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bioRxiv2026

Genetic Architecture of Reproduction and Longevity in Historical Dutch Cohorts

Life-history theory predicts genetic trade-offs between reproduction and survival, often invoked to explain ageing through antagonistic pleiotropy, but evidence in humans is mixed. We analysed a historical Dutch genealogical dataset (birth cohorts 1850-1915) to estimate pedigree-based heritabilities and genetic correlations among lifespan, offspring number, and ages at first and last birth (AFB and ALB). Bayesian animal models were fitted by sex in samples surviving beyond ages 45 and 13, with year of birth modelled by a natural spline. Lifespan heritability was approximately 0.19-0.23. Reproductive-trait heritabilities varied by sex and were generally higher in men. Cross-sex genetic correlations were substantial. Genetic correlations between offspring number and lifespan were weakly negative in women (-0.10), near zero in men aged 45+, and weakly positive in men aged 13+. AFB was positively genetically correlated with lifespan, especially in the 13+ sample. Offspring number was strongly genetically correlated with earlier AFB and later ALB, while AFB and ALB shared positive genetic covariance. Phenotypic associations changed little after adjustment for birth year, province, and urban/rural marriage location. Overall, results provide little support for a strong additive genetic fertility-longevity trade-off and instead highlight shared genetic influences on reproductive timing and viability

evolutionary biology↗

bioRxiv2026

Demographic history, geographic distance, and landscape features shape the genetic divergence of wild tigers in northeast India

Habitat fragmentation creates small, isolated populations vulnerable to inbreeding, genetic drift, and high genetic load. For conservation management, it is essential to distinguish contemporary landscape resistance from historical demographic processes as drivers of these genetic patterns, especially for conservation priority regions such as northeast India which intersects two major tiger conservation landscapes. We studied the genetic structure and landscape connectivity of tigers across four protected areas in northeast India: Kaziranga, Manas, Orang, and Nameri, using faecal samples. From 741 samples collected over two field seasons, we identified 654 confirmed tiger specimens. Using methylation-based enrichment and ddRAD-seq of 176 samples, we generated a high-quality dataset of 3091 SNPs across 44 individuals. Population structure analyses identified three genetically distinct clusters: Kaziranga-Nameri, Manas, and Orang. Isolation-by-distance and landscape resistance explained 13% and 17% of the observed genetic divergence, respectively, with human settlements influencing gene flow. Orangs pronounced divergence from Kaziranga, despite geographic proximity and corridors, suggests a post-bottleneck founder effect, as evidenced by reduced heterozygosity (Ho = 0.24), nucleotide diversity (pi = 0.24), and effective population size (Ne = 1.3). These findings reveal that demographic and genetic recovery can decouple: Orangs population has recently grown, yet genome-wide evidence shows ongoing genetic erosion that monitoring has not detected. Similar patterns have been reported in other Indian tiger populations, indicating that such decoupling may be systemic. Target 4 of the Kunming-Montreal Global Biodiversity Framework requires explicit genetic diversity monitoring; this study demonstrates that non-invasive genomics can operationalise that mandate at a conservation-relevant scale.

evolutionary biology↗

bioRxiv2026

Genetic architecture of the murine serum metabolome reveals carboxyl esterases as master regulators of circulating fatty acid metabolism

BackgroundThe systemic biochemical diversity of circulating metabolites and lipids reflects the integrated effects of genetic variation and environmental exposure. Metabolite quantitative trait locus (mQTL) studies in humans have established gene-metabolite associations, but genetic contributions can be obscured by sex, diet, age, medication use, and environmental exposures. Genetically diverse model systems offer a powerful complementary strategy to isolate genetic contributions to the biochemical diversity of the circulating metabolome. Methodology/Principal FindingsWe applied mass spectrometry profiling to serum samples in 541 mice from the Diversity Outbred (DO) population and identified 1,933 mQTL across 240 metabolites, 561 lipids, 43 oxylipins, and 4,465 MS/MS features. Co-mapping QTL, i.e., QTL hotspots, on chromosomes 8 and 17 implicated carboxyl esterase gene clusters (Ces1 and Ces2) as major regulators of circulating lipid remodeling and demonstrated genetic control of circulating protein/peptide-like features at the major histocompatibility complex and complement C3 loci. QTL hotspots on chromosomes 9 and 10 revealed previously unknown genetic drivers of lipid and amino acid metabolism. Comparisons with matched red blood cell mQTL revealed widespread compartment-specific genetic control. Conclusions/SignificanceCollectively, these findings provide a high-resolution map of the genetic regulation of the circulating metabolome, offering mechanistic insights that complement and extend human metabolic genetics.

