The effects of human population structure on large genetic association studies

Large-scale cross-trait genetic analysis highlights shared genetic backgrounds of autoimmune diseases

Disorders associated with the immune system burden multiple organs, although the shared biology exists across the diseases. Preceding family-based studies reveal that immune diseases are heritable to varying degrees, providing the basis for immunogenomics. The recent cost reduction in genetic analysis intensively promotes biobank-scale studies and the development of frameworks for statistical genetics. The accumulating multi-layer omics data, including genome-wide association studies (GWAS) and RNA-sequencing at single-cell resolution, enable us to dissect the genetic backgrounds of immune-related disorders. Although autoimmune and allergic diseases are generally categorized into different disease categories, epidemiological studies reveal the high incidence of autoimmune and allergic disease complications, suggesting the shared genetics and biology between the disease categories. Biobank resources and consortia cover multiple immune-related disorders to accumulate phenome-wide associations of genetic variants and enhance researchers to analyze the shared and heterogeneous genetic backgrounds. The emerging post-GWAS and integrative multi-omics analyses provide genetic and biological insights into the multicategorical disease associations.

Circum-Mediterranean influence in the Y-chromosome lineages associated with prostate cancer in Mexican men: A Converso heritage founder effect?

Prostate cancer is the second most common neoplasia amongst men worldwide. Hereditary susceptibility and ancestral heritage are well-established risk factors that explain the disparity trends across different ethnicities, populations, and regions even within the same country. The Y-chromosome has been considered a prototype biomarker for male health. African, European, Middle Eastern, and Hispanic ancestries exhibit the highest incidences of such neoplasia; Asians have the lowest rates. Nonetheless, the contribution of ancestry patterns has been scarcely explored among Latino males. The Mexican population has an extremely diverse genetic architecture where all the aforementioned ancestral backgrounds converge. Trans-ethnic research could illuminate the aetiology of prostate cancer, involving the migratory patterns, founder effects, and the ethnic contributions to its disparate incidence rates. The contribution of the ancestral heritage to prostate cancer risk were explored through a case-control study (152 cases and 372 controls) study in Mexican Mestizo males. Seventeen microsatellites were used to trace back the ancestral heritage using two Bayesian predictor methods. The lineage R1a seems to contribute to prostate cancer (ORadjusted:8.04, 95%CI:1.41-45.80) development, whereas E1b1a/E1b1b and GHIJ contributed to well-differentiated (Gleason ≤ 7), and late-onset prostate cancer. Meta-analyses reinforced our findings. The mentioned lineages exhibited a connection with the Middle Eastern and North African populations that enriched the patrilineal diversity to the southeast region of the Iberian Peninsula. This ancestral legacy arrived at the New World with the Spanish and Sephardim migrations. Our findings reinforced the contribution of family history and ethnic background to prostate cancer risk, although should be confirmed using a large sample size. Nonetheless, given its complex aetiology, in addition to the genetic component, the lifestyle and xenobiotic exposition could also influence the obtained results.

Copy-number variants differ in frequency across genetic ancestry groups

Copy-number variants (CNVs) have been implicated in a variety of neuropsychiatric and cognitive phenotypes. We found that deleterious CNVs are less prevalent in non-European ancestry groups than they are in European ancestry groups of both the UK Biobank (UKBB) and a US replication cohort (SPARK). We also identified specific recurrent CNVs that consistently differ in frequency across ancestry groups in both the UKBB and SPARK. These ancestry-related differences in CNV prevalence present in both an unselected community population and a family cohort enriched with individuals diagnosed with autism spectrum disorder (ASD) strongly suggest that genetic ancestry should be considered when probing associations between CNVs and health outcomes.

Genomic Characterization of Quality Wool Traits in Spanish Merino Sheep

The native Spanish Merino breed was the founder of all the other Merino and Merino-derived breeds worldwide. Despite the fact that this breed was created and improved to produce the highest quality fine wool, the global wool market crisis led to the wholescale crossing of most of the herds with breeds for meat purposes. Nevertheless, there are still some purebred animals with a high potential for producing quality wool. The objective of this study was to characterize the current wool quality of the breed and identify genes associated with these parameters. To achieve this, over 12,800 records from the most representative animals of the breed (registered in the herd book) were analyzed using the Australian OFDA 2000 system, for parameters such as fiber diameter (FD), standard deviation (SD), coefficient of variation (CV), fibers over 15 microns (>15%), staple length (SL), and comfort factor (CRV). Additionally, animals with the most extreme FD values were whole-genome sequenced using NGS. Genome-wide association studies (GWAS) determined the association of 74 variants with the different traits studied, which were located in 70 different genes. Of these genes, EDN2, COL18A1, and LRP1B, associated with fibers over 15%, and FGF12 and ADAM17, associated with SL, play a key role in hair follicle growth and development. Our study reveals the great potential for recovering this breed for fine wool production, and identifies five candidate genes whose understanding may aid in that selection process.

Genetic history of esophageal cancer group in southwestern China revealed by Y-chromosome STRs and genomic evolutionary connection analysis

Genetic and environmental factors play crucial roles in the development of esophageal cancer (EC) and contribute uniquely or cooperatively to human cancer susceptibility. Sichuan is located in the interior of southwestern China, and the northern part of Sichuan is one of the regions with a high occurrence of EC. However, the factors influencing the high incidence rate of EC in the Sichuan Han Chinese population and its corresponding genetic background and origins are still poorly understood. Here, we utilized genome-wide single nucleotide polymorphisms (SNPs) and Y-chromosome short tandem repeats (Y-STRs) to characterize the genetic structure, connection, and origin of cancer groups and general populations. We generated Y-STR-based haplotype data from 214 Sichuan individuals, including the Han Chinese EC population and a control group of Han Chinese individuals. Our results, obtained from Y-STR-based population statistical methods (analysis of molecular variance (AMOVA), principal component analysis (PCA), and phylogenetic analysis), demonstrated that there was a genetic substructure difference between the EC population in the high-incidence area of northern Sichuan Province and the control population. Additionally, there was a strong genetic relationship between the EC population in the northern Sichuan high-incidence area and those at high risk in both the Fujian and Chaoshan areas. In addition, we obtained high-density SNP data from saliva samples of 60 healthy Han Chinese individuals from three high-prevalence areas of EC in China: Sichuan Nanchong, Fujian Quanzhou, and Henan Xinxiang. As inferred from the allele frequency of SNPs and sharing patterns of haplotype segments, the evolutionary history and admixture events suggested that the Han population from Nanchong in northern Sichuan Province shared a close genetic relationship with the Han populations from Xinxiang in Henan Province and Quanzhou in Fujian Province, both of which are regions with a high prevalence of EC. Our study illuminated the genetic profile and connection of the Northern Sichuan Han population and enriched the genomic resources and features of the Han Chinese populations in China, especially for the Y-STR genetic data of the Han Chinese EC population. Populations living in different regions with high incidences of EC may share similar genetic backgrounds, which offers new insights for further exploring the genetic mechanisms underlying EC.

Adjusting for principal components can induce spurious associations in genome-wide association studies in admixed populations

Principal component analysis (PCA) is widely used to control for population structure in genome-wide association studies (GWAS). Top principal components (PCs) typically reflect population structure, but challenges arise in deciding how many PCs are needed and ensuring that PCs do not capture other artifacts such as regions with atypical linkage disequilibrium (LD). In response to the latter, many groups suggest performing LD pruning or excluding known high LD regions prior to PCA. However, these suggestions are not universally implemented and the implications for GWAS are not fully understood, especially in the context of admixed populations. In this paper, we investigate the impact of pre-processing and the number of PCs included in GWAS models in African American samples from the Women's Women's Health Initiative SNP Health Association Resource and two Trans-Omics for Precision Medicine Whole Genome Sequencing Project contributing studies (Jackson Heart Study and Genetic Epidemiology of Chronic Obstructive Pulmonary Disease Study). In all three samples, we find the first PC is highly correlated with genome-wide ancestry whereas later PCs often capture local genomic features. The pattern of which, and how many, genetic variants are highly correlated with individual PCs differs from what has been observed in prior studies focused on European populations and leads to distinct downstream consequences: adjusting for such PCs yields biased effect size estimates and elevated rates of spurious associations due to the phenomenon of collider bias. Excluding high LD regions identified in previous studies does not resolve these issues. LD pruning proves more effective, but the optimal choice of thresholds varies across datasets. Altogether, our work highlights unique issues that arise when using PCA to control for ancestral heterogeneity in admixed populations and demonstrates the importance of careful pre-processing and diagnostics to ensure that PCs capturing multiple local genomic features are not included in GWAS models.

