ArCH: improving the performance of clonal hematopoiesis variant calling and interpretation.

MOTIVATION: The acquisition of somatic mutations in hematopoietic stem and progenitor stem cells with resultant clonal expansion, termed clonal hematopoiesis (CH), is associated with increased risk of hematologic malignancies and other adverse outcomes. CH is generally present at low allelic fractions, but clonal expansion and acquisition of additional mutations leads to hematologic cancers in a small proportion of individuals. With high depth and high sensitivity sequencing, CH can be detected in most adults and its clonal trajectory mapped over time. However, accurate CH variant calling is challenging due to the difficulty in distinguishing low frequency CH mutations from sequencing artifacts. The lack of well-validated bioinformatic pipelines for CH calling may contribute to lack of reproducibility in studies of CH. RESULTS: Here, we developed ArCH, an Artifact filtering Clonal Hematopoiesis variant calling pipeline for detecting single nucleotide variants and short insertions/deletions by combining the output of four variant calling tools and filtering based on variant characteristics and sequencing error rate estimation. ArCH is an end-to-end cloud-based pipeline optimized to accept a variety of inputs with customizable parameters adaptable to multiple sequencing technologies, research questions, and datasets. Using deep targeted sequencing data generated from six acute myeloid leukemia patient tumor: normal dilutions, 31 blood samples with orthogonal validation, and 26 blood samples with technical replicates, we show that ArCH improves the sensitivity and positive predictive value of CH variant detection at low allele frequencies compared to standard application of commonly used variant calling approaches. AVAILABILITY AND IMPLEMENTATION: The code for this workflow is available at: https://github.com/kbolton-lab/ArCH.

Differences in the number of de novo mutations between individuals are due to small family-specific effects and stochasticity.

The number of de novo mutations (DNMs) in the human germline is correlated with parental age at conception, but this explains only part of the observed variation. We investigated whether there is a family-specific contribution to the number of DNMs in offspring. The analysis of DNMs in 111 dizygotic twin pairs did not identify a substantial family-specific contribution. This result was corroborated by comparing DNMs of 1669 siblings to those of age-matched unrelated offspring following correction for parental age. In addition, by modeling DNM data from 1714 multi-offspring families, we estimated that the family-specific contribution explains ∼5.2% of the variation in DNM number. Furthermore, we found no substantial difference between the observed number of DNMs and those predicted by a stochastic Poisson process. We conclude that there is a small family-specific contribution to DNM number and that stochasticity explains a large proportion of variation in DNM counts.

Moving Toward Findable, Accessible, Interoperable, Reusable Practices in Epidemiologic Research.

Data sharing is essential for reproducibility of epidemiologic research, replication of findings, pooled analyses in consortia efforts, and maximizing study value to address multiple research questions. However, barriers related to confidentiality, costs, and incentives often limit the extent and speed of data sharing. Epidemiological practices that follow Findable, Accessible, Interoperable, Reusable (FAIR) principles can address these barriers by making data resources findable with the necessary metadata, accessible to authorized users, and interoperable with other data, to optimize the reuse of resources with appropriate credit to its creators. We provide an overview of these principles and describe approaches for implementation in epidemiology. Increasing degrees of FAIRness can be achieved by moving data and code from on-site locations to remote, accessible ("Cloud") data servers, using machine-readable and nonproprietary files, and developing open-source code. Adoption of these practices will improve daily work and collaborative analyses and facilitate compliance with data sharing policies from funders and scientific journals. Achieving a high degree of FAIRness will require funding, training, organizational support, recognition, and incentives for sharing research resources, both data and code. However, these costs are outweighed by the benefits of making research more reproducible, impactful, and equitable by facilitating the reuse of precious research resources by the scientific community.

Genomic and molecular characterization of preterm birth.