genomics↗

bioRxiv2026

Genetic determinism and inheritance of differential DNA methylation profiles across three successive generations in quail.

Environmental exposures can induce epigenetic modifications that persist across generations, potentially contributing to the transmission of environmentally-induced phenotypes. However, the extent to which such persistence of molecular changes are maintained independently of genetic mechanisms remains unclear. In this study, we investigated the evolution and genetic determinism of DNA methylation (DNAm) variation across three successive generations of Japanese quails (G0 to G2) belonging to two epilines, defined according to whether their female ancestor had received genistein supplementation (epi+) or not (epi-). Using reduced representation bisulfite sequencing (RRBS), we characterised CpG methylation patterns and identified differentially methylated cytosines (DMCs) and regions (DMRs) between control and genistein-supplemented epilines. Out of 112,745 CpG sites analysed, no DMCs or DMRs were detected between epilines in the first generation following exposure (G0), whereas 621 DMCs (36 DMRs) were identified in G1, and 1,381 DMCs (101 DMRs) in G2. Despite this progressive increase of differential methylation sites, mean differences in methylation rate between epilines were globally small. Similarly, genome-wide genetic differentiation between epilines was very limited. Overall, SNP-based heritability of DNA methylation was 0.19 indicating that genetic variation accounted for only a small part of the global variation of DNAm levels. By contrast, DNAm at differentially methylated CpG sites was extensively under genetic control, with a mean SNP-based heritability > 0.5, and meQTL analysis further identified significant associations between SNPs and methylation levels. Together, these results indicate that local DNAm differentiation in quail is under substantial genetic regulation, while the limited genetic differentiation between groups suggests that genetic variation alone can not fully explain the progressive accumulation of methylation differences following an ancestral exposure to genistein. These results support DNAm as a plausible molecular candidate mechanism in the multigenerational response to environmental exposure and highlight the complex interplay between genetic and epigenetic regulations in the inheritance of phenotypes.

genomics↗

bioRxiv2013

The evolution of sex differences in disease genetics

There are significant differences in the biology of males and females, ranging from biochemical pathways to behavioural responses, which are relevant to modern medicine. Broad-sense heritability estimates differ between the sexes for many common medical disorders, indicating that genetic architecture can be sex-dependent. Recent genome-wide association studies (GWAS) have successfully identified sex-specific and sex-biased effects, where in addition to sex-specific effects on gene expression, twenty-two medical traits have sex-specific or sex-biased loci. Sex-specific genetic architecture of complex traits is also extensively documented in model organisms using genome-wide linkage or association mapping, and in gene disruption studies. The evolutionary origins of sex-specific genetic architecture and sexual dimorphism lie in the fact that males and females share most of their genetic variation yet experience different selection pressures. At the extreme is sexual antagonism, where selection on an allele acts in opposite directions between the sexes. Sexual antagonism has been repeatedly identified via a number of experimental methods in a range of different taxa. Although the molecular basis remains to be identified, mathematical models predict the maintenance of deleterious variants that experience selection in a sex-dependent manner. There are multiple mechanisms by which sexual antagonism and alleles under sex-differential selection could contribute toward the genetics of common, complex disorders. The evidence we review clearly indicates that further research into sex-dependent selection and the sex-specific genetic architecture of diseases would be rewarding. This would be aided by studies of laboratory and wild animal populations, and by modelling sex-specific effects in genome-wide association data with joint, gene-by-sex interaction tests. We predict that even sexually monomorphic diseases may harbour cryptic sex-specific genetic architecture. Furthermore, empirical evidence suggests that investigating sex-dependent epistasis may be especially rewarding. Finally, the prevalent nature of sex-specific genetic architecture in disease offers scope for the development of more effective, sex-specific therapies.\n\nFundingThis work was supported by the European Research Council (WPG and EHM; Starting Grant #280632), a Royal Society University Research Fellowship (EHM), the Swedish Research Council (JKA), and the Volkswagen Foundation (JKA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\n\nCompeting interestsThe authors declare that they have no competing financial interests.

