Quality control and analytic best practices for testing genetic models of sex differences in large populations

Methodological opportunities in genomic data analysis to advance health equity

The causes and consequences of inequities in genomic research and medicine are complex and widespread. However, it is widely acknowledged that underrepresentation of diverse populations in human genetics research risks exacerbating existing health disparities. Efforts to improve diversity are ongoing, but an often-overlooked source of inequity is the choice of analytical methods used to process, analyse and interpret genomic data. This choice can influence all areas of genomic research, from genome-wide association studies and polygenic score development to variant prioritization and functional genomics. New statistical and machine learning techniques to understand, quantify and correct for the impact of biases in genomic data are emerging within the wider genomic research and genomic medicine ecosystems. At this crucial time point, it is important to clarify where improvements in methods and practices can, or cannot, have a role in improving equity in genomics. Here, we review existing approaches to promote equity and fairness in statistical analysis for genomics, and propose future methodological developments that are likely to yield the most impact for equity.

Some lessons learned from genomic and epigenomic studies of the placenta

Recent attention to the "replicability crisis" in research has led to greater efforts to outline research practices that might improve the quality of research results. Steps to improve replicability through standardisation and control of covariates can be field specific. In this review, we highlight lessons learned through our own and other genome-wide studies of genetic, epigenetic and gene expression variation in the placenta. We share our understanding of the impact of placental genetics, possible confounders, sources of sampling variation, dataset stratification, and adjustment for covariates. Overall, we hope to raise awareness of these study considerations amongst placental researchers.

Early Manifestations of Neurodevelopmental Copy Number Variants in Children: A Population-Based Investigation

BACKGROUND

There is clinical interest in recognizing copy number variants (CNVs) in children because many have immediate and long-term health implications. Neurodevelopmental (ND) CNVs are associated with intellectual disability, autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD), conditions typically diagnosed by medical practitioners. However, ND CNVs may have additional, early developmental impacts that have yet to be examined in unselected populations.

METHODS

Carriers of known ND CNVs were identified in 2 UK birth cohorts: ALSPAC (Avon Longitudinal Study of Parents and Children) (carriers = 144, controls = 6217) and MCS (Millennium Cohort Study) (carriers = 151, controls = 6559). In ALSPAC, we assessed associations between CNV carrier status and birth complications; preschool development; cognitive ability; ND conditions (ASD, ADHD, reading, language, and motor difficulties); and psychiatric, social, and educational outcomes. Corresponding phenotypes were identified in MCS and meta-analyzed, where available.

RESULTS

In ALSPAC, ND CNVs were associated with low cognitive ability, ADHD, and ASD. ND CNV carriers showed a greater likelihood of preterm birth, fine and gross motor delay, difficulties in motor coordination, language, and reading, and special educational needs (SEND). Meta-analysis with available measures in MCS identified elevated likelihood of ASD, ADHD, low birth weight, reading difficulties, SEND, and peer problems.

CONCLUSIONS

ND CNVs are associated with a broad range of developmental impacts. While clinicians who see children with intellectual disability, ASD, or ADHD may be aware of the impacts of CNVs and consider genetic testing, our investigation suggests that this training and awareness may need to extend to other professional groups (e.g., speech and language therapists).

Distinct explanations underlie gene-environment interactions in the UK Biobank

The role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized but currently unknown. Here, we apply three statistical approaches to quantify and distinguish three different types of GxE interaction for a given trait and environmental (E) variable. First, we detect locus-specific GxE interaction by testing for genetic correlation (rg) < 1 across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRSs) to test for significant PRSxE in a regression of phenotypes on PRS, E, and PRSxE, together with differences in SNP heritability across E bins. Third, we detect genome-wide proportional amplification of genetic and environmental effects as a function of the E variable by testing for significant PRSxE with no differences in SNP heritability across E bins. We applied our framework to 33 UK Biobank traits (25 quantitative traits and 8 diseases; average n = 325,000) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs with rg significantly <1 (false discovery rate < 5%); 28 trait-E pairs with significant PRSxE and significant SNP heritability differences across E bins; and 15 trait-E pairs with significant PRSxE but no SNP heritability differences across E bins. Across the three scenarios, eight of the trait-E pairs involved disease traits, whose interpretation is complicated by scale effects. Analyses using biological sex as the E variable produced additional significant findings in each of these scenarios. Overall, we infer a significant contribution of GxE and GxSex effects to complex trait variance.

