Biology subjects

Thompson, W. K.

Publications and source records attributed to Thompson, W. K..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

The genetic architecture of human cortical folding

The folding of the human cerebral cortex is a highly genetically regulated process that allows for a much larger surface area to fit into the cranial vault and optimizes functional organization. Sulcal depth is a robust, yet understudied measure of localized folding, previously associated with a range of neurodevelopmental disorders. Here, we report the first genome-wide association study of sulcal depth. Through the Multivariate Omnibus Statistical Test (MOSTest) applied to vertexwise measures from 33,748 participants of the UK Biobank (mean age 64.3 years, 52.0% female) we identified 856 genetic loci associated with sulcal depth at genome-wide significance (=5x10-8). Comparison with two other measures of cortical morphology, cortical thickness and surface area, indicated that sulcal depth has higher yield in terms of loci discovered, higher heritability and higher effective sample size. There was a large amount of genetic overlap between the three traits, with gene-based analyses indicating strong associations with neurodevelopmental processes. Our findings demonstrate sulcal depth is a promising MRI phenotype that may enhance our understanding of human cortical morphology.

genomics↗

bioRxiv2020

Multivariate genome-wide association study identifies 1735 unique genetic loci associated with cortical morphology.

Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we exploit the distributed nature of genetic effects across the brain and apply the Multivariate Omnibus Statistical Test (MOSTest) to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N=35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology. This reflects an approximate 10-fold increase compared to the commonly applied univariate GWAS methods. Power analysis indicates that applying MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. Functional follow up including gene-based analyses implicate 10% of all protein-coding genes and point towards pathways involved in neurogenesis and cell differentiation.

genetics↗

bioRxiv2020

Neural vulnerability and hurricane-related media predict posttraumatic stress in youth

As natural disasters increase in frequency and severity1, 2, mounting evidence reveals that their human toll extends beyond death, injury, and loss. Posttraumatic stress (PTS) can be common among directly exposed individuals, and children are particularly vulnerable3, 4. Curiously, PTS can even be found among youth far removed from harms way, and media-based exposure may partially account for this phenomenon5-8. Unfortunately, susceptibility to media effects has been difficult to characterize because most research is initiated post-event, precluding examination of pre-disaster factors. In this study, we mitigate this issue with data from nearly 400 9-to 11-year-old children collected prior to and after Hurricane Irma. We evaluate whether preexisting neural patterns moderate associations between hurricane experiences and later Irma-related PTS. We show that "dose" of both objective exposure and Irma-related media exposure predicted Irma-related PTS, the latter even among children dwelling thousands of kilometers away from the hurricane. Furthermore, we show, using pre-hurricane functional magnetic resonance imaging data, that neural responses in brain regions associated with anxiety and stress confer particular vulnerability to the psychological effects of hurricane exposure among certain children. Surprisingly, this was even the case for media exposure- we found that that right amygdala reactivity to fearful stimuli moderated the association between Irma-related media exposure and PTS symptoms, with the media-PTS association strongest for children showing pre-hurricane heightened amygdala reactivity to Fear vs Neutral Faces. In contrast, in bilateral orbitofrontal cortex and left parahippocampal gyrus, children showing a weak response to the Fear condition relative to the Neutral condition were especially susceptible to PTS as a result of Irma-related media exposure. Collectively, these findings run counter to outdated "bullseye" models of disaster exposure that assume negative effects are narrowly circumscribed around a disasters geographic epicenter9. In contrast, for some youth with measurable preexisting vulnerability, consumption of extensive disaster-related media appears to offer an alternative pathway to disaster exposure that transcends geography and objective risk. This preventable exposure should be considered in disaster-related mental health efforts.