Genetics↗

bioRxiv2014

Genetic influences on translation in yeast

Heritable differences in gene expression between individuals are an important source of phenotypic variation. The question of how closely the effects of genetic variation on protein levels mirror those on mRNA levels remains open. Here, we addressed this question by using ribosome profiling to examine how genetic differences between two strains of the yeast S. cerevisiae affect translation. Strain differences in translation were observed for hundreds of genes. Allele specific measurements in the diploid hybrid between the two strains revealed roughly half as many cis-acting effects on translation as were observed for mRNA levels. In both the parents and the hybrid, most effects on translation were of small magnitude, such that the direction of an mRNA difference was typically reflected in a concordant footprint difference. The relative importance of cis and trans acting variation on footprint levels was similar to that for mRNA levels. There was a tendency for translation to cause larger footprint differences than expected given the respective mRNA differences. This is in contrast to translational differences between yeast species that have been reported to more often oppose than reinforce mRNA differences. Finally, we catalogued instances of premature translation termination in the two yeast strains and also found several instances where erroneous reference gene annotations lead to apparent nonsense mutations that in fact reside outside of the translated gene body. Overall, genetic influences on translation subtly modulate gene expression differences, and translation does not create strong discrepancies between genetic influences on mRNA and protein levels.\n\nAuthor summaryIndividuals in a species differ from each other in many ways. For many traits, a fraction of this variation is genetic - it is caused by DNA sequence variants in the genome of each individual. Some of these variants influence traits by altering how much certain genes are expressed, i.e. how many mRNA and protein molecules are made in different individuals. Surprisingly, earlier work has found that the effects of genetic variants on mRNA and protein levels for the same genes appear to be very different. Many variants appeared to influence only mRNA (but not protein) levels, and vice versa. In this paper, we studied this question by using a technique called \"ribosome profiling\" to measure translation (the cellular process of reading mRNA molecules and synthesizing protein molecules) in two yeast strains. We found that the genetic differences between these two strains influence translation for hundreds of genes. Because most of these effects were small in magnitude, they explain at most a small fraction of the discrepancies between the effects of genetic variants on mRNA and protein levels.

Genetics↗

bioRxiv2015

Standing genetic variation as a major contributor to adaptation in the Virginia chicken lines selection experiment

Artificial selection has, for decades, provided a powerful approach to study the genetics of adaptation. Using selective-sweep mapping, it is possible to identify genomic regions in populations where the allele-frequencies have diverged during selection. To avoid misleading signatures of selection, it is necessary to show that a sweep has an effect on the selected trait before it can be considered adaptive. Here, we confirm candidate selective-sweeps on a genome-wide scale in one of the longest, on-going bi-directional selection experiments in vertebrates, the Virginia high and low body-weight selected chicken lines. The candidate selective-sweeps represent standing genetic variants originating from the common base-population. Using a deep-intercross between the selected lines, 16 of 99 evaluated regions were confirmed to contain adaptive selective-sweeps based on their association with the selected trait, 56-day body-weight. Although individual additive effects were small, the fixation for alternative alleles in the high and low body-weight lines across these loci contributed at least 40% of the divergence between them and about half of the additive genetic variance present within and between the lines after 40 generations of selection. The genetic variance contributed by the sweeps corresponds to about 85% of the additive genetic variance of the base-population, illustrating that these loci were major contributors to the realised selection-response. Thus, the gradual, continued, long-term selection response in the Virginia lines was likely due to a considerable standing genetic variation in a highly polygenic genetic architecture in the base-population with contributions from a steady release of selectable genetic variation from new mutations and epistasis throughout the course of selection.