Socioeconomic inequalities in cardiovascular disease: a causal perspective

Socioeconomic inequalities in cardiovascular disease (CVD) persist in high-income countries despite marked overall declines in CVD-related morbidity and mortality. After decades of research, the field has struggled to unequivocally answer a crucial question: is the association between low socioeconomic position (SEP) and the development of CVD causal? We review relevant evidence from various study designs and disciplinary perspectives. Traditional observational, family-based and Mendelian randomization studies support the widely accepted view that low SEP causally influences CVD. However, results from quasi-experimental and experimental studies are both limited and equivocal. While more experimental and quasi-experimental studies are needed to aid causal understanding and inform policy, high-quality descriptive studies are also required to document inequalities, investigate their contextual dependence and consider SEP throughout the lifespan; no simple hierarchy of evidence exists for an exposure as complex as SEP. The COVID-19 pandemic illustrates the context-dependent nature of CVD inequalities, with the generation of potentially new causal pathways linking SEP and CVD. The linked goals of understanding the causal nature of SEP and CVD associations, their contextual dependence, and their remediation by policy interventions necessitate a detailed understanding of society, its change over time and the phenotypes of CVD. Interdisciplinary research is therefore key to advancing both causal understanding and policy translation.

Indigenous Australian genomes show deep structure and rich novel variation

The Indigenous peoples of Australia have a rich linguistic and cultural history. How this relates to genetic diversity remains largely unknown because of their limited engagement with genomic studies. Here we analyse the genomes of 159 individuals from four remote Indigenous communities, including people who speak a language (Tiwi) not from the most widespread family (Pama-Nyungan). This large collection of Indigenous Australian genomes was made possible by careful community engagement and consultation. We observe exceptionally strong population structure across Australia, driven by divergence times between communities of 26,000-35,000 years ago and long-term low but stable effective population sizes. This demographic history, including early divergence from Papua New Guinean (47,000 years ago) and Eurasian groups1, has generated the highest proportion of previously undescribed genetic variation seen outside Africa and the most extended homozygosity compared with global samples. A substantial proportion of this variation is not observed in global reference panels or clinical datasets, and variation with predicted functional consequence is more likely to be homozygous than in other populations, with consequent implications for medical genomics2. Our results show that Indigenous Australians are not a single homogeneous genetic group and their genetic relationship with the peoples of New Guinea is not uniform. These patterns imply that the full breadth of Indigenous Australian genetic diversity remains uncharacterized, potentially limiting genomic medicine and equitable healthcare for Indigenous Australians.

A Wolf in Sheep's Clothing: Idealisations and the aims of polygenic scores

Research in pharmacogenomics and precision medicine has recently introduced the concept of Polygenic Scores (PGSs), namely, indexes that aggregate the effects that many genetic variants are predicted to have on individual disease risk. The popularity of PGSs is increasing rapidly, but surprisingly little attention has been paid to the idealisations they make about phenotypic development. Indeed, PGSs rely on quantitative genetics models and methods, which involve considerable theoretical assumptions that have been questioned on various grounds. This comes with epistemological and ethical concerns about the use of PGSs in clinical decision-making. In this paper, I investigate to what extent idealisations in genetics models can impact the data gathering and clinical interpretation of genomics findings, particularly the calculation and predictive accuracy of PGSs. Although idealisations are considered ineliminable components of scientific models, they may be legitimate or not depending on the epistemic aims of a model. I thus analyse how various idealisations have been introduced in classical models and progressively readapted throughout the history of genetic theorising. Notably, this process involved important changes in the epistemic purpose of such idealisations, which raises the question of whether they are legitimate in the context of contemporary genomics.

Influence of Admixture on Phenotypes

Individuals of European descent have historically been the focus of genetic studies and possess relatively homogenous genomes. As a result, analytical methods have been developed and optimized with such genomes in mind. African-descent and Latino individuals generally possess genomes of greater architectural complexity due to mosaic genomic ancestry, which can extensively and intricately impact phenotypic expression. As such, genetic analyses of admixed individuals require that genetic admixture be quantified to accurately model the impact of genetic variation on phenotypic expression. In this overview, we explore how fundamental genetic concepts such as linkage disequilibrium and differential allele frequency interact with genetic admixture to uniquely influence phenotypes in admixed individuals. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.

Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle

Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.

Germline genetic variants and pediatric rhabdomyosarcoma outcomes: a report from the Children's Oncology Group

BACKGROUND

Relative to other pediatric cancers, survival for rhabdomyosarcoma (RMS) has not improved in recent decades, suggesting the need to enhance risk stratification. Therefore, we conducted a genome-wide association study for event-free survival (EFS) and overall survival (OS) to identify genetic variants associated with outcomes in individuals with RMS.

METHODS

The study included 920 individuals with newly diagnosed RMS who were enrolled in Children's Oncology Group protocols. To assess the association of each single nucleotide polymorphism (SNP) with EFS and OS, we estimated hazard ratios (HRs) and 95% confidence intervals (CIs) using multivariable Cox proportional hazards models, adjusted for clinical covariates. All statistical tests were two sided. We also performed stratified analyses by histological subtype (alveolar and embryonal RMS) and carried out sensitivity analyses of statistically significant SNPs by PAX3/7-FOXO1 fusion status and genetic ancestry group.

RESULTS

We identified that rs17321084 was associated with worse EFS (HR = 2.01, 95% CI = 1.59 to 2.53, P = 5.39 × 10-9) and rs10094840 was associated with worse OS (HR = 1.84, 95% CI = 1.48 to 2.27, P = 2.13 × 10-8). Using publicly available data, we found that rs17321084 lies in a binding region for transcription factors GATA2 and GATA3, and rs10094840 is associated with SPAG1 and RNF19A expression. We also identified that CTNNA3 rs2135732 (HR = 3.75, 95% CI = 2.34 to 5.99, P = 3.54 × 10-8) and MED31 rs74504320 (HR = 3.21, 95% CI = 2.12 to 4.86, P = 3.60 × 10-8) were associated with worse OS among individuals with alveolar RMS.

CONCLUSIONS

We demonstrated that common germline variants are associated with EFS and OS among individuals with RMS. Additional replication and investigation of these SNP effects may further support their consideration in risk stratification protocols.

A scoping review of smoking cessation pharmacogenetic studies to advance future research across racial, ethnic, and ancestral populations

Abstinence rates among smokers attempting to quit remain low despite the wide availability and accessibility of pharmacological smoking cessation treatments. In addition, the prevalence of cessation attempts and abstinence differs by individual-level social factors such as race and ethnicity. Clinical treatment of nicotine dependence also continues to be challenged by individual-level variability in effectiveness to promote abstinence. The use of tailored smoking cessation strategies that incorporate information on individual-level social and genetic factors hold promise, although additional pharmacogenomic knowledge is still needed. In particular, genetic variants associated with pharmacological responses to smoking cessation treatment have generally been conducted in populations with participants that self-identify as White race or who are determined to be of European genetic ancestry. These results may not adequately capture the variability across all smokers as a result of understudied differences in allele frequencies across genetic ancestry populations. This suggests that much of the current pharmacogenetic study results for smoking cessation may not apply to all populations. Therefore, clinical application of pharmacogenetic results may exacerbate health inequities by racial and ethnic groups. This scoping review examines the extent to which racial, ethnic, and ancestral groups that experience differences in smoking rates and smoking cessation are represented in the existing body of published pharmacogenetic studies of smoking cessation. We will summarize results by race, ethnicity, and ancestry across pharmacological treatments and study designs. We will also explore current opportunities and challenges in conducting pharmacogenomic research on smoking cessation that encourages greater participant diversity, including practical barriers to clinical utilization of pharmacological smoking cessation treatment and clinical implementation of pharmacogenetic knowledge.