Preterm birth (PTB) complications are the leading cause of long-term morbidity and mortality in children. By using whole blood samples, we integrated whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and DNA methylation data for 270 PTB and 521 control families. We analyzed this combined dataset to identify genomic variants associated with PTB and secondary analyses to identify variants associated with very early PTB (VEPTB) as well as other subcategories of disease that may contribute to PTB. We identified differentially expressed genes (DEGs) and methylated genomic loci and performed expression and methylation quantitative trait loci analyses to link genomic variants to these expression and methylation changes. We performed enrichment tests to identify overlaps between new and known PTB candidate gene systems. We identified 160 significant genomic variants associated with PTB-related phenotypes. The most significant variants, DEGs, and differentially methylated loci were associated with VEPTB. Integration of all data types identified a set of 72 candidate biomarker genes for VEPTB, encompassing genes and those previously associated with PTB. Notably, PTB-associated genes RAB31 and RBPJ were identified by all three data types (WGS, RNA-seq, and methylation). Pathways associated with VEPTB include EGFR and prolactin signaling pathways, inflammation- and immunity-related pathways, chemokine signaling, IFN-γ signaling, and Notch1 signaling. Progress in identifying molecular components of a complex disease is aided by integrated analyses of multiple molecular data types and clinical data. With these data, and by stratifying PTB by subphenotype, we have identified associations between VEPTB and the underlying biology.

Mother-child histocompatibility and risk of rheumatoid arthritis and systemic lupus erythematosus among mothers.

The study objective was to test the hypothesis that having histocompatible children increases the risk of rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE), possibly by contributing to the persistence of fetal cells acquired during pregnancy. We conducted a case control study using data from the UC San Francisco Mother Child Immunogenetic Study and studies at the Inova Translational Medicine Institute. We imputed human leukocyte antigen (HLA) alleles and minor histocompatibility antigens (mHags). We created a variable of exposure to histocompatible children. We estimated an average sequence similarity matching (SSM) score for each mother based on discordant mother-child alleles as a measure of histocompatibility. We used logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals. A total of 138 RA, 117 SLE, and 913 control mothers were analyzed. Increased risk of RA was associated with having any child compatible at HLA-B (OR 1.9; 1.2-3.1), DPB1 (OR 1.8; 1.2-2.6) or DQB1 (OR 1.8; 1.2-2.7). Compatibility at mHag ZAPHIR was associated with reduced risk of SLE among mothers carrying the HLA-restriction allele B*07:02 (n = 262; OR 0.4; 0.2-0.8). Our findings support the hypothesis that mother-child histocompatibility is associated with risk of RA and SLE.

Gut microbial composition difference between pediatric ALL survivors and siblings.

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer with high cure rates leading to rising numbers of long-term survivors. Adult survivors of childhood ALL are at increased risk of obesity, cardiovascular disease, and other chronic illnesses. We hypothesize that ALL therapy is associated with long-term gut microbiome alterations that contribute to predisposition to chronic medical conditions. We conducted a pilot study to test whether differences can be detected between stool microbiota of pediatric ALL survivors and their siblings. Stool samples were collected from 38 individuals under age 19 who were at least 1 year after completion of therapy for ALL. Stool samples collected from 16 healthy siblings served as controls. 16S ribosomal RNA gene sequencing was performed on the stool samples. Comparing microbiota of survivors to sibling controls, no statistically significant differences were found in alpha or beta diversity. However, among the top 10 operational taxonomic units (OTUs) from component 1 in sparse partial least squares discriminant analysis (sPLS-DA) with different relative abundance in survivors versus siblings, OTUs mapping to the genus Faecalibacterium were depleted in survivors. Differences in gut microbial composition were found between pediatric survivors of childhood ALL and their siblings. Specifically, the protective Faecalibacterium is depleted in survivors, which is reminiscent of gut microbiota alteration found in adult survivors of childhood ALL and reported in obesity, suggesting that microbiota alterations in pediatric ALL survivors start in childhood and may play a role in predisposition to chronic illness in later years of survivorship.

Clinical and social factors associated with excess weight in Hispanic and non-Hispanic White children.

