Phenome-wide functional dissection of pleiotropic effects highlights key molecular pathways for human complex traits

A phenome-wide association and Mendelian randomisation study of alcohol use variants in a diverse cohort comprising over 3 million individuals

BACKGROUND

Alcohol consumption is associated with numerous negative social and health outcomes. These associations may be direct consequences of drinking, or they may reflect common genetic factors that influence both alcohol consumption and other outcomes.

METHODS

We performed exploratory phenome-wide association studies (PheWAS) of three of the best studied protective single nucleotide polymorphisms (SNPs) in genes encoding ethanol metabolising enzymes (ADH1B: rs1229984-T, rs2066702-A; ADH1C: rs698-T) using up to 1109 health outcomes across 28 phenotypic categories (e.g., substance-use, mental health, sleep, immune, cardiovascular, metabolic) from a diverse 23andMe cohort, including European (N ≤ 2,619,939), Latin American (N ≤ 446,646) and African American (N ≤ 146,776) populations to uncover new and perhaps unexpected associations. These SNPs have been consistently implicated by both candidate gene studies and genome-wide association studies of alcohol-related behaviours but have not been investigated in detail for other relevant phenotypes in a hypothesis-free approach in such a large cohort of multiple ancestries. To provide insight into potential causal effects of alcohol consumption on the outcomes significant in the PheWAS, we performed univariable two-sample and one-sample Mendelian randomisation (MR) analyses.

FINDINGS

The minor allele rs1229984-T, which is protective against alcohol behaviours, showed the highest number of PheWAS associations across the three cohorts (N = 232, European; N = 29, Latin American; N = 7, African American). rs1229984-T influenced multiple domains of health. We replicated associations with alcohol-related behaviours, mental and sleep conditions, and cardio-metabolic health. We also found associations with understudied traits related to neurological (migraines, epilepsy), immune (allergies), musculoskeletal (fibromyalgia), and reproductive health (preeclampsia). MR analyses identified evidence of causal effects of alcohol consumption on liability for 35 of these outcomes in the European cohort.

INTERPRETATION

Our work demonstrates that polymorphisms in genes encoding alcohol metabolising enzymes affect multiple domains of health beyond alcohol-related behaviours. Understanding the underlying mechanisms of these effects could have implications for treatments and preventative medicine.

FUNDING

MVJ, NCK, SBB, SSR and AAP were supported by T32IR5226 and 28IR-0070. SSR was also supported by NIDA DP1DA054394. NCK and RBC were also supported by R25MH081482. ASH was supported by funds from NIAAA K01AA030083. JLMO was supported by VA 1IK2CX002095. JLMO and JJMM were also supported by NIDA R21DA050160. JJMM was also supported by the Kavli Postdoctoral Award for Academic Diversity. EGA was supported by K01MH121659 from the NIMH/NIH, the Caroline Wiess Law Fund for Research in Molecular Medicine and the ARCO Foundation Young Teacher-Investigator Fund at Baylor College of Medicine. MSA was supported by the Instituto de Salud Carlos III and co-funded by the European Union Found: Fondo Social Europeo Plus (FSE+) (P19/01224, PI22/00464 and CP22/00128).

Advancements and Prospects of Genome-Wide Association Studies (GWAS) in Maize

Genome-wide association studies (GWAS) have emerged as a powerful tool for unraveling intricate genotype-phenotype association across various species. Maize (Zea mays L.), renowned for its extensive genetic diversity and rapid linkage disequilibrium (LD), stands as an exemplary candidate for GWAS. In maize, GWAS has made significant advancements by pinpointing numerous genetic loci and potential genes associated with complex traits, including responses to both abiotic and biotic stress. These discoveries hold the promise of enhancing adaptability and yield through effective breeding strategies. Nevertheless, the impact of environmental stress on crop growth and yield is evident in various agronomic traits. Therefore, understanding the complex genetic basis of these traits becomes paramount. This review delves into current and future prospectives aimed at yield, quality, and environmental stress resilience in maize and also addresses the challenges encountered during genomic selection and molecular breeding, all facilitated by the utilization of GWAS. Furthermore, the integration of omics, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics has enriched our understanding of intricate traits in maize, thereby enhancing environmental stress tolerance and boosting maize production. Collectively, these insights not only advance our understanding of the genetic mechanism regulating complex traits but also propel the utilization of marker-assisted selection in maize molecular breeding programs, where GWAS plays a pivotal role. Therefore, GWAS provides robust support for delving into the genetic mechanism underlying complex traits in maize and enhancing breeding strategies.

