Breast cancer is the most common solid organ malignancy and the most frequent cause of cancer death among women worldwide. Previous research has yielded insights into its genetic etiology, but there remains a gap in the understanding of genetic factors that contribute to risk, and particularly in the biological mechanisms by which genetic variation modulates risk. The National Cancer Institute’s “Up for a Challenge” (U4C) competition provided an opportunity to further elucidate the genetic basis of the disease. Our group leveraged the seven datasets made available by the U4C organizers and data from the publicly available UK Biobank cohort to examine associations between imputed gene expression and breast cancer risk. In particular, we used reference datasets describing the breast tissue and whole blood transcriptomes to impute expression levels in breast cancer cases and controls. In trans-ethnic meta-analyses of U4C and UK Biobank data, we found significant associations between breast cancer risk and the expression of RCCD1 (joint p-value: 3.6x10^-06) and DHODH (p-value: 7.1x10^-06) in breast tissue, as well as a suggestive association for ANKLE1 (p-value: 9.3x10^-05). Expression of RCCD1 in whole blood was also suggestively associated with disease risk (p-value: 1.2x10^-05), as were expression of ACAP1 (p-value: 1.9x10^-05) and LRRC25 (p-value: 5.2x10^-05). While genome-wide association studies (GWAS) have implicated RCCD1 and ANKLE1 in breast cancer risk, they have not identified the remaining three genes. Among the genetic variants that contributed to the predicted expression of the five genes, we found 23 nominally (p-value < 0.05) associated with breast cancer risk, among which 15 are not in high linkage disequilibrium with risk variants previously identified by GWAS. In summary, we used a transcriptome-based approach to investigate the genetic underpinnings of breast carcinogenesis. This approach provided an avenue for deciphering the functional relevance of genes and genetic variants involved in breast cancer. There is a clear genetic basis of breast cancer, and previous work has identified numerous genetic variants that increase risk of this common disease. However, much of the underlying genetic variation in breast cancer remains unexplained. To address this void, as part of the National Cancer Institute’s “Up for a Challenge” (U4C) competition, we undertook a large-scale study of genetically regulated gene expression and breast cancer. Specifically, we estimated gene expression levels based on germline genetics for subjects in the seven breast cancer studies provided by U4C and for subjects in the UK Biobank. We then evaluated associations between gene expression and breast cancer and detected three novel and two known breast cancer genes. These genes exhibit potential biological mechanisms for impacting breast carcinogenesis. Our work highlights the value of leveraging different sources of data to more thoroughly study the genetic basis of complex diseases.

Genetics clearly plays a major role in the etiology of autism spectrum disorders (ASDs), but studies to date are only beginning to characterize the causal genetic variants responsible. Until recently, studies using multiple extended multi-generation families to identify ASD risk genes had not been undertaken.

Importance The novel Coronavirus Disease 2019 (COVID-19), declared a pandemic in March 2020, may present with disproportionately higher rates in underrepresented racial/ethnic minority populations in the United States, including African American communities who have traditionally been over-represented in negative health outcomes. Study Objective To understand the impact of the density of African American communities (defined as the percentage of African Americans in a county) on COVID-19 prevalence and death rate within the three most populous counties in each U.S. state and territory (n=152). Design An ecological study using linear regression was employed for the study. Setting The top three most populous counties of each U.S. state and territory were included in analyses for a final sample size of n=152 counties. Participants Confirmed COVID-19 cases and deaths that were accumulated between January 22, 2020 and April 12, 2020 in each of the three most populous counties in each U.S. state and territory were included. Main outcome measures Linear regression was used to determine the association between African American density and COVID-19 prevalence (defined as the percentage of cases for the county population), and death rate (defined as number of deaths per 100,000 population). The models were adjusted for median age and poverty. Results There was a direct association between African American density and COVID-19 prevalence; COVID-19 prevalence increased 5% for every 1% increase in county AA density (p<.01). There was also an association between county AA density and COVID-19 deaths, such; the death rate increased 2 per 100,000 for every percentage increase in county AA density (p=.02). Conclusion These study findings indicate that communities with a high African American density have been disproportionately burdened with COVID-19. Further study is needed to indicate if this burden is related to environmental factors or individual factors such as types of employment or comorbidities that members of these community have. Question What is the impact of the density of African American communities (defined as the percentage of African Americans in a county) on COVID-19 prevalence and death rate by U.S. counties. Findings This ecological study including the three most populous counties in each U.S. state and territory, found that COVID-19 prevalence increased 5% for every percentage increase in county African American density, and that the death rate increased 2 per hundred thousand for every percentage increase in county African American density. Both were statistically significant increases. Meaning These study findings indicate that communities with a high African American density have been disproportionately burdened with COVID-19.

