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Infection by high-risk human papillomavirus is known to exacerbate cervical cancer development. The host immune response is crucial in disease regression. Large-scale genetic association studies for cervical cancer have identified few susceptibility variants, mainly at the human leukocyte antigen locus on chromosome 6. We hypothesized that the host immune response modifies cervical cancer risk and performed three genome-wide association analyses for HPV16, HPV18 and HPV16/18 seropositivity in 7814, 7924, and 7924 samples from the UK Biobank, followed by validation genotyping in the German Cervigen case-control series of cervical cancer and dysplasia. In GWAS analyses, we identified two loci associated with HPV16 seropositivity (6p21.32 and 15q26.2), two loci associated with HPV18 seropositivity (5q31.2 and 14q24.3), and one locus for HPV16 and/or HPV18 seropositivity (at 6p21.32). MAGMA gene-based analysis identified HLA-DQA1 and HLA-DQB1 as genome-wide significant (GWS) genes. In validation genotyping, the genome-wide significant lead variant at 6p21.32, rs9272293 associated with overall cervical disease (OR = 0.86, p = 0.004, 95% CI = 0.78-0.95, n = 3710) and HPV16 positive invasive cancer (OR = 0.73, p = 0.005, 95% CI = 0.59-0.91, n = 1431). This variant was found to be a robust eQTL for HLA-DRB1, HLA-DQB1-AS1, C4B, HLA-DRB5, HLA-DRB6, HLA-DQB1, and HLA-DPB1 in a series of cervical epithelial tissue samples. We additionally genotyped twenty-four HPV seropositivity variants below the GWS threshold out of which eleven variants were found to be associated with cervical disease in our cohort, suggesting that further seropositivity variants may determine cervical disease outcome. Our study identifies novel genomic risk loci that associate with HPV type-specific cervical cancer and dysplasia risk and provides evidence for candidate genes at one of the risk loci.

Multienvironment trials (METs) are crucial for identifying varieties that perform well across a target population of environments. However, METs are typically too small to sufficiently represent all relevant environment-types, and face challenges from changing environment-types due to climate change. Statistical methods that enable prediction of variety performance for new environments beyond the METs are needed. We recently developed MegaLMM, a statistical model that can leverage hundreds of trials to significantly improve genetic value prediction accuracy within METs. Here, we extend MegaLMM to enable genomic prediction in new environments by learning regressions of latent factor loadings on Environmental Covariates (ECs) across trials. We evaluated the extended MegaLMM using the maize Genome-To-Fields dataset, consisting of 4,402 varieties cultivated in 195 trials with 87.1% of phenotypic values missing, and demonstrated its high accuracy in genomic prediction under various breeding scenarios. Furthermore, we showcased MegaLMMs superiority over univariate GBLUP in predicting trait performance of experimental genotypes in new environments. Finally, we explored the use of higher-dimensional quantitative ECs and discussed when and how detailed environmental data can be leveraged for genomic prediction from METs. We propose that MegaLMM can be applied to plant breeding of diverse crops and different fields of genetics where large-scale linear mixed models are utilized.

BACKGROUND: The 313-variant polygenic risk score (PRS313) provides a promising tool for clinical breast cancer risk prediction. However, evaluation of the PRS313 across different European populations which could influence risk estimation has not been performed. METHODS: We explored the distribution of PRS313 across European populations using genotype data from 94,072 females without breast cancer diagnosis, of European-ancestry from 21 countries participating in the Breast Cancer Association Consortium (BCAC) and 223,316 females without breast cancer diagnosis from the UK Biobank. The mean PRS was calculated by country in the BCAC dataset and by country of birth in the UK Biobank. We explored different approaches to reduce the observed heterogeneity in the mean PRS across the countries, and investigated the implications of the distribution variability in risk prediction. RESULTS: The mean PRS313 differed markedly across European countries, being highest in individuals from Greece and Italy and lowest in individuals from Ireland. Using the overall European PRS313 distribution to define risk categories, leads to overestimation and underestimation of risk in some individuals from these countries. Adjustment for principal components explained most of the observed heterogeneity in the mean PRS. The mean estimates derived when using an empirical Bayes approach were similar to the predicted means after principal component adjustment. CONCLUSIONS: Our results demonstrate that PRS distribution differs even within European ancestry populations leading to underestimation or overestimation of risk in specific European countries, which could potentially influence clinical management of some individuals if is not appropriately accounted for. Population-specific PRS distributions may be used in breast cancer risk estimation to ensure predicted risks are correctly calibrated across risk categories.