Gene as a dynamical notion: An extensive and integrative vision. Redefining the gene concept, from traditional to genic-interaction, as a new dynamical version

The current concept of gene has been very useful during the 20th and 21st centuries. However, recent advances in molecular biology and bioinformatics, which have further diversified the functional and adaptive profile of genetic information and its integration with cell physiology and environmental response, have contributed to focusing on additional new gene properties besides the traditional definition. Considering the inherent complexity of gene expression, whose adaptive objective must be referred to the Tortoise-Hare model, in which two tendencies converge, one focused on rapid adaptation to achieve survival, and the other that prevents an over-adaptation effect. In this context, a revision of the gene concept must be made, which must include these new mechanisms and approaches. In this paper, we propose a new conception of the idea of a gene that moves from a static and defined version of hereditary information to a dynamic idea that preponderates gene interaction (circumscribed to that established between protein-protein, protein-nucleic acid, and nucleic acid-nucleic acid) and the selection it exerts, as the irreducible element that works in a coordinated way in a genomic regulatory network (GRN).

Recent natural selection conferred protection against schizophrenia by non-antagonistic pleiotropy

Schizophrenia is a debilitating psychiatric disorder associated with a reduced fertility and decreased life expectancy, yet common predisposing variation substantially contributes to the onset of the disorder, which poses an evolutionary paradox. Previous research has suggested balanced selection, a mechanism by which schizophrenia risk alleles could also provide advantages under certain environments, as a reliable explanation. However, recent studies have shown strong evidence against a positive selection of predisposing loci. Furthermore, evolutionary pressures on schizophrenia risk alleles could have changed throughout human history as new environments emerged. Here in this study, we used 1000 Genomes Project data to explore the relationship between schizophrenia predisposing loci and recent natural selection (RNS) signatures after the human diaspora out of Africa around 100,000 years ago on a genome-wide scale. We found evidence for significant enrichment of RNS markers in derived alleles arisen during human evolution conferring protection to schizophrenia. Moreover, both partitioned heritability and gene set enrichment analyses of mapped genes from schizophrenia predisposing loci subject to RNS revealed a lower involvement in brain and neuronal related functions compared to those not subject to RNS. Taken together, our results suggest non-antagonistic pleiotropy as a likely mechanism behind RNS that could explain the persistence of schizophrenia common predisposing variation in human populations due to its association to other non-psychiatric phenotypes.

Elucidating the patterns of pleiotropy and its biological relevance in maize

Pleiotropy-when a single gene controls two or more seemingly unrelated traits-has been shown to impact genes with effects on flowering time, leaf architecture, and inflorescence morphology in maize. However, the genome-wide impact of biological pleiotropy across all maize phenotypes is largely unknown. Here, we investigate the extent to which biological pleiotropy impacts phenotypes within maize using GWAS summary statistics reanalyzed from previously published metabolite, field, and expression phenotypes across the Nested Association Mapping population and Goodman Association Panel. Through phenotypic saturation of 120,597 traits, we obtain over 480 million significant quantitative trait nucleotides. We estimate that only 1.56-32.3% of intervals show some degree of pleiotropy. We then assess the relationship between pleiotropy and various biological features such as gene expression, chromatin accessibility, sequence conservation, and enrichment for gene ontology terms. We find very little relationship between pleiotropy and these variables when compared to permuted pleiotropy. We hypothesize that biological pleiotropy of common alleles is not widespread in maize and is highly impacted by nuisance terms such as population structure and linkage disequilibrium. Natural selection on large standing natural variation in maize populations may target wide and large effect variants, leaving the prevalence of detectable pleiotropy relatively low.

