Biology subjects

Lind, P. A.

Publications and source records attributed to Lind, P. A..

16 recordsLinked to original sources

bioRxiv2025

Forecasting multi-trait resistance evolution under antibiotic stress

Many bacteria rely on efflux pumps to survive antibiotic stress, and exposure to antibiotics often leads to mutations in pump genes or their regulators that increase pump expression. Predicting the spectrum of these mutations is important for designing effective antibiotic treatments, but the underlying regulatory networks are large and complex, making them difficult to map experimentally. To address this challenge, we developed a mathematical framework that integrates dynamical equations for efflux pump regulation with a genetic algorithm for parameter estimation and evolutionary simulations. Using this framework, we simulated in silico evolution of Pseudomonas aeruginosa under exposure to the antibiotics meropenem, tobramycin, and ciprofloxacin. The simulations revealed mutational spectra affecting the expression of four RND efflux pumps and their shared regulatory network. The most frequently mutated genes were single-target regulators that matched well with previous observations in clinical and in vitro studies. The model also showed that the shared use of the OprM protein by two pumps is a key factor shaping their distinct mutational patterns. Mutations often produced multi-trait phenotypes, manifesting as collateral sensitivity or cross-resistance to antibiotics not used for selection. While cross-resistance evolved readily, its extent depended on initial pump expression levels and thus may vary between strains. Finally, simulations of changing environments showed that efflux pump genes tend to be lost in the absence of antibiotics, suggesting a potential strategy to steer bacterial evolution toward reduced capacity to re-evolve resistance.

microbiology↗

bioRxiv2024

A blended genome and exome sequencing method captures genetic variation in an unbiased, high-quality, and cost-effective manner

We deployed the Blended Genome Exome (BGE), a DNA library blending approach that generates low pass whole genome (1-4x mean depth) and deep whole exome (30-40x mean depth) data in a single sequencing run. This technology is cost-effective, empowers most genomic discoveries possible with deep whole genome sequencing, and provides an unbiased method to capture the diversity of common SNP variation across the globe. To evaluate this new technology at scale, we applied BGE to sequence >53,000 samples from the Populations Underrepresented in Mental Illness Associations Studies (PUMAS) Project, which included participants across African, African American, and Latin American populations. We evaluated the accuracy of BGE imputed genotypes against raw genotype calls from the Illumina Global Screening Array. All PUMAS cohorts had R2 concordance [≥]95% among SNPs with MAF[≥]1%, and never fell below [≥]90% R2 for SNPs with MAF<1%. Furthermore, concordance rates among local ancestries within two recently admixed cohorts were consistent among SNPs with MAF[≥]1%, with only minor deviations in SNPs with MAF<1%. We also benchmarked the discovery capacity of BGE to access protein-coding copy number variants (CNVs) against deep whole genome data, finding that deletions and duplications spanning at least 3 exons had a positive predicted value of [~]90%. Our results demonstrate BGE scalability and efficacy in capturing SNPs, indels, and CNVs in the human genome at 28% of the cost of deep whole-genome sequencing. BGE is poised to enhance access to genomic testing and empower genomic discoveries, particularly in underrepresented populations.

genomics↗

bioRxiv2024

An extreme mutational hotspot in nlpD depends on transcriptional induction of rpoS

Mutation rate varies within and between genomes. Within genomes, tracts of nucleotides, including short sequence repeats and palindromes, can cause localised elevation of mutation rate. Additional mechanisms remain poorly understood. Here we report an instance of extreme mutational bias in Pseudomonas fluorescens SBW25 associated with a single base-pair change in nlpD. These mutants frequently evolve in static microcosms, and have a cell-chaining (CC) phenotype. Analysis of 153 replicate populations revealed 137 independent instances of a C565T loss-of-function mutation at codon 189 (CAG to TAG (Q189*)). Fitness measures of alternative nlpD mutants showed molecular parallelism to be unconnected to selective advantage. Recognising that transcription can be mutagenic, and that codon 189 overlaps with a predicted promoter (rpoSp) for the adjacent stationary phase sigma factor, rpoS, transcription across this promoter region was measured. This confirmed rpoSp is induced in stationary phase and that C565T mutation caused significant elevation of transcription. The latter provided opportunity to determine the C565T mutation rate using a reporter-gene fused to rpoSp. Fluctuation assays demonstrate the C565T mutation rate to be 5,700-fold higher than expected. In Pseudomonas, transcription of rpoS requires the positive activator PsrA, which we show also holds for SBW25. Fluctuation assays performed in a {Delta}psrA background showed a 60-fold reduction in mutation rate confirming that the elevated rate of mutation at C565T mutation rate is dependent on induction of transcription. This hotspot suggests a generalisable phenomenon where the induction of transcription causes elevated mutation rates within defining regions of promoters.

