Genetic dissection of complex traits

Association of the TNFRSF1B-rs1061622 variant with nonresponse to infliximab in ulcerative colitis

For severe forms of ulcerative colitis (UC), a chronic inflammatory bowel disease (IBD), biological therapies, including tumor necrosis factor inhibitors (anti-TNF), are often used. However, these drugs have a high variability in treatment response. Multiple factors, such as genetic variants, can affect this variability. The goal of the study was to verify if selected candidate variants could affect response to anti-TNF in UC treatment. This association study included 76 participants suffering from UC and past or current users of anti-TNF. Clinical data for phenotyping was collected through a single visit with the participant and a medical chart review. Blood or saliva samples were collected to extract DNA and to genotype eight selected candidate variants in genes TNF, TNFAIP3, TNFRSF1 A and TNFRSF1B. For anti-TNF users, 30% of individuals were non-responders, 70% suffered from AE and none of the studied variants was associated with the response's phenotype. However, for infliximab users only (n = 44), the TNFRSF1B-rs1061622 variant was associated with nonresponse to infliximab for the first time in a cohort of UC patients (p-value = 0.028). Next steps are to replicate this association in independent cohorts and to perform functional studies to gain more evidence on the variant.

Protein structural domain-disease association prediction based on heterogeneous networks

BACKGROUND

Domains can be viewed as portable units of protein structure, folding, function, evolution, and design. Small proteins are often found to be composed of only a single domain, while most large proteins consist of multiple domains for achieving various composite cellular functions. A dysfunction in domains may affect the function of proteins in some disease. Inferring the disease-related domains will help our understanding of the mechanism of human complex diseases.

RESULTS

In this study, we firstly build a global heterogeneous information network based on structural-based domains, proteins, and diseases. Then the topological features of the network are extracted according to the meta-paths between domain and disease nodes. Finally, we train a binary classifier based on the XGBOOST (eXtreme Gradient Boosting) algorithm to predict the potential associations between domains and diseases. The results show that the binary classification model using the XGBOOST algorithm performs significantly better than models using other machine learning algorithms, achieving an AUC (Area Under Curve) score of 0.94 in the leave-one-out cross-validation experiment.

CONCLUSIONS

We develop a method to build a binary classifier using the topological features based on meta-paths and predict the potential associations between domains and diseases. Based on its predictive performance in independent test sets, the method is proved to be powerful. Moreover, representing domains and diseases through integrating more multi-omic data will further optimize predictive performance.

Improving complex agronomic and domestication traits in the perennial grain crop intermediate wheatgrass with genetic mapping and genomic prediction

The perennial grass Thinopyrum intermedium (intermediate wheatgrass [IWG]) is being domesticated as a food crop. With a deep root system and high biomass, IWG can help reduce soil and water erosion and limit nutrient runoff. As a novel grain crop undergoing domestication, IWG lags in yield, seed size, and other agronomic traits compared to annual grains. Better characterization of trait variation and identification of genetic markers associated with loci controlling the traits could help in further improving this crop. The University of Minnesota's Cycle 5 IWG breeding population of 595 spaced plants was evaluated at two locations in 2021 and 2022 for agronomic traits plant height, grain yield, and spike weight, and domestication traits shatter resistance, free grain threshing, and seed size. Pairwise trait correlations were weak to moderate with the highest correlation observed between seed size and height (0.41). Broad-sense trait heritabilities were high (0.68-0.77) except for spike weight (0.49) and yield (0.44). Association mapping using 24,284 genome-wide single nucleotide polymorphism markers identified 30 main quantitative trait loci (QTLs) across all environments and 32 QTL-by-environment interactions (QTE) at each environment. The genomic prediction model significantly improved predictions when parents were used in the training set and significant QTLs and QTEs used as covariates. Seed size was the best predicted trait with model predictive ability (r) of 0.72; yield was predicted moderately well (r = 0.45). We expect this discovery of significant genomic loci and mostly high trait predictions from genomic prediction models to help improve future IWG breeding populations.

Genetic Mutations Leading to Dento-Maxillofacial Abnormalities in Mice: A Systematic Review

INTRODUCTION

To systematically review the available literature reporting on genetic mutations leading to dento-maxillofacial malformations in mice.

MATERIALS AND METHODS

An electronic search was performed across Embase, PubMed, Web of Science, and Scopus databases up to May 2024, targeting all in vivo studies on gene mutations causing dento-maxillofacial deformities in mice. Studies reporting oral clefts were excluded. Data collected included genetic background, sex distribution, observation times, sample sizes, interventions, affected genes, zygosity, dento-maxillofacial anomalies, and associated human syndromes. Risk of bias was evaluated using the SYRCLE tool.

RESULTS

Of 12,968 articles, 215 were included. The most common genetic background was C57BL6/J (B6) (n = 83), and knock-out was the most common intervention (n = 142). A total of 172 studies included homozygous mice. The five most studied genes were Amelx, Bmp-2, Dspp, Enam, and Runx2. Dento-alveolar anomalies were more commonly reported (n = 175) than skeletal (n = 65). Skeletal anomalies were mostly related to micrognathia (n = 14), agnathia (n = 5), dysplasia (n = 1), or reduced jaw size (n = 14). Risk of bias was moderate.

CONCLUSIONS

Key genes such as Amelx, Bmp-2, Dspp, Enam, and Runx2 implicated in dento-maxillofacial abnormalities in mice, detailing the most prevalent skeletal and dento-alveolar anomalies. These findings offer insights for developing gene therapy and diagnosing congenital malformations.

Adjusting for principal components can induce collider bias in genome-wide association studies

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 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.

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these 2 fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we lay out a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., genome-wide association studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur analytically and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study, we re-examine an analysis testing for coevolution of expression levels between genes across a fungal phylogeny and show that including eigenvectors of the covariance matrix as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

Cracking the type 1 diabetes code: Genes, microbes, immunity, and the early life environment

Type 1 diabetes (T1D) results from a complex interplay of genetic predisposition, immunological dysregulation, and environmental triggers, that culminate in the destruction of insulin-secreting pancreatic β cells. This review provides a comprehensive examination of the multiple factors underpinning T1D pathogenesis, to elucidate key mechanisms and potential therapeutic targets. Beginning with an exploration of genetic risk factors, we dissect the roles of human leukocyte antigen (HLA) haplotypes and non-HLA gene variants associated with T1D susceptibility. Mechanistic insights gleaned from the NOD mouse model provide valuable parallels to the human disease, particularly immunological intricacies underlying β cell-directed autoimmunity. Immunological drivers of T1D pathogenesis are examined, highlighting the pivotal contributions of both effector and regulatory T cells and the multiple functions of B cells and autoantibodies in β-cell destruction. Furthermore, the impact of environmental risk factors, notably modulation of host immune development by the intestinal microbiome, is examined. Lastly, the review probes human longitudinal studies, unveiling the dynamic interplay between mucosal immunity, systemic antimicrobial antibody responses, and the trajectories of T1D development. Insights garnered from these interconnected factors pave the way for targeted interventions and the identification of biomarkers to enhance T1D management and prevention strategies.