A pleiotropic variant in DNAJB4 is associated with multiple myeloma risk

Pleiotropy, which consists of a single gene or allelic variant affecting multiple unrelated traits, is common across cancers, with evidence for genome-wide significant loci shared across cancer and noncancer traits. This feature is particularly relevant in multiple myeloma (MM) because several susceptibility loci that have been identified to date are pleiotropic. Therefore, the aim of this study was to identify novel pleiotropic variants involved in MM risk using 28 684 independent single nucleotide polymorphisms (SNPs) from GWAS Catalog that reached a significant association (P < 5 × 10^-8 ) with their respective trait. The selected SNPs were analyzed in 2434 MM cases and 3446 controls from the International Lymphoma Epidemiology Consortium (InterLymph). The 10 SNPs showing the strongest associations with MM risk in InterLymph were selected for replication in an independent set of 1955 MM cases and 1549 controls from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and 418 MM cases and 147 282 controls from the FinnGen project. The combined analysis of the three studies identified an association between DNAJB4-rs34517439-A and an increased risk of developing MM (OR = 1.22, 95%CI 1.13-1.32, P = 4.81 × 10^-7 ). rs34517439-A is associated with a modified expression of the FUBP1 gene, which encodes a multifunctional DNA and RNA-binding protein that it was observed to influence the regulation of various genes involved in cell cycle regulation, among which various oncogenes and oncosuppressors. In conclusion, with a pleiotropic scan approach we identified DNAJB4-rs34517439 as a potentially novel MM risk locus.

Biobanking as a Tool for Genomic Research: From Allele Frequencies to Cross-Ancestry Association Studies

In recent years, great advances have been made in the field of collection, storage, and analysis of biological samples. Large collections of samples, biobanks, have been established in many countries. Biobanks typically collect large amounts of biological samples and associated clinical information; the largest collections include over a million samples. In this review, we summarize the main directions in which biobanks aid medical genetics and genomic research, from providing reference allele frequency information to allowing large-scale cross-ancestry meta-analyses. The largest biobanks greatly vary in the size of the collection, and the amount of available phenotype and genotype data. Nevertheless, all of them are extensively used in genomics, providing a rich resource for genome-wide association analysis, genetic epidemiology, and statistical research into the structure, function, and evolution of the human genome. Recently, multiple research efforts were based on trans-biobank data integration, which increases sample size and allows for the identification of robust genetic associations. We provide prominent examples of such data integration and discuss important caveats which have to be taken into account in trans-biobank research.

Exploring the genetic overlap between twelve psychiatric disorders

The widespread comorbidity among psychiatric disorders demonstrated in epidemiological studies1-5 is mirrored by non-zero, positive genetic correlations from large-scale genetic studies6-10. To identify shared biological processes underpinning this observed phenotypic and genetic covariance and enhance molecular characterization of general psychiatric disorder liability11-13, we used several strategies aimed at uncovering pleiotropic, that is, cross-trait-associated, single-nucleotide polymorphisms (SNPs), genes and biological pathways. We conducted cross-trait meta-analysis on 12 psychiatric disorders to identify pleiotropic SNPs. The meta-analytic signal was driven by schizophrenia, hampering interpretation and joint biological characterization of the cross-trait meta-analytic signal. Subsequent pairwise comparisons of psychiatric disorders identified substantial pleiotropic overlap, but mainly among pairs of psychiatric disorders, and mainly at less stringent P-value thresholds. Only annotations related to evolutionarily conserved genomic regions were significant for multiple (9 out of 12) psychiatric disorders. Overall, identification of shared biological mechanisms remains challenging due to variation in power and genetic architecture between psychiatric disorders.