evolutionary biology↗

bioRxiv2024

Adaptive evolutionary trajectories in complexity: repeated transitions between unicellularity and differentiated multicellularity

Multicellularity spans a wide gamut in terms of complexity, from simple clonal clusters of cells to large-scale organisms composed of differentiated cells and tissues. While recent experiments have demonstrated that simple forms of multicellularity can readily evolve in response to different selective pressures, it is unknown if continued exposure to those same selective pressures will result in the evolution of increased multicellular complexity. We use mathematical models to consider the adaptive trajectories of unicellular organisms exposed to periodic bouts of abiotic stress, such as drought or antibiotics. Populations can improve survival in response to the stress by evolving multicellularity or cell differentiation--or both; however, these responses have associated costs when the stress is absent. We define a parameter space of fitness-relevant traits and identify where multicellularity, differentiation, or their combination is fittest. We then study the effects of adaptation by allowing populations to fix mutations that improve their fitness. We find that while the same mutation can be beneficial to phenotypes with different complexity, e.g. unicellularity and differentiated multicellularity, the magnitudes of their effects can differ and alter which phenotype is fittest. As a result, we observe adaptive trajectories that gain and lose complexity. We also show that the order of mutations, historical contingency, can cause some transitions to be permanent in the absence of neutral evolution. Ultimately, we find that continued exposure to a selective driver for multicellularity can either lead to increasing complexity or a return to unicellularity.

evolutionary biology↗

bioRxiv2024

Forecasting of phenotypic and genetic outcomes of experimental evolution in Pseudomonas syringae and Pseudomonas savastanoi

Microbial experimental evolution is commonly highly repeatable under identical conditions, indicating a potential for short-term evolutionary forecasting. However, it is unclear to what extent evolutionary predictions can be extrapolated to related species adapting in similar environments, which would enable direct testing of general forecasting models and biological assumptions. To further develop a model system for evolutionary forecasting based on adaptation to static culture conditions, we experimentally tested previous predictions for Pseudomonas syringae and Pseudomonas savastanoi. In addition to sequence divergence, these species also differ in their repertoire of diguanylate cyclases that can be mutationally activated to produce the adaptive wrinkly spreader (WS) phenotype and genes for biosynthesis of exopolysaccharides. After experimental evolution, we isolated 32 independent WS mutants for P. syringae and 37 WS mutants for P. savastanoi that had increased ability to colonize the air-liquid interface and reduced motility. As predicted, most mutants had mutations in the wsp operon followed by rarer promoter mutations upstream of uncharacterized diguanylate cyclases. Surprisingly, no mutations were found in wspF, the most commonly mutated gene in the previously characterized species, which was explained by differences in relative fitness. While prediction of mutated regions was largely successful for WspA, mutations in WspE had a divergent pattern for both species. Surprisingly, deletion of known exopolysaccharide loci previously shown to contribute to the adaptive WS phenotype in other species did not reduce fitness, suggesting the presence of additional adhesive components under c-di-GMP control. This study shows that evolutionary forecasts can be extended to related species, but that differences in the genotype-phenotype-fitness map and mutational biases limit predictability on a detailed molecular level. Author summaryBiological evolution is often observed to be repeatable in the short-term, which suggests that it might be possible to forecast and ultimately steer evolution. Evolutionary processes are fundamental to biology but are also central to major societal problems, including antibiotic resistance, cancer, and adaptation to climate change. Experimental evolution with microbes makes it possible to study evolutionary processes in real-time over many generations to allow direct tests of evolutionary forecasts. However, a fundamental problem is that predictive models are usually based on previous experimental data which limits the novelty of the prediction beyond simple repeatability. A more challenging issue is to predict to what degree similar species evolve in similar ways in similar environments. Here we show that one of the best characterized bacterial experimental evolution model systems, biofilm formation at the surface of static tubes in Pseudomonas, can be extended to related species evolving in similar environments. This allowed us to directly test previous evolutionary forecasts to show that similar phenotypes evolved in similar environments, but that predictions of molecular details often fail. This study also elucidates the causes for failed forecasts to allow continuous improvements in predictive models and to delineate the limits of evolutionary forecasting.

