplex CH families from the Alzheimer Disease Sequencing Project (ADSP). We developed, validated, and applied an implementation of a logistic mixed model for admixture mapping with binary traits that leverages genetic ancestry to identify ancestry-of-origin loci contributing to AD. We identified three loci on chromosome 13q33.3 associated with reduced risk of AD, where associations were driven by Native American (NAM) ancestry. This AD admixture mapping signal spans the FAM155A, ABHD13, TNFSF13B, LIG4, and MYO16 genes and was supported by evidence for association in an independent sample from the Alzheimer's Genetics in Argentina-Alzheimer Argentina consortium (AGA-ALZAR) study with considerable NAM ancestry. We also provide evidence of NAM haplotypes and key variants within 13q33.3 that segregate with AD in the ADSP whole-genome sequencing data. Interestingly, the widely used genome-wide association study approach failed to identify associations in this region. Our findings underscore the potential of leveraging genetic ancestry diversity in recently admixed populations to improve genetic mapping, in this case for AD-relevant loci.