Aggregation of Genome-Wide Association Data from FinnGen and UK Biobank Replicates Multiple Risk Loci for Pregnancy Complications

Complications endangering mother or fetus affect around one in seven pregnant women. Investigation of the genetic susceptibility to such diseases is of high importance for better understanding of the disease biology as well as for prediction of individual risk. In this study, we collected and analyzed GWAS summary statistics from the FinnGen cohort and UK Biobank for 24 pregnancy complications. In FinnGen, we identified 11 loci associated with pregnancy hypertension, excessive vomiting, and gestational diabetes. When UK Biobank and FinnGen data were combined, we discovered six loci reaching genome-wide significance in the meta-analysis. These include rs35954793 in FGF5 (p=6.1×10-9), rs10882398 in PLCE1 (p=8.9×10-9), and rs167479 in RGL3 (p=5.2×10-9) for pregnancy hypertension, rs10830963 in MTNR1B (p=4.5×10-41) and rs36090025 in TCF7L2 (p=3.4×10-15) for gestational diabetes, and rs2963457 in the EBF1 locus (p=6.5×10-9) for preterm birth. In addition to the identified genome-wide associations, we also replicated 14 out of 40 previously reported GWAS markers for pregnancy complications, including four more preeclampsia-related variants. Finally, annotation of the GWAS results identified a causal relationship between gene expression in the cervix and gestational hypertension, as well as both known and previously uncharacterized genetic correlations between pregnancy complications and other traits. These results suggest new prospects for research into the etiology and pathogenesis of pregnancy complications, as well as early risk prediction for these disorders.

Maintenance of quantitative genetic variance in complex, multi-trait phenotypes: The contribution of rare, large effect variants in two Drosophila species

The interaction of evolutionary processes to determine quantitative genetic variation has implications for contemporary and future phenotypic evolution, as well as for our ability to detect causal genetic variants. While theoretical studies have provided robust predictions to discriminate among competing models, empirical assessment of these has been limited. In particular, theory highlights the importance of pleiotropy in resolving observations of selection and mutation, but empirical investigations have typically been limited to few traits. Here, we applied high-dimensional Bayesian Sparse Factor Genetic modeling to gene expression datasets in 2 species, Drosophila melanogaster and Drosophila serrata, to explore the distributions of genetic variance across high-dimensional phenotypic space. Surprisingly, most of the heritable trait covariation was due to few lines (genotypes) with extreme [>3 interquartile ranges (IQR) from the median] values. Intriguingly, while genotypes extreme for a multivariate factor also tended to have a higher proportion of individual traits that were extreme, we also observed genotypes that were extreme for multivariate factors but not for any individual trait. We observed other consistent differences between heritable multivariate factors with outlier lines vs those factors without extreme values, including differences in gene functions. We use these observations to identify further data required to advance our understanding of the evolutionary dynamics and nature of standing genetic variation for quantitative traits.

WebCSEA: web-based cell-type-specific enrichment analysis of genes

Human complex traits and common diseases show tissue- and cell-type- specificity. Recently, single-cell RNA sequencing (scRNA-seq) technology has successfully depicted cellular heterogeneity in human tissue, providing an unprecedented opportunity to understand the context-specific expression of complex trait-associated genes in human tissue-cell types (TCs). Here, we present the first web-based application to quickly assess the cell-type-specificity of genes, named Web-based Cell-type Specific Enrichment Analysis of Genes (WebCSEA, available at https://bioinfo.uth.edu/webcsea/). Specifically, we curated a total of 111 scRNA-seq panels of human tissues and 1,355 TCs from 61 different general tissues across 11 human organ systems. We adapted our previous decoding tissue-specificity (deTS) algorithm to measure the enrichment for each tissue-cell type (TC). To overcome the potential bias from the number of signature genes between different TCs, we further developed a permutation-based method that accurately estimates the TC-specificity of a given inquiry gene list. WebCSEA also provides an interactive heatmap that displays the cell-type specificity across 1355 human TCs, and other interactive and static visualizations of cell-type specificity by human organ system, developmental stage, and top-ranked tissues and cell types. In short, WebCSEA is a one-click application that provides a comprehensive exploration of the TC-specificity of genes among human major TC map.