Deep learning identified genetic variants for COVID-19-related mortality among 28,097 affected cases in UK Biobank

Analysis of host genetic components provides insights into the susceptibility and response to viral infection such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). To reveal genetic determinants of susceptibility to COVID-19 related mortality, we train a deep learning model to identify groups of genetic variants and their interactions that contribute to the COVID-19 related mortality risk using the UK Biobank data (28,097 affected cases and 1656 deaths). We refer to such groups of variants as super variants. We identify 15 super variants with various levels of significance as susceptibility loci for COVID-19 mortality. Specifically, we identify a super variant (odds ratio [OR] = 1.594, p = 5.47 × 10-9 ) on Chromosome 7 that consists of the minor allele of rs76398985, rs6943608, rs2052130, 7:150989011_CT_C, rs118033050, and rs12540488. We also discover a super variant (OR = 1.353, p = 2.87 × 10-8 ) on Chromosome 5 that contains rs12517344, rs72733036, rs190052994, rs34723029, rs72734818, 5:9305797_GTA_G, and rs180899355.

Identifying the genetic basis of viral spillover using Lassa virus as a test case

The rate at which zoonotic viruses spill over into the human population varies significantly over space and time. Remarkably, we do not yet know how much of this variation is attributable to genetic variation within viral populations. This gap in understanding arises because we lack methods of genetic analysis that can be easily applied to zoonotic viruses, where the number of available viral sequences is often limited, and opportunistic sampling introduces significant population stratification. Here, we explore the feasibility of using patterns of shared ancestry to correct for population stratification, enabling genome-wide association methods to identify genetic substitutions associated with spillover into the human population. Using a combination of phylogenetically structured simulations and Lassa virus sequences collected from humans and rodents in Sierra Leone, we demonstrate that existing methods do not fully correct for stratification, leading to elevated error rates. We also demonstrate, however, that the Type I error rate can be substantially reduced by confining the analysis to a less-stratified region of the phylogeny, even in an already-small dataset. Using this method, we detect two candidate single-nucleotide polymorphisms associated with spillover in the Lassa virus polymerase gene and provide generalized recommendations for the collection and analysis of zoonotic viruses.

Hybrid autoencoder with orthogonal latent space for robust population structure inference

Analysis of population structure and genomic ancestry remains an important topic in human genetics and bioinformatics. Commonly used methods require high-quality genotype data to ensure accurate inference. However, in practice, laboratory artifacts and outliers are often present in the data. Moreover, existing methods are typically affected by the presence of related individuals in the dataset. In this work, we propose a novel hybrid method, called SAE-IBS, which combines the strengths of traditional matrix decomposition-based (e.g., principal component analysis) and more recent neural network-based (e.g., autoencoders) solutions. Namely, it yields an orthogonal latent space enhancing dimensionality selection while learning non-linear transformations. The proposed approach achieves higher accuracy than existing methods for projecting poor quality target samples (genotyping errors and missing data) onto a reference ancestry space and generates a robust ancestry space in the presence of relatedness. We introduce a new approach and an accompanying open-source program for robust ancestry inference in the presence of missing data, genotyping errors, and relatedness. The obtained ancestry space allows for non-linear projections and exhibits orthogonality with clearly separable population groups.

Unsupervised discovery of ancestry-informative markers and genetic admixture proportions in biobank-scale datasets

Admixture estimation plays a crucial role in ancestry inference and genome-wide association studies (GWASs). Computer programs such as ADMIXTURE and STRUCTURE are commonly employed to estimate the admixture proportions of sample individuals. However, these programs can be overwhelmed by the computational burdens imposed by the 10⁵ to 10⁶ samples and millions of markers commonly found in modern biobanks. An attractive strategy is to run these programs on a set of ancestry-informative SNP markers (AIMs) that exhibit substantially different frequencies across populations. Unfortunately, existing methods for identifying AIMs require knowing ancestry labels for a subset of the sample. This supervised learning approach creates a chicken and the egg scenario. In this paper, we present an unsupervised, scalable framework that seamlessly carries out AIM selection and likelihood-based estimation of admixture proportions. Our simulated and real data examples show that this approach is scalable to modern biobank datasets. OpenADMIXTURE, our Julia implementation of the method, is open source and available for free.

Genetic and ecological drivers of molt in a migratory bird

The ability of animals to sync the timing and location of molting (the replacement of hair, skin, exoskeletons or feathers) with peaks in resource availability has important implications for their ecology and evolution. In migratory birds, the timing and location of pre-migratory feather molting, a period when feathers are shed and replaced with newer, more aerodynamic feathers, can vary within and between species. While hypotheses to explain the evolution of intraspecific variation in the timing and location of molt have been proposed, little is known about the genetic basis of this trait or the specific environmental drivers that may result in natural selection for distinct molting phenotypes. Here we take advantage of intraspecific variation in the timing and location of molt in the iconic songbird, the Painted Bunting (Passerina ciris) to investigate the genetic and ecological drivers of distinct molting phenotypes. Specifically, we use genome-wide genetic sequencing in combination with stable isotope analysis to determine population genetic structure and molting phenotype across thirteen breeding sites. We then use genome-wide association analysis (GWAS) to identify a suite of genes associated with molting and pair this with gene-environment association analysis (GEA) to investigate potential environmental drivers of genetic variation in this trait. Associations between genetic variation in molt-linked genes and the environment are further tested via targeted SNP genotyping in 25 additional breeding populations across the range. Together, our integrative analysis suggests that molting is in part regulated by genes linked to feather development and structure (GLI2 and CSPG4) and that genetic variation in these genes is associated with seasonal variation in precipitation and aridity. Overall, this work provides important insights into the genetic basis and potential selective forces behind phenotypic variation in what is arguably one of the most important fitness-linked traits in a migratory bird.

Surnames in south-eastern France: structure of the rural population during the 19th century through isonymy

An analysis of the distribution of surnames through time and space allows us to understand the structure of human groups, their exchanges or even their possible isolation. The French population has already been studied through surnames and it has been shown that the Sud-Provence-Alpes-Côte d'Azur region differed from the rest of France in both the 20th and 21st centuries (Mourrieras et al., ; Scapoli et al., ). The objective of this study was to understand the population evolution and particularities of the Sud-Provence-Alpes-Côte d'Azur region through an analysis of the distribution of surnames over an earlier period: the 19th century. For this work, 806,069 birth records from 521 communes between 1810 and 1890 were recorded and a total of 23,340 surnames were collected. The estimation of various isonymic parameters has allowed a description of this corpus never exploited before. In order to appreciate the population evolution, the data set was divided into three periods of 25 years. The canton was the geographical unit of this study, and similarities and differences between each of them were evaluated using Lasker distances, which allow the construction of dendrograms. A positive and significant correlation (p<0.0001) was found between Lasker distances and geographical distances using the Mantel test. The lowest inbreeding estimates were found in the Durance Valley. Migration, estimated from the v-index of Karlin and McGregor (), showed higher values in the south-western quarter of the region. The decrease in R_st values across the three periods is consistent with a homogenization of the patronymic between the cantons. This three-period approach showed a population evolution influenced by linguistic, cultural, historical and migratory phenomena since the Middle Ages, disrupted by the socioeconomic changes of the 19th century.