Polygenic Indices (a.k.a. Polygenic Scores) in Social Science: A Guide for Interpretation and Evaluation

Polygenic indices (PGI)-the new recommended label for polygenic scores (PGS) in social science-are genetic summary scales often used to represent an individual's liability for a disease, trait, or behavior based on the additive effects of measured genetic variants. Enthusiasm for linking genetic data with social outcomes and the inclusion of premade PGIs in social science datasets have facilitated increased uptake of PGIs in social science research-a trend that will likely continue. Yet, most social scientists lack the expertise to interpret and evaluate PGIs in social science research. Here, we provide a primer on PGIs for social scientists focusing on key concepts, unique statistical genetic considerations, and best practices in calculation, estimation, reporting, and interpretation. We summarize our recommended best practices as a checklist to aid social scientists in evaluating and interpreting studies with PGIs. We conclude by discussing the similarities between PGIs and standard social science scales and unique interpretative considerations.

Increased risk of venous thromboembolism in young and middle-aged individuals with hereditary angioedema: a family study

ABSTRACT

Hereditary angioedema (HAE), caused by C1 inhibitor protein deficiency, was recently shown to be associated with an increased risk for venous thromboembolism (VTE). To our knowledge, this is the first national family study of HAE, which aimed to determine the familial risk of VTE. The Swedish Multi-Generation Register was linked to the Swedish National Patient Register for the period of 1964 to 2018. Only patients with HAE with a validated diagnosis were included in the study and were linked to their family members. Hazard ratios (HRs) and 95% confidence intervals (CIs) for VTE were calculated for patients with HAE in comparison with relatives without HAE. Among 2006 individuals (from 276 pedigrees of 365 patients with HAE), 103 individuals were affected by VTE. In total, 35 (9.6%) patients with HAE were affected by VTE, whereas 68 (4.1%) non-HAE relatives were affected (P < .001). The adjusted HR for VTE among patients with HAE was 2.51 (95% CI, 1.67-3.77). Patients with HAE were younger at the first VTE than their non-HAE relatives (mean age, 51 years vs 63 years; P < .001). Before the age of 70 years, the HR for VTE among patients with HAE was 3.62 (95% CI, 2.26-5.80). The HR for VTE for patients with HAE born after 1964 was 8.29 (95% CI, 2.90-23.71). The HR for VTE for patients with HAE who were born in 1964 or earlier was 1.82 (95% CI, 1.14-2.91). HAE is associated with VTE among young and middle-aged individuals in Swedish families with HAE. The effect size of the association is in the order of other thrombophilias. We suggest that HAE may be considered a new rare thrombophilia.

An integrated QTL and RNA-seq analysis revealed new petal morphology loci in Brassica napus L

BACKGROUND

Rapeseed (Brassica napus L.) is one of the most important oil crops and a wildly cultivated horticultural crop. The petals of B. napus serve to protect the reproductive organs and attract pollinators and tourists. Understanding the genetic basis of petal morphology regulation is necessary for B. napus breeding.

RESULTS

In the present study, the quantitative trait locus (QTL) analysis for six B. napus petal morphology parameters in a double haploid (DH) population was conducted across six microenvironments. A total of 243 QTLs and five QTL hotspots were observed, including 232 novel QTLs and three novel QTL hotspots. The spatiotemporal transcriptomic analysis of the diversiform petals was also conducted, which indicated that the expression of plant hormone metabolic and cytoskeletal binding protein genes was variant among diversiform petals.

CONCLUSIONS

The integration of QTL and RNA-seq analysis revealed that plant hormones (including cytokinin, auxin, and gibberellin) and cytoskeleton were key regulators of the petal morphology. Subsequently, 61 high-confidence candidate genes of petal morphology regulation were identified, including Bn.SAUR10, Bn.ARF18, Bn.KIR1, Bn.NGA2, Bn.PRF1, and Bn.VLN4. The current study provided novel QTLs and candidate genes for further breeding B. napus varieties with diversiform petals.

Determination of genomic regions associated with early storage root formation and bulking in cassava

Early cassava storage root formation and bulking is a medium of escape that farmers and processors tend to adopt in cases of abiotic and biotic stresses like drought, flood, and destruction by domestic animals. In this study, 220 cassava genotypes from the International Institute of Tropical Agriculture (IITA), National Root Crops Research Institute (NRCRI), International Center for Tropical Agriculture (CIAT), local farmers (from farmer's field), and NextGen project were evaluated in three locations (Umudike, Benue, and Ikenne). The trials were laid out using a split plot in a randomized incomplete block design (alpha lattice) with two replications in 2 years. The storage roots for each plant genotype were sampled or harvested at 3, 6, 9, and 12 month after planting (MAP). All data collected were analyzed using the R-statistical package. The result showed moderate to high heritability among the traits, and there were significant differences (p< 0.05) among the performances of the genotypes. The genome-wide association mapping using the BLINK model detected 45 single-nucleotide polymorphism (SNP) markers significantly associated with the four early storage root bulking and formation traits on Chromosomes 1, 2, 3, 4, 5, 6, 8, 9, 10, 13, 14, 17, and 18. A total of 199 putative candidate genes were found to be directly linked to early storage root bulking and formation. The functions of these candidate genes were further characterized to regulate i) phytohormone biosynthesis, ii) cellular growth and development, and iii) biosynthesis of secondary metabolites for accumulation of starch and defense. Genome-wide association study (GWAS) also revealed the presence of four pleiotropic SNPs, which control starch content, dry matter content, dry yield, and bulking and formation index. The information on the GWAS could be used to develop improved cassava cultivars by breeders. Five genotypes (W940006, NR090146, TMS982123, TMS13F1060P0014, and NR010161) were selected as the best early storage root bulking and formation genotypes across the plant age. These selected cultivars should be used as sources of early storage root bulking and formation in future breeding programs.

Testing for differences in polygenic scores in the presence of confounding

Polygenic scores have become an important tool in human genetics, enabling the prediction of individuals' phenotypes from their genotypes. Understanding how the pattern of differences in polygenic score predictions across individuals intersects with variation in ancestry can provide insights into the evolutionary forces acting on the trait in question, and is important for understanding health disparities. However, because most polygenic scores are computed using effect estimates from population samples, they are susceptible to confounding by both genetic and environmental effects that are correlated with ancestry. The extent to which this confounding drives patterns in the distribution of polygenic scores depends on patterns of population structure in both the original estimation panel and in the prediction/test panel. Here, we use theory from population and statistical genetics, together with simulations, to study the procedure of testing for an association between polygenic scores and axes of ancestry variation in the presence of confounding. We use a general model of genetic relatedness to describe how confounding in the estimation panel biases the distribution of polygenic scores in a way that depends on the degree of overlap in population structure between panels. We then show how this confounding can bias tests for associations between polygenic scores and important axes of ancestry variation in the test panel. Specifically, for any given test, there exists a single axis of population structure in the GWAS panel that needs to be controlled for in order to protect the test. Based on this result, we propose a new approach for directly estimating this axis of population structure in the GWAS panel. We then use simulations to compare the performance of this approach to the standard approach in which the principal components of the GWAS panel genotypes are used to control for stratification.

