Biology subjects

Hayes, M. G.

Publications and source records attributed to Hayes, M. G..

12 recordsLinked to original sources

bioRxiv2022

The epigenetic modifier DOT1L regulates gene regulatory networks necessary for cardiac patterning and cardiomyocyte cell cycle withdrawal

Mechanisms by which specific histone modifications regulate distinct gene regulatory networks remain little understood. We investigated how H3K79me2, a modification catalyzed by DOT1L and previously considered a general transcriptional activation mark, regulates gene expression in mammalian cardiogenesis. Early embryonic cardiomyocyte ablation of Dot1l revealed that H3K79me2 does not act as a general transcriptional activator, but rather regulates highly specific gene regulatory networks at two critical cardiogenic junctures: left ventricle patterning and postnatal cardiomyocyte cell cycle withdrawal. Mechanistic analyses revealed that H3K79me2 in two distinct domains, gene bodies and regulatory elements, synergized to promote expression of genes activated by DOT1L. Surprisingly, these analyses also revealed that H3K79me2 in specific regulatory elements contributed to silencing genes usually not expressed in cardiomyocytes. As DOT1L mutants had increased numbers of postnatal mononuclear cardiomyocytes and prolonged cardiomyocyte cell cycle activity, controlled inhibition of DOT1L might be a strategy to promote cardiac regeneration post-injury.

developmental biology↗

bioRxiv2022

Integrating phylogenetic and functional data in microbiome studies

Microbiome functional data are frequently analyzed to identify associations between microbial gene families and sample groups of interest. This is most often performed with approaches focused on the metagenome-wide relative abundance of microbial functions. Although such approaches can provide valuable insights, it is challenging to distinguish between different possible explanations for variation in community-wide functional profiles by considering functions alone. To help address this problem, we have developed a novel, phylogeny-aware framework to expand taxonomic balance tree-based approaches to identify enriched functions more robustly. The key focus of our approach, termed POMS, is on identifying functions that are consistently enriched in sample groups across independent taxonomic lineages. Based on simulated data we demonstrate that POMS can more accurately identify gene families that confer a selective advantage compared with commonly used differential abundance approaches. We also show that POMS can identify enriched functions in real-world metagenomics datasets that are potential targets of strong selection on multiple members of the microbiome. While this framework may not be able to identify all potential functional enrichments, the enrichments it does identify are more interpretable and conservative compared with those identified by existing differential abundance approaches. More generally, POMS is a novel approach for exploring microbiome functional data, which could be used to complement standard analyses. POMS is freely available as an R package at: https://github.com/gavinmdouglas/POMS.

bioinformatics↗

bioRxiv2021

Microbiome differential abundance methods produce disturbingly different results across 38 datasets

Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods have been applied for this purpose, which are largely used interchangeably in the literature. Although it has been observed that these tools can produce different results, there have been very few large-scale comparisons to describe the scale and significance of these differences. In addition, it is challenging for microbiome researchers to know which differential abundance tools are appropriate for their study and how these tools compare to one another. Here, we have investigated these questions by analyzing 38 16S rRNA gene datasets with two sample groups for differential abundance testing. We tested for differences in amplicon sequence variants and operational taxonomic units (referred to as ASVs for simplicity) between these groups with 14 commonly used differential abundance tools. Our findings confirmed that these tools identified drastically different numbers and sets of significant ASVs, however, for many tools the number of features identified correlated with aspects of the tested study data, such as sample size, sequencing depth, and effect size of community differences. We also found that the ASVs identified by each method were dependent on whether the abundance tables were prevalence-filtered before testing. ALDEx2 and ANCOM produced the most consistent results across studies and agreed best with the intersect of results from different approaches. In contrast, several methods, such as LEfSe, limma voom, and edgeR, produced inconsistent results and in some cases were unable to control the false discovery rate. In addition to these observations, we were unable to find supporting evidence for a recent recommendation that limma voom, corncob, and DESeq2 are more reliable overall compared with other methods. Although ALDEx2 and ANCOM are two promising conservative methods, we argue that those researchers requiring more sensitive methods should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations.

bioinformatics↗

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

Testing the Effect of the Toba Volcanic Eruption on Population Sizes in Worldwide Mammal Species

The volcanic eruption of Toba in northern Sumatra at 71 kyBP ({+/-}5 kyBP) emitted sulfur gas and deposited thick layers of dust throughout the surrounding region. It is thought to have had a significant and dramatic cooling impact on the paleoclimate worldwide. Ambrose [1] conjectured this to be the cause of the contemporaneous (50-100 kyBP) population bottleneck observed in humans. We hypothesize that a volcanic winter of sufficient magnitude to cause a population bottleneck in humans would similarly affect other mammals. To test this hypothesis, we estimated pairwise mismatch distributions using mtDNA control region sequences of 28 mammal species archived on NCBI to assess whether each species underwent a population bottleneck. For any species fitting the sudden expansion model, we estimated the timing of the bottleneck and compared it to the date range of the Toba eruption. Only 3 of the 28 species show evidence of rapid population expansion overlapping in time with the Toba eruption. Therefore, the hypothesis that the volcanic winter triggered by the Toba eruption caused a significant bottleneck impacting mammal species worldwide is not supported by mitochondrial evidence. Our results question the hypothesis that the Toba eruption contributed to the bottleneck observed in humans at this time.