Privacy-aware estimation of relatedness in admixed populations

BACKGROUND

Estimation of genetic relatedness, or kinship, is used occasionally for recreational purposes and in forensic applications. While numerous methods were developed to estimate kinship, they suffer from high computational requirements and often make an untenable assumption of homogeneous population ancestry of the samples. Moreover, genetic privacy is generally overlooked in the usage of kinship estimation methods. There can be ethical concerns about finding unknown familial relationships in third-party databases. Similar ethical concerns may arise while estimating and reporting sensitive population-level statistics such as inbreeding coefficients for the concerns around marginalization and stigmatization.

RESULTS

Here, we present SIGFRIED, which makes use of existing reference panels with a projection-based approach that simplifies kinship estimation in the admixed populations. We use simulated and real datasets to demonstrate the accuracy and efficiency of kinship estimation. We present a secure federated kinship estimation framework and implement a secure kinship estimator using homomorphic encryption-based primitives for computing relatedness between samples in two different sites while genotype data are kept confidential. Source code and documentation for our methods can be found at https://doi.org/10.5281/zenodo.7053352.

CONCLUSIONS

Analysis of relatedness is fundamentally important for identifying relatives, in association studies, and for estimation of population-level estimates of inbreeding. As the awareness of individual and group genomic privacy is growing, privacy-preserving methods for the estimation of relatedness are needed. Presented methods alleviate the ethical and privacy concerns in the analysis of relatedness in admixed, historically isolated and underrepresented populations.

SHORT ABSTRACT

Genetic relatedness is a central quantity used for finding relatives in databases, correcting biases in genome wide association studies and for estimating population-level statistics. Methods for estimating genetic relatedness have high computational requirements, and occasionally do not consider individuals from admixed ancestries. Furthermore, the ethical concerns around using genetic data and calculating relatedness are not considered. We present a projection-based approach that can efficiently and accurately estimate kinship. We implement our method using encryption-based techniques that provide provable security guarantees to protect genetic data while kinship statistics are computed among multiple sites.

Quality Control Procedures for Genome-Wide Association Studies

Genome-wide association studies (GWAS) are being conducted at an unprecedented rate in population-based cohorts and have increased our understanding of the pathophysiology of many complex diseases. Regardless of the context, the practical utility of this information ultimately depends upon the quality of the data used for statistical analyses. Quality control (QC) procedures for GWAS are constantly evolving. Here, we enumerate some of the challenges in QC of genotyped GWAS data and describe the approaches involving genotype imputation of a sample dataset along with post-imputation quality assurance, thereby minimizing potential bias and error in GWAS results. We discuss common issues associated with QC of the GWAS data (genotyped and imputed), including data file formats, software packages for data manipulation and analysis, sex chromosome anomalies, sample identity, sample relatedness, population substructure, batch effects, and marker quality. We provide detailed guidelines along with a sample dataset to suggest current best practices and discuss areas of ongoing and future research. © 2022 Wiley Periodicals LLC.

Unveiling the genetic architecture for lodging resistance in rice (Oryza sativa. L) by genome-wide association analyses

Lodging is one of the major abiotic stresses, affecting the total crop yield and quality. The improved lodging resistance and its component traits potentially reduce the yield losses. The section modulus (SM), bending moment at breaking (M), pushing resistance (PR), and coefficient of lodging resistance (cLr) are the key elements to estimate the lodging resistance. Understanding the genetic architecture of lodging resistance-related traits will help to improve the culm strength and overall yield potential. In this study, a natural population of 795 globally diverse genotypes was further divided into two (indica and japonica) subpopulations and was used to evaluate the lodging resistance and culm strength-related traits. Significant diversity was observed among the studied traits. We carried out the genome-wide association evaluation of four lodging resistance traits with 3.3 million deep resolution single-nucleotide polymorphic (SNP) markers. The general linear model (GLM) and compressed mixed linear model (MLM) were used for the whole population and two subpopulation genome-wide association studies (GWAS), and a 1000-time permutation test was performed to remove the false positives. A total of 375 nonredundant QTLs were observed for four culm strength traits on 12 chromosomes of the rice genome. Then, 33 pleiotropic loci governing more than one trait were mined. A total of 4031 annotated genes were detected within the candidate genomic region of 33 pleiotropic loci. The functional annotations and metabolic pathway enrichment analysis showed cellular localization and transmembrane transport as the top gene ontological terms. The in silico and in vitro expression analyses were conducted to validate the three candidate genes in a pleiotropic QTL on chromosome 7. It validated OsFBA2 as a candidate gene to contribute to lodging resistance in rice. The haplotype analysis for the candidate gene revealed a significant functional variation in the promoter region. Validation and introgression of alleles that are beneficial to induce culm strength may be used in rice breeding for lodging resistance.

Parsimonious model for mass-univariate vertexwise analysis

Purpose: Covariance between gray-matter measurements can reflect structural or functional brain networks though it has also been shown to be influenced by confounding factors (e.g., age, head size, and scanner), which could lead to lower mapping precision (increased size of associated clusters) and create distal false positives associations in mass-univariate vertexwise analyses. Approach: We evaluated this concern by performing state-of-the-art mass-univariate analyses (general linear model, GLM) on traits simulated from real vertex-wise gray matter data (including cortical and subcortical thickness and surface area). We contrasted the results with those from linear mixed models (LMMs), which have been shown to overcome similar issues in omics association studies. Results: We showed that when performed on a large sample ( N = 8662 , UK Biobank), GLMs yielded greatly inflated false positive rate (cluster false discovery rate > 0.6 ). We showed that LMMs resulted in more parsimonious results: smaller clusters and reduced false positive rate but at a cost of increased computation. Next, we performed mass-univariate association analyses on five real UKB traits (age, sex, BMI, fluid intelligence, and smoking status) and LMM yielded fewer and more localized associations. We identified 19 significant clusters displaying small associations with age, sex, and BMI, which suggest a complex architecture of at least dozens of associated areas with those phenotypes. Conclusions: The published literature could contain a large proportion of redundant (possibly confounded) associations that are largely prevented using LMMs. The parsimony of LMMs results from controlling for the joint effect of all vertices, which prevents local and distal redundant associations from reaching significance.

Quantitative evaluation of nonlinear methods for population structure visualization and inference

Population structure (also called genetic structure and population stratification) is the presence of a systematic difference in allele frequencies between subpopulations in a population as a result of nonrandom mating between individuals. It can be informative of genetic ancestry, and in the context of medical genetics, it is an important confounding variable in genome-wide association studies. Recently, many nonlinear dimensionality reduction techniques have been proposed for the population structure visualization task. However, an objective comparison of these techniques has so far been missing from the literature. In this article, we discuss the previously proposed nonlinear techniques and some of their potential weaknesses. We then propose a novel quantitative evaluation methodology for comparing these nonlinear techniques, based on populations for which pedigree is known a priori either through artificial selection or simulation. Based on this evaluation metric, we find graph-based algorithms such as t-SNE and UMAP to be superior to principal component analysis, while neural network-based methods fall behind.

Genome-Wide Association Study Identifies Candidate Genes for Stripe Pattern Feather Color of Rhode Island Red Chicks

Feather colors of chickens are not only characteristics of breeds but also as phenotypic markers in chicken breeding. Pure-bred Rhode Island Red (RIR) chicks have a stripe pattern and a non-stripe pattern on the back. The stripe pattern of RIR is generally shown as four longitudinal black stripes on the back and is more likely to appear in females. In this study, we performed a genome-wide association study (GWAS) to identify candidate genes controlling the stripe pattern of RIR chicks, and then, based on physical location and biological functions, quantitative RT-PCR analysis was used to validate the differential expression of candidate genes between stripe pattern and non-stripe pattern back skin tissue. The GWAS showed that a major signal contains 768 significant single nucleotide polymorphisms (SNPs) and 87 significant small insertions-deletions (INDELs) spanning 41.78 to 43.05 Mb (~1.27 Mb) on GGA1, corresponding to 16 genes associated with stripe pattern phenotype. Among these 16 genes, KITLG and TMTC3 could be considered candidate genes as they showed different expressions between back skin tissues of stripe pattern and non-stripe pattern chicks in value (p = 0.062) and the significant level (p < 0.05), respectively. This study provided novel insight into the mechanisms underlying feather pigmentation and stripe formation in RIR chicks.