Intravitreal MPTP drives retinal ganglion cell loss with oral nicotinamide treatment providing robust neuroprotection

Neurodegenerative diseases have common underlying pathological mechanisms including progressive neuronal dysfunction, axonal and dendritic retraction, and mitochondrial dysfunction resulting in neuronal death. The retina is often affected in common neurodegenerative diseases such as Parkinson's and Alzheimer's disease. Studies have demonstrated that the retina in patients with Parkinson's disease undergoes changes that parallel the dysfunction in the brain. These changes classically include decreased levels of dopamine, accumulation of alpha-synuclein in the brain and retina, and death of dopaminergic nigral neurons and retinal amacrine cells leading to gross neuronal loss. Exploring this disease's retinal phenotype and vision-related symptoms is an important window for elucidating its pathophysiology and progression, and identifying novel ways to diagnose and treat Parkinson's disease. 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is commonly used to model Parkinson's disease in animal models. MPTP is a neurotoxin converted to its toxic form by astrocytes, transported to neurons through the dopamine transporter, where it causes mitochondrial Complex I inhibition and neuron degeneration. Systemic administration of MPTP induces retinal changes in different animal models. In this study, we assessed the effects of MPTP on the retina directly via intravitreal injection in mice (5 mg/mL and 50 mg/mL to 7, 14 and 21 days post-injection). MPTP treatment induced the reduction of retinal ganglion cells-a sensitive neuron in the retina-at all time points investigated. This occurred without a concomitant loss of dopaminergic amacrine cells or neuroinflammation at any of the time points or concentrations tested. The observed neurodegeneration which initially affected retinal ganglion cells indicated that this method of MPTP administration could yield a fast and straightforward model of retinal ganglion cell neurodegeneration. To assess whether this model could be amenable to neuroprotection, mice were treated orally with nicotinamide (a nicotinamide adenine dinucleotide precursor) which has been demonstrated to be neuroprotective in several retinal ganglion cell injury models. Nicotinamide was strongly protective following intravitreal MPTP administration, further supporting intravitreal MPTP use as a model of retinal ganglion cell injury. As such, this model could be utilized for testing neuroprotective treatments in the context of Parkinson's disease and retinal ganglion cell injury.

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.

Interpreting population- and family-based genome-wide association studies in the presence of confounding

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding and can also absorb the "indirect" genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect-size estimates are used in polygenic scores (PGSs). We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding.

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these two fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we derive a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., Genome-Wide Association Studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur using analytical theory and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate this by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study of this, we re-examine an analysis testing for co-evolution of expression levels between genes across a fungal phylogeny, and show that including covariance matrix eigenvectors as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

Next-generation sequencing-based gene panel tests for the detection of rare variants and hypomorphic alleles associated with primary open-angle glaucoma

Primary open-angle glaucoma (POAG) is a complex disease with a strong hereditably component. Several genetic variants have recently been associated with POAG, partially due to technological improvements such as next-generation sequencing (NGS). The aim of this study was to genetically analyze patients with POAG to determine the contribution of rare variants and hypomorphic alleles associated with glaucoma as a future method of diagnosis and early treatment. Seventy-two genes potentially associated with adult glaucoma were studied in 61 patients with POAG. Additionally, we sequenced the coding sequence of CYP1B1 gene in 13 independent patients to deep analyze the potential association of hypomorphic CYP1B1 alleles in the pathogenesis of POAG. We detected nine rare variants in 16% of POAG patients studied by NGS. Those rare variants are located in CYP1B1, SIX6, CARD10, MFN1, OPTC, OPTN, and WDR36 glaucoma-related genes. Hypomorphic variants in CYP1B1 and SIX6 genes have been identified in 8% of the total POAG patient assessed. Our findings suggest that NGS could be a valuable tool to clarify the impact of genetic component on adult glaucoma. However, in order to demonstrate the contribution of these rare variants and hypomorphic alleles to glaucoma, segregation and functional studies would be necessary. The identification of new variants and hypomorphic alleles in glaucoma patients will help to configure the genetic identity of these patients, in order to make an early and precise molecular diagnosis.

CaeNDR, the Caenorhabditis Natural Diversity Resource

Studies of model organisms have provided important insights into how natural genetic differences shape trait variation. These discoveries are driven by the growing availability of genomes and the expansive experimental toolkits afforded to researchers using these species. For example, Caenorhabditis elegans is increasingly being used to identify and measure the effects of natural genetic variants on traits using quantitative genetics. Since 2016, the C. elegans Natural Diversity Resource (CeNDR) has facilitated many of these studies by providing an archive of wild strains, genome-wide sequence and variant data for each strain, and a genome-wide association (GWA) mapping portal for the C. elegans community. Here, we present an updated platform, the Caenorhabditis Natural Diversity Resource (CaeNDR), that enables quantitative genetics and genomics studies across the three Caenorhabditis species: C. elegans, C. briggsae and C. tropicalis. The CaeNDR platform hosts several databases that are continually updated by the addition of new strains, whole-genome sequence data and annotated variants. Additionally, CaeNDR provides new interactive tools to explore natural variation and enable GWA mappings. All CaeNDR data and tools are accessible through a freely available web portal located at caendr.org.

Harnessing population diversity: in search of tools of the trade

Big neuroscience datasets are not big small datasets when it comes to quantitative data analysis. Neuroscience has now witnessed the advent of many population cohort studies that deep-profile participants, yielding hundreds of measures, capturing dimensions of each individual's position in the broader society. Indeed, there is a rebalancing from small, strictly selected, and thus homogenized cohorts toward always larger, more representative, and thus diverse cohorts. This shift in cohort composition is prompting the revision of incumbent modeling practices. Major sources of population stratification increasingly overshadow the subtle effects that neuroscientists are typically studying. In our opinion, as we sample individuals from always wider diversity backgrounds, we will require a new stack of quantitative tools to realize diversity-aware modeling. We here take inventory of candidate analytical frameworks. Better incorporating driving factors behind population structure will allow refining our understanding of how brain-behavior relationships depend on human subgroups.

Population Neuroscience: Principles and Advances

In population neuroscience, three disciplines come together to advance our knowledge of factors that shape the human brain: neuroscience, genetics, and epidemiology (Paus, Human Brain Mapping 31:891-903, 2010). Here, I will come back to some of the background material reviewed in more detail in our previous book (Paus, Population Neuroscience, 2013), followed by a brief overview of current advances and challenges faced by this integrative approach.

Familial risk of vasospastic angina: a nationwide family study in Sweden

OBJECTIVES

Vasospastic angina (VSA) is a complex coronary vasomotor disorder associated with an increased risk of myocardial infarction and sudden death. Despite considerable advances in understanding VSA pathophysiology, the interplay between genetic and environmental factors remains elusive. Accordingly, we aimed to determine the familial VSA risk among first-degree relatives of affected individuals.