BACKGROUND: Hispanic children are disproportionately affected by obesity, with this disparity starting at a young age, and there is a paucity of data comparing factors associated with excess weight in the first year of life in Hispanic vs. non-Hispanic populations. METHODS: Excess weight was defined as weight-for-length ≥95th percentile. The associations of potential risk factors were compared by ethnicity stratification. RESULTS: Of the 1009 children, 302 (30.0%) were Hispanic and 707 (70.0%) were non-Hispanic White. The rate of excess weight was 30.1% and 13.6% among Hispanic and non-Hispanic White children, respectively. Factors associated with excess weight for non-Hispanic White children were higher than recommended weight gain during pregnancy (odds ratio (OR) 1.8 (1.2-3.1)), higher paternal body mass index (BMI) (OR 1.1 (1.02-1.15)), higher birth weight (OR 1.001 (1.001-1.002)), and lower breast milk feedings at 6 months (OR 0.98 (0.96-0.98)). Factors associated with excess weight for Hispanic children were lower maternal education (OR 2.37 (1.1-4.5)) and lower breast milk feedings at 6 months (OR 0.98 (0.96-0.99)). CONCLUSION: There are differential risk factors associated with excess weight at 12 months between Hispanic and non-Hispanic White children. Identification of differential factors in different ethnicities may allow for more targeted anticipatory guidance reduce obesity in at-risk populations.

Common genetic causes of holoprosencephaly are limited to a small set of evolutionarily conserved driver genes of midline development coordinated by TGF-ß, hedgehog, and FGF signaling.

Here, we applied targeted capture to examine 153 genes representative of all the major vertebrate developmental pathways among 333 probands to rank their relative significance as causes for holoprosencephaly (HPE). We now show that comparisons of variant transmission versus nontransmission among 136 HPE Trios indicates some reported genes now lack confirmation, while novel genes are implicated. Furthermore, we demonstrate that variation of modest intrinsic effect can synergize with these driver mutations as gene modifiers.

Clinical, Social, and Genetic Factors Associated with Obesity at 12 Months of Age.

OBJECTIVE: To examine genomic, social, and clinical risk factors of ≥85 weight for length percentile (WFLP) at 12 months. STUDY DESIGN: Children in this study had whole-genome sequencing, and clinical and social data were collected. WFLPs at 12 months of age were grouped as follows: (1) <85th, (2) ≥85th to <95th, (3) ≥95th to <99th, and (4) ≥99th. Whole-genome sequencing data were used to analyze rare and common variants, and association of clinical and social factors was examined. RESULTS: A total of 690 children were included; WFLPs were 422 (61.2%) <85th, 112 (16.2%) ≥85th-<95th, 89 (12.9%) ≥95th-<99th, and 67 (9.7%) ≥99th. Family-related risk factors associated with greater WFLP were greater paternal body mass index, WFLP ≥99th OR 1.10 (1.03-1.16), and greater than recommended weight gain in pregnancy, WFLP ≥85th-<95th OR 1.90 (1.09-3.26). More breast milk at 6 months was protective factor: WFLP ≥85th-<95th, OR 0.98 (0.97-0.99), WFLP ≥95th-<99th OR 0.98 (0.97-0.99), and WFLP ≥99th OR 0.98 (0.96-0.99). Although none of the variants reached genome-wide significance, there was a trend toward increased prevalence of genetic variants within or near genes previously associated with obesity in children with WFLP ≥99th. CONCLUSION: This cross-sectional study identified several modifiable factors, including increased weight gain in pregnancy and decreased breast milk at 6 months, associated with greater WFLP at 12 months. Strong genetic factors were not identified.

Rate of de novo mutations and the importance of father's age to disease risk.

Mutations generate sequence diversity and provide a substrate for selection. The rate of de novo mutations is therefore of major importance to evolution. Here we conduct a study of genome-wide mutation rates by sequencing the entire genomes of 78 Icelandic parent-offspring trios at high coverage. We show that in our samples, with an average father's age of 29.7, the average de novo mutation rate is 1.20 × 10(-8) per nucleotide per generation. Most notably, the diversity in mutation rate of single nucleotide polymorphisms is dominated by the age of the father at conception of the child. The effect is an increase of about two mutations per year. An exponential model estimates paternal mutations doubling every 16.5 years. After accounting for random Poisson variation, father's age is estimated to explain nearly all of the remaining variation in the de novo mutation counts. These observations shed light on the importance of the father's age on the risk of diseases such as schizophrenia and autism.

Utility of whole-genome sequencing for detection of newborn screening disorders in a population cohort of 1,696 neonates.