The contribution of common regulatory and protein-coding TYR variants to the genetic architecture of albinism

Genetic diseases have been historically segregated into rare Mendelian disorders and common complex conditions. Large-scale studies using genome sequencing are eroding this distinction and are gradually unmasking the underlying complexity of human traits. Here, we analysed data from the Genomics England 100,000 Genomes Project and from a cohort of 1313 individuals with albinism aiming to gain insights into the genetic architecture of this archetypal rare disorder. We investigated the contribution of protein-coding and regulatory variants both rare and common. We focused on TYR, the gene encoding tyrosinase, and found that a high-frequency promoter variant, TYR c.-301C>T [rs4547091], modulates the penetrance of a prevalent, albinism-associated missense change, TYR c.1205G>A (p.Arg402Gln) [rs1126809]. We also found that homozygosity for a haplotype formed by three common, functionally-relevant variants, TYR c.[-301C;575C>A;1205G>A], is associated with a high probability of receiving an albinism diagnosis (OR>82). This genotype is also associated with reduced visual acuity and with increased central retinal thickness in UK Biobank participants. Finally, we report how the combined analysis of rare and common variants can increase diagnostic yield and can help inform genetic counselling in families with albinism.

Corin Missense Variants, Blood Pressure, and Hypertension in 11 322 Black Individuals: Insights From REGARDS and the Jackson Heart Study

Background Corin enzyme contributes to the processing of inactive natriuretic peptides to bioactive hormones. In Black individuals, Corin gene variants (rs111253292 [Q568P] and rs75770792 [T555I]) have been previously reported to have a modest association with blood pressure (BP) and hypertension. Methods and Results We evaluated the association of Corin genotype with BP traits, prevalent hypertension, and incident hypertension among self-identified 11 322 Black Americans in the REGARDS (Reasons for Geographic and Racial Differences in Stroke) study and the JHS (Jackson Heart Study) using multivariable-adjusted regression modeling. Multivariable-adjusted genotype-stratified differences in NT-proBNP (N-terminal pro-B-type natriuretic peptide) and BNP (B-type natriuretic peptide) levels were assessed. Genotype-stratified NPPA and NPPB expression differences in healthy organ donor left atrial and left ventricular heart tissue (N=15) were also examined. The rs111253292 genotype was not associated with systolic BP (β±SE, 0.42±0.58; -1.24±0.82), diastolic BP (0.51±0.33; -0.41±0.46), mean arterial pressure (0.48±0.38; -0.68±0.51), and prevalent hypertension (odds ratio [OR], 0.93 [95% CI, 0.80-1.09]; OR, 0.79 [95% CI, 0.61-1.01]) in both REGARDS and JHS, respectively. The rs75770792 genotype was not associated with systolic BP (0.48±0.58; -1.26±0.81), diastolic BP (0.52±0.33; -0.33±0.45), mean arterial pressure (0.50±0.38; -0.63±0.50), and prevalent hypertension (OR, 1.02 [95% CI, 0.84-1.23]; OR, 0.87 [95% CI, 0.67-1.13]) in both cohorts, respectively. The Corin genotype was also not associated with incident hypertension (OR, 1.35 [95% CI, 0.94-1.93]; OR, 0.95 [95% CI, 0.64-1.39]) in the study cohorts. The NT-proBNP levels in REGARDS and BNP levels in JHS were similar between the Corin genotype groups. In heart tissue, the NPPA and NPPB expression was similar between the genotype groups. Conclusions Corin gene variants observed more commonly in Black individuals are not associated with differences in NP expression, circulating NP levels, and BP or hypertension as previously reported in candidate gene studies. Understanding the genetic determinants of complex cardiovascular traits in underrepresented populations requires further evaluation.

Dissection of genetic architecture for tiller angle in rice (Oryza sativa. L) by multiple genome-wide association analyses

Background

The rice plant architecture is determined by spatially and temporally domesticated tiller angle. The deeper insight into the genetic mechanism for rice plant architecture will allow more efficient light capture by increasing the planting density, reproducibility, and the ability to survive in a stressful environment.

Methods

In this study, a natural population of 795 genotypes further divided into japonica and indica subpopulations, was evaluated for tiller angle. A significant variation with a wide range was observed. Genome-wide association analysis was performed by the general linear model (GLM), and compressed mix linear model (cMLM) for three populations to disclose the genomic associations. The population principal components and kinship matrix in 1,000 permutations were used to remove the false positives. The candidate genes were evaluated for their functional annotations and specific molecular pathways. The sequencing-based haplotype analysis was further performed to reveal the functional variation among candidate genomic regions.

Results

As a result, 37 significant QTLs with 93 annotated loci were identified. Among the loci, a known tiller angle controlling locus TAC1 was also identified. The introduction of the sequence pooling technique was observed fruitful to screen the 12 significant QTLs with 22 annotated loci. For ten of these loci, the functional variations were identified by haplotype analysis. These results were not only providing a better understanding of the genetic bases of rice plant architecture but also provide significant information for future breeding programs.

Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy

Mendelian Randomization (MR) studies are threatened by population stratification, batch effects, and horizontal pleiotropy. Although a variety of methods have been proposed to mitigate those problems, residual biases may still remain, leading to highly statistically significant false positives in large databases. Here we describe a suite of sensitivity analysis tools that enables investigators to quantify the robustness of their findings against such validity threats. Specifically, we propose the routine reporting of sensitivity statistics that reveal the minimal strength of violations necessary to explain away the MR results. We further provide intuitive displays of the robustness of the MR estimate to any degree of violation, and formal bounds on the worst-case bias caused by violations multiple times stronger than observed variables. We demonstrate how these tools can aid researchers in distinguishing robust from fragile findings by examining the effect of body mass index on diastolic blood pressure and Townsend deprivation index.

Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy

Mendelian Randomization (MR) studies are threatened by population stratification, batch effects, and horizontal pleiotropy. Although a variety of methods have been proposed to mitigate those problems, residual biases may still remain, leading to highly statistically significant false positives in large databases. Here we describe a suite of sensitivity analysis tools that enables investigators to quantify the robustness of their findings against such validity threats. Specifically, we propose the routine reporting of sensitivity statistics that reveal the minimal strength of violations necessary to explain away the MR results. We further provide intuitive displays of the robustness of the MR estimate to any degree of violation, and formal bounds on the worst-case bias caused by violations multiple times stronger than observed variables. We demonstrate how these tools can aid researchers in distinguishing robust from fragile findings by examining the effect of body mass index on diastolic blood pressure and Townsend deprivation index.

DeepNull models non-linear covariate effects to improve phenotypic prediction and association power

Genome-wide association studies (GWASs) examine the association between genotype and phenotype while adjusting for a set of covariates. Although the covariates may have non-linear or interactive effects, due to the challenge of specifying the model, GWAS often neglect such terms. Here we introduce DeepNull, a method that identifies and adjusts for non-linear and interactive covariate effects using a deep neural network. In analyses of simulated and real data, we demonstrate that DeepNull maintains tight control of the type I error while increasing statistical power by up to 20% in the presence of non-linear and interactive effects. Moreover, in the absence of such effects, DeepNull incurs no loss of power. When applied to 10 phenotypes from the UK Biobank (n = 370K), DeepNull discovered more hits (+6%) and loci (+7%), on average, than conventional association analyses, many of which are biologically plausible or have previously been reported. Finally, DeepNull improves upon linear modeling for phenotypic prediction (+23% on average).