METHODS

A population-based multigenerational cohort study was conducted, including full-sibling pairs born to Swedish parents between 1932 and 2018. Register-based diagnoses were ascertained through linkage to the Swedish Multigeneration Register and National Patient Register. Incidence rate ratios (IRRs) and adjusted HRs were calculated for relatives of individuals with VSA compared with relatives of individuals without VSA.

RESULTS

The total study population included 5 764 770 individuals. Overall, 3461 (0.06%) individuals (median age at disease onset 59 years, IQR: 63-76) were diagnosed with VSA. Of these, 2236 (64.61%) were women. The incidence rate of VSA for individuals with an affected sibling was 0.31 (95% CI: 0.24 to 0.42) per 1000 person-years compared with 0.04 (95% CI: 0.04 to 0.04) per 1000 person-years for those without an affected sibling, yielding an IRR of 7.58 (95% CI: 5.71 to 10.07). The risk of VSA for siblings with an affected sibling was significantly increased in the fully adjusted model (HR: 2.56; 95% CI: 1.73 to 3.79). No increased risk of VSA was observed in spouses of affected individuals (HR: 0.63; 95% CI: 0.19 to 2.09).

CONCLUSIONS

In this nationwide family study, we identified high familial risk for VSA independent of shared environmental risk factors. Our findings indicate that VSA tends to cluster in families, emphasising the need to explore genetic and non-genetic factors that may contribute.

Increased development of T-bet+CD11c+ B cells predisposes to lupus in females: Analysis in BXD2 mouse and genetic crosses

Cardinal features of lupus include elevated B cell activation and autoantibody production with a female sex preponderance. We quantified interactions of sex and genetic variation on the development of autoimmune B-cell phenotypes and autoantibodies in the BXD2 murine model of lupus using a cohort of backcrossed progeny (BXD2 x C57BL/6J) x BXD2. Sex was the key factor leading to increased total IgG, IgG2b, and autoantibodies. The percentage of T-bet+CD11c+ IgD+ activated naive B cells (aNAV) was higher in females and was associated with increased T-bet+CD11c+ IgD- age-related B cells, Fas+GL7+ germinal center B cells, Cxcr5-Icos+ peripheral T-helper cells, and Cxcr5+Icos+ follicular T-helper cells. IFN-β was elevated in females. Variation in aNAV cells was mapped to Chr 7 in a locus that shows significant interactions between the female sex and heterozygous B/D variant. Our results suggest that activation of naive B cells forms the basis for the female-predominant development of autoantibodies in lupus-susceptible BXD2 mice.

Impact of inclusion non-additive effects on genome-wide association and variance's components in Scottish black sheep

CONTEXT

It's well-documented that most economic traits have a complex genetic structure that is controlled by additive and non-additive gene actions. Hence, knowledge of the underlying genetic architecture of such complex traits could aid in understanding how these traits respond to the selection in breeding and mating programs. Computing and having estimates of the non-additive effect for economic traits in sheep using genome-wide information can be important because; non-additive genes play an important role in the prediction accuracy of genomic breeding values and the genetic response to the selection.

AIM

This study aimed to assess the impact of non-additive effects (dominance and epistasis) on the estimation of genetic parameters for body weight traits in sheep.

METHODS

This study used phenotypic and genotypic belonging to 752 Scottish Blackface lambs. Three live weight traits considered in this study were included in body weight at 16, 20, and 24 weeks). Three genetic models including additive (AM), additive + dominance (ADM), and additive + dominance + epistasis (ADEM), were used.

KEY RESULTS

The narrow sense heritability for weight at 16 weeks of age (BW16) were 0.39, 0.35, and 0.23, for 20 weeks of age (BW20) were 0.55, 0.54, and 0.42, and finally for 24 weeks of age (BW24) were 0.16, 0.12, and 0.02, using the AM, ADM, and ADEM models, respectively. The additive genetic model significantly outperformed the non-additive genetic model (p < 0.01). The dominance variance of the BW16, BW20, and BW24 accounted for 38, 6, and 30% of the total phenotypic, respectively. Moreover, the epistatic variance accounted for 39, 0.39, and 47% of the total phenotypic variances of these traits, respectively. In addition, our results indicated that the most important SNPs for live weight traits are on chromosomes 3 (three SNPS including s12606.1, OAR3_221188082.1, and OAR3_4106875.1), 8 (OAR8_16468019.1, OAR8_18067475.1, and OAR8_18043643.1), and 19 (OAR19_18010247.1), according to the genome-wide association analysis using additive and non-additive genetic model.

CONCLUSIONS

The results emphasized that the non-additive genetic effects play an important role in controlling body weight variation at the age of 16-24 weeks in Scottish Blackface lambs.

IMPLICATIONS

It is expected that using a high-density SNP panel and the joint modeling of both additive and non-additive effects can lead to better estimation and prediction of genetic parameters.

Distinct patterns of inheritance shape life-history traits in steelhead trout

Life-history variation is the raw material of adaptation, and understanding its genetic and environmental underpinnings is key to designing effective conservation strategies. We used large-scale genetic pedigree reconstruction of anadromous steelhead trout (Oncorhynchus mykiss) from the Russian River, CA, USA, to elucidate sex-specific patterns of life-history traits and their heritability. SNP data from adults returning from sea over a 14-year period were used to identify 13,474 parent-offspring trios. These pedigrees were used to determine age structure, size distributions and family sizes for these fish, as well as to estimate the heritability of two key life-history traits, spawn date and age at maturity (first reproduction). Spawn date was highly heritable (h2  = 0.73) and had a cross-sex genetic correlation near unity. We provide the first estimate of heritability for age at maturity in ocean-going fish from this species and found it to be highly heritable (h2 from 0.29 to 0.62, depending on sex and method), with a much lower genetic correlation across sexes. We also evaluated genotypes at a migration-associated inversion polymorphism and found sex-specific correlations with age at maturity. The significant heritability of these two key reproductive traits in these imperiled fish, and their patterns of inheritance in the two sexes, is consistent with predictions of both natural and sexually antagonistic selection (sexes experience opposing selection pressures). This emphasizes the importance of anthropogenic factors, including hatchery practices and ecosystem modifications, in shaping the fitness of this species, thus providing important guidance for management and conservation efforts.

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.