PURPOSE: To assess the potential of whole-genome sequencing (WGS) to replicate and augment results from conventional blood-based newborn screening (NBS). METHODS: Research-generated WGS data from an ancestrally diverse cohort of 1,696 infants and both parents of each infant were analyzed for variants in 163 genes involved in disorders included or under discussion for inclusion in US NBS programs. WGS results were compared with results from state NBS and related follow-up testing. RESULTS: NBS genes are generally well covered by WGS. There is a median of one (range: 0-6) database-annotated pathogenic variant in the NBS genes per infant. Results of WGS and NBS in detecting 28 state-screened disorders and four hemoglobin traits were concordant for 88.6% of true positives (n = 35) and 98.9% of true negatives (n = 45,757). Of the five infants affected with a state-screened disorder, WGS identified two whereas NBS detected four. WGS yielded fewer false positives than NBS (0.037 vs. 0.17%) but more results of uncertain significance (0.90 vs. 0.013%). CONCLUSION: WGS may help rule in and rule out NBS disorders, pinpoint molecular diagnoses, and detect conditions not amenable to current NBS assays.

Tracking medical genetic literature through machine learning.

There has been remarkable progress in identifying the causes of genetic conditions as well as understanding how changes in specific genes cause disease. Though difficult (and often superficial) to parse, an interesting tension involves emphasis on basic research aimed to dissect normal and abnormal biology versus more clearly clinical and therapeutic investigations. To examine one facet of this question and to better understand progress in Mendelian-related research, we developed an algorithm that classifies medical literature into three categories (Basic, Clinical, and Management) and conducted a retrospective analysis. We built a supervised machine learning classification model using the Azure Machine Learning (ML) Platform and analyzed the literature (1970-2014) from NCBI's Entrez Gene2Pubmed Database (http://www.ncbi.nlm.nih.gov/gene) using genes from the NHGRI's Clinical Genomics Database (http://research.nhgri.nih.gov/CGD/). We applied our model to 376,738 articles: 288,639 (76.6%) were classified as Basic, 54,178 (14.4%) as Clinical, and 24,569 (6.5%) as Management. The average classification accuracy was 92.2%. The rate of Clinical publication was significantly higher than Basic or Management. The rate of publication of article types differed significantly when divided into key eras: Human Genome Project (HGP) planning phase (1984-1990); HGP launch (1990) to publication (2001); following HGP completion to the "Next Generation" advent (2009); the era following 2009. In conclusion, in addition to the findings regarding the pace and focus of genetic progress, our algorithm produced a database that can be used in a variety of contexts including automating the identification of management-related literature.

A Child's HLA-DRB1 genotype increases maternal risk of systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) disproportionately affects women of reproductive age. During pregnancy, women are exposed to various sources of fetal material possibly constituting a significant immunologic exposure relevant to the development of SLE. The objective of this study was to investigate whether having any children who carry DRB1 alleles associated with SLE increase the risk of maternal SLE. This case-control study is based on the University of California, San Francisco Mother-Child Immunogenetic Study and from studies at the Inova Translational Medicine Institute. Analyses were conducted using data for 1304 mothers (219 cases/1085 controls) and their respective 1664 children. We selected alleles based on their known association with risk of SLE (DRB1*03:01, *15:01, or *08:01) or Epstein-Barr virus (EBV) glycoproteins (*04:01) due to the established EBV association with SLE risk. We used logistic regression models to estimate odds ratios (OR) and 95% confidence intervals (CI) for each allele of interest, taking into account maternal genotype and number of live births. We found an increase in risk of maternal SLE associated with exposure to children who inherited DRB1*04:01 from their father (OR 1.9; 95% CI, 1.1-3.2), among *04:01 allele-negative mothers. Increased risk was only present among mothers who were positive for one or more SLE risk-associated alleles (*03:01, *15:01 and/or *08:01). We did not find increased risk of maternal SLE associated with any other tested allele. These findings support the hypothesis that a child's alleles inherited from the father influence a mother's subsequent risk of SLE.

Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs.