Study Design.: A retrospective study at a single academic institution. Objective.: The purpose of this study is to utilize machine learning to predict hospital length of stay (LOS) and discharge disposition following adult elective spine surgery, and to compare performance metrics of machine learning models to the American College of Surgeon’s National Surgical Quality Improvement Program’s (ACS NSQIP) prediction calculator. Summary of Background Data.: A total of 3678 adult patients undergoing elective spine surgery between 2014 and 2019, acquired from the electronic health record. Methods.: Patients were divided into three stratified cohorts: cervical degenerative, lumbar degenerative, and adult spinal deformity groups. Predictive variables included demographics, body mass index, surgical region, surgical invasiveness, surgical approach, and comorbidities. Regression, classification trees, and least absolute shrinkage and selection operator (LASSO) were used to build predictive models. Validation of the models was conducted on 16% of patients (N=587), using area under the receiver operator curve (AUROC), sensitivity, specificity, and correlation. Patient data were manually entered into the ACS NSQIP online risk calculator to compare performance. Outcome variables were discharge disposition (home vs. rehabilitation) and LOS (days). Results.: Of 3678 patients analyzed, 51.4% were male (n=1890) and 48.6% were female (n=1788). The average LOS was 3.66 days. In all, 78% were discharged home and 22% discharged to rehabilitation. Compared with NSQIP (Pearson R ²=0.16), the predictions of poisson regression (R ²=0.29) and LASSO (R ²=0.29) models were significantly more correlated with observed LOS (P=0.025 and 0.004, respectively). Of the models generated to predict discharge location, logistic regression yielded an AUROC of 0.79, which was statistically equivalent to the AUROC of 0.75 for NSQIP (P=0.135). Conclusion.: The predictive models developed in this study can enable accurate preoperative estimation of LOS and risk of rehabilitation discharge for adult patients undergoing elective spine surgery. The demonstrated models exhibited better performance than NSQIP for prediction of LOS and equivalent performance to NSQIP for prediction of discharge location.

Ehlers Danlos Syndrome is a rare form of inherited connective tissue disorder, which primarily affects skin, joints, muscle, and blood cells. The current study aimed at finding the mutation that causing EDS type VII C also known as “Dermatosparaxis” in this family. Through systematic data querying of the electronic medical records (EMRs) of over 80,000 individuals, we recently identified an EDS family that indicate an autosomal dominant inheritance. The family was consented for genomic analysis of their de-identified data. After a negative screen for known mutations, we performed whole genome sequencing on the male proband, his affected father, and unaffected mother. We filtered the list of non-synonymous variants that are common between the affected individuals. The analysis of non-synonymous variants lead to identifying a novel mutation in the ADAMTSL2 (p. Gly421Ser) gene in the affected individuals. Sanger sequencing confirmed the mutation. Our work is significant not only because it sheds new light on the pathophysiology of EDS for the affected family and the field at large, but also because it demonstrates the utility of unbiased large-scale clinical recruitment in deciphering the genetic etiology of rare mendelian diseases. With unbiased large-scale clinical recruitment we strive to sequence as many rare mendelian diseases as possible, and this work in EDS serves as a successful proof of concept to that effect.

The etiology of monoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma (MM) is still obscure as are the processes that enable the progression of MGUS to MM. Understanding the unique vs. shared transcriptomes can potentially elucidate why individuals develop one or the other. Furthermore, highlighting key pathways and genes involved in the pathogenesis of MM or the development of MGUS to MM may allow the discovery of novel drug targets and therapies.