Human-genome single nucleotide polymorphisms affecting transcription factor binding and their role in pathogenesis

Single nucleotide polymorphisms (SNPs) are the most common type of variation in the human genome. The vast majority of SNPs identified in the human genome do not have any effect on the phenotype; however, some can lead to changes in the function of a gene or the level of its expression. Most SNPs associated with certain traits or pathologies are mapped to regulatory regions of the genome and affect gene expression by changing transcription factor binding sites. In recent decades, substantial effort has been invested in searching for such regulatory SNPs (rSNPs) and understanding the mechanisms by which they lead to phenotypic differences, primarily to individual differences in susceptibility to diseases and in sensitivity to drugs. The development of the NGS (next-generation sequencing) technology has contributed not only to the identification of a huge number of SNPs and to the search for their association (genome-wide association studies, GWASs) with certain diseases or phenotypic manifestations, but also to the development of more productive approaches to their functional annotation. It should be noted that the presence of an association does not allow one to identify a functional, truly disease-associated DNA sequence variant among multiple marker SNPs that are detected due to linkage disequilibrium. Moreover, determination of associations of genetic variants with a disease does not provide information about the functionality of these variants, which is necessary to elucidate the molecular mechanisms of the development of pathology and to design effective methods for its treatment and prevention. In this regard, the functional analysis of SNPs annotated in the GWAS catalog, both at the genome-wide level and at the level of individual SNPs, became especially relevant in recent years. A genome-wide search for potential rSNPs is possible without any prior knowledge of their association with a trait. Thus, mapping expression quantitative trait loci (eQTLs) makes it possible to identify an SNP for which - among transcriptomes of homozygotes and heterozygotes for its various alleles - there are differences in the expression level of certain genes, which can be located at various distances from the SNP. To predict rSNPs, approaches based on searches for allele-specific events in RNA-seq, ChIP-seq, DNase-seq, ATAC-seq, MPRA, and other data are also used. Nonetheless, for a more complete functional annotation of such rSNPs, it is necessary to establish their association with a trait, in particular, with a predisposition to a certain pathology or sensitivity to drugs. Thus, approaches to finding SNPs important for the development of a trait can be categorized into two groups: (1) starting from data on an association of SNPs with a certain trait, (2) starting from the determination of allele-specific changes at the molecular level (in a transcriptome or regulome). Only comprehensive use of strategically different approaches can considerably enrich our knowledge about the role of genetic determinants in the molecular mechanisms of trait formation, including predisposition to multifactorial diseases.

Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures

Large-effect loci-those statistically significant loci discovered by genome-wide association studies or linkage mapping-associated with key traits segregate amidst a background of minor, often undetectable, genetic effects in wild and domesticated plants and animals. Accurately attributing mean differences and variance explained to the correct components in the linear mixed model analysis is vital for selecting superior progeny and parents in plant and animal breeding, gene therapy, and medical genetics in humans. Marker-assisted prediction and its successor, genomic prediction, have many advantages for selecting superior individuals and understanding disease risk. However, these two approaches are less often integrated to study complex traits with different genetic architectures. This simulation study demonstrates that the average semivariance can be applied to models incorporating Mendelian, oligogenic, and polygenic terms simultaneously and yields accurate estimates of the variance explained for all relevant variables. Our previous research focused on large-effect loci and polygenic variance separately. This work aims to synthesize and expand the average semivariance framework to various genetic architectures and the corresponding mixed models. This framework independently accounts for the effects of large-effect loci and the polygenic genetic background and is universally applicable to genetics studies in humans, plants, animals, and microbes.

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.

Familial aggregation of multimorbidity in Sweden: national explorative family study

Objectives

To examine whether multimorbidity aggregates in families in Sweden.

Design

National explorative family study.

Setting

Swedish Multigeneration Register linked to the National Patient Register, 1997-2015. Multimorbidity was assessed with a modified counting method of 45 chronic non-communicable diseases according to ICD-10 (international classification of diseases, 10th revision) diagnoses.

Participants

2 694 442 Swedish born individuals (48.73% women) who could be linked to their Swedish born first, second, and third degree relatives. Twins were defined as full siblings born on the same date.

Main outcome measures

Multimorbidity was defined as two or more non-communicable diseases. Familial associations for one, two, three, four, and five or more non-communicable diseases were assessed to examine risks depending on the number of non-communicable diseases. Familial adjusted odds ratios for multimorbidity were calculated for individuals with a diagnosis of multimorbidity compared with relatives of individuals unaffected by multimorbidity (reference). An initial principal component decomposition followed by a factor analysis with a principal factor method and an oblique promax rotation was used on the correlation matrix of tetrachoric correlations between 45 diagnoses in patients to identify disease clusters.

Results

The odds ratios for multimorbidity were 2.89 in twins (95% confidence interval 2.56 to 3.25), 1.81 in full siblings (1.78 to 1.84), 1.26 in half siblings (1.24 to 1.28), and 1.13 in cousins (1.12 to 1.14) of relatives with a diagnosis of multimorbidity. The odds ratios for multimorbidity increased with the number of diseases in relatives. For example, among twins, the odds ratios for multimorbidity were 1.73, 2.84, 4.09, 4.63, and 6.66 for an increasing number of diseases in relatives, from one to five or more, respectively. Odds ratios were highest at younger ages: in twins, the odds ratio was 3.22 for those aged ≤20 years, 3.14 for those aged 21-30 years, and 2.29 for those aged >30 years at the end of follow-up. Nine disease clusters (factor clusters 1-9) were identified, of which seven aggregated in families. The first three disease clusters in the principal component decomposition were cardiometabolic disease (factor 1), mental health disorders (factor 2), and disorders of the digestive system (factor 3). Odds ratios for multimorbidity in twins, siblings, half siblings, and cousins for the factor 1 cluster were 2.79 (95% confidence interval 0.97 to 8.06), 2.62 (2.39 to 2.88), 1.52 (1.34 to 1.73), and 1.31 (1.23 to 1.39), and for the factor 2 cluster, 5.79 (4.48 to 7.48) 3.24 (3.13 to 3.36), 1.51 (1.45 to 1.57), and 1.37 (1.341.40).

Conclusions

The results of this explorative family study indicated that multimorbidity aggregated in Swedish families. The findings suggest that map clusters of diseases should be used for the genetic study of common diseases to show new genetic patterns of non-communicable diseases.

Interpreting population and family-based genome-wide association studies in the presence of confounding

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding, and can also absorb the 'indirect' genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of Mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect size estimates are used in polygenic scores. We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. In addition to known biases that can arise in family-based GWASs when interactions between family members are ignored, we show that biases can also arise from gene-by-environment (G×E) interactions when parental genotypes are not distributed identically across interacting environmental and genetic backgrounds. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding and interactions.

Advancing the Genetics of Lewy Body Disorders with Disease-Modifying Treatments in Mind

In this article, a caveat for advancing the genetics of Lewy body disorders is raised, given the nosological controversy about whether to consider dementia with Lewy bodies (DLB) and Parkinson's disease (PD) as one entity or two separate entities. Using the framework of the sufficient and component causes model of causation, as further developed into an evolution-based model of causation, it is proposed that a disease of complex etiology is defined as having a relatively high degree of sharing of the component causes (a genetic or environmental factor), that is, a low degree of heterogeneity of the sufficient causes. Based on this definition, only if the sharing of component causes within each of two diseases is similar to their combined sharing can lumping be warranted. However, it is not known whether the separate and combined sharing are similar before conducting the etiologic studies. This means that lumping DLB and PD can be counterproductive as it can decrease the ability to detect component causes despite the potential benefit of conducting studies with larger sample sizes. In turn, this is relevant to the development of disease-modifying treatments, because non-overlapping causal genetic factors may result in distinct pathogenetic pathways providing promising targets for interventions.