Systems genetics in the rat HXB/BXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose

Neurogenesis in the adult hippocampus contributes to learning and memory in the healthy brain but is dysregulated in metabolic and neurodegenerative diseases. The molecular relationships between neural stem cell activity, adult neurogenesis, and global metabolism are largely unknown. Here we applied unbiased systems genetics methods to quantify genetic covariation among adult neurogenesis and metabolic phenotypes in peripheral tissues of a genetically diverse family of rat strains, derived from a cross between the spontaneously hypertensive (SHR/OlaIpcv) strain and Brown Norway (BN-Lx/Cub). The HXB/BXH family is a very well established model to dissect genetic variants that modulate metabolic and cardiovascular diseases and we have accumulated deep phenome and transcriptome data in a FAIR-compliant resource for systematic and integrative analyses. Here we measured rates of precursor cell proliferation, survival of new neurons, and gene expression in the hippocampus of the entire HXB/BXH family, including both parents. These data were combined with published metabolic phenotypes to detect a neurometabolic quantitative trait locus (QTL) for serum glucose and neuronal survival on Chromosome 16: 62.1-66.3 Mb. We subsequently fine-mapped the key phenotype to a locus that includes the Telo2-interacting protein 2 gene (Tti2)-a chaperone that modulates the activity and stability of PIKK kinases. To verify the hypothesis that differences in neurogenesis and glucose levels are caused by a polymorphism in Tti2, we generated a targeted frameshift mutation on the SHR/OlaIpcv background. Heterozygous SHR-Tti2+/- mutants had lower rates of hippocampal neurogenesis and hallmarks of dysglycemia compared to wild-type littermates. Our findings highlight Tti2 as a causal genetic link between glucose metabolism and structural brain plasticity. In humans, more than 800 genomic variants are linked to TTI2 expression, seven of which have associations to protein and blood stem cell factor concentrations, blood pressure and frontotemporal dementia.

Identification of Genetic Risk Factors of Severe COVID-19 Using Extensive Phenotypic Data: A Proof-of-Concept Study in a Cohort of Russian Patients

The COVID-19 pandemic has drawn the attention of many researchers to the interaction between pathogen and host genomes. Over the last two years, numerous studies have been conducted to identify the genetic risk factors that predict COVID-19 severity and outcome. However, such an analysis might be complicated in cohorts of limited size and/or in case of limited breadth of genome coverage. In this work, we tried to circumvent these challenges by searching for candidate genes and genetic variants associated with a variety of quantitative and binary traits in a cohort of 840 COVID-19 patients from Russia. While we found no gene- or pathway-level associations with the disease severity and outcome, we discovered eleven independent candidate loci associated with quantitative traits in COVID-19 patients. Out of these, the most significant associations correspond to rs1651553 in MYH14p = 1.4 × 10^-7), rs11243705 in SETX (p = 8.2 × 10^-6), and rs16885 in ATXN1 (p = 1.3 × 10^-5). One of the identified variants, rs33985936 in SCN11A, was successfully replicated in an independent study, and three of the variants were found to be associated with blood-related quantitative traits according to the UK Biobank data (rs33985936 in SCN11A, rs16885 in ATXN1, and rs4747194 in CDH23). Moreover, we show that a risk score based on these variants can predict the severity and outcome of hospitalization in our cohort of patients. Given these findings, we believe that our work may serve as proof-of-concept study demonstrating the utility of quantitative traits and extensive phenotyping for identification of genetic risk factors of severe COVID-19.