Genetics↗

bioRxiv2016

Exploiting single-cell quantitative data to map genetic variants having probabilistic effects

Despite the recent progress in sequencing technologies, genome-wide association studies (GWAS) remain limited by a statistical-power issue: many polymorphisms contribute little to common trait variation and therefore escape detection. The small contribution sometimes corresponds to incomplete penetrance, which may result from probabilistic effects on molecular regulations. In such cases, genetic mapping may benefit from the wealth of data produced by single-cell technologies. We present here the development of a novel genetic mapping method that allows to scan genomes for single-cell Probabilistic Trait Loci that modify the statistical properties of cellular-level quantitative traits. Phenotypic values are acquired on thousands of individual cells, and genetic association is obtained from a multivariate analysis of a matrix of Kantorovich distances. No prior assumption is required on the mode of action of the genetic loci involved and, by exploiting all single-cell values, the method can reveal non-deterministic effects. Using both simulations and yeast experimental datasets, we show that it can detect linkages that are missed by classical genetic mapping. A probabilistic effect of a single SNP on cell shape was detected and validated. The method also detected a novel locus associated with elevated gene expression noise of the yeast galactose regulon. Our results illustrate how single-cell technologies can be exploited to improve the genetic dissection of certain common traits.\n\nAUTHOR SUMMARYGenetic association studies are usually conducted on phenotypes measured at the scale of whole tissues or individuals, and not at the scale of individual cells. However, some common traits, such as cancer, can result from a minority of cells that adopted a special behavior. From one individual to another, DNA variants can modify the frequency of such cellular behaviors. The body of one of the individuals then harbours more misbehaving cells and is therefore predisposed to a macroscopic phenotypic change, such as disease. Such genetic effects are probabilistic, they contribute little to trait variation at the macroscopic level and therefore largely escape detection in classical studies. We have developed a novel statistical method that uses single-cell measurements to detect variants of the genome that have non-deterministic effects on cellular traits. The approach is based on a comparison of distributions of single-cell traits. We applied it to colonies of yeast cells and showed that it can detect mutations that change cellular morphology or molecular regulations in a probabilistic manner. This opens the way to study multicellular organisms from a novel angle, by exploiting single-cell technologies to detect genetic variants that predispose to certain diseases or common traits.

Genetics↗

bioRxiv2016

Sex differences in disease genetics

There is long-standing evidence for gene-by-sex interactions in disease risk, which can now be tested in genome-wide association studies with participant numbers in the hundreds of thousands. Contemporary methods start with a separate test for each sex, but simulations suggest a more powerful approach should be to use sex as an interaction term in a single test. The traits currently with the most compelling evidence for sex-dependent genetic effects are for adiposity (predictive of cardiac disease), type II diabetes, asthma and inflammatory bowel disease. Sexually dimorphic gene expression varies dynamically, by age, tissue type, and chromosome, so sex dependent genetic effects are expected for a wide range of diseases.\n\nKey conceptsO_LICompelling findings of sex-dependent genetic effects on disease have been made in adiposity-related anthropometric traits, type II diabetes, and inflammatory bowel disease. Other disorders remain to be more fully investigated, regardless of what sexual differences they exhibit in prevalence and presentation.\nC_LIO_LICurrent evidence indicates that sex difference in gene expression is not required for a SNP to have a sex-dependent effect. However, sex differences in gene expression vary dynamically, by organ and age, so generalisations may be inaccurate without comprehensive data.\nC_LIO_LISex-dependent risk alleles are predicted to be of greater effect size than conventional ones, because natural selection acts only against the sex which has the disease. There is evidence for this from a high-powered GWAS of adiposity-related traits.\nC_LIO_LIMany of the large GWAS meta-analyses look for sex-dependent genetic effects by testing male and female groups separately. However, this may be under-powered compared to a whole-sample, gene-by-sex interaction test.\nC_LI\n\nGlossaryO_LIGenome-wide association study (GWAS). Method for identifying molecular genetic variation that controls heritable traits, in a population sample. Involves assessing the correlation between allele frequencies and phenotype value, at millions of markers of common genetic variation across the genome.\nC_LIO_LISexual dimorphism. A difference between males and females in a population for the value of a particular trait. May include anything from anatomical measurements to expression level of a gene.\nC_LIO_LISex-dependent genetic effect. A disease risk allele is termed sex-specific when it increases risk in one sex only but has no effect on the disease in the other sex. The term sex-biased is used for an allele causes a significant increase in risk of disease in both sexes, but for which the magnitude of the risk increase is significantly different between males and females. There are also reports where an allele that increases risk of a disease in one sex reduces risk of the same disease in the other sex but none have been replicated, and there is no biochemical reason why this could be true. It effectively constitutes a sexually antagonistic effect, but should be distinguished from intra-locus sexual conflict which explicitly requires than an allele have opposing effects on the evolutionary fitness of males and females (Bonduriansky and Chenoweth 2009). All of the above relationships constitute a form of sex-dependent genetic effect.\nC_LI