Neglect in Childhood, Problem Behavior in Adulthood

Few studies have reported problem behaviors in adulthood related to the timing of child neglect. The objective was to examine the relationship between classes of child neglect and later behavior. The sample included 473 participants from the prospective Longitudinal Studies of Child Abuse and Neglect (LONGSCAN); their mean age was 23.8 years. They completed an online survey regarding behaviors and experiences in early adulthood. Neglect was assessed via Child Protective Services (CPS) and self-reports of neglect. Latent class analysis (LCA) identified three classes: Late Neglect, Chronic Neglect, and Limited Neglect. There were significant differences between Limited and Late Neglect regarding later intimate partner aggression and violence (IPAV) and psychological distress, and among all classes for criminal behavior. High-risk youth experiencing neglect beginning in mid-adolescence appear especially vulnerable to later criminal behavior, psychological distress, and IPAV. Those working with such youth can help ensure that their needs are adequately met, to prevent or mitigate problems in adulthood.

Multi-environment genomic prediction for soluble solids content in peach (Prunus persica)

Genotype-by-environment interaction (G × E) is a common phenomenon influencing genetic improvement in plants, and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G × E in horticultural tree crops, mostly due to evaluation costs, leading to a focus on the development and deployment of locally adapted germplasm. Using sweetness (measured as soluble solids content, SSC) in peach/nectarine assessed at four trials from three US peach-breeding programs as a case study, we evaluated the hypotheses that (i) complex data from multiple breeding programs can be connected using GBLUP models to improve the knowledge of G × E for breeding and deployment and (ii) accounting for a known large-effect quantitative trait locus (QTL) improves the prediction accuracy. Following a structured strategy using univariate and multivariate models containing additive and dominance genomic effects on SSC, a model that included a previously detected QTL and background genomic effects was a significantly better fit than a genome-wide model with completely anonymous markers. Estimates of an individual's narrow-sense and broad-sense heritability for SSC were high (0.57-0.73 and 0.66-0.80, respectively), with 19-32% of total genomic variance explained by the QTL. Genome-wide dominance effects and QTL effects were stable across environments. Significant G × E was detected for background genome effects, mostly due to the low correlation of these effects across seasons within a particular trial. The expected prediction accuracy, estimated from the linear model, was higher than the realised prediction accuracy estimated by cross-validation, suggesting that these two parameters measure different qualities of the prediction models. While prediction accuracy was improved in some cases by combining data across trials, particularly when phenotypic data for untested individuals were available from other trials, this improvement was not consistent. This study confirms that complex data can be combined into a single analysis using GBLUP methods to improve understanding of G × E and also incorporate known QTL effects. In addition, the study generated baseline information to account for population structure in genomic prediction models in horticultural crop improvement.

New insights on the role of human leukocyte antigen complex in primary biliary cholangitis

Primary Biliary Cholangitis (PBC) is a rare autoimmune cholangiopathy. Genetic studies have shown that the strongest statistical association with PBC has been mapped in the human leukocyte antigen (HLA) locus, a highly polymorphic area that mostly contribute to the genetic variance of the disease. Furthermore, PBC presents high variability throughout different population groups, which may explain the different geoepidemiology of the disease. A major role in defining HLA genetic contribution has been given by genome-wide association studies (GWAS) studies; more recently, new technologies have been developed to allow a deeper understanding. The study of the altered peptides transcribed by genetic alterations also allowed the development of novel therapeutic strategies in the context of immunotolerance. This review summarizes what is known about the immunogenetics of PBC with a focus on the HLA locus, the different distribution of HLA alleles worldwide, and how HLA modifications are associated with the pathogenesis of PBC. Novel therapeutic strategies are also outlined.

Inflammation and Autophagy: A Convergent Point between Autism Spectrum Disorder (ASD)-Related Genetic and Environmental Factors: Focus on Aluminum Adjuvants

Autism spectrum disorder (ASD), schizophrenia, and bipolar disorder are genetically complex and heterogeneous neurodevelopmental disorders (NDDs) resulting from genetic factors and gene-environment (GxE) interactions for which onset occurs in early brain development. Recent progress highlights the link between ASD and (i) immunogenetics, neurodevelopment, and inflammation, and (ii) impairments of autophagy, a crucial neurodevelopmental process involved in synaptic pruning. Among various environmental factors causing risk for ASD, aluminum (Al)-containing vaccines injected during critical periods have received special attention and triggered relevant scientific questions. The aim of this review is to discuss the current knowledge on the role of early inflammation, immune and autophagy dysfunction in ASD as well as preclinical studies which question Al adjuvant impacts on brain and immune maturation. We highlight the most recent breakthroughs and the lack of epidemiological, pharmacokinetic and pharmacodynamic data constituting a "scientific gap". We propose additional research, such as genetic studies that could contribute to identify populations at genetic risk, improving diagnosis, and potentially the development of new therapeutic tools.

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 mapping within a local Arabidopsis thaliana population more fully reveals the genetic architecture for defensive metabolite diversity

A paradoxical finding from genome-wide association studies (GWAS) in plants is that variation in metabolite profiles typically maps to a small number of loci, despite the complexity of underlying biosynthetic pathways. This discrepancy may partially arise from limitations presented by geographically diverse mapping panels. Properties of metabolic pathways that impede GWAS by diluting the additive effect of a causal variant, such as allelic and genetic heterogeneity and epistasis, would be expected to increase in severity with the geographical range of the mapping panel. We hypothesized that a population from a single locality would reveal an expanded set of associated loci. We tested this in a French Arabidopsis thaliana population (less than 1 km transect) by profiling and conducting GWAS for glucosinolates, a suite of defensive metabolites that have been studied in depth through functional and genetic mapping approaches. For two distinct classes of glucosinolates, we discovered more associations at biosynthetic loci than the previous GWAS with continental-scale mapping panels. Candidate genes underlying novel associations were supported by concordance between their observed effects in the TOU-A population and previous functional genetic and biochemical characterization. Local populations complement geographically diverse mapping panels to reveal a more complete genetic architecture for metabolic traits. This article is part of the theme issue 'Genetic basis of adaptation and speciation: from loci to causative mutations'.

Genomic analyses of 10,376 individuals in the Westlake BioBank for Chinese (WBBC) pilot project

We initiate the Westlake BioBank for Chinese (WBBC) pilot project with 4,535 whole-genome sequencing (WGS) individuals and 5,841 high-density genotyping individuals, and identify 81.5 million SNPs and INDELs, of which 38.5% are absent in dbSNP Build 151. We provide a population-specific reference panel and an online imputation server ( https://wbbc.westlake.edu.cn/ ) which could yield substantial improvement of imputation performance in Chinese population, especially for low-frequency and rare variants. By analyzing the singleton density of the WGS data, we find selection signatures in SNX29, DNAH1 and WDR1 genes, and the derived alleles of the alcohol metabolism genes (ADH1A and ADH1B) emerge around 7,000 years ago and tend to be more common from 4,000 years ago in East Asia. Genetic evidence supports the corresponding geographical boundaries of the Qinling-Huaihe Line and Nanling Mountains, which separate the Han Chinese into subgroups, and we reveal that North Han was more homogeneous than South Han.