neuroscience↗

bioRxiv2020

Towards Reproducible Brain-Wide Association Studies

Magnetic resonance imaging (MRI) continues to drive many important neuroscientific advances. However, progress in uncovering reproducible associations between individual differences in brain structure/function and behavioral phenotypes (e.g., cognition, mental health) may have been undermined by typical neuroimaging sample sizes (median N=25)1,2. Leveraging the Adolescent Brain Cognitive Development (ABCD) Study3 (N=11,878), we estimated the effect sizes and reproducibility of these brain-wide associations studies (BWAS) as a function of sample size. The very largest, replicable brain-wide associations for univariate and multivariate methods were r=0.14 and r=0.34, respectively. In smaller samples, typical for brain-wide association studies (BWAS), irreproducible, inflated effect sizes were ubiquitous, no matter the method (univariate, multivariate). Until sample sizes started to approach consortium-levels, BWAS were underpowered and statistical errors assured. Multiple factors contribute to replication failures4-6; here, we show that the pairing of small brain-behavioral phenotype effect sizes with sampling variability is a key element in wide-spread BWAS replication failure. Brain-behavioral phenotype associations stabilize and become more reproducible with sample sizes of N{gap}2,000. While investigator-initiated brain-behavior research continues to generate hypotheses and propel innovation, large consortia are needed to usher in a new era of reproducible human brain-wide association studies.

neuroscience↗

bioRxiv2020

Multi-ancestry Genome- and Phenome-wide Association Studies of Diverticular Disease in Electronic Health Records with Natural Language Processing enriched phenotype algorithm

Background and aimsDiverticular disease is among the most prevalent conditions encountered by gastroenterologists, affecting [~]50% of Americans before the age of 60. Our aim was to identify genetic risk variants and clinical phenotypes associated with diverticular disease, utilizing the electronic health record (EHR) with Natural Language Processing (NLP). MethodsWe developed a NLP-enriched phenotype algorithm that incorporated colonoscopy or abdominal imaging reports to accurately identify patients with diverticulosis and diverticulitis from multicenter EHRs. We performed genome-wide association studies (GWAS) of diverticular disease in European, African and multi-ancestry participants, followed by phenome-wide association studies (PheWAS) of the risk variants to identify their potential comorbid/pleiotropic effects in the clinical phenome. For more in-depth investigation of associated clinical phenotypes, we also performed PheWAS with the previously reported 52 GWAS susceptibility variants for diverticular disease. ResultsAncestry-stratified GWAS analyses confirmed the well-established associations between ARHGAP15 loci with diverticular disease in European cohorts, and found similar positive effect sizes in African cohorts but with non-significant p-values. With overall intensified GWAS signals in diverticulitis patients compared to diverticulosis patients, we found substantial genetic correlations between diverticulosis and diverticulitis, up to 0.997 in European ancestry. PheWAS analyses identified associations between the diverticular disease GWAS variants and circulatory system, genitourinary, and neoplastic EHR phenotypes. ConclusionOur multiancestry GWAS-PheWAS study demonstrated an effective use of multidimensional EHR information in disease case/control classification with NLP for more comprehensive and scalable phenotyping, and implementation of an integrative analytical pipeline to facilitate etiological investigation of a disease from a clinical perspective.

bioinformatics↗

bioRxiv2020

Maturational trajectories of white matter microstructure underlying the right presupplementary motor area reflect individual improvements in motor response cancellation in children and adolescents

The ability to effectively suppress motor response tendencies is essential for focused and goal-directed behavior. Here, we tested the hypothesis that developmental improvement in the ability to cancel a motor response is reflected by maturational changes in the white matter underlying the right presupplementary motor area (preSMA) and posterior inferior frontal gyrus (IFG), two cortical key areas of the fronto-basal ganglia "stopping" network. Eighty-eight typically-developing children and adolescents, aged 7-19 years, were longitudinally assessed with the stop-signal task (SST) and diffusion tensor imaging (DTI) of the brain over a period of six years. Participants were examined from two to nine times with an average of 6.6 times, resulting in 576 SST-DTI datasets. We applied tract-based spatial statistics to extract mean fractional anisotropy (FA) from regions-of-interest in the white matter underlying the right IFG (IFGFA) and right preSMA (preSMAFA) at each time point. Motor response cancelation performance, estimated with the stop-signal reaction time (SSRT), improved with age. Initially well performing children plateaued around the age of 11 years, while initially poor performers caught up at the age of 13-14 years. White matter microstructure continued to mature across the investigated age range. Males generally displayed linear maturational trajectories, while females displayed more curvilinear trajectories that leveled off around 12-14 years of age. Maturational increases in right preSMAFA but not right IFGFA were associated with developmental improvements in SSRT. This association differed depending on the mean right preSMAFA across the individual maturational trajectory. Children with lower mean right preSMAFA exhibited poorer SSRT performance at younger ages but steeper developmental trajectories of SSRT improvement. Children with higher mean right preSMAFA exhibited flatter trajectories of SSRT improvement along with faster SSRT already at the first assessments. The results suggest that no further improvement in motor response cancellation is achieved once a certain level of maturity in the white matter underlying the right preSMA is reached. Similar dynamics may apply to other behavioral read-outs and brain structures and, thus, need to be considered in longitudinal MRI studies designed to map brain structural correlates of behavioral changes during development. HighlightsO_LIMotor response cancellation, i.e. SSRT, improvement plateaued at 13-14 years of age C_LIO_LIFractional anisotropy (FA) captured maturation of white matter (WM) microstructure C_LIO_LIFA in the WM underlying right preSMA (preSMAFA) reflected SSRT improvement with age C_LIO_LIIndividual SSRT improvement depended on mean right preSMAFA across all DTI sessions C_LIO_LIChildren with lower mean right preSMAFA had the steepest improvements in SSRT C_LI