BACKGROUND: Patients with inflammatory bowel disease (IBD) exhibit considerable interindividual variability in medication response, highlighting the need for precision medicine approaches to optimize and tailor treatment. Pharmacogenetics (PGx) offers the ability to individualize dosing by examining genetic factors underlying the metabolism of medications such as thiopurines. Pharmacogenetic testing can identify individuals who may be at risk for thiopurine dose-dependent adverse reactions including myelosuppression. We aimed to evaluate PGx variation in genes supported by clinical guidelines that inform dosing of thiopurines and characterize differences in the distribution of actionable PGx variation among diverse ancestral groups. METHODS: Pharmacogenetic variation in TPMT and NUDT15 was captured by genome-wide genotyping of 1083 pediatric IBD patients from a diverse Canadian cohort. Genetic ancestry was inferred using principal component analysis. The proportion of PGx variation and associated metabolizer status phenotypes was compared across 5 genetic ancestral groups within the cohort (Admixed American, African, East Asian, European, and South Asian) and to prior global estimates from corresponding populations. RESULTS: Collectively, 11% of the cohort was categorized as intermediate or poor metabolizers of thiopurines, which would warrant a significant dose reduction or selection of alternate therapy. Clinically actionable variation in TPMT was more prevalent in participants of European and Admixed American/Latino ancestry (8.7% and 7.5%, respectively), whereas variation in NUDT15 was more prevalent in participants of East Asian and Admixed American/Latino ancestry (16% and 15% respectively). CONCLUSIONS: These findings demonstrate the considerable interpopulation variability in PGx variation underlying thiopurine metabolism, which should be factored into testing diverse patient populations.

Precision medicine has the potential to provide more accurate diagnosis, appropriate treatment and timely prevention strategies by considering patients biological makeup. However, this cannot be realized without integrating clinical and omics data in a data-sharing framework that achieves large sample sizes. Systems that integrate clinical and genetic data from multiple sources are scarce due to their distinct data types, interoperability, security and data ownership issues. Here we present a secure framework that allows immutable storage, querying and analysis of clinical and genetic data using blockchain technology. Our platform allows clinical and genetic data to be harmonized by combining them under a unified framework. It supports combined genotype-phenotype queries and analysis, gives institutions control of their data and provides immutable user access logs, improving transparency into how and when health information is used. We demonstrate the value of our framework for precision medicine by creating genotype-phenotype cohorts and examining relationships within them. We show that combining data across institutions using our secure platform increases statistical power for rare disease analysis. By offering an integrated, secure and decentralized framework, we aim to enhance reproducibility and encourage broader participation from communities and patients in data sharing.

As of 2023, it is estimated that 6.7 million individuals in the United States live with Alzheimer's disease (AD). Prior research indicates that AD disproportionality affects females; females have a greater incidence rate, perform worse on a variety of neuropsychological tasks, and have greater total brain atrophy. Recent research shows that hippocampal functional connectivity differs by sex and may be related to the observed sex differences in AD, and apolipoprotein E (ApoE) ε4 carriers have reduced hippocampal functional connectivity. The purpose of this study was to determine if the ApoE genotype plays a role in the observed sex differences in hippocampal functional connectivity in Alzheimer's disease. The resting state fMRI and T2 MRI of individuals with AD (n = 30, female = 15) and cognitively normal individuals (n = 30, female = 15) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed using the functional connectivity toolbox (CONN). Our results demonstrated intrahippocampal functional connectivity differed between those without an ε4 allele and those with at least one ε4 allele in each group. Additionally, intrahippocampal functional connectivity differed only by sex when Alzheimer's participants had at least one ε4 allele. These results improve our current understanding of the role of the interacting relationship between sex, ApoE genotype, and hippocampal function in AD. Understanding these biomarkers may aid in the development of sex-specific interventions for improved AD treatment.