Using multiomic integration to improve blood biomarkers of major depressive disorder: a case-control study

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disability, with a twofold increase in prevalence in women compared to men. Over the last few years, identifying molecular biomarkers of MDD has proven challenging, reflecting interactions among multiple environmental and genetic factors. Recently, epigenetic processes have been proposed as mediators of such interactions, with the potential for biomarker development.

METHODS

We characterised gene expression and two mechanisms of epigenomic regulation, DNA methylation (DNAm) and microRNAs (miRNAs), in blood samples from a cohort of individuals with MDD and healthy controls (n = 169). Case-control comparisons were conducted for each omic layer. We also defined gene coexpression networks, followed by step-by-step annotations across omic layers. Third, we implemented an advanced multiomic integration strategy, with covariate correction and feature selection embedded in a cross-validation procedure. Performance of MDD prediction was systematically compared across 6 methods for dimensionality reduction, and for every combination of 1, 2 or 3 types of molecular data. Feature stability was further assessed by bootstrapping.

FINDINGS

Results showed that molecular and coexpression changes associated with MDD were highly sex-specific and that the performance of MDD prediction was greater when the female and male cohorts were analysed separately, rather than combined. Importantly, they also demonstrated that performance progressively increased with the number of molecular datasets considered.

INTERPRETATION

Informational gain from multiomic integration had already been documented in other medical fields. Our results pave the way toward similar advances in molecular psychiatry, and have practical implications for developing clinically useful MDD biomarkers.

FUNDING

This work was supported by the Centre National de la Recherche Scientifique (contract UPR3212), the University of Strasbourg, the Université Sorbonne Paris Nord, the Université Paris Cité, the Fondation de France (FdF N° Engt:00081244 and 00148126; ECI, IY, RB, PEL), the French National Research Agency (ANR-18-CE37-0002, BE, CMC, ADD, PEL, ECI; ANR-18-CE17-0009, ADD; ANR-19-CE37-0010, PEL; ANR-21-RHUS-009, ADD, BE, CMC, CCB; ANR-22-PESN-0013, ADD), the Fondation pour la Recherche sur le Cerveau (FRC 2019, PEL), Fondation de France (2018, BE, CMC, ADD) and American Foundation for Suicide Prevention (AFSP YIG-1-102-19; PEL).

Comparison of the multivariate genetic architecture of eight major psychiatric disorders across sex

Differences in the patterning of genetic sharing between groups of individuals may arise from biological pathways, social mechanisms, phenotyping and ascertainment. We expand genomic structural equation modeling to allow for testing genomic structural invariance (GSI), that is, the formal comparison of multivariate genetic architecture across groups. We apply GSI to compare the autosomal multivariate genetic architecture of eight psychiatric disorders spanning three factors (psychotic, neurodevelopmental and internalizing) between cisgender males and females. We find that the genetic factor structure is largely similar across sex, permitting meaningful comparisons of associations at the level of the factors. However, in females, problematic alcohol use and posttraumatic stress disorder loaded more strongly on the internalizing factor, while the neurodevelopmental disorder factor exhibited weaker genetic correlations with the other factors. Four phenotypes (educational attainment, insomnia, smoking and deprivation) showed significant, albeit small, sex-differentiated associations with the psychotic factor. As genome-wide association study samples grow and diversify, GSI will become increasingly valuable for comparing multivariate genetic architecture across groups.