KIBRA, MTNR1B, and FKBP5 genotypes are associated with decreased odds of incident delirium in elderly post-surgical patients

Despite the association between cognitive impairment and delirium, little is known about whether genetic differences that confer cognitive resilience also confer resistance to delirium. To investigate whether older adults without postoperative delirium, compared with those with postoperative delirium, are more likely to have specific single nucleotide polymorphisms (SNPs) in the FKBP5, KIBRA, KLOTHO, MTNR1B, and SIRT1 genes known to be associated with cognition or delirium. This prospective nested matched exploratory case-control study included 94 older adults who underwent orthopedic surgery and screened for postoperative delirium. Forty-seven subjects had incident delirium, and 47 age-matched controls were not delirious. The primary study outcome was genotype frequency for the five SNPs. Compared with participants with delirium, those without delirium had higher adjusted odds of KIBRA SNP rs17070145 CT/TT [vs. CC; adjusted odds ratio (aOR) 2.80, 95% confidence interval (CI) 1.03, 7.54; p = 0.04] and MTNR1B SNP rs10830963 CG/GG (vs. CC; aOR 4.14, 95% CI 1.36, 12.59; p = 0.01). FKBP5 SNP rs1360780 CT/TT (vs. CC) demonstrated borderline increased adjusted odds of not developing delirium (aOR 2.51, 95% CI 1.00, 7.34; p = 0.05). Our results highlight the relevance of KIBRA, MTNR1B, and FKBP5 in understanding the complex relationship between delirium, cognition, and sleep, which warrant further study in larger, more diverse populations.

Efficient approaches for large-scale GWAS with genotype uncertainty

Association studies using genetic data from SNP-chip-based imputation or low-depth sequencing data provide a cost-efficient design for large-scale association studies. We explore methods for performing association studies applicable to such genetic data and investigate how using different priors when estimating genotype probabilities affects the association results. Our proposed method, ANGSD-asso’s latent model, models the unobserved genotype as a latent variable in a generalized linear model framework. The software is implemented in C/C++ and can be run multi-threaded. ANGSD-asso is based on genotype probabilities, which can be estimated using either the sample allele frequency or the individual allele frequencies as a prior. We explore through simulations how genotype probability-based methods compare with using genetic dosages. Our simulations show that in a structured population using the individual allele frequency prior has better power than the sample allele frequency. In scenarios with sequencing depth and phenotype correlation ANGSD-asso’s latent model has higher statistical power and less bias than using dosages. Adding additional covariates to the linear model of ANGSD-asso’s latent model has higher statistical power and less bias than other methods that accommodate genotype uncertainty, while also being much faster. This is shown with imputed data from UK Biobank and simulations.

A guidance of model selection for genomic prediction based on linear mixed models for complex traits

Brain imaging outcomes are important for Alzheimer's disease (AD) detection, and their prediction based on both genetic and demographic risk factors can facilitate the ongoing prevention and treatment of AD. Existing studies have identified numerous significantly AD-associated SNPs. However, how to make the best use of them for prediction analyses remains unknown. In this research, we first explored the relationship between genetic architecture and prediction accuracy of linear mixed models via visualizing the Manhattan plots generated based on the data obtained from the Wellcome Trust Case Control Consortium, and then constructed prediction models for eleven AD-related brain imaging outcomes using data from United Kingdom Biobank and Alzheimer's Disease Neuroimaging Initiative studies. We found that the simple Manhattan plots can be informative for the selection of prediction models. For traits that do not exhibit any significant signals from the Manhattan plots, the simple genomic best linear unbiased prediction (gBLUP) model is recommended due to its robust and accurate prediction performance as well as its computational efficiency. For diseases and traits that show spiked signals on the Manhattan plots, the latent Dirichlet process regression is preferred, as it can flexibly accommodate both the oligogenic and omnigenic models. For the prediction of AD-related traits, the Manhattan plots suggest their polygenic nature, and gBLUP has achieved robust performance for all these traits. We found that for these AD-related traits, genetic factors themselves only explain a very small proportion of the heritability, and the well-known AD risk factors can substantially improve the prediction model.

Targeted re-sequencing and genome-wide association analysis for wood property traits in breeding population of Eucalyptus tereticornis × E. grandis

Globally, Eucalyptus plantations occupy 22 million ha area and is one of the preferred hardwood species due to their short rotation, rapid growth, adaptability and wood properties. In this study, we present results of GWAS in parents and 100 hybrids of Eucalyptus tereticornis × E. grandis using 762 genes presumably involved in wood formation. Comparative analysis between parents predicted 32,202 polymorphic SNPs with high average read depth of 269-562× per individual per nucleotide. Seventeen wood related traits were phenotyped across three diverse environments and GWAS was conducted using 13,610 SNPs. A total of 45 SNP-trait associations were predicted across two locations. Seven large effect markers were identified which explained more than 80% of phenotypic variation for fibre area. This study has provided an array of candidate genes which may govern fibre morphology in this genus and has predicted potential SNPs which can guide future breeding programs in tropical Eucalyptus.

Problems and promises: How to tell the story of a Genome Wide Association Study?

The promise of treatments for common complex diseases (CCDs) is understood as an important force driving large scale genetics research over the last few decades. This paper considers the phenomenon of the Genome Wide Association Study (GWAS) via one high profile example, the Wellcome Trust Case Control Consortium (WTCCC). The WTCCC despite not fulfilling promises of new health interventions is still understood as an important step towards tackling CCDs clinically. The 'sociology of expectations' has considered many examples of failure to fulfil promises and the subsequent negative consequences including disillusionment, disappointment and disinvestment. In order to explore why some domains remain resilient in the face of apparent failure, I employ the concept of the 'problematic' found in the work of Giles Deleuze. This alternative theoretical framework challenges the idea that the failure to reach promised goals results in largely negative outcomes for a given field. I will argue that collective scientific action is motivated not only by hopes for the future but also by the drive to create solutions to the actual setbacks and successes which scientists encounter in their day-to-day work. I draw on eighteen interviews.

Ancestry inference using reference labeled clusters of haplotypes

BACKGROUND

We present ARCHes, a fast and accurate haplotype-based approach for inferring an individual's ancestry composition. Our approach works by modeling haplotype diversity from a large, admixed cohort of hundreds of thousands, then annotating those models with population information from reference panels of known ancestry.

RESULTS

The running time of ARCHes does not depend on the size of a reference panel because training and testing are separate processes, and the inferred population-annotated haplotype models can be written to disk and reused to label large test sets in parallel (in our experiments, it averages less than one minute to assign ancestry from 32 populations using 10 CPU). We test ARCHes on public data from the 1000 Genomes Project and the Human Genome Diversity Project (HGDP) as well as simulated examples of known admixture.

CONCLUSIONS

Our results demonstrate that ARCHes outperforms RFMix at correctly assigning both global and local ancestry at finer population scales regardless of the amount of population admixture.