ABSTRACTHistoplasmosis is an endemic mycosis in North America frequently reported along the Ohio and Mississippi River Valleys, although autochthonous cases occur in non-endemic areas. In the United States, the disease is provoked by two genetically distinct clades of Histoplasma capsulatum sensu lato, Histoplasma mississippiense (Nam1) and H. ohiense (Nam2). To bridge the molecular epidemiological gap, we genotyped 93 Histoplasma isolates (62 novel genomes) including clinical, environmental, and veterinarian samples from a broader geographical range by whole-genome sequencing, followed by evolutionary and species niche modelling analyses. We show that histoplasmosis is caused by two major lineages, H. ohiense and H. mississippiense; with sporadic cases caused by H. suramericanum in California and Texas. While H. ohiense is prevalent in eastern states, H. mississipiense was found to be prevalent in the central and western portions of the United States, but also geographically overlapping in some areas suggesting that these species might co-occur. Species Niche Modelling revealed that H. ohiense thrives in places with warmer and drier conditions, while H. mississippiense is endemic to areas with cooler temperatures and more precipitation. In addition, we predicted multiple areas of secondary contact zones where the two species co-occur, potentially facilitating gene exchange and hybridization. This study provides the most comprehensive understanding of the genomic epidemiology of histoplasmosis in the USA and lays a blueprint for the study of invasive fungal diseases.

Human leukocyte antigen (HLA) class I/killer cell immunoglobulin-like receptor (KIR) genotypes influence human immunodeficiency virus type 1 (HIV-1) disease progression and viral load, but their role in primary infection is uncertain. Inconsistent results from previous studies suggest that the inoculum size and transmission route-parenteral versus sexual-may influence this association. We conducted a genome-wide association study in a population of people with HIV-1 and HIV-1-exposed seronegative individuals exposed to the virus through the sexual route. Our data do not support any role of the HLA/KIR system in susceptibility to sexually transmitted HIV-1 infection. The genetics basis of HIV-1 viral load and disease progression are distinct from the genetics of HIV resistance, a paradox worth exploring.

Genome-wide association studies typically evaluate the autosomes and sometimes the X Chromosome, but seldom consider the Y or mitochondrial (MT) Chromosomes. We genotyped the Y and MT Chromosomes in heterogeneous stock (HS) rats (Rattus norvegicus), an outbred population created from 8 inbred strains. We identified 8 distinct Y and 4 distinct MT Chromosomes among the 8 founders. However, only 2 types of each nonrecombinant chromosome were observed in our modern HS rat population (generations 81-97). Despite the relatively large sample size, there were virtually no significant associations for behavioral, physiological, metabolome, or microbiome traits after correcting for multiple comparisons. However, both Y and MT Chromosomes were strongly associated with the expression of a few genes located on those chromosomes, which provided a positive control. Our results suggest that within modern HS rats there are no Y and MT Chromosomes differences that strongly influence behavioral or physiological traits. These results do not address other ancestral Y and MT Chromosomes that do not appear in modern HS rats, nor do they address effects that may exist in other rat populations, or in other species.

Large ancient DNA (aDNA) studies offer the chance to examine genomic changes over time, providing direct insights into human evolution. While recent studies have used time-stratified aDNA for selection scans, most focus on single-locus methods. We conducted a multi-locus genotype scan on 708 samples spanning 7000 years of European history. We show that the G12 statistic, originally designed for unphased diploid data, can effectively detect selection in aDNA processed to create 'pseudo-haplotypes'. In simulations and at known positive control loci (e.g., lactase persistence), G12 outperforms the allele frequency-based selection statistic, SweepFinder2, previously used on aDNA. Applying our approach, we identified 14 candidate regions of selection across four time periods, with half the signals detectable only in the earliest period. Our findings suggest that selective events in European prehistory, including from the onset of animal domestication, have been obscured by neutral processes like genetic drift and demographic shifts such as admixture.