Challenging the utility of polygenic scores for social science: Environmental confounding, downward causation, and unknown biology

The sociogenomics revolution is upon us, we are told. Whether revolutionary or not, sociogenomics is poised to flourish given the ease of incorporating polygenic scores (or PGSs) as "genetic propensities" for complex traits into social science research. Pointing to evidence of ubiquitous heritability and the accessibility of genetic data, scholars have argued that social scientists not only have an opportunity but a duty to add PGSs to social science research. Social science research that ignores genetics is, some proponents argue, at best partial and likely scientifically flawed, misleading, and wasteful. Here, I challenge arguments about the value of genetics for social science and with it the claimed necessity of incorporating PGSs into social science models as measures of genetic influences. In so doing, I discuss the impracticability of distinguishing genetic influences from environmental influences because of non-causal gene-environment correlations, especially population stratification, familial confounding, and downward causation. I explain how environmental effects masquerade as genetic influences in PGSs, which undermines their raison d'être as measures of genetic propensity, especially for complex socially contingent behaviors that are the subject of sociogenomics. Additionally, I draw attention to the partial, unknown biology, while highlighting the persistence of an implicit, unavoidable reductionist genes versus environments approach. Leaving sociopolitical and ethical concerns aside, I argue that the potential scientific rewards of adding PGSs to social science are few and greatly overstated and the scientific costs, which include obscuring structural disadvantages and cultural influences, outweigh these meager benefits for most social science applications.

Leveraging LD eigenvalue regression to improve the estimation of SNP heritability and confounding inflation

Heritability is a fundamental concept in genetic studies, measuring the genetic contribution to complex traits and bringing insights about disease mechanisms. The advance of high-throughput technologies has provided many resources for heritability estimation. Linkage disequilibrium (LD) score regression (LDSC) estimates both heritability and confounding biases, such as cryptic relatedness and population stratification, among single-nucleotide polymorphisms (SNPs) by using only summary statistics released from genome-wide association studies. However, only partial information in the LD matrix is utilized in LDSC, leading to loss in precision. In this study, we propose LD eigenvalue regression (LDER), an extension of LDSC, by making full use of the LD information. Compared to state-of-the-art heritability estimating methods, LDER provides more accurate estimates of SNP heritability and better distinguishes the inflation caused by polygenicity and confounding effects. We demonstrate the advantages of LDER both theoretically and with extensive simulations. We applied LDER to 814 complex traits from UK Biobank, and LDER identified 363 significantly heritable phenotypes, among which 97 were not identified by LDSC.

Chile Pepper (Capsicum) Breeding and Improvement in the "Multi-Omics" Era

Chile pepper (Capsicum spp.) is a major culinary, medicinal, and economic crop in most areas of the world. For more than hundreds of years, chile peppers have "defined" the state of New Mexico, USA. The official state question, "Red or Green?" refers to the preference for either red or the green stage of chile pepper, respectively, reflects the value of these important commodities. The presence of major diseases, low yields, decreased acreages, and costs associated with manual labor limit production in all growing regions of the world. The New Mexico State University (NMSU) Chile Pepper Breeding Program continues to serve as a key player in the development of improved chile pepper varieties for growers and in discoveries that assist plant breeders worldwide. Among the traits of interest for genetic improvement include yield, disease resistance, flavor, and mechanical harvestability. While progress has been made, the use of conventional breeding approaches has yet to fully address producer and consumer demand for these traits in available cultivars. Recent developments in "multi-omics," that is, the simultaneous application of multiple omics approaches to study biological systems, have allowed the genetic dissection of important phenotypes. Given the current needs and production constraints, and the availability of multi-omics tools, it would be relevant to examine the application of these approaches in chile pepper breeding and improvement. In this review, we summarize the major developments in chile pepper breeding and present novel tools that can be implemented to facilitate genetic improvement. In the future, chile pepper improvement is anticipated to be more data and multi-omics driven as more advanced genetics, breeding, and phenotyping tools are developed.

The Genetic Architecture of Nitrogen Use Efficiency in Switchgrass (Panicum virgatum L.)

Switchgrass (Panicum virgatum L.) has immense potential as a bioenergy crop with the aim of producing biofuel as an end goal. Nitrogen (N)-related sustainability traits, such as nitrogen use efficiency (NUE) and nitrogen remobilization efficiency (NRE), are important factors affecting switchgrass quality and productivity. Hence, it is imperative to develop nitrogen use-efficient switchgrass accessions by exploring the genetic basis of NUE in switchgrass. For that, we used 331 diverse field-grown switchgrass accessions planted under low and moderate N fertility treatments. We performed a genome wide association study (GWAS) in a holistic manner where we not only considered NUE as a single trait but also used its related phenotypic traits, such as total dry biomass at low N and moderate N, and nitrogen use index, such as NRE. We have evaluated the phenotypic characterization of the NUE and the related traits, highlighted their relationship using correlation analysis, and identified the top ten nitrogen use-efficient switchgrass accessions. Our GWAS analysis identified 19 unique single nucleotide polymorphisms (SNPs) and 32 candidate genes. Two promising GWAS candidate genes, caffeoyl-CoA O-methyltransferase (CCoAOMT) and alfin-like 6 (AL6), were further supported by linkage disequilibrium (LD) analysis. Finally, we discussed the potential role of nitrogen in modulating the expression of these two genes. Our findings have opened avenues for the development of improved nitrogen use-efficient switchgrass lines.

Factors Influencing Gallstone Formation: A Review of the Literature

Gallstone disease is a common pathology of the digestive system with nearly a 10–20% incidence rate among adults. The mainstay of treatment is cholecystectomy, which is commonly associated with physical pain and may also seriously affect a patient’s quality of life. Clinical research suggests that cholelithiasis is closely related to the age, gender, body mass index, and other basic physical characteristics of patients. Clinical research further suggests that the occurrence of cholelithiasis is related to obesity, diabetes, non-alcoholic fatty liver, and other diseases. For this reason, we reviewed the following: genetic factors; excessive liver cholesterol secretion (causing cholesterol supersaturation in gallbladder bile); accelerated growth of cholesterol crystals and solid cholesterol crystals; gallbladder motility impairment; and cardiovascular factors. Herein, we summarize and analyze the causes and mechanisms of cholelithiasis, discuss its correlation with the pathogenesis of related diseases, and discuss possible mechanisms.

Lottery, luck, or legacy. A review of "The Genetic Lottery: Why DNA matters for social equality"

A book review of "The genetic lottery: why DNA matters for social equality." (Princeton University Press, 2021) by Kathryn Paige Harden.

Germplasm Resources and Strategy for Genetic Breeding of Lycium Species: A Review

Lycium species (goji), belonging to Solanaceae, are widely spread in the arid to semiarid environments of Eurasia, Africa, North and South America, among which most species have affinal drug and diet functions, resulting in their potential to be a superior healthy food. However, compared with other crop species, scientific research on breeding Lycium species lags behind. This review systematically introduces the present germplasm resources, cytological examination and molecular-assisted breeding progress in Lycium species. Introduction of the distribution of Lycium species around the world could facilitate germplasm collection for breeding. Karyotypes of different species could provide a feasibility analysis of fertility between species. The introduction of mapping technology has discussed strategies for quantitative trait locus (QTL) mapping in Lycium species according to different kinds of traits. Moreover, to extend the number of traits and standardize the protocols of trait detection, we also provide 1,145 potential traits (275 agronomic and 870 metabolic) in different organs based on different reference studies on Lycium, tomato and other Solanaceae species. Finally, perspectives on goji breeding research are discussed and concluded. This review will provide breeders with new insights into breeding Lycium species.