evolutionary biology↗

bioRxiv2022

Distribution of mutation rates challenges evolutionary predictability

Natural selection is commonly assumed to act on extensive standing genetic variation. Yet, accumulating evidence highlights the role of mutational processes creating this genetic variation: to become evolutionarily successful, adaptive mutants must not only reach fixation, but also emerge in the first place, i.e. have a high enough mutation rate relative to the population size. Here, we use numerical simulations to investigate how mutational biases impact our ability to observe rare mutational pathways in the laboratory and to predict outcomes in experimental evolution. We find the number of available mutations and the unevenness of their mutation rates to increase, sometimes impractically, the experimental effort required to discover all of them. Thus, most experimental studies lack power to directly observe the full range of adaptive mutations. Modelling mutation rates as a distribution, we show that a substantially larger target size ensures that a pathway mutates more commonly. Therefore, we predict that commonly mutated pathways are conserved between closely related species, contrary to rare pathways. This approach formalizes our proposal that most mutations have a lower mutation rate than the average mutation rate measured experimentally. We suggest that the extent of genetic variation is overestimated when based on the average mutation rate.

evolutionary biology↗

bioRxiv2022

Bacteria evolve macroscopic multicellularity via the canalization of phenotypically plastic cell clustering

The evolutionary transition from unicellular to multicellular life was a key innovation in the history of life. Given scarce fossil evidence, experimental evolution has been an important tool to study the likely first step of this transition, namely the formation of undifferentiated cellular clusters. Although multicellularity first evolved in bacteria, the extant experimental evolution literature on this subject has primarily used eukaryotes. Moreover, it focuses on mutationally driven (and not environmentally induced) phenotypes. Here we show that both Gram-negative and Gram-positive bacteria exhibit phenotypically plastic (i.e., environmentally induced) cell clustering. Under high salinity, they grow as elongated ~ 2 cm long clusters (not as individual planktonic cells). However, under habitual salinity, the clusters disintegrate and grow planktonically. We used experimental evolution with Escherichia coli to show that such clustering can be canalized successfully: the evolved bacteria inherently grow as macroscopic multicellular clusters, even without environmental induction. Highly parallel mutations in genes linked to cell wall assembly formed the genomic basis of canalized multicellularity. While the wildtype also showed cell shape plasticity across high versus low salinity, it was either canalized or reversed after evolution. Interestingly, a single mutation could canalize multicellularity by modulating plasticity at multiple levels of organization. Taken together, we show that phenotypic plasticity can prime bacteria for evolving undifferentiated macroscopic multicellularity.

evolutionary biology↗

bioRxiv2021

Within-sibship GWAS improve estimates of direct genetic effects

Estimates from genome-wide association studies (GWAS) represent a combination of the effect of inherited genetic variation (direct effects), demography (population stratification, assortative mating) and genetic nurture from relatives (indirect genetic effects). GWAS using family-based designs can control for demography and indirect genetic effects, but large-scale family datasets have been lacking. We combined data on 159,701 siblings from 17 cohorts to generate population (between-family) and within-sibship (within-family) estimates of genome-wide genetic associations for 25 phenotypes. We demonstrate that existing GWAS associations for height, educational attainment, smoking, depressive symptoms, age at first birth and cognitive ability overestimate direct effects. We show that estimates of SNP-heritability, genetic correlations and Mendelian randomization involving these phenotypes substantially differ when calculated using within-sibship estimates. For example, genetic correlations between educational attainment and height largely disappear. In contrast, analyses of most clinical phenotypes (e.g. LDL-cholesterol) were generally consistent between population and within-sibship models. We also report compelling evidence of polygenic adaptation on taller human height using within-sibship data. Large-scale family datasets provide new opportunities to quantify direct effects of genetic variation on human traits and diseases.