No evidence for sex-differential transcriptomes driving genome-wide sex-differential natural selection

Sex differences in human transcriptomes have been argued to drive sex-differential selection (SDS). Here, we show that previous evidence supporting this hypothesis has been largely unfounded. We develop a method to test for a genome-wide relationship between sex differences in expression and selection on expression-influencing alleles (expression quantitative trait loci [eQTLs]). We apply it across 34 human tissues and find no evidence for a general relationship. We offer possible explanations for the lack of evidence, including that it is due in part to eQTL ascertainment bias toward sites under weak selection. We conclude that the drivers of ongoing SDS in humans remain to be identified.

OncoSexome: the landscape of sex-based differences in oncologic diseases

The NIH policy on sex as biological variable (SABV) emphasized the importance of sex-based differences in precision oncology. Over 50% of clinically actionable oncology genes are sex-biased, indicating differences in drug efficacy. Research has identified sex differences in non-reproductive cancers, highlighting the need for comprehensive sex-based cancer data. We therefore developed OncoSexome, a multidimensional knowledge base describing sex-based differences in cancer (https://idrblab.org/OncoSexome/) across four key topics: antineoplastic drugs and responses (SDR), oncology-related biomarkers (SBM), risk factors (SRF) and microbial landscape (SML). SDR covers sex-based differences in 2051 anticancer drugs; SBM describes 12 551 sex-differential biomarkers; SRF illustrates 350 sex-dependent risk factors; SML demonstrates 1386 microbes with sex-differential abundances associated with cancer development. OncoSexome is unique in illuminating multifaceted influences of biological sex on cancer, providing both external and endogenous contributors to cancer development and describing sex-based differences for the broadest oncological classes. Given the increasing global research interest in sex-based differences, OncoSexome is expected to impact future precision oncology practices significantly.

Women in Dental Clinical and Translational Research

This article presents a summary of the third American Association for Dental, Oral, and Craniofacial Research Meeting within a Meeting, which was held during the 2024 annual meeting in New Orleans, Louisiana. Speakers were challenged to reflect on how women are differentially affected by health and societal issues by factors that go beyond biology. The goal was to promote current research on women's issues that are relevant to dental, oral, and craniofacial scientists.

Sex-specific Genetic Determinants of Right Ventricular Structure and Function

RATIONALE

While sex differences in right heart phenotypes have been observed, the molecular drivers remain unknown.

OBJECTIVES

To provide biological insights into sex differences in the structure and function of the right ventricle (RV) using common genetic variation.

METHODS

RV phenotypes were obtained from cardiac magnetic resonance imaging in 18,156 women and 16,171 men from the UK Biobank. Observational analyses and sex-stratified genome-wide association studies were performed. Candidate female-specific loci were evaluated against invasively measured cardiac performance in 479 female patients with idiopathic or heritable pulmonary arterial hypertension (PAH), recruited to the UK NIHR BioResource Rare Diseases study.

MEASUREMENTS AND MAIN RESULTS

Sex was associated with differences in RV volumes and ejection fraction in models adjusting for left heart counterparts, blood pressure, lung function and sex hormone levels. Six genome-wide significant loci (13%) revealed heterogeneity of allelic effects between women and men, and significant sex-by-genotype interaction. These included two sex-specific candidate loci present in women only: a locus for RV ejection fraction in BMPR1A and a locus for RV end-systolic volume near DMRT2. Epigenetic data in RV tissue indicate that variation at the BMPR1A locus likely alters transcriptional regulation. In female patients with PAH, a variant located in the promoter of BMPR1A was significantly associated with cardiac index (effect size 0.16 l/min/m2), despite similar RV afterload.

CONCLUSIONS

BMPR1A has emerged as a biologically plausible candidate gene for female-specific genetic determination of RV function, showing associations with cardiac performance under chronically increased afterload in female patients with PAH.