An integrated framework for local genetic correlation analysis

Genetic correlation (r_g) analysis is used to identify phenotypes that may have a shared genetic basis. Traditionally, r_g is studied globally, considering only the average of the shared signal across the genome, although this approach may fail when the r_g is confined to particular genomic regions or in opposing directions at different loci. Current tools for local r_g analysis are restricted to analysis of two phenotypes. Here we introduce LAVA, an integrated framework for local r_g analysis that, in addition to testing the standard bivariate local r_gs between two phenotypes, can evaluate local heritabilities and analyze conditional genetic relations between several phenotypes using partial correlation and multiple regression. Applied to 25 behavioral and health phenotypes, we show considerable heterogeneity in the bivariate local r_gs across the genome, which is often masked by the global r_g patterns, and demonstrate how our conditional approaches can elucidate more complex, multivariate genetic relations.

Revisiting the malaria hypothesis: accounting for polygenicity and pleiotropy

The malaria hypothesis predicts local, balancing selection of deleterious alleles that confer strong protection from malaria. Three protective variants, recently discovered in red cell genes, are indeed more common in African than European populations. Still, up to 89% of the heritability of severe malaria is attributed to many genome-wide loci with individually small effects. Recent analyses of hundreds of genome-wide association studies (GWAS) in humans suggest that most functional, polygenic variation is pleiotropic for multiple traits. Interestingly, GWAS alleles and red cell traits associated with small reductions in malaria risk are not enriched in African populations. We propose that other selective and neutral forces, in addition to malaria prevalence, explain the global distribution of most genetic variation impacting malaria risk.

Polygenic risk scores: An overview from bench to bedside for personalised medicine

Since the first polygenic risk score (PRS) in 2007, research in this area has progressed significantly. The increasing number of SNPs that have been identified by large scale GWAS analyses has fuelled the development of a myriad of PRSs for a wide variety of diseases and, more recently, to PRSs that potentially identify differential response to specific drugs. PRSs constitute a composite genomic biomarker and potential applications for PRSs in clinical practice encompass risk prediction and disease screening, early diagnosis, prognostication, and drug stratification to improve efficacy or reduce adverse drug reactions. Nevertheless, to our knowledge, no PRSs have yet been adopted into routine clinical practice. Beyond the technical considerations of PRS development, the major challenges that face PRSs include demonstrating clinical utility and circumnavigating the implementation of novel genomic technologies at scale into stretched healthcare systems. In this review, we discuss progress in developing disease susceptibility PRSs across multiple medical specialties, development of pharmacogenomic PRSs, and future directions for the field.

Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multitrait GWA studies

Genetic correlations between traits may cause correlated responses to selection. Previous models described the conditions under which genetic correlations are expected to be maintained. Selection, mutation, and migration are all proposed to affect genetic correlations, regardless of whether the underlying genetic architecture consists of pleiotropic or tightly linked loci affecting the traits. Here, we investigate the conditions under which pleiotropy and linkage have different effects on the genetic correlations between traits by explicitly modeling multiple genetic architectures to look at the effects of selection strength, degree of correlational selection, mutation rate, mutational variance, recombination rate, and migration rate. We show that at mutation-selection(-migration) balance, mutation rates differentially affect the equilibrium levels of genetic correlation when architectures are composed of pairs of physically linked loci compared to architectures of pleiotropic loci. Even when there is perfect linkage (no recombination within pairs of linked loci), a lower genetic correlation is maintained than with pleiotropy, with a lower mutation rate leading to a larger decrease. These results imply that the detection of causal loci in multitrait association studies will be affected by the type of underlying architectures, whereby pleiotropic variants are more likely to be underlying multiple detected associations. We also confirm that tighter linkage between nonpleiotropic causal loci maintains higher genetic correlations at the traits and leads to a greater proportion of false positives in association analyses.

Pleiotropy data resource as a primer for investigating co-morbidities/multi-morbidities and their role in disease

Most current biomedical and protein research focuses only on a small proportion of genes, which results in a lost opportunity to identify new gene-disease associations and explore new opportunities for therapeutic intervention. The International Mouse Phenotyping Consortium (IMPC) focuses on elucidating gene function at scale for poorly characterized and/or under-studied genes. A key component of the IMPC initiative is the implementation of a broad phenotyping pipeline, which is facilitating the discovery of pleiotropy. Characterizing pleiotropy is essential to identify gene-disease associations, and it is of particular importance when elucidating the genetic causes of syndromic disorders. Here we show how the IMPC is effectively uncovering pleiotropy and how the new mouse models and gene function hypotheses generated by the IMPC are increasing our understanding of the mammalian genome, forming the basis of new research and identifying new gene-disease associations.