genetics↗

bioRxiv2019

The genetic architecture of sporadic and recurrent miscarriage

Miscarriage is a common complex trait that affects 10-25% of clinically confirmed pregnancies1,2. Here we present the first large-scale genetic association analyses with 69,118 cases from five different ancestries for sporadic miscarriage and 750 cases of European ancestry for recurrent miscarriage, and up to 359,469 female controls. We identify one genome-wide significant association on chromosome 13 (rs146350366, minor allele frequency (MAF) 1.2%, Pmeta=3.2x -8 (CI) 1.2-1.6) for sporadic miscarriage in our European ancestry meta-analysis (50,060 cases and 174,109 controls), located near FGF9 involved in pregnancy maintenance3 and progesterone production4. Additionally, we identified three genome-wide significant associations for recurrent miscarriage, including a signal on chromosome 9 (rs7859844, MAF=6.4%, Pmeta=1.3x -8 in controlling extravillous trophoblast motility5. We further investigate the genetic architecture of miscarriage with biobank-scale Mendelian randomization, heritability and, genetic correlation analyses. Our results implicate that miscarriage etiopathogenesis is partly driven by genetic variation related to gonadotropin regulation, placental biology and progesterone production.

genetics↗

bioRxiv2018

Genetic variation in the Major Histocompatibility Complex and association with depression

BackgroundThe prevalence of depression is higher in individuals suffering from autoimmune diseases, but the mechanisms underlying the observed comorbidities are unknown. Epidemiological findings point to a bi-directional relationship - that depression increases the risk of developing an autoimmune disease, and vice-versa. Shared genetic etiology is a plausible explanation for the overlap between depression and autoimmune diseases. In this study we tested whether genetic variation in the Major Histocompatibility Complex (MHC), which is associated with risk for autoimmune diseases, is also associated with risk for depression.\n\nMethodWe fine-mapped the classical MHC (chr6: 29.6-33.1 Mb), imputing 216 Human Leukocyte Antigen (HLA) alleles and four Complement Component 4 (C4) haplotypes in studies from the Psychiatric Genomics Consortium (PGC) Major Depressive Disorder (MDD) working group and the UK Biobank (UKB). In the 26 PGC-MDD studies, cases met a lifetime diagnosis of MDD, determined by a structured diagnostic interview. In the UKB, cases and controls were identified from an online mental health questionnaire. The total sample size was 45,149 depression cases and 86,698 controls. We tested for association between depression status and imputed MHC variants in each study and performed an inverse-variance weighted meta-analysis across the PGC-MDD and UKB samples, applying both a conservative region-wide significance threshold (3.9-e6) and a candidate threshold (1.6e-4).\n\nResultsNo HLA alleles or C4 haplotypes were associated with depression at the conservative threshold in the PGC, UKB or meta-analysis. HLA-B*08:01 was associated with modest protection for depression at the candidate threshold in the meta-analysis. Under the conservative threshold, 70 SNPs were detected in the UKB and 143 SNPs were detected in the meta-analysis, mirroring previous findings from highly powered GWAS of depression.\n\nDiscussionWe found no evidence that HLA alleles, which play a major role in the genetic susceptibility to autoimmune diseases, or C4 haplotypes, which are strongly associated with schizophrenia, confer risk for depression. These results indicate that autoimmune diseases and depression do not share common risk loci of moderate or large effect in the MHC.

genetics↗

bioRxiv2018

Genome-wide association and functional studies identify 46 novel loci for alcohol consumption and suggest common genetic mechanisms with neuropsychiatric disorders

Excessive alcohol consumption is one of the main causes of death and disability worldwide. Alcohol consumption is a heritable complex trait. We conducted a genome-wide association study (GWAS) of alcohol use in ~480,000 people of European descent to decipher the genetic architecture of alcohol intake. We identified 46 novel, common loci, and investigated their potential functional significance using magnetic resonance imaging data, gene expression and behavioral studies in Drosophila. Our results identify new genetic pathways associated with alcohol consumption and suggest common genetic mechanisms with several neuropsychiatric disorders including schizophrenia.

genetics↗

bioRxiv2018

Evidence of causal effect of major depression on alcohol dependence: Findings from the Psychiatric Genomics Consortium

