Molecular Study of Preterm Birth: Genomic Ancestry, Race, and Ethnicity

BACKGROUND: Inova Translational Medicine Institute (ITMI) initiated a study to identify genomic markers predictive of preterm birth (PTB). Molecular Study of Preterm Birth evaluated ancestral reference genomes, self-reported country of birth, race and ethnicity, as well as data from the electronic medical records (EMR). Family and racial predispositions to PTB suggest genomic characterizations may confer increased risk. OBJECTIVE: To investigate genomic ancestry utilizing the electronic medical record, self-reported race/ethnicity, and principle component analysis to determine the molecular characterization of genomics and preterm birth

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

Germline Variation in Cancer-Susceptibility Genes in a Healthy, Ancestrally Diverse Cohort: Implications for Individual Genome Sequencing

<div><p>Technological advances coupled with decreasing costs are bringing whole genome and whole exome sequencing closer to routine clinical use. One of the hurdles to clinical implementation is the high number of variants of unknown significance. For cancer-susceptibility genes, the difficulty in interpreting the clinical relevance of the genomic variants is compounded by the fact that most of what is known about these variants comes from the study of highly selected populations, such as cancer patients or individuals with a family history of cancer. The genetic variation in known cancer-susceptibility genes in the general population has not been well characterized to date. To address this gap, we profiled the nonsynonymous genomic variation in 158 genes causally implicated in carcinogenesis using high-quality whole genome sequences from an ancestrally diverse cohort of 681 healthy individuals. We found that all individuals carry multiple variants that may impact cancer susceptibility, with an average of 68 variants per individual. Of the 2,688 allelic variants identified within the cohort, most are very rare, with 75% found in only 1 or 2 individuals in our population. Allele frequencies vary between ancestral groups, and there are 21 variants for which the minor allele in one population is the major allele in another. Detailed analysis of a selected subset of 5 clinically important cancer genes, <i>BRCA1</i>, <i>BRCA2</i>, <i>KRAS</i>, <i>TP53</i>, and <i>PTEN</i>, highlights differences between germline variants and reported somatic mutations. The dataset can serve a resource of genetic variation in cancer-susceptibility genes in 6 ancestry groups, an important foundation for the interpretation of cancer risk from personal genome sequences.</p></div

Variation prevalence per gene.

<p>Distribution of the number of individuals with a variant per gene for (A) all variants (B) rare variants.</p

Number of cancer-gene variants per individual by ancestry.

<p>The distribution of the number of nonsynonymous genes per subject for each of the 6 ancestry-based subpopulations.</p

Profile of the variability per individual.

<p>(A) Boxplot of the total number of variants, the number of variants listed in HGMD, the number of likely deleterious variants, and the number of variants of unknown significance per individual for cancer-associated genes. (B) Distribution of the number of cancer genes with at least one nonsynonymous variant per individual.</p

Correlation between the number of variants and coding length.

<p>The number of nonsynonymous variants vs. total number of coding bases for each of the 158 cancer-susceptibility genes.</p

Average numbers of cancer-gene variants per individual by ancestry.

<p>Average numbers of cancer-gene variants per individual by ancestry.</p