MOTIVATION: Whole genome and exome sequencing of matched tumor-normal sample pairs is becoming routine in cancer research. The consequent increased demand for somatic variant analysis of paired samples requires methods specialized to model this problem so as to sensitively call variants at any practical level of tumor impurity. RESULTS: We describe Strelka, a method for somatic SNV and small indel detection from sequencing data of matched tumor-normal samples. The method uses a novel Bayesian approach which represents continuous allele frequencies for both tumor and normal samples, while leveraging the expected genotype structure of the normal. This is achieved by representing the normal sample as a mixture of germline variation with noise, and representing the tumor sample as a mixture of the normal sample with somatic variation. A natural consequence of the model structure is that sensitivity can be maintained at high tumor impurity without requiring purity estimates. We demonstrate that the method has superior accuracy and sensitivity on impure samples compared with approaches based on either diploid genotype likelihoods or general allele-frequency tests. AVAILABILITY: The Strelka workflow source code is available at ftp://strelka@ftp.illumina.com/. CONTACT: csaunders@illumina.com

Protein interaction networks define the genetic architecture of preterm birth.

The likely genetic architecture of complex diseases is that subgroups of patients share variants in genes in specific networks sufficient to express a shared phenotype. We combined high throughput sequencing with advanced bioinformatic approaches to identify such subgroups of patients with variants in shared networks. We performed targeted sequencing of patients with 2 or 3 generations of preterm birth on genes, gene sets and haplotype blocks that were highly associated with preterm birth. We analyzed the data using a multi-sample, protein-protein interaction (PPI) tool to identify significant clusters of patients associated with preterm birth. We identified shared protein interaction networks among preterm cases in two statistically significant clusters, p < 0.001. We also found two small control-dominated clusters. We replicated these data on an independent, large birth cohort. Separation testing showed significant similarity scores between the clusters from the two independent cohorts of patients. Canonical pathway analysis of the unique genes defining these clusters demonstrated enrichment in inflammatory signaling pathways, the glucocorticoid receptor, the insulin receptor, EGF and B-cell signaling, These results support a genetic architecture defined by subgroups of patients that share variants in genes in specific networks and pathways which are sufficient to give rise to the disease phenotype.

Population sequencing data reveal a compendium of mutational processes in the human germ line.

Biological mechanisms underlying human germline mutations remain largely unknown. We statistically decompose variation in the rate and spectra of mutations along the genome using volume-regularized nonnegative matrix factorization. The analysis of a sequencing dataset (TOPMed) reveals nine processes that explain the variation in mutation properties between loci. We provide a biological interpretation for seven of these processes. We associate one process with bulky DNA lesions that are resolved asymmetrically with respect to transcription and replication. Two processes track direction of replication fork and replication timing, respectively. We identify a mutagenic effect of active demethylation primarily acting in regulatory regions and a mutagenic effect of long interspersed nuclear elements. We localize a mutagenic process specific to oocytes from population sequencing data. This process appears transcriptionally asymmetric.

Differences in maternal gene expression in Cesarean section delivery compared with vaginal delivery.

Cesarean section (CS) is recognized as being a shared environmental risk factor associated with chronic immune disease. A study of maternal gene expression changes between different delivery modes can add to our understanding of how CS contributes to disease patterns later in life. We evaluated the association of delivery mode with postpartum gene expression using a cross-sectional study of 324 mothers who delivered full-term (≥ 37 weeks) singletons. Of these, 181 mothers had a vaginal delivery and 143 had a CS delivery (60 with and 83 without labor). Antimicrobial peptides (AMP) were upregulated in vaginal delivery compared to CS with or without labor. Peptidase inhibitor 3 (PI3), a gene in the antimicrobial peptide pathway and known to be involved in antimicrobial and anti-inflammatory activities, showed a twofold increase in vaginal delivery compared to CS with or without labor (adjusted p-value 1.57 × 10-11 and 3.70 × 10-13, respectively). This study evaluates differences in gene expression by delivery mode and provides evidence of antimicrobial peptide upregulation in vaginal delivery compared to CS with or without labor. Further exploration is needed to determine if AMP upregulation provides protection against CS-associated diseases later in life.

Association of Ancestral Genetic Admixture and Excess Weight at Twelve Months of Age.