BackgroundDespite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood.\n\nMethodsThis study was conducted using genome-wide data from the Psychiatric Genomics Consortium (MD: 135,458 cases and 344,901 controls; AD: 10,206 cases and 28,480 controls) and UK Biobank (AC-Frequency: from \"daily or almost daily\" to \"never\", 438,308 individuals; AC-Quantity: total units of alcohol per week, 307,098 individuals). Linkage disequilibrium score regression and Mendelian Randomization (MR) analyses were applied to investigate shared genetic mechanisms (horizontal pleiotropy) and causal relationships (mediated pleiotropy) among these traits.\n\nOutcomesPositive genetic correlation was observed between MD and AD (rgMD-AD=+0.47, P=6.6x10-10). AC-Quantity showed positive genetic correlation with both AD (rgAD-AC-Quantity=+0.75, P=1.8x10-14) and MD (rgMD-AC-Quantity=+0.14, P=2.9x10-7), while there was negative correlation of AC-Frequency with MD (rgMD-AC-Frequency=-0.17, P=1.5x10-10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e., causal relationship) with a causal role of MD on AD (beta=0.28, P=1.29x10-6) that does not appear to be biased by confounding such as horizontal pleiotropy. No evidence of reverse causation was observed as the AD genetic instrument did not show a causal effect on MD.\n\nInterpretationResults support a causal role for MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity not only addresses important public health concerns but also has the potential to facilitate prevention and intervention efforts.\n\nFundingNational Institute of Mental Health and National Institute on Drug Abuse.\n\nPutting data into contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed up to August 24, 2018, for research studies that investigated causality among alcohol-and depression related phenotypes using Mendelian randomization approaches. We used the search terms \"alcohol\" AND \"depression\" AND \"Mendelian Randomization\". No restrictions were applied to language, date, or article type. Ten articles were retrieved, but only two were focused on alcohol consumption and depression-related traits. The studies were based on genetic variants in alcohol dehydrogenase (ADH) genes only, did not find evidence for a causal effect of alcohol consumption on depression phenotypes, with one study finding a causal effect of alcohol consumption on alcoholism. Both studies noted that future studies are needed with increased sample sizes and clinically derived phenotypes. To our knowledge, no previous study has applied two-sample Mendelian randomization to investigate causal relationships between alcohol dependence and major depression.\n\nTwin studies show genetic factors influence susceptibility to MD, AD, and alcohol consumption. Differently from observational approaches where several studies have investigated the relationship between alcohol-and depression-related phenotypes, very limited use of molecular genetic data has been applied to investigate this issue. Additionally, the use of genetic information has been shown to be less biased by confounders and reverse causation than observation data. However, genetic approaches, like Mendelian randomization, require large sample sizes to be informative.\n\nAdded value of this studyIn this study, we used genome-wide data from the Psychiatric Genomic Consortium and UK Biobank, which include information regarding hundred thousands of individuals, to test the presence of shared genetic mechanisms and causal relationships among major depression, alcohol dependence, and alcohol consumption. The results support a causal influence of MD on AD, while alcohol consumption showed shared genetic mechanisms with respect to both major depression and alcohol dependence.\n\nImplications of all the available evidenceGiven the significant morbidity and mortality associated with MD, AD, and the comorbid condition, understanding mechanisms underlying these associations not only address important public health concerns but also has the potential to facilitate prevention and intervention efforts.

genetics↗

bioRxiv2018

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet we know little about the specific genetic loci influencing human cortical structure. To identify genetic variants, including structural variants, impacting cortical structure, we conducted a genome-wide association meta-analysis of brain MRI data from 51,662 individuals. We analysed the surface area and average thickness of the whole cortex and 34 regions with known functional specialisations. We identified 255 nominally significant loci (P [≤] 5 x 10-8); 199 survived multiple testing correction (P [≤] 8.3 x 10-10; 187 surface area; 12 thickness). We found significant enrichment for loci influencing total surface area within regulatory elements active during prenatal cortical development, supporting the radial unit hypothesis. Loci impacting regional surface area cluster near genes in Wnt signalling pathways, known to influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinsons disease, insomnia, depression and ADHD.\n\nOne Sentence SummaryCommon genetic variation is associated with inter-individual variation in the structure of the human cortex, both globally and within specific regions, and is shared with genetic risk factors for some neuropsychiatric disorders.

genetics↗

bioRxiv2018

Evolutionary forecasting of phenotypic and genetic outcomes of experimental evolution in Pseudomonas