neuroscience↗

bioRxiv2020

Fluid and crystallised intelligence are associated with distinct regionalisation patterns of cortical morphology

Cognitive performance in children is predictive of academic and social outcomes; therefore, understanding neurobiological mechanisms underlying individual differences in cognition during development may be important for improving quality of life. The belief that a single, psychological construct underlies many cognitive processes is pervasive throughout society. However, it is unclear if there is a consistent neural substrate underlying many cognitive processes. Here we show that a distributed configuration of cortical surface area and apparent thickness, when controlling for global imaging measures, is differentially associated with cognitive performance on different types of tasks in a large sample (N=10,145) of 9-11 year old children from the Adolescent Brain and Cognitive DevelopmentSM (ABCD) study. The minimal overlap in these regionalization patterns of association has implications for competing theories about developing intellectual functions. Surprisingly, not controlling for sociodemographic factors increased the similarity between these regionalization patterns. This highlights the importance of understanding the shared variance between sociodemographic factors, cognition and brain structure, particularly with a population-based sample such as ABCD.

neuroscience↗

bioRxiv2020

The effect of statistical normalisation on network propagation scores

MotivationNetwork diffusion and label propagation are fundamental tools in computational biology, with applications like gene-disease association, protein function prediction and module discovery. More recently, several publications have introduced a permutation analysis after the propagation process, due to concerns that network topology can bias diffusion scores. This opens the question of the statistical properties and the presence of bias of such diffusion processes in each of its applications. In this work, we characterised some common null models behind the permutation analysis and the statistical properties of the diffusion scores. We benchmarked seven diffusion scores on three case studies: synthetic signals on a yeast interactome, simulated differential gene expression on a protein-protein interaction network and prospective gene set prediction on another interaction network. For clarity, all the datasets were based on binary labels, but we also present theoretical results for quantitative labels. ResultsDiffusion scores starting from binary labels were affected by the label codification, and exhibited a problem-dependent topological bias that could be removed by the statistical normalisation. Parametric and non-parametric normalisation addressed both points by being codification-independent and by equalising the bias. We identified and quantified two sources of bias -mean value and variance- that yielded performance differences when normalising the scores. We provided closed formulae for both and showed how the null covariance is related to the spectral properties of the graph. Despite none of the proposed scores systematically outperformed the others, normalisation was preferred when the sought positive labels were not aligned with the bias. We conclude that the decision on bias removal should be problem and data-driven, i.e. based on a quantitative analysis of the bias and its relation to the positive entities. AvailabilityThe code is publicly available at https://github.com/b2slab/diffuBench Contactsergi.picart@upc.edu

bioinformatics↗

bioRxiv2019

Quantifying the polygenic architecture of the human cerebral cortex: Extensive genetic overlap between cortical thickness and surface area