Quantification of escape from X chromosome inactivation with single-cell omics data reveals heterogeneity across cell types and tissues

Several X-linked genes escape from X chromosome inactivation (XCI), while differences in escape across cell types and tissues are still poorly characterized. Here, we developed scLinaX for directly quantifying relative gene expression from the inactivated X chromosome with droplet-based single-cell RNA sequencing (scRNA-seq) data. The scLinaX and differentially expressed gene analyses with large-scale blood scRNA-seq datasets consistently identified the stronger escape in lymphocytes than in myeloid cells. An extension of scLinaX to a 10x multiome dataset (scLinaX-multi) suggested a stronger escape in lymphocytes than in myeloid cells at the chromatin-accessibility level. The scLinaX analysis of human multiple-organ scRNA-seq datasets also identified the relatively strong degree of escape from XCI in lymphoid tissues and lymphocytes. Finally, effect size comparisons of genome-wide association studies between sexes suggested the underlying impact of escape on the genotype-phenotype association. Overall, scLinaX and the quantified escape catalog identified the heterogeneity of escape across cell types and tissues.

A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk

Polygenic scores (PGSs) offer the ability to predict genetic risk for complex diseases across the life course; a key benefit over short-term prediction models. To produce risk estimates relevant to clinical and public health decision-making, it is important to account for varying effects due to age and sex. Here, we develop a novel framework to estimate country-, age-, and sex-specific estimates of cumulative incidence stratified by PGS for 18 high-burden diseases. We integrate PGS associations from seven studies in four countries (N = 1,197,129) with disease incidences from the Global Burden of Disease. PGS has a significant sex-specific effect for asthma, hip osteoarthritis, gout, coronary heart disease and type 2 diabetes (T2D), with all but T2D exhibiting a larger effect in men. PGS has a larger effect in younger individuals for 13 diseases, with effects decreasing linearly with age. We show for breast cancer that, relative to individuals in the bottom 20% of polygenic risk, the top 5% attain an absolute risk for screening eligibility 16.3 years earlier. Our framework increases the generalizability of results from biobank studies and the accuracy of absolute risk estimates by appropriately accounting for age- and sex-specific PGS effects. Our results highlight the potential of PGS as a screening tool which may assist in the early prevention of common diseases.

Leveraging sex-genetic interactions to understand brain disorders: recent advances and current gaps

It is established that there are sex differences in terms of prevalence, age of onset, clinical manifestations, and response to treatment for a variety of brain disorders, including neurodevelopmental, psychiatric, and neurodegenerative disorders. Cohorts of increasing sample sizes with diverse data types collected, including genetic, transcriptomic and/or phenotypic data, are providing the building blocks to permit analytical designs to test for sex-biased genetic variant-trait associations, and for sex-biased transcriptional regulation. Such molecular assessments can contribute to our understanding of the manifested phenotypic differences between the sexes for brain disorders, offering the future possibility of delivering personalized therapy for females and males. With the intention of raising the profile of this field as a research priority, this review aims to shed light on the importance of investigating sex-genetic interactions for brain disorders, focusing on two areas: (i) variant-trait associations and (ii) transcriptomics (i.e. gene expression, transcript usage and regulation). We specifically discuss recent advances in the field, current gaps and provide considerations for future studies.

Distinct explanations underlie gene-environment interactions in the UK Biobank

The role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized, but currently unknown. Here, we apply three statistical approaches to quantify and distinguish three different types of GxE interaction for a given trait and E variable. First, we detect locus-specific GxE interaction by testing for genetic correlation r g < 1 across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRS) to test for significant PRSxE in a regression of phenotypes on PRS, E, and PRSxE, together with differences in SNP-heritability across E bins. Third, we detect genome-wide proportional amplification of genetic and environmental effects as a function of the E variable by testing for significant PRSxE with no differences in SNP-heritability across E bins. Simulations show that these approaches achieve high sensitivity and specificity in distinguishing these three GxE scenarios. We applied our framework to 33 UK Biobank traits (25 quantitative traits and 8 diseases; average N = 325 K ) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs with r g significantly < 1 (FDR<5%) (average r g = 0.95 ); for example, white blood cell count had r g = 0.95 (s.e. 0.01) between smokers and non-smokers. Second, we identified 28 trait-E pairs with significant PRSxE and significant SNP-heritability differences across E bins; for example, BMI had a significant PRSxE for physical activity (P=4.6e-5) with 5% larger SNP-heritability in the largest versus smallest quintiles of physical activity (P=7e-4). Third, we identified 15 trait-E pairs with significant PRSxE with no SNP-heritability differences across E bins; for example, waist-hip ratio adjusted for BMI had a significant PRSxE effect for time spent watching television (P=5e-3) with no SNP-heritability differences. Across the three scenarios, 8 of the trait-E pairs involved disease traits, whose interpretation is complicated by scale effects. Analyses using biological sex as the E variable produced additional significant findings in each of the three scenarios. Overall, we infer a significant contribution of GxE and GxSex effects to complex trait and disease variance.