Genetics↗

bioRxiv2016

Visualizing the Geography of Genetic Variants

One of the key characteristics of any genetic variant is its geographic distribution. The geographic distribution can shed light on where an allele first arose, what populations it has spread to, and in turn on how migration, genetic drift, and natural selection have acted. The distribution of a genetic variant can also be of great utility for medical/clinical geneticists. Collectively the geographic distribution of many genetic variants can reveal population structure. As a result, visual inspection of geographic maps for genetic variants is common practice in genetic studies. Here we develop an interactive visualization tool for rapidly displaying the geographic distribution of genetic variants. Through a REST API and dynamic front-end the Geography of Genetic Variants (GGV) browser provides maps of allele frequencies in populations distributed across the globe.

Genetics↗

bioRxiv2016

Evidence that low socioeconomic position accentuates genetic susceptibility to obesity

Susceptibility to obesity in todays environment has a strong genetic component. Lower socioeconomic position (SEP) is associated with a higher risk of obesity but it is not known if it accentuates genetic susceptibility to obesity. We aimed to use up to 120,000 individuals from the UK Biobank study to test the hypothesis that measures of socioeconomic position accentuate genetic susceptibility to obesity. We used the Townsend deprivation index (TDI) as the main measure of socioeconomic position, and a 69-variant genetic risk score (GRS) as a measure of genetic susceptibility to obesity. We also tested the hypothesis that interactions between BMI genetics and socioeconomic position would result in evidence of interaction with individual measures of the obesogenic environment and behaviours that correlate strongly with socioeconomic position, even if they have no obesogenic role. These measures included self-reported TV watching, diet and physical activity, and an objective measure of activity derived from accelerometers. We performed several negative control tests, including a simulated environment correlated with BMI but not TDI, and sun protection use. We found evidence of gene-environment interactions with TDI (Pinteraction=3x10-10) such that, within the group of 50% living in the most relatively deprived situations, carrying 10 additional BMI-raising alleles was associated with approximately 3.8 kg extra weight in someone 1.73m tall. In contrast, within the group of 50% living in the least deprivation, carrying 10 additional BMI-raising alleles was associated with approximately 2.9 kg extra weight. We also observed evidence of interaction between sun protection use and BMI genetics, suggesting that residual confounding may result in evidence of non-causal interactions. Our findings provide evidence that relative social deprivation best captures aspects of the obesogenic environment that accentuate the genetic predisposition to obesity in the UK.