evolutionary biology↗

bioRxiv2019

Phenotypic clustering reveals distinct subtypes of polycystic ovary syndrome with novel genetic associations

BackgroundPolycystic ovary syndrome (PCOS) is a common, complex genetic disorder affecting up to 15% of reproductive age women worldwide, depending on the diagnostic criteria applied. These diagnostic criteria are based on expert opinion and have been the subject of considerable controversy. The phenotypic variation observed in PCOS is suggestive of an underlying genetic heterogeneity, but a recent meta-analysis of European ancestry PCOS cases found that the genetic architecture of PCOS defined by different diagnostic criteria was generally similar, suggesting that the criteria do not identify biologically distinct disease subtypes. We performed this study to test the hypothesis that there are biologically relevant subtypes of PCOS.\n\nMethods and FindingsUnsupervised hierarchical cluster analysis was performed on quantitative anthropometric, reproductive, and metabolic traits in a genotyped discovery cohort of 893 PCOS cases and an ungenotyped validation cohort of 263 PCOS cases. We identified two PCOS subtypes: a \"reproductive\" group (21-23%) characterized by higher luteinizing hormone (LH) and sex hormone binding globulin (SHBG) levels with relatively low body mass index (BMI) and insulin levels; and a \"metabolic\" group (37-39%), characterized by higher BMI, glucose, and insulin levels with lower SHBG and LH levels. We performed a GWAS on the genotyped cohort, limiting the cases to either the reproductive or metabolic subtypes. We identified alleles in four novel loci that were associated with the reproductive subtype at genome-wide significance (PRDM2/KAZN1, P=2.2x10-10; IQCA1, P=2.8x10-9; BMPR1B/UNC5C, P=9.7x10-9; CDH10, P=1.2x10-8) and one locus that was significantly associated with the metabolic subtype (KCNH7/FIGN, P=1.0x10-8). We have previously reported that rare variants in DENND1A, a gene regulating androgen biosynthesis, were associated with PCOS quantitative traits in a family-based whole genome sequencing analysis. We classified the reproductive and metabolic subtypes in this family-based PCOS cohort and found that the subtypes tended to cluster in families and that carriers of rare DENND1A variants were significantly more likely to have the reproductive subtype of PCOS. Limitations of our study were that only PCOS cases of European ancestry diagnosed by NIH criteria were included, the sample sizes for the subtype GWAS were small, and the GWAS findings were not replicated.\n\nConclusionsIn conclusion, we have found stable reproductive and metabolic subtypes of PCOS. Further, these subtypes were associated with novel susceptibility loci. Our results suggest that these subtypes are biologically relevant since they have distinct genetic architectures. This study demonstrates how precise phenotypic delineation can be more powerful than increases in sample size for genetic association studies.

genomics↗

bioRxiv2019

A polygenic and phenotypic risk prediction for Polycystic Ovary Syndrome evaluated by Phenome-wide association studies

PurposeAs many as 75% of patients with Polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice. Utilizing polygenic risk prediction, we aim to identify the phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment.\n\nMethods and FindingsLeveraging the electronic health records (EHRs) of 124,852 individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores (PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). We evaluated its predictive capability across different ancestries and perform a PRS-based phenome-wide association study (PheWAS) to assess the phenomic expression of the heightened risk of PCOS. The integrated polygenic prediction improved the average performance (pseudo-R2) for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null model across European, African, and multi-ancestry participants respectively. The subsequent PRS-powered PheWAS identified a high level of shared biology between PCOS and a range of metabolic and endocrine outcomes, especially with obesity and diabetes: morbid obesity, type 2 diabetes, hypercholesterolemia, disorders of lipid metabolism, hypertension and sleep apnea reaching phenome-wide significance.\n\nConclusionsOur study has expanded the methodological utility of PRS in patient stratification and risk prediction, especially in a multifactorial condition like PCOS, across different genetic origins. By utilizing the individual genome-phenome data available from the EHR, our approach also demonstrates that polygenic prediction by PRS can provide valuable opportunities to discover the pleiotropic phenomic network associated with PCOS pathogenesis.

bioinformatics↗

bioRxiv2019

Women with isolated fasting hyperglycemia in pregnancy are at a higher genetic risk for type 2 diabetes