Experimental evolution with microbes is often highly repeatable under identical conditions, suggesting the possibility to predict short-term evolution. However, it is not clear to what degree evolutionary forecasts can be extended to related species in non-identical environments, which would allow testing of general predictive models and fundamental biological assumptions. To develop an extended model system for evolutionary forecasting, we used previous data and models of the genotype-to-phenotype map from the wrinkly spreader system in Pseudomonas fluorescens SBW25 to make predictions of evolutionary outcomes on different biological levels for Pseudomonas protegens Pf-5. In addition to sequence divergence (78% amino acid and 81% nucleotide identity) for the genes targeted by mutations, these species also differ in the inability of Pf-5 to make cellulose, which is the main structural basis for the adaptive phenotype in SBW25. The experimental conditions were also changed compared to the SBW25 system to test the robustness of forecasts to environmental variation. Forty-three mutants with increased ability to colonize the air-liquid interface were isolated, and the majority had reduced motility and was partly dependent on the pel exopolysaccharide as a structural component. Most (38/43) mutations are expected to disrupt negative regulation of the same three diguanylate cyclases as in SBW25, with a smaller number of mutations in promoter regions, including that of an uncharacterized polysaccharide operon. A mathematical model developed for SBW25 predicted the order of the three main pathways and the genes targeted by mutations, but differences in fitness between mutants and mutational biases also appear to influence outcomes. Mutated regions in proteins could be predicted in most cases (16/22), but parallelism at the nucleotide level was low and mutational hot spots were not conserved. This study demonstrates the potential of short-term evolutionary forecasting in experimental populations and provides testable predictions for evolutionary outcomes in other Pseudomonas species. Author SummaryBiological evolution is often repeatable in the short-term suggesting the possibility of forecasting and controlling evolutionary outcomes. In addition to its fundamental importance for biology, evolutionary processes are at the core of several major societal problems, including infectious diseases, cancer and adaptation to climate change. Experimental evolution allows study of evolutionary processes in real time and seems like an ideal way to test the predictability of evolution and our ability to make forecasts. However, lack of model systems where forecasts can be extended to other species evolving under different conditions has prevented studies that first predict evolutionary outcomes followed by direct testing. We showed that a well-characterized bacterial experimental evolution system, based on biofilm formation by Pseudomonas fluorescens at the surface of static growth tubes, can be extended to the related species Pseudomonas protegens. We tested evolutionary forecasts experimentally and showed that mutations mainly appear in the predicted genes resulting in similar phenotypes. We also identified factors that we cannot yet predict, such as variation in mutation rates and differences in fitness. Finally, we make forecasts for other Pseudomonas species to be tested in future experiments.

evolutionary biology↗

bioRxiv2018

Disentangling the effects of genetic architecture, mutational bias and selection on evolutionary forecasting

Predicting evolutionary change poses numerous challenges. Here we take advantage of the model bacterium Pseudomonas fluorescens in which the genotype-to-phenotype map determining evolution of the adaptive \"wrinkly spreader\" (WS) type is known. We present mathematical descriptions of three necessary regulatory pathways and use these to predict both the rate at which each mutational route is used and the expected mutational targets. To test predictions, mutation rates and targets were determined for each pathway. Unanticipated mutational hotspots caused experimental observations to depart from predictions but additional data led to refined models. A mismatch was observed between the spectra of WS-causing mutations obtained with and without selection due to low fitness of previously undetected WS-causing mutations. Our findings contribute toward the development of mechanistic models for forecasting evolution, highlight current limitations, and draw attention to challenges in predicting locus-specific mutational biases and fitness effects.\n\nImpact statementA combination of genetics, experimental evolution and mathematical modelling defines information necessary to predict the outcome of short-term adaptive evolution.

evolutionary biology↗

bioRxiv2018

Genome-wide association analysis identifies 27 novel loci associated with uterine leiomyomata revealing common genetic origins with endometriosis

Uterine leiomyomata (UL), also known as uterine fibroids, are the most common neoplasms of the reproductive tract and the primary cause for hysterectomy, leading to considerable impact on womens lives as well as high economic burden1,2. Genetic epidemiologic studies indicate that heritable risk factors contribute to UL pathogenesis3. Previous genome-wide association studies (GWAS) identified five loci associated with UL at genome-wide significance (P < 5 x 10-8)4-6. We conducted GWAS meta-analysis in 20,406 cases and 223,918 female controls of white European ancestry, identifying 24 genome-wide significant independent loci; 17 replicated in an unrelated cohort of 15,068 additional cases and 43,587 female controls. Aggregation of discovery and replication studies (35,474 cases and 267,505 female controls) revealed six additional significant loci. Interestingly, four of the 17 loci identified and replicated in these analyses have also been associated with risk for endometriosis - another common gynecologic disorder. These findings increase our understanding of the biological mechanisms underlying UL development, and suggest overlapping genetic origins with endometriosis.

genomics↗