Genetics↗

bioRxiv2017

Genetical genomics reveals Ras/MAPK modifier loci

The oncogenic Ras/MAPK pathway is evolutionary conserved across metazoans and is essential for many cellular functions. Mutant screens in the model nematode Caenorhabditis elegans have been invaluable for elucidating Ras/MAPK pathway characteristics and identification of the genes involved. Almost all of these screens have been conducted in a single genetic background. However, phenotypic traits of induced mutations can vary widely depending on the genetic background. At the moment, we lack insight into how different genetic backgrounds modulate Ras/MAPK-signaling and which genetic modifiers are involved.\n\nWe previously introduced a gain-of-function mutation in the Ras/MAPK pathway gene let-60 in over 200 recombinant inbred lines (mutant introgressed RILs: miRILs) and detected genetic modifiers affecting this pathway by studying variation in vulval development. In the present study, we investigate how gene expression regulation is affected by the let-60 gain-of-function mutation and the genetic background by mapping eQTL using 33 miRILs. We found that the majority ([~]73%) of the 1516 detected cis-eQTL are not specific for the let-60 mutation, whereas most ([~]76%) of the 898 detected trans-eQTL are associated with the let-60 mutation. We detected 6 eQTL trans-bands that were specific for the interaction between the genetic background and the mutation. One of these eQTL hotspots co-localizes with the previously identified polymorphic Ras/MAPK modifier amx-2. Comparing gene expression profiles between transgenic lines expressing either the N2 or the CB4856 alleles of amx-2 showed the involvement of amx-2 in 79% of the trans-eQTLs for genes mapping to this trans-band.\n\nTogether, our results have revealed hidden loci affecting Ras/MAPK signaling using sensitized backgrounds in C. elegans. These loci harbor putative polymorphic modifier genes that would not have been detected using mutant screens in single genetic backgrounds.

genetics↗

bioRxiv2017

Genetic validation of bipolar disorder identified by automated phenotyping using electronic health records

Bipolar disorder (BD) is a heritable mood disorder characterized by episodes of mania and depression. Although genomewide association studies (GWAS) have successfully identified genetic loci contributing to BD risk, sample size has become a rate-limiting obstacle to genetic discovery. Electronic health records (EHRs) represent a vast but relatively untapped resource for high-throughput phenotyping. As part of the International Cohort Collection for Bipolar Disorder (ICCBD), we previously validated automated EHR-based phenotyping algorithms for BD against in-person diagnostic interviews (Castro et al. 2015). Here, we establish the genetic validity of these phenotypes by determining their genetic correlation with traditionally-ascertained samples. Case and control algorithms were derived from structured and narrative text in the Partners Healthcare system comprising more than 4.6 million patients over 20 years. Genomewide genotype data for 3,330 BD cases and 3,952 controls of European ancestry were used to estimate SNP-based heritability (h2g) and genetic correlation(rg) between EHR-based phenotype definitions and traditionally-ascertained BD cases in GWAS by the ICCBD and Psychiatric Genomics Consortium (PGC) using LD score regression. We evaluated BD cases identified using 4 EHR-based algorithms: an NLP-based algorithm (95-NLP) and 3 rule-based algorithms using codified EHR with decreasing levels of stringency - \"coded-strict\", \"coded-broad\", and \"coded-broad based on a single clinical encounter\" (coded-broad-SV). The analytic sample comprised 862 95-NLP, 1,968 coded-strict, 2,581 coded-broad, 408 coded-broad-SV BD cases, and 3,952 controls. The estimated h2g were 0.24 (p=0.015), 0.09 (p=0.064), 0.13 (p=0.003), 0.00 (p=0.591) for 95-NLP, coded-strict, coded-broad and coded-broad-SV BD, respectively. The h2g for all EHR-based cases combined except coded-broad-SV (excluded due to 0 h2g) was 0.12 (p=0.004). These h2g were lower or similar to the h2g observed by the ICCBD+PGCBD (0.23, p=3.17E-80, total N=33,181). However, the rg between ICCBD+PGCBD and the EHR-based cases were high for 95-NLP (0.66, p=3.69x10-5), coded-strict (1.00, p=2.40x10-4), and coded-broad (0.74, p=8.11x10-7). The rg between EHR-based BDs ranged from 0.90 to 0.98. These results provide the first genetic validation of automated EHR-based phenotyping for BD and suggest that this approach identifies cases that are highly genetically correlated with those ascertained through conventional methods. High throughput phenotyping using the large data resources available in EHRs represents a viable method for accelerating psychiatric genetic research.