IntroductionThe thickness of the cerebral cortical sheet and its surface area are highly heritable traits thought to have largely distinct polygenic architectures. Despite large-scale efforts, the majority of their genetic determinants remains unknown. Our ability to identify causal genetic variants can be improved by employing better delineated, less noisy brain measures that better map onto the biology we seek to understand. Such measures may have fewer variants but with larger effects, i.e. lower polygenicity and higher discoverability. MethodsUsing Gaussian mixture modeling, we estimated the number of causal variants shared between mean cortical thickness and total surface area. We further determined the polygenicity and discoverability of regional cortical measures from five often-employed parcellation schemes. We made use of UK Biobank data from 31,312 healthy White European individuals (mean age 55.5, standard deviation (SD) 7.4, 52.1% female). ResultsContrary to previous reports, we found large genetic overlap between total surface area and mean thickness, sharing 4427 out of 7150 causal variants. Regional surface area was more discoverable (p=4.1x10-6) and less polygenic (p=.007) than regional thickness measures. We further found that genetically-informed and less granular parcellation schemes had highest discoverability, with no differences in polygenicity. ConclusionsThese findings may serve as a roadmap for improved future GWAS studies; Knowledge of which measures or parcellations are most discoverable, as well as the genetic overlap between these measures, may be used to boost identification of genetic predictors and thereby gain a better understanding of brain morphology.

neuroscience↗

bioRxiv2019

Economic, social, and physiological resilience predict brain structure and cognitive performance in 9 - 10-year-old children

While children with economic disadvantage are at risk for poorer outcomes in cognitive and brain development, less understood is the contribution of other factors in the broader socioeconomic context that may more closely index the underlying mechanisms influencing risk and resilience. We examined brain structure and cognitive test performance in association with economic disadvantage and 22 measures in the broader socioeconomic context among n = 8,158 demographically diverse 9-10-year-old children from the ABCD Study. Total cortical surface area and total cognition scores increased as a function of income-to-needs, with the steepest differences most apparent among children below and near poverty relative to their wealthier peers. We found three latent factors encompassing distinct relationships among our proximal measures, including social, economic, and physiological well-being, each associated with brain structure and cognitive performance independently of economic advantage. Our findings will inform future studies of risk and resilience in developmental outcomes for children with economic disadvantage.

neuroscience↗

bioRxiv2019

Determining the association between cortical morphology and cognition in 10,145 children from the Adolescent Brain and Cognitive Development (ABCD) study using the MOSTest

Individuals undergo protracted changes in cortical morphology during childhood and adolescence, coinciding with cognitive development. Studies quantifying the association between brain structure and cognition do not always assess regional cortical morphology relative to global brain measures and typically rely on mass univariate statistics or ROI-based analyses. After controlling for global brain measures, it is possible to detect a residual regionalisation pattern indicating the size or thickness of different regions relative to the total cortical surface area or mean thickness. Individual variability in regionalisation may be important for understanding and predicting between subject variability in cognitive performance. Here we sought to determine whether the relative configuration of cortical architecture across the whole cortex was associated with cognition using a novel multivariate omnibus statistical test (MOSTest) in 10,145 children aged 9-10 years from the Adolescent Brain and Cognitive Development (ABCD) Study. MOSTest is better powered to detect associations that are widely distributed across the cortex compared to methods that assume sparse associations. We then quantified the magnitude of the association between vertex-wise cortical morphology and cognitive performance using a linear weighted sum across vertices, based on the estimated vertex-wise effect sizes. We show that the relative pattern of cortical architecture, after removing the effects of global brain measures, predicted unique variance associated with cognition across different imaging modalities and cognitive domains. SIGNIFICANCE STATEMENTThis paper demonstrates a significant advance in our understanding of the relationship between cortical morphology and individual variability in cognition. There is increasing evidence that brain-behaviour associations are distributed across the cortex. Using the unprecedented sample from the Adolescent Brain and Cognitive Development (ABCD) study and a novel application of a multivariate statistical approach (MOSTest), we have discovered specific distributed regionalization patterns across the cortex associated with cognition across multiple cognitive domains. This furthers our understanding of the relationship between brain structure and cognition, namely that these associations are not sparse and localized as assumed with traditional neuroimaging analyses. This multivariate method is extremely versatile and can be used in several different applications.

neuroscience↗

bioRxiv2019

The polyvertex score (PVS): a whole-brain phenotypic prediction framework for neuroimaging studies