Multidimensional Approach Assessing the Role of Interleukin 1 Beta in Mesial Temporal Lobe Epilepsy

We aimed to investigate the role of interleukin-1 beta (IL-1β) in the mechanisms underlying mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE+HS). We assessed a cohort of 194 patients with MTLE+HS and 199 healthy controls. Patients were divided into those with positive and negative antecedent febrile seizures (FS). We used a multidimensional approach, including (i) genetic association with single nucleotide polymorphisms (SNPs) in the IL1B gene; (ii) quantification of the IL1B transcript in the hippocampal tissue of patients with refractory seizures; and (iii) quantification of the IL-1β protein in the plasma. We found a genetic association signal for two SNPs, rs2708928 and rs3730364^*C in the IL1B gene, regardless of the presence of FS (adjusted p = 9.62e–11 and 5.14e–07, respectively). We found no difference between IL1B transcript levels when comparing sclerotic hippocampal tissue from patients with MTLE+HS, without FS, and hippocampi from autopsy controls (p > 0.05). Nevertheless, we found increased IL-1β in the plasma of patients with MTLE+HS with FS compared with controls (p = 0.0195). Our results support the hypothesis of a genetic association between MTLE+HS and the IL1B gene

Putative pathogen-selected polymorphisms in the PKLR gene are associated with mycobacterial susceptibility in Brazilian and African populations

Pyruvate kinase (PK), encoded by the PKLR gene, is a key player in glycolysis controlling the integrity of erythrocytes. Due to Plasmodium selection, mutations for PK deficiency, which leads to hemolytic anemia, are associated with resistance to malaria in sub-Saharan Africa and with susceptibility to intracellular pathogens in experimental models. In this case-control study, we enrolled 4,555 individuals and investigated whether PKLR single nucleotide polymorphisms (SNPs) putatively selected for malaria resistance are associated with susceptibility to leprosy across Brazil (Manaus-North; Salvador-Northeast; Rondonópolis-Midwest and Rio de Janeiro-Southeast) and with tuberculosis in Mozambique. Haplotype T/G/G (rs1052176/rs4971072/rs11264359) was associated with leprosy susceptibility in Rio de Janeiro (OR = 2.46, p = 0.00001) and Salvador (OR = 1.57, p = 0.04), and with tuberculosis in Mozambique (OR = 1.52, p = 0.07). This haplotype downregulates PKLR expression in nerve and skin, accordingly to GTEx, and might subtly modulate ferritin and haptoglobin levels in serum. Furthermore, we observed genetic signatures of positive selection in the HCN3 gene (xpEHH>2 -recent selection) in Europe but not in Africa, involving 6 SNPs which are PKLR/HCN3 eQTLs. However, this evidence was not corroborated by the other tests (FST, Tajima's D and iHS). Altogether, we provide evidence that a common PKLR locus in Africans contribute to mycobacterial susceptibility in African descent populations and also highlight, for first, PKLR as a susceptibility gene for leprosy and TB.

Novel genetic variants associated with brain functional networks in 18,445 adults from the UK Biobank

Here, we investigated the genetics of weighted functional brain network graph theory measures from 18,445 participants of the UK Biobank (44–80 years). The eighteen measures studied showed low heritability (mean h²_SNP = 0.12) and were highly genetically correlated. One genome-wide significant locus was associated with strength of somatomotor and limbic networks. These intergenic variants were located near the PAX8 gene on chromosome 2. Gene-based analyses identified five significantly associated genes for five of the network measures, which have been implicated in sleep duration, neuronal differentiation/development, cancer, and susceptibility to neurodegenerative diseases. Further analysis found that somatomotor network strength was phenotypically associated with sleep duration and insomnia. Single nucleotide polymorphism (SNP) and gene level associations with functional network measures were identified, which may help uncover novel biological pathways relevant to human brain functional network integrity and related disorders that affect it.

Increased burden of rare variants in genes of the endosomal Toll-like receptor pathway in patients with systemic lupus erythematosus

OBJECTIVE

To compare the frequency of rare variants in genes of the pathophysiologically relevant endosomal Toll-like receptor (eTLR) pathway and any quantifiable differences in variant rarity, predicted deleteriousness, or molecular proximity in patients with systemic lupus erythematosus (SLE) and healthy controls.

PATIENTS AND METHODS

65 genes associated with the eTLR pathway were identified by literature search and pathway analysis. Using next generation sequencing techniques, these were compared in two randomised cohorts of patients with SLE (n = 114 and n = 113) with 197 healthy controls. Genetically determined ethnicity was used to normalise minor allele frequencies (MAF) for the identified genetic variants and these were then compared by their frequency: rare (MAF < 0.005), uncommon (MAF 0.005-0.02), and common (MAF >0.02). This was compared to the results for 65 randomly selected genes.

RESULTS

Patients with SLE are more likely to carry a rare nonsynonymous variant affecting proteins within the eTLR pathway than healthy controls. Furthermore, individuals with SLE are more likely to have multiple rare variants in this pathway. There were no differences in rarity, Combined Annotation Dependent Depletion (CADD) score, or molecular proximity for rare eTLR pathway variants.

CONCLUSIONS

Rare non-synonymous variants are enriched in patients with SLE in the eTLR pathway. This supports the hypothesis that SLE arises from several rare variants of relatively large effect rather than many common variants of small effect.

Association Analysis of Candidate Variants in Admixed Brazilian Patients With Genetic Generalized Epilepsies

Genetic generalized epilepsies (GGEs) include well-established epilepsy syndromes with generalized onset seizures: childhood absence epilepsy, juvenile myoclonic epilepsy (JME), juvenile absence epilepsy (JAE), myoclonic absence epilepsy, epilepsy with eyelid myoclonia (Jeavons syndrome), generalized tonic–clonic seizures, and generalized tonic–clonic seizures alone. Genome-wide association studies (GWASs) and exome sequencing have identified 48 single-nucleotide polymorphisms (SNPs) associated with GGE. However, these studies were mainly based on non-admixed, European, and Asian populations. Thus, it remains unclear whether these results apply to patients of other origins. This study aims to evaluate whether these previous results could be replicated in a cohort of admixed Brazilian patients with GGE. We obtained SNP-array data from 87 patients with GGE, compared with 340 controls from the BIPMed public dataset. We could directly access genotypes of 17 candidate SNPs, available in the SNP array, and the remaining 31 SNPs were imputed using the BEAGLE v5.1 software. We performed an association test by logistic regression analysis, including the first five principal components as covariates. Furthermore, to expand the analysis of the candidate regions, we also interrogated 14,047 SNPs that flank the candidate SNPs (1 Mb). The statistical power was evaluated in terms of odds ratio and minor allele frequency (MAF) by the genpwr package. Differences in SNP frequencies between Brazilian and Europeans, sub-Saharan African, and Native Americans were evaluated by a two-proportion Z-test. We identified nine flanking SNPs, located on eight candidate regions, which presented association signals that passed the Bonferroni correction (rs12726617; rs9428842; rs1915992; rs1464634; rs6459526; rs2510087; rs9551042; rs9888879; and rs8133217; p-values <3.55e^–06). In addition, the two-proportion Z-test indicates that the lack of association of the remaining candidate SNPs could be due to different genomic backgrounds observed in admixed Brazilians. This is the first time that candidate SNPs for GGE are analyzed in an admixed Brazilian population, and we could successfully replicate the association signals in eight candidate regions. In addition, our results provide new insights on how we can account for population structure to improve risk stratification estimation in admixed individuals.

Lack of Effects of the Genetic Polymorphisms of Interleukin-10 in Clinical Outcomes of COVID-19

Interleukin-10 (IL-10) gene polymorphisms have been associated with severity and outcomes in patients with respiratory and nonrespiratory viral infections. The aim of this study was to assess whether rs1800871 and rs1800872 polymorphisms of IL-10 gene are associated with the clinical outcomes of COVID-19 in a Mexican population. Study subjects were 193 COVID-19 patients. The genotyping was carried out with real-time PCR and serum IL-10 levels were measured with enzyme-linked immunosorbent assay. Logistic regression analysis was used for analysis association with clinical outcomes. There was no evidence of an association between alleles, genotypes, or haplotypes frequencies between patient groups according to severity and outcomes. The rs1800871 and rs1800872 polymorphisms might not be genetic risk factors for severity and mortality for COVID-19 in Mexican mestizos patients from northwest Mexico.

Towards fine-scale population stratification modeling based on kernel principal component analysis and random forest

BACKGROUND

Population stratification modeling is essential in Genome-Wide Association Studies.

OBJECTIVE

In this paper, we aim to build a fine-scale population stratification model to efficiently infer individual genetic ancestry.