genetics↗

bioRxiv2017

Age at first birth in women is genetically associated with increased risk of schizophrenia

Previous studies have shown an increased risk for a range of mental health issues in children born to both younger and older parents compared to children of average-aged parents. However, until recently, it was not clear if these increased risks are due to psychosocial factors associated with age or if parents at higher genetic risk for psychiatric disorders tend to have children at an earlier or later age. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age of mothers at the birth of their first child (AFB). Here, we use independent data from the UK Biobank (N=38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, end to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value=1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value=3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is -0.16 (SE=0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE=0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health.

genetics↗

bioRxiv2020

Common maternal and fetal genetic variants show expected polygenic effects on the probability of being born small- or large-for-gestational-age (SGA or LGA), except in the smallest 3% of babies

Babies born clinically Small- or Large-for-Gestational-Age (SGA or LGA; sex- and gestational age-adjusted birth weight (BW) <10th or >90th percentile, respectively), are at higher risks of complications. SGA and LGA include babies who have experienced growth-restriction or overgrowth, respectively, and babies who are naturally small or large. However, the relative proportions within each group are unclear. We aimed to assess the extent to which the genetics of normal variation in birth weight influence the probability of SGA/LGA. We calculated independent fetal and maternal genetic scores (GS) for BW in 12,125 babies and 5,187 mothers. These scores capture the direct fetal and indirect maternal (via intrauterine environment) genetic contributions to BW, respectively. We also calculated maternal fasting glucose (FG) and systolic blood pressure (SBP) GS. We tested associations between each GS and probability of SGA or LGA. For the BW GS, we used simulations to assess evidence of deviation from an expected polygenic model. Higher BW GS were strongly associated with lower odds of SGA and higher odds of LGA (ORfetal=0.65 (0.60,0.71) and 1.47 (1.36,1.59); ORmaternal=0.80 (0.76,0.87) and 1.23 (1.15,1.31), respectively per 1 decile higher GS). Associations were in accordance with a polygenic model except in the smallest 3% of babies (Pfetal=0.0034, Pmaternal=0.023). Higher maternal GS for FG and SBP were associated with higher odds of LGA and SGA respectively (both P<0.01). While lower maternal FG and SBP are generally considered healthy in pregnancy, we found some evidence of association with higher odds of SGA (P=0.015) and LGA (P=0.14) respectively. We conclude that common genetic variants contribute to risk of SGA and LGA, but that additional factors become more important for risk of SGA in the smallest 3% of babies. Naturally low maternal glucose and blood pressure levels may additionally contribute to risk of SGA and LGA, respectively. Author SummaryBabies in the lowest or highest 10% of the population distribution of birth weight (BW) for a given gestational age are referred to as Small- or Large-for-Gestational-Age (SGA or LGA) respectively. These babies have higher risks of complications compared to babies with BW closer to the mean. SGA and LGA babies may have experienced growth restriction or overgrowth, respectively, but may alternatively just be at the tail ends of the normal growth distribution. The relative proportions of normal vs. sub-optimal growth within these groups is unclear. To examine the role of common genetic variation in SGA and LGA, we tested their associations with a fetal genetic score (GS) for BW in 12,125 European-ancestry individuals. We also tested associations with maternal GS (5,187 mothers) for offspring BW, fasting glucose and systolic blood pressure, each of which influences fetal growth via the in utero environment. We found all fetal and maternal GS were associated with SGA and LGA, supporting strong maternal and fetal genetic contributions to birth weight in both tails of the distribution. However, within the smallest 3% of babies, the maternal and fetal GS for BW were higher than expected, suggesting factors additional to common genetic variation are more important in determining birth weight in these very small babies.

genetics↗

bioRxiv2020

Shared genetic etiology of age of menarche and sosocioeconomic variables: No evidence for geneticoverlap with psychiatric traits