The traditional brain mapping approach has greatly advanced our understanding of the localized effect of the brain on behavior. However, the statistically significant brain regions identified by standard mass univariate models only explain minimal variance in behavior despite increased sample sizes and statistical power. This is potentially due to the generalizable explanatory signal in the brain being non-sparse, therefore not captured by the thresholded, localized model. Here we introduced the Bayesian polyvertex score (PVS-B), a whole-brain prediction framework that aggregates the effect sizes across all vertices to predict individual variability in behavior. The PVS-B estimates the posterior mean effect size at each vertex with mass univariate summary statistics and the correlation structure of the imaging phenotype, and weights the imaging phenotype of participants from an independent sample with these posterior mean effect sizes to estimate the generalizable effect of a brain-behavior association. Empirical data showed that the PVS-B was able to double the variance explained in general cognitive ability by an n-back fMRI contrast when compared to prediction models based on the mass univariate parameter estimates as well as models in which only vertices thresholded based on p-value were included. A fivefold improvement in variance explained by the PVS-B was observed using a stop signal task fMRI contrast to predict individual variability in the stop signal reaction time. We believe that the PVS-B can shed light on the multivariate investigation of brain-behavioral associations and will empower small scale neuroimaging studies with more reliable and accurate effect size estimates.

neuroscience↗

bioRxiv2019

Genetic factors underlying the bidirectional relationship between autoimmune and mental disorders - findings from a Danish population-based study

BackgroundPrevious studies have indicated the bidirectionality between autoimmune and mental disorders. However, genetic studies underpinning the co-occurrence of the two disorders have been lacking. In this study, we examined the potential genetic contribution to the association between autoimmune and mental disorders.\n\nMethodsWe used diagnostic information for patients with seven autoimmune diseases and six mental disorders from the Danish population-based case-cohort sample (iPSYCH2012). We explored the epidemiological association using survival analysis and modelled the effect of polygenic risk scores (PRSs) on two diseases. The genetic factors were investigated using GWAS and HLA imputation data based on iPSYCH cohort.\n\nResultsAmong 64,039 individuals, a total of 43,902 (68.6%) were diagnosed with mental disorders and 1,383 (2.2%) with autoimmune diseases. There was a significant comorbidity between the two diseases (P=2.67x10-7, OR=1.38, 95%CI=1.22-1.56), with an overall bidirectional association wherein individuals with autoimmune diseases had an increased risk of subsequent mental disorders (HR=1.13, 95%CI: 1.07-1.21, P=7.95x10-5) and vice versa (HR=1.27, 95%CI=1.16-1.39, P=8.77x10-15). Though PRSs were significantly correlated with both types of diagnosis, PRSs had little effect on the bidirectional relationship. Importantly, we for the first time observed 12 human leukocyte antigen (HLA) loci and 20 HLA alleles strongly associated with overall autoimmune diseases, but we did not find significant evidence of their associations with overall mental disorders.\n\nConclusionsOur findings confirm the overall comorbidity and bidirectionality between autoimmune and mental disorders and discover HLA genes which are significantly associated with overall autoimmune diseases, but not with overall mental disorders.

bioinformatics↗

bioRxiv2019

Polygenic Score of Intelligence is More Predictive of Crystallized than Fluid Performance Among Children

BackgroundFindings in adults have shown more culturally sensitive crystallized measures of intelligence have greater heritability, these results were not able to be shown in children. MethodsWith data from 8,518 participants, aged 9 to 11, from the Adolescent Brain Cognitive Development (ABCD) Study(R), we used polygenic predictors of intelligence test performance (based on genome-wide association meta-analyses of data from 269,867 individuals) and of educational attainment (based on data from 1.1 million individuals), associating these predictors with neurocognitive performance. We then assessed the extent of mediation of these associations by a measure of recreational reading. Resultsmore culturally sensitive crystallized measures were more strongly associated with the polygenic predictors than were less culturally sensitive fluid measures. This mirrored heritability differences reported previously in adults and suggests similar associations in children. Recreational reading more strongly statistically mediated the genetic associations with crystallized than those with fluid measures of cognition. ConclusionThis is consistent with a prominent role of gene-environment correlation in cognitive development measured by "crystallized" intelligence tests. Such experiential mediators may represent malleable targets for improving cognitive outcomes.