METHODS

Kernel Principal Component Analysis (PCA) and random forest are adopted to build the population stratification model, together with parameter optimization. We explore different PCA methods, including standard PCA and kernel PCA to extract relevant features from the genotype data that is transformed by vcf2geno, a pipeline from LASER software. These extracted features are fed into a random forest for ensemble learning. Parameter tuning is performed to jointly find the optimal number of principal components, kernel function for PCA and parameters of the random forest.

RESULTS

Experiments based on HGDP dataset show that kernel PCA with Sigmoid function and Gaussian function can achieve higher prediction accuracy than the standard PCA. Compared to standard PCA with the two principal components, the accuracy by using KPCA-Sigmoid with the optimal number of principal components can achieve around 100% and 200% improvement for East Asian and European populations, respectively.

CONCLUSION

With the optimal parameter configuration on both PCA and random forest, our proposed method can infer the individual genetic ancestry more accurately, given their variants.

Penalized linear mixed models for structured genetic data

Many genetic studies that aim to identify genetic variants associated with complex phenotypes are subject to unobserved confounding factors arising from environmental heterogeneity. This poses a challenge to detecting associations of interest and is known to induce spurious associations when left unaccounted for. Penalized linear mixed models (LMMs) are an attractive method to correct for unobserved confounding. These methods correct for varying levels of relatedness and population structure by modeling it as a random effect with a covariance structure estimated from observed genetic data. Despite an extensive literature on penalized regression and LMMs separately, the two are rarely discussed together. The aim of this review is to do so while examining the statistical properties of penalized LMMs in the genetic association setting. Specifically, the ability of penalized LMMs to accurately estimate genetic effects in the presence of environmental confounding has not been well studied. To clarify the important yet subtle distinction between population structure and environmental heterogeneity, we present a detailed review of relevant concepts and methods. In addition, we evaluate the performance of penalized LMMs and competing methods in terms of estimation and selection accuracy in the presence of a number of confounding structures.

The Translational Machine: A novel machine-learning approach to illuminate complex genetic architectures

The Translational Machine (TM) is a machine learning (ML)-based analytic pipeline that translates genotypic/variant call data into biologically contextualized features that richly characterize complex variant architectures and permit greater interpretability and biological replication. It also reduces potentially confounding effects of population substructure on outcome prediction. The TM consists of three main components. First, replicable but flexible feature engineering procedures translate genome-scale data into biologically informative features that appropriately contextualize simple variant calls/genotypes within biological and functional contexts. Second, model-free, nonparametric ML-based feature filtering procedures empirically reduce dimensionality and noise of both original genotype calls and engineered features. Third, a powerful ML algorithm for feature selection is used to differentiate risk variant contributions across variant frequency and functional prediction spectra. The TM simultaneously evaluates potential contributions of variants operative under polygenic and heterogeneous models of genetic architecture. Our TM enables integration of biological information (e.g., genomic annotations) within conceptual frameworks akin to geneset-/pathways-based and collapsing methods, but overcomes some of these methods' limitations. The full TM pipeline is executed in R. Our approach and initial findings from its application to a whole-exome schizophrenia case-control data set are presented. These TM procedures extend the findings of the primary investigation and yield novel results.

Heterogeneous genetic basis of age at maturity in salmonid fishes

Understanding the genetic basis of repeated evolution of the same phenotype across taxa is a fundamental aim in evolutionary biology and has applications in conservation and management. However, the extent to which interspecific life-history trait polymorphisms share evolutionary pathways remains underexplored. Here, we address this gap by studying the genetic basis of a key life-history trait, age at maturity, in four species of Pacific salmonids (genus Oncorhynchus) that exhibit intra- and interspecific variation in this trait-Chinook Salmon, Coho Salmon, Sockeye Salmon, and Steelhead Trout. We tested for associations in all four species between age at maturity and two genome regions, six6 and vgll3, that are strongly associated with the same trait in Atlantic Salmon (Salmo salar). We also conducted a genome-wide association analysis in Steelhead to assess whether additional regions were associated with this trait. We found the genetic basis of age at maturity to be heterogeneous across salmonid species. Significant associations between six6 and age at maturity were observed in two of the four species, Sockeye and Steelhead, with the association in Steelhead being particularly strong in both sexes (p = 4.46 × 10^-9 after adjusting for genomic inflation). However, no significant associations were detected between age at maturity and the vgll3 genome region in any of the species, despite its strong association with the same trait in Atlantic Salmon. We discuss possible explanations for the heterogeneous nature of the genetic architecture of this key life-history trait, as well as the implications of our findings for conservation and management.

Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure

Background

In genome-wide association studies the extent and impact of confounding due to population structure have been well recognized. Inadequate handling of such confounding is likely to lead to spurious associations, hampering replication, and the identification of causal variants. Several strategies have been developed for protecting associations against confounding, the most popular one is based on Principal Component Analysis. In contrast, the extent and impact of confounding due to population structure in gene-gene interaction association epistasis studies are much less investigated and understood. In particular, the role of nonlinear genetic population substructure in epistasis detection is largely under-investigated, especially outside a regression framework.

Methods

To identify causal variants in synergy, to improve interpretability and replicability of epistasis results, we introduce three strategies based on a model-based multifactor dimensionality reduction approach for structured populations, namely MBMDR-PC, MBMDR-PG, and MBMDR-GC.

Results

Simulation results comparing the performance of various approaches show that in the presence of population structure MBMDR-PC and MBMDR-PG consistently better control type I error rate at the nominal level than MBMDR-GC. Moreover, our proposed three methods of population structure correction outperform MDR-SP in terms of statistical power.

Conclusion

We demonstrate through extensive simulation studies the effect of various degrees of genetic population structure and relatedness on epistasis detection and propose appropriate remedial measures based on linear and nonlinear sample genetic similarity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13040-021-00247-w.

Genome-wide association studies detects candidate genes for wool traits by re-sequencing in Chinese fine-wool sheep

BACKGROUND

The quality and yield of wool determine the economic value of the fine-wool sheep. Therefore, discovering markers or genes relevant to wool traits is the cornerstone for the breeding of fine-wool sheep. In this study, we used the Illumina HiSeq X Ten platform to re-sequence 460 sheep belonging to four different fine-wool sheep breeds, namely, Alpine Merino sheep (AMS), Chinese Merino sheep (CMS), Aohan fine-wool sheep (AHS) and Qinghai fine-wool sheep (QHS). Eight wool traits, including fiber diameter (FD), fiber diameter coefficient of variance (FDCV), fiber diameter standard deviation (FDSD), staple length (SL), greasy fleece weight (GFW), clean wool rate (CWR), staple strength (SS) and staple elongation (SE) were examined. A genome-wide association study (GWAS) was performed to detect the candidate genes for the eight wool traits.

RESULTS

A total of 8.222 Tb of raw data was generated, with an average of approximately 8.59X sequencing depth. After quality control, 12,561,225 SNPs were available for analysis. And a total of 57 genome-wide significant SNPs and 30 candidate genes were detected for the desired wool traits. Among them, 7 SNPs and 6 genes are related to wool fineness indicators (FD, FDCV and FDSD), 10 SNPs and 7 genes are related to staple length, 13 SNPs and 7 genes are related to wool production indicators (GFW and CWR), 27 SNPs and 10 genes associated with staple elongation. Among these candidate genes, UBE2E3 and RHPN2 associated with fiber diameter, were found to play an important role in keratinocyte differentiation and cell proliferation. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results, revealed that multitude significant pathways are related to keratin and cell proliferation and differentiation, such as positive regulation of canonical Wnt signaling pathway (GO:0090263).

CONCLUSION

This is the first GWAS on the wool traits by using re-sequencing data in Chinese fine-wool sheep. The newly detected significant SNPs in this study can be used in genome-selective breeding for the fine-wool sheep. And the new candidate genes would provide a good theoretical basis for the fine-wool sheep breeding.