Highly pleiotropic variants of human traits are enriched in genomic regions with strong background selection

Recent studies have shown the ubiquity of pleiotropy for variants affecting human complex traits. These studies also show that rare variants tend to be less pleiotropic than common ones, suggesting that purifying natural selection acts against highly pleiotropic variants of large effect. Here, we investigate the mean frequency, effect size and recombination rate associated with pleiotropic variants, and focus particularly on whether highly pleiotropic variants are enriched in regions with putative strong background selection. We evaluate variants for 41 human traits using data from the NHGRI-EBI GWAS Catalog, as well as data from other three studies. Our results show that variants involving a higher degree of pleiotropy tend to be more common, have larger mean effect sizes, and contribute more to heritability than variants with a lower degree of pleiotropy. This is consistent with the fact that variants of large effect and frequency are more likely detected by GWAS. Using data from four different studies, we also show that more pleiotropic variants are enriched in genome regions with stronger background selection than less pleiotropic variants, suggesting that highly pleiotropic variants are subjected to strong purifying selection. From the above results, we hypothesized that a number of highly pleiotropic variants of low effect/frequency may pass undetected by GWAS.

Correlational selection in the age of genomics

Ecologists and evolutionary biologists are well aware that natural and sexual selection do not operate on traits in isolation, but instead act on combinations of traits. This long-recognized and pervasive phenomenon is known as multivariate selection, or-in the particular case where it favours correlations between interacting traits-correlational selection. Despite broad acknowledgement of correlational selection, the relevant theory has often been overlooked in genomic research. Here, we discuss theory and empirical findings from ecological, quantitative genetic and genomic research, linking key insights from different fields. Correlational selection can operate on both discrete trait combinations and quantitative characters, with profound implications for genomic architecture, linkage, pleiotropy, evolvability, modularity, phenotypic integration and phenotypic plasticity. We synthesize current knowledge and discuss promising research approaches that will enable us to understand how correlational selection shapes genomic architecture, thereby linking quantitative genetic approaches with emerging genomic methods. We suggest that research on correlational selection has great potential to integrate multiple fields in evolutionary biology, including developmental and functional biology, ecology, quantitative genetics, phenotypic polymorphisms, hybrid zones and speciation processes.

A Data-Driven Review of the Genetic Factors of Pregnancy Complications

Over the recent years, many advances have been made in the research of the genetic factors of pregnancy complications. In this work, we use publicly available data repositories, such as the National Human Genome Research Institute GWAS Catalog, HuGE Navigator, and the UK Biobank genetic and phenotypic dataset to gain insights into molecular pathways and individual genes behind a set of pregnancy-related traits, including the most studied ones—preeclampsia, gestational diabetes, preterm birth, and placental abruption. Using both HuGE and GWAS Catalog data, we confirm that immune system and, in particular, T-cell related pathways are one of the most important drivers of pregnancy-related traits. Pathway analysis of the data reveals that cell adhesion and matrisome-related genes are also commonly involved in pregnancy pathologies. We also find a large role of metabolic factors that affect not only gestational diabetes, but also the other traits. These shared metabolic genes include IGF2, PPARG, and NOS3. We further discover that the published genetic associations are poorly replicated in the independent UK Biobank cohort. Nevertheless, we find novel genome-wide associations with pregnancy-related traits for the FBLN7, STK32B, and ACTR3B genes, and replicate the effects of the KAZN and TLE1 genes, with the latter being the only gene identified across all data resources. Overall, our analysis highlights central molecular pathways for pregnancy-related traits, and suggests a need to use more accurate and sophisticated association analysis strategies to robustly identify genetic risk factors for pregnancy complications.