Better together against genetic heterogeneity: A sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank

Genetic effects can be sex-specific, particularly for traits such as testosterone, a sex hormone. While sex-stratified analysis provides easily interpretable sex-specific effect size estimates, the presence of sex-differences in SNP effect implies a SNP×sex interaction. This suggests the usage of the often overlooked joint test, testing for an SNP's main and SNP×sex interaction effects simultaneously. Notably, even without individual-level data, the joint test statistic can be derived from sex-stratified summary statistics through an omnibus meta-analysis. Utilizing the available sex-stratified summary statistics of the UK Biobank, we performed such omnibus meta-analyses for 290 quantitative traits. Results revealed that this approach is robust to genetic effect heterogeneity and can outperform the traditional sex-stratified or sex-combined main effect-only tests. Therefore, we advocate using the omnibus meta-analysis that captures both the main and interaction effects. Subsequent sex-stratified analysis should be conducted for sex-specific effect size estimation and interpretation.

Genetic correlations of alcohol consumption and alcohol use disorder with sex hormone levels in females and males

Background

Alcohol consumption behaviors and alcohol use disorder risk and presentation differ by sex, and these complex traits are associated with blood concentrations of the steroid sex hormones, testosterone and estradiol, and their regulatory binding proteins, sex hormone binding globulin (SHBG) and albumin. Genetic variation is associated with alcohol consumption and alcohol use disorder, as well as levels of steroid sex hormones and their binding proteins.

Methods

To assess the contribution of genetic factors to previously described phenotypic associations between alcohol-use traits and sex-hormone levels, we estimated genetic correlations (rg) using summary statistics from prior published, large sample size genome-wide association studies (GWAS) of alcohol consumption, alcohol dependence, testosterone, estradiol, SHBG, and albumin.

Results

For alcohol consumption, we observed positive genetic correlation (i.e. genetic effects in the same direction) with total testosterone in males (rg = 0.084, p = 0.007) and trends toward positive genetic correlation with bioavailable testosterone (rg = 0.060, p = 0.084) and SHBG in males (rg = 0.056, p = 0.086) and with albumin in a sex-combined cohort (rg = 0.082, p = 0.015); however in females, we observed positive genetic correlation with SHBG (rg = 0.089, p = 0.004) and a trend toward negative genetic correlation (i.e. genetic effects in opposite directions) with bioavailable testosterone (rg = -0.064, p = 0.032). For alcohol dependence, we observed a trend toward negative genetic correlation with total testosterone in females (rg = -0.106, p = 0.024) and positive genetic correlation with BMI-adjusted SHBG in males (rg = 0.119, p = 0.017). Several of these genetic correlations differed between females and males and were not in the same direction as the corresponding phenotypic associations.

Conclusions

Findings suggest that shared genetic effects may contribute to positive associations of alcohol consumption with albumin in both sexes, as well as positive associations between alcohol consumption and bioavailable testosterone and between alcohol dependence and SHBG in males. However, relative contributions of heritable and environmental factors to associations between alcohol-use traits and sex-hormone levels may differ by sex, with genetic factors contributing more in males and environmental factors contributing more in females.