BackgroundUsing genetic scores for fasting plasma glucose (FPG GS) and type 2 diabetes (T2D GS), we investigated whether the fasting, 1-hour and 2-hour glucose thresholds from the WHO 2013 criteria for gestational diabetes (GDM) have different implications for genetic susceptibility to raised fasting glucose and type 2 diabetes in women from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) and Atlantic Diabetes in Pregnancy (DIP) studies. MethodsCases were divided into three subgroups: (i) FPG [≥]5.1 mmol/L only, n=222; (ii) 1-hour glucose post 75 g oral glucose load [≥]10 mmol/L only, n=154 (iii) 2-hour glucose [≥]8.5 mmol/L only, n=73); and (iv) both FPG [≥]5.1 mmol/L and either of a 1-hour glucose [≥]10 mmol/L or 2-hour glucose [≥]8.5 mmol/L, n=172. We compared the FPG and T2D GS of these groups with controls (n=3,091) in HAPO and DIP separately. ResultsIn HAPO and DIP, the mean FPG GS in women with a FPG [≥]5.1 mmol/L, either on its own or with 1-hour glucose [≥]10 mmol/L or 2-hour glucose [≥]8.5 mmol/L, was higher than controls (all P <0.01). Mean T2D GS in women with a raised FPG alone or with either a raised 1-hour or 2-hour glucose was higher than controls (all P <0.05). GDM defined by 1-hour or 2-hour hyperglycaemia only was also associated with a higher T2D GS than controls (all P <0.05). ConclusionsThe different diagnostic categories that are part of the WHO 2013 criteria for GDM identify women with a genetic predisposition to type 2 diabetes as well as a risk for adverse pregnancy outcomes.

genetics↗

bioRxiv2019

Concordance of Race Documented in Electronic Health Records and Genetic Ancestry

ObjectiveGenetic screening is the gold standard for biogeographical ancestry (i.e. race), but this information is often unavailable to those developing research studies. We assessed agreement between census- and electronic health record (EHR)-derived demographic data with genetic ancestry to determine if these sources could support selection of diverse cohorts.\n\nMaterials and MethodsWe identified a population of 4,837 genotyped patients and determined concordance between genetic measures of ancestry against race derived from decennial nationwide census, electronic medical records, and self-report.\n\nResultsWe identified a 90% or greater concordance between the EHR-derived data and genetic ancestry. Census data had a high concordance (97%) with genetic and self-reported data for patients of European ancestry but low concordance for patients of African ancestry (64%).\n\nDiscussion and ConclusionsThe high concordance between EHR-derived race and genetic ancestry suggests that EHR-derived information could be an effective proxy for race when recruiting for diverse research cohorts.

bioinformatics↗

bioRxiv2018

Family-based quantitative trait meta-analysis implicates rare noncoding variants in DENND1A in pathogenesis of polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is among the most common endocrine disorders of premenopausal women, affecting 5-15% of this population depending on the diagnostic criteria applied. It is characterized by hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. PCOS is a leading risk factor for type 2 diabetes in young women. PCOS is highly heritable, but only a small proportion of this heritability can be accounted for by the common genetic susceptibility variants identified to date. To test the hypothesis that rare genetic variants contribute to PCOS pathogenesis, we performed whole-genome sequencing on DNA from 62 families with one or more daughters with PCOS. We tested for associations of rare variants with PCOS and its concomitant hormonal traits using a quantitative trait meta-analysis. We found rare variants in DENND1A (P=5.31x10-5, Padj=0.019) that were significantly associated with reproductive and metabolic traits in PCOS families. Common variants in DENND1A have previously been associated with PCOS diagnosis in genome-wide association studies. Subsequent studies indicated that DENND1A is an important regulator of human ovarian androgen biosynthesis. Our findings provide additional evidence that DENND1A plays a central role in PCOS and suggest that rare noncoding variants contribute to disease pathogenesis.

genomics↗

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↗

bioRxiv2017

Paleo-Eskimo genetic legacy across North America

Paleo-Eskimos were the first people to settle vast regions of the American Arctic around 5,000 years ago, and were subsequently joined and largely displaced around 1,000 years ago by ancestors of the present-day Inuit and Yupik. The genetic relationship between Paleo-Eskimos and Native American populations remains uncertain. We analyze ancient and present-day genome-wide data from the Americas and Siberia, including new data from Alaskan Inupiat and West Siberian populations, and the first genome-wide DNA from ancient Aleutian Islanders, ancient northern Athabaskans, and a 4,250-year-old individual of the Chukotkan Ust'-Belaya culture. Employing new methods based on rare allele and haplotype sharing as well as established methods based on allele frequency correlations, we show that Paleo-Eskimo ancestry is widespread among populations who speak Na-Dene and Eskimo-Aleut languages. Using phylogenetic modelling with allele frequency correlations and rare variation, we present a comprehensive model for the complex peopling of North America.

genetics↗