The Four Core Genotypes (FCG) mouse model has become a valuable model to study the mechanistic basis for biological sex differences. This model allows discrimination between influences of gonadal sex (ovaries or testes) from those associated with genetic sex (presence of XX or XY chromosome complement). FCG mice have illuminated distinct effects of gonadal and chromosomal sex on traits ranging from brain structure and behavior to vulnerability to obesity, atherosclerosis, multiple sclerosis, Alzheimers and other diseases. A recent study determined that the YSry- chromosome used in a specific line of C57BL/6J FCG mice harbors nine genes that have been duplicated from the X chromosome. This report raised concern that scores of publications that previously used the FCG model may therefore be flawed, but did not provide details regarding how studies can be evaluated for potential impact (or lack of impact) of the translocation. Here we (1) provide a practical description of the genetic translocation for researchers using the FCG model, (2) document that a majority of the studies cited in the recent report are unlikely to be affected by the translocation, (3) provide a scheme for interpreting data from studies with FCG mice harboring the YSry- translocation, and (4) delineate expression levels of the nine translocated genes across tissue/cell types as a filter for evaluating their potential involvement in specific phenotypes.

Dietary fats influence gene expression and several metabolic pathways. Therefore, it is crucial to study the role of personal genotypes in the interaction between fat consumption and cardiometabolic markers. This research aimed to determine the interaction of the rs708272 polymorphism of CETP and the fatty acid intake with changes in the HOMA-IR in adults living with overweight or obesity. The current study was a secondary analysis of an 8-week controlled clinical trial. The final sample for this analysis comprised 78 Mexican adults with the Cholesteryl Ester Transfer Protein (CETP) rs708272 polymorphism who followed a dietary intervention. Using an interaction analysis, we evaluated the fatty acid intake and the genotypes of rs708272, with changes in blood glucose, insulin, and the HOMA-IR from baseline to endpoint. Our findings suggest a significant interaction between the trans fatty acid intake and the GG genotype with changes in glucose (p = 0.024), insulin (p = 0.004), and the HOMA-IR (p = 0.002). The higher the consumption of trans fatty acids, the less these markers of glucose metabolism were reduced. carriers of the GG genotype may benefit from limiting dietary trans fatty acid intake, as there was no reduction in plasma glucose and insulin despite a hypocaloric dietary intervention in adults with overweight and obesity.

Increasing heatwaves are threatening forest ecosystems globally. Leaf thermal regulation and tolerance are important for plant survival during heatwaves, though the interaction between these processes and water availability is unclear. Genotypes of the widely distributed foundation tree species Populus fremontii were studied in a controlled common garden during a record summer heatwave-where air temperature exceeded 48 °C. When water was not limiting, all genotypes cooled leaves 2 to 5 °C below air temperatures. Homeothermic cooling was disrupted for weeks following a 72-h reduction in soil water, resulting in leaf temperatures rising 3 °C above air temperature and 1.3 °C above leaf thresholds for physiological damage, despite the water stress having little effect on leaf water potentials. Tradeoffs between leaf thermal safety and hydraulic safety emerged but, regardless of water use strategy, all genotypes experienced significant leaf mortality following water stress. Genotypes from warmer climates showed greater leaf cooling and less leaf mortality after water stress in comparison with genotypes from cooler climates. These results illustrate how brief soil water limitation disrupts leaf thermal regulation and potentially compromises plant survival during extreme heatwaves, thus providing insight into future scenarios in which ecosystems will be challenged with extreme heat and unreliable soil water access.