Sex-stratified genome-wide association and transcriptome-wide Mendelian randomization studies reveal drug targets of heart failure

The prevalence of heart failure (HF) subtypes, which are classified by left ventricular ejection fraction (LVEF), demonstrate significant sex differences. Here, we perform sex-stratified genome-wide association studies (GWASs) on LVEF and transcriptome-wide Mendelian randomization (MR) on LVEF, all-cause HF, HF with reduced ejection fraction (HFrEF), and HF with preserved ejection fraction (HFpEF). The sex-stratified GWASs of LVEF identified three sex-specific loci that were exclusively detected in the sex-stratified GWASs. Three drug target genes show sex-differential effects on HF/HFrEF via influencing LVEF, with NPR2 as the target gene for the HF drug Cenderitide under phase 2 clinical trial. Our study highlights the importance of considering sex-differential genetic effects in sex-balanced diseases such as HF and emphasizes the value of sex-stratified GWASs and MR in identifying putative genetic variants, causal genes, and candidate drug targets for HF, which is not identifiable using a sex-combined strategy.

Genomics Research of Lifetime Depression in the Netherlands: The BIObanks Netherlands Internet Collaboration (BIONIC) Project

In this cohort profile article we describe the lifetime major depressive disorder (MDD) database that has been established as part of the BIObanks Netherlands Internet Collaboration (BIONIC). Across the Netherlands we collected data on Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) lifetime MDD diagnosis in 132,850 Dutch individuals. Currently, N = 66,684 of these also have genomewide single nucleotide polymorphism (SNP) data. We initiated this project because the complex genetic basis of MDD requires large population-wide studies with uniform in-depth phenotyping. For standardized phenotyping we developed the LIDAS (LIfetime Depression Assessment Survey), which then was used to measure MDD in 11 Dutch cohorts. Data from these cohorts were combined with diagnostic interview depression data from 5 clinical cohorts to create a dataset of N = 29,650 lifetime MDD cases (22%) meeting DSM-5 criteria and 94,300 screened controls. In addition, genomewide genotype data from the cohorts were assembled into a genomewide association study (GWAS) dataset of N = 66,684 Dutch individuals (25.3% cases). Phenotype data include DSM-5-based MDD diagnoses, sociodemographic variables, information on lifestyle and BMI, characteristics of depressive symptoms and episodes, and psychiatric diagnosis and treatment history. We describe the establishment and harmonization of the BIONIC phenotype and GWAS datasets and provide an overview of the available information and sample characteristics. Our next step is the GWAS of lifetime MDD in the Netherlands, with future plans including fine-grained genetic analyses of depression characteristics, international collaborations and multi-omics studies.

Gender Control of Mouse Embryos by Activation of TLR7/8 on X Sperm via Ligands dsRNA-40 and dsRNA-DR

Gender control technologies are promising for enhancing the production efficiency of the farm animal industry, and preventing sex-linked hereditary diseases in humans. It has been shown that the X sperm of mammalian animals specifically expresses X-chromosome-derived toll-like receptor 7/8 (TLR7/8), and the activation of TLR7/8 on the X sperm by their agonist, R848, can separate X and Y sperm via the specific inhibition of X sperm motility. The use of R848-preselected sperm for fertilization resulted in sex-ratio-skewed embryos or offspring. In this study, we aimed to investigate whether two other TLR7/8 ligands, double-stranded RNA-40 (dsRNA-40) and double-stranded RNA-DR (dsRNA-DR), are also effective in the separation of mouse X and Y sperm and the subsequent generation of gender-ratio-skewed in vitro fertilization (IVF) embryos. Our results indicated that cholesterol modification significantly enhances the transfection of dsRNA-40 and dsRNA-DR into sperm cells. dsRNA-40 and dsRNA-DR incubation with mouse sperm could separate X and Y sperm by the specific suppression of X sperm motility by decreasing its ATP level and mitochondrial activity. The use of a dsRNA-40- or dsRNA-DR-preselected upper layer of sperm, which predominantly contains high-motility Y sperm, for IVF caused a male-biased sex ratio shift in resulting embryos (with 65.90-74.93% of embryos being male). This study develops a simple new method for the efficient separation of mammalian X and Y sperm, enabling the selective production of male or female progenies.

Toward a deeper understanding of gene-by-sex interaction models

In this issue of Cell Genomics, Zhu et al.1 propose amplification as the primary mode for gene-by-sex interactions in complex traits. Khramtsova et al. preview their modeling approach and discuss implications for the future work on the genomics of sex differences.