Earlier research has shown observational associations of early pubertal timing and poor mental health. Mendelian randomization (MR) studies demonstrated a transient effect of pubertal timing on mental health during adolescence, but not later in life. MR studies also showed that there is a likely causal association of pubertal timing with life history traits. However, the strongest causal effects and genetic correlations with age of menarche have been found for Body Mass Index (BMI). As high BMI is associated with lower socioeconomic status and with poor mental health, the shared genetic etiology of socioeconomic status, BMI and poor mental health is not yet fully understood. BMI correlates negatively with socioeconomic status and several mental health outcomes. Despite their substantial genetic overlap, the underlying genetic etiology of these phenotypes remains unclear. In this study we applied Linkage Disequi-librium score regression to test genetic correlations of age of menarche with 33 socioeconomic, life history, social interaction, personality and psychiatric traits, and BMI. We further applied spectral decomposition and hierarchical clustering to the genetic correlation matrix. After controlling for multiple testing, we could only identify significant genetic correlations with BMI and three socioeconomic traits (household income, deprivation and parental longevity). The results suggest that genome-wide association studies on age of menarche also contain socioeconomic information. Future MR studies aiming to test the unconfounded effect of pubertal timing should make sure that genetic instruments have no pleiotropic effect on socioeconomic variables, or (if possible) also control for socioeconomic status on the observational level.

genetics↗

bioRxiv2020

Examining sex-differentiated genetic effects across neuropsychiatric and behavioral traits

BackgroundThe origin of sex differences in prevalence and presentation of neuropsychiatric and behavioral traits is largely unknown. Given established genetic contributions and correlations across these traits, we tested for a sex-differentiated genetic architecture within and between traits. MethodsUsing genome-wide association study (GWAS) summary statistics for 20 neuropsychiatric and behavioral traits, we tested for differences in SNP-based heritability (h2) and genetic correlation (rg<1) between sexes. For each trait, we computed z-scores from sex-stratified GWAS regression coefficients and identified genes with sex-differentiated effects. We calculated Pearson correlation coefficients between z-scores for each trait pair, to assess whether specific pairs share variants with sex-differentiated effects. Finally, we tested for sex differences in between-trait genetic correlations. ResultsWith current sample sizes (and power), we found no significant, consistent sex differences in SNP-based h2. Between-sex, within-trait genetic correlations were consistently high, although significantly less than 1 for educational attainment and risk-taking behavior. We identified genome-wide significant genes with sex-differentiated effects for eight traits. Several trait pairs shared sex-differentiated effects. The top 0.1% of genes with sex-differentiated effects across traits overlapped with neuron- and synapse-related gene sets. Most between-trait genetic correlation estimates were similar across sex, with several exceptions (e.g. educational attainment & risk-taking behavior). ConclusionsSex differences in the common autosomal genetic architecture of neuropsychiatric and behavioral phenotypes are small and polygenic, requiring large sample sizes. Genes with sex-differentiated effects are enriched for neuron-related gene sets. This work motivates further investigation of genetic, as well as environmental, influences on sex differences.

genetics↗

bioRxiv2020

Variation in genetic relatedness is determined by the aggregate recombination process

The genomic proportion that two relatives share identically by descent--their genetic relatedness-- can vary depending on the history of recombination and segregation in their pedigree. This variation is important in many applications of genetics, including pedigree-based estimation of the genetic variance and heritability of traits, and estimation of pedigree relationships from sequence data. Here, we calculate the variance of genetic relatedness for general pedigree relationships, making no assumptions about the recombination process. For the specific relationships of grandparent-grandoffspring and siblings, the variance of genetic relatedness is a simple decreasing function of [Formula], the average proportion of locus pairs that recombine in meiosis. For general pedigree relationships, the variance of genetic relatedness is likewise the average of some function of pairwise recombination rates. Therefore, features of the aggregate recombination process that affect [Formula] and analogs also affect variance in genetic relatedness. Such features include the number of chromosomes and heterogeneity in their size, and the number of crossovers and their location along chromosomes. Our calculations help to explain several recent observations about variance in genetic relatedness, including that it is reduced by crossover interference (which is known to increase [Formula]). Our methods further allow us to calculate the neutral variance of ancestry among F2s in a hybrid cross, enabling precise statistical inference in F2-based tests for various kinds of selection.

genetics↗