Classic Thrombophilias and Thrombotic Risk Among Middle-Aged and Older Adults: A Population-Based Cohort Study

Background

Five classic thrombophilias have been recognized: factor V Leiden (rs6025), the prothrombin G20210A variant (rs1799963), and protein C, protein S, and antithrombin deficiencies. This study aimed to determine the thrombotic risk of classic thrombophilias in a cohort of middle‐aged and older adults.

Methods and Results

Factor V Leiden, prothrombin G20210A and protein‐coding variants in the PROC (protein C), PROS1 (protein S), and SERPINC1 (antithrombin) anticoagulant genes were determined in 29 387 subjects (born 1923–1950, 60% women) who participated in the Malmö Diet and Cancer study (1991–1996). The Human Gene Mutation Database was used to define 68 disease‐causing mutations. Patients were followed up from baseline until the first event of venous thromboembolism (VTE), death, or Dec 31, 2018. Carriership (n=908, 3.1%) for disease‐causing mutations in the PROC, PROS1, and SERPINC1 genes was associated with incident VTE: Hazard ratio (HR) was 1.6 (95% CI, 1.3–1.9). Variants not in Human Gene Mutation Database were not linked to VTE (HR, 1.1; 95% CI, 0.8–1.5). Heterozygosity for rs6025 and rs1799963 was associated with incident VTE: HR, 1.8 (95% CI, 1.6–2.0) and HR, 1.6 (95% CI, 1.3–2.0), respectively. The HR for carrying 1 classical thrombophilia variant was 1.7 (95% CI, 1.6–1.9). HR was 3.9 (95% CI, 3.1–5.0) for carriers of ≥2 thrombophilia variants.

Conclusions

The 5 classic thrombophilias are associated with a dose‐graded risk of VTE in middle‐aged and older adults. Disease‐causing variants in the PROC, PROS1, and SERPINC1 genes were more common than the rs1799963 variant but the conferred genetic risk was comparable with the rs6025 and rs1799963 variants.

Familial Mortality Risks in Patients With Ischemic Stroke: A Swedish Sibling Study

BACKGROUND

The influence of familial factors on the prognosis of ischemic stroke (IS) is unknown. This nationwide follow-up study evaluated familial mortality risks of IS among Swedish sibling pairs hospitalized for IS.

METHODS

We linked Swedish nationwide registers for the identification of 1380 Swedish born sibling pairs (2760 cases), where both siblings were hospitalized for first-time IS between 1991 and 2010. Median age was 62 years (range, 26-78 years). Sibling pairs with cancer were excluded. Familial hazard ratios (FHRs) for mortality after first IS hospitalization were determined with Cox regression. The influence of proband survival after IS was categorized as short sibling survival (<1, 2, 3, 4, or 5 years) or long sibling survival (≥1, 2, 3, 4, or 5 years) after IS. FHRs were adjusted for age at onset, sex, education, county, year of diagnosis, days of hospitalization, and comorbidities.

RESULTS

Short sibling survival (ie, <3 or <4 years) after IS was associated with an adjusted FHR of 1.29 (95% CI, 1.05-1.58) and 1.24 (95% CI, 1.02-1.51), respectively, for overall mortality after IS. Stratified analysis showed that short sibling survival (ie, <2-<5 years) was stronger and significant only among younger individuals (<62 years) and males. Highest FHR for short sibling survival (ie, <4 years) was 1.42 (95% CI, 1.08-1.88) for younger individuals and 1.50 (95% CI, 1.21-1.87) for males. For young male subjects, FHR was 1.80 (95% CI, 1.33-2.46). In the adjusted model, mortality was also associated with sex, education, age at onset, year of diagnosis, days of hospitalization, coronary heart disease, diabetes, dementia, heart failure, obesity, alcoholism, and hyperlipidemia.

CONCLUSIONS

Our results suggest that family history of short survival in siblings after IS is associated with mortality after IS for younger male subjects. Additional studies are needed to characterize possible genetic and nongenetic familial environmental causes of this association.

Ancestry-Matched and Cross-Ancestry Genetic Risk Scores of Type 2 Diabetes in Pregnant Women and Fetal Growth: A Study in an Ancestrally Diverse Cohort

Maternal genetic variants associated with offspring birth weight and adult type 2 diabetes (T2D) risk loci show some overlap. Whether T2D genetic risk influences longitudinal fetal weight and the gestational timing when these relationships begin is unknown. We investigated the associations of T2D genetic risk scores (GRS) with longitudinal fetal weight and birth weight among 1,513 pregnant women from four ancestral groups. Women had up to five ultrasonography examinations. Ancestry-matched GRS were constructed separately using 380 European- (GRSeur), 104 African- (GRSafr), and 189 East Asian- (GRSeas) related T2D loci discovered in different population groups. Among European Americans, the highest quartile GRSeur was significantly associated with 53.8 g higher fetal weight (95% CI 19.2-88.5) over the pregnancy. The associations began at gestational week 24 and continued through week 40, with a 106.8 g (95% CI 6.5-207.1) increase in birth weight. The findings were similar in analysis further adjusted for maternal glucose challenge test results. No consistent association was found using ancestry-matched or cross-ancestry GRS in non-Europeans. In conclusion, T2D genetic susceptibility may influence fetal growth starting at midsecond trimester among Europeans. Absence of similar associations in non-Europeans urges the need for further genetic T2D studies in diverse ancestries.

A framework for research into continental ancestry groups of the UK Biobank

BACKGROUND

The UK Biobank is a large prospective cohort, based in the UK, that has deep phenotypic and genomic data on roughly a half a million individuals. Included in this resource are data on approximately 78,000 individuals with "non-white British ancestry." While most epidemiology studies have focused predominantly on populations of European ancestry, there is an opportunity to contribute to the study of health and disease for a broader segment of the population by making use of the UK Biobank's "non-white British ancestry" samples. Here, we present an empirical description of the continental ancestry and population structure among the individuals in this UK Biobank subset.

RESULTS

Reference populations from the 1000 Genomes Project for Africa, Europe, East Asia, and South Asia were used to estimate ancestry for each individual. Those with at least 80% ancestry in one of these four continental ancestry groups were taken forward (N = 62,484). Principal component and K-means clustering analyses were used to identify and characterize population structure within each ancestry group. Of the approximately 78,000 individuals in the UK Biobank that are of "non-white British" ancestry, 50,685, 6653, 2782, and 2364 individuals were associated to the European, African, South Asian, and East Asian continental ancestry groups, respectively. Each continental ancestry group exhibits prominent population structure that is consistent with self-reported country of birth data and geography.

CONCLUSIONS

Methods outlined here provide an avenue to leverage UK Biobank's deeply phenotyped data allowing researchers to maximize its potential in the study of health and disease in individuals of non-white British ancestry.