SNP heritability, the proportion of phenotypic variation explained by genotyped SNPs, is an important parameter in understanding the genetic architecture underlying various diseases and traits. Methods that aim to estimate SNP heritability from individual genotype and phenotype data are limited by their ability to scale to Biobank-scale data sets and by the restrictions in access to individual-level data. These limitations have motivated the development of methods that only require summary statistics. Although the availability of publicly accessible summary statistics makes them widely applicable, these methods lack the accuracy of methods that utilize individual genotypes. Here we present a SUMmary-statistics-based Randomized Haseman-Elston regression (SUM-RHE), a method that can estimate the SNP heritability of complex phenotypes with accuracies comparable to approaches that require individual genotypes, while exclusively relying on summary statistics. SUM-RHE employs Genome-Wide Association Study (GWAS) summary statistics and statistics obtained on a reference population, which can be efficiently estimated and readily shared for public use. Our results demonstrate that SUM-RHE obtains estimates of SNP heritability that are substantially more accurate compared with other summary statistic methods and on par with methods that rely on individual-level data.

Non-invasive prenatal testing (NIPT) employs ultra-low-pass sequencing of maternal plasma cell-free DNA to detect fetal trisomy. Its global adoption has established NIPT as a large human genetic resource for exploring genetic variations and their associations with phenotypes. Here, we present methods for analyzing large-scale, low-depth NIPT data, including customized algorithms and software for genetic variant detection, genotype imputation, family relatedness, population structure inference, and genome-wide association analysis of maternal genomes. Our results demonstrate accurate allele frequency estimation and high genotype imputation accuracy (R2>0.84) for NIPT sequencing depths from 0.1× to 0.3×. We also achieve effective classification of duplicates and first-degree relatives, along with robust principal-component analysis. Additionally, we obtain an R2>0.81 for estimating genetic effect sizes across genotyping and sequencing platforms with adequate sample sizes. These methods offer a robust theoretical and practical foundation for utilizing NIPT data in medical genetic research.

Predicting how new mutations alter phenotypes is difficult because mutational effects vary across genotypes and environments. Recently discovered global epistasis, in which the fitness effects of mutations scale with the fitness of the background genotype, can improve predictions, but how the environment modulates this scaling is unknown. We measured the fitness effects of ~100 insertion mutations in 42 strains of Saccharomyces cerevisiae in six laboratory environments and found that the global epistasis scaling is nearly invariant across environments. Instead, the environment tunes one global parameter, the background fitness at which most mutations switch sign. As a consequence, the distribution of mutational effects is predictable across genotypes and environments. Our results suggest that the effective dimensionality of genotype-to-phenotype maps across environments is surprisingly low.

MOTIVATION: Malaria parasite genetic data can provide insight into parasite phenotypes, evolution, and transmission. However, estimating key parameters such as allele frequencies, multiplicity of infection (MOI), and within-host relatedness from genetic data is challenging, particularly in the presence of multiple related coinfecting strains. Existing methods often rely on single nucleotide polymorphism (SNP) data and do not account for within-host relatedness. RESULTS: We present Multiplicity Of Infection and allele frequency REcovery (MOIRE), a Bayesian approach to estimate allele frequencies, MOI, and within-host relatedness from genetic data subject to experimental error. MOIRE accommodates both polyallelic and SNP data, making it applicable to diverse genotyping panels. We also introduce a novel metric, the effective MOI (eMOI), which integrates MOI and within-host relatedness, providing a robust and interpretable measure of genetic diversity. Extensive simulations and real-world data from a malaria study in Namibia demonstrate the superior performance of MOIRE over naive estimation methods, accurately estimating MOI up to seven with moderate-sized panels of diverse loci (e.g. microhaplotypes). MOIRE also revealed substantial heterogeneity in population mean MOI and mean relatedness across health districts in Namibia, suggesting detectable differences in transmission dynamics. Notably, eMOI emerges as a portable metric of within-host diversity, facilitating meaningful comparisons across settings when allele frequencies or genotyping panels differ. Compared to existing software, MOIRE enables more comprehensive insights into within-host diversity and population structure. AVAILABILITY AND IMPLEMENTATION: MOIRE is available as an R package at https://eppicenter.github.io/moire/.