Background/Objective: Understanding the influence of genetically determined ancestry may give insight into the disparities of obesity seen in different ethnic groups beginning at a very early age. Aim: To investigate the relationship between children's ancestral genetic proportions and excess weight at 12 months of age. Methods: Eight hundred twenty-one 12-month-old children were included in this cross-sectional study. Their genetic admixture was estimated using the ancestry and kinship tool kit by projecting the samples into the 1000 Genomes principal component database. Weight-for-length percentile (WFLP) at 12 months of age was categorized as <95th percentile or ≥95th percentile. Multiple logistic regression analysis was performed to calculate odds ratios (ORs) with 95% confidence intervals (CIs) for the association of admixture proportions, including European (EUR), admixed American (AMR), African (AFR), South Asian (SAS), and East Asian (EAS) populations, with WFLP categories, adjusting for maternal education, birth weight, frequency of breastfeeding, and juice consumption. Results: Eight hundred twenty-one children were included; WFLP <95th percentile = 671 (81.7%) and WFLP ≥95th percentile = 150 (18.3%). Crude ORs showed that the EUR admixture was protective [OR 0.45 (95% CI 0.27-0.74)], whereas AMR [OR 3.85 (95% CI 1.92-7.70)] and AFR [OR 5.70 (95% CI 2.19-14.85)] admixtures were positively associated with excess weight. After adjusting for confounding variables, only the AFR admixture was associated with WFLP ≥95th percentile [OR 7.38 (95% CI 2.31-23.59)]. Conclusions: AFRs remain associated with early excess weight after accounting for confounding variables, suggesting that this ancestral genetic background may contribute to the differences seen in early childhood obesity.

Assessment of the Urinary Microbiome in Children Younger Than 48 Months.

BACKGROUND: The urinary tract was once thought to be sterile, and little is known about the urinary microbiome in children. This study aimed to examine the urinary microbiome of young children across demographic and clinical factors. METHODS: Children <48 months, undergoing a urinary catheterization for clinical purposes in the Pediatric Emergency Department were recruited and urine samples collected. Detailed demographic and clinical information were recorded. Urine samples underwent DNA extraction and 16S ribosomal RNA gene sequencing, urinalysis and urine culture. RESULTS: Eighty-five children were included; a urinary microbiome was identified in every child. Nine children had Escherichia coli urinary tract infections (UTIs) identified. Those with UTIs had a significantly decreased alpha diversity (t test, P < 0.001) and the composition of the microbiome clustered separately (P = 0.001) compared with those without UTIs. CONCLUSIONS: A urinary microbiome was identified in every child, even neonates. Differences in microbiome diversity and composition were observed in patients with a standard culture positive UTI. The urinary microbiome has just begun to be explored, and the implications on long-term disease processes deserve further investigation.

Prenatal and Peripartum Exposure to Antibiotics and Cesarean Section Delivery Are Associated with Differences in Diversity and Composition of the Infant Meconium Microbiome.

The meconium microbiome may provide insight into intrauterine and peripartum exposures and the very earliest intestinal pioneering microbes. Prenatal antibiotics have been associated with later obesity in children, which is thought to be driven by microbiome dependent mechanisms. However, there is little data regarding associations of prenatal or peripartum antibiotic exposure, with or without cesarean section (CS), with the features of the meconium microbiome. In this study, 16S ribosomal RNA gene sequencing was performed on bacterial DNA of meconium samples from 105 infants in a birth cohort study. After multivariable adjustment, delivery mode (p = 0.044), prenatal antibiotic use (p = 0.005) and peripartum antibiotic use (p < 0.001) were associated with beta diversity of the infant meconium microbiome. CS (vs. vaginal delivery) and peripartum antibiotics were also associated with greater alpha diversity of the meconium microbiome (Shannon and Simpson, p < 0.05). Meconium from infants born by CS (vs. vaginal delivery) had lower relative abundance of the genus Escherichia (p < 0.001). Prenatal antibiotic use and peripartum antibiotic use (both in the overall analytic sample and when restricting to vaginally delivered infants) were associated with differential abundance of several bacterial taxa in the meconium. Bacterial taxa in the meconium microbiome were also differentially associated with infant excess weight at 12 months of age, however, sample size was limited for this comparison. In conclusion, prenatal and peripartum antibiotic use along with CS delivery were associated with differences in the diversity and composition of the meconium microbiome. Whether or not these differences in the meconium microbiome portend risk for long-term health outcomes warrants further exploration.