neuroscience↗

bioRxiv2019

Sex-dependent polygenic effects on the clinical progressions of Alzheimer’s disease

Sex differences in the manifestations of Alzheimers disease (AD) are under intense investigations 1,2. Despite the emerging importance of polygenic predictions for AD 3-8, the sex-dependent polygenic effects have not been demonstrated. Here, using a sex crossover analysis, we show that sex-dependent autosomal genetic effects on AD can be revealed by characterizing disease progress via the hazard function. We first performed sex-stratified genome-wide associations, and then applied derived sex-dependent weights to two independent cohorts. Sex-matched polygenic hazard scores (PHS) have significantly stronger associations with age-at-disease-onset, clinical progressions, amyloid depositions, neurofibrillary tangles, and composite neuropathological scores, than sex-mismatched PHS, independent of apolipoprotein E. Models without using hazard weights, i.e. polygenic risk scores (PRS), have lower predictive power than PHS and show no evidence for sex differences. Our results indicate revealing sex-dependent genetic architecture requires the consideration of temporal processes of AD. This has strong implications not only for the genetic underpinning of AD but also for how we estimate sex-dependent polygenic effects for clinical use.

genetics↗

bioRxiv2018

Prediction of Neurocognitive Profiles in Youth From Resting State fMRI

Difficulties with higher-order cognitive functions in youth are a potentially important vulnerability factor for the emergence of problematic behaviors and a range of psychopathologies. This study examined 2,013 9-10 year olds in the first data release from the Adolescent Brain Cognitive Development 21-site consortium study in order to identify resting state functional connectivity patterns that predict individual-differences in three domains of higher-order cognitive functions: General Ability, Speed/Flexibility, and Learning/Memory. We found that connectivity patterns involving task control networks and default mode network were prominently implicated in predicting individual differences across participants across all three domains. In addition, for General Ability scores specifically, we observed consistent cross-site generalizability, with statistically significant predictions in 14 out of 15 held-out sites. These findings demonstrate that resting state connectivity can be leveraged to produce generalizable markers of neurocognitive functioning. Additionally, they highlight the importance of task control-default mode network inter-connections as a major locus of individual differences in cognitive functioning in early adolescence.

neuroscience↗

bioRxiv2018

Image processing and analysis methods for the Adolescent Brain Cognitive Development Study

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The ABCD Study is a collaborative effort, including a Coordinating Center, 21 data acquisition sites across the United States, and a Data Analysis and Informatics Center (DAIC). The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data will provide a resource of unprecedented scale and depth for studying typical and atypical development. Here, we describe the baseline neuroimaging processing and subject-level analysis methods used by the ABCD DAIC in the centralized processing and extraction of neuroanatomical and functional imaging phenotypes. Neuroimaging processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI.\n\nHighlightsO_LIAn overview of the MRI processing pipeline for the ABCD Study\nC_LIO_LIA discussion on the challenges of large, multisite population studies\nC_LIO_LIA methodological reference for users of publicly shared data from the ABCD Study\nC_LI

neuroscience↗

bioRxiv2018

Variants in the fetal genome near pro-inflammatory cytokine genes on 2q13 are associated with gestational duration

The duration of pregnancy is influenced by fetal and maternal genetic and non-genetic factors. We conducted a fetal genome-wide association meta-analysis of gestational duration, and early preterm, preterm, and postterm birth in 84,689 infants. One locus on chromosome 2q13 was associated with gestational duration; the association was replicated in 9,291 additional infants (combined P = 3.96 x 10-14). Analysis of 15,536 mother-child pairs showed that the association was driven by fetal rather than maternal genotype. Functional experiments showed that the lead SNP, rs7594852, alters the binding of the HIC1 transcriptional repressor. Genes at the locus include several interleukin 1 family members with roles in pro-inflammatory pathways that are central to the process of parturition. Further understanding of the underlying mechanisms will be of great public health importance, since giving birth either before or after the window of term gestation is associated with increased morbidity and mortality.

genetics↗