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Differences in obesity and body fat distribution across gender and race/ethnicity have been extensively described. We sought to replicate these differences and evaluate newly emerging data from the All of Us Research Program (AoU). We compared body mass index (BMI), waist circumference, and waist-to-hip ratio from the baseline physical examination, and alanine aminotransferase (ALT) from the electronic health record in up to 88,195 Non-Hispanic White (NHW), 40,770 Non-Hispanic Black (NHB), 35,640 Hispanic, and 5,648 Asian participants. We compared AoU sociodemographic variable distribution to National Health and Nutrition Examination Survey (NHANES) data and applied the pseudo-weighting method for adjusting selection biases of AoU recruitment. Our findings replicate previous observations with respect to gender differences in BMI. In particular, we replicate the large gender disparity in obesity rates among NHB participants, in which obesity and mean BMI are much higher in NHB women than NHB men (33.34 kg/m2 versus 28.40 kg/m2 respectively; p<2.22x10-308). The overall age-adjusted obesity prevalence in AoU participants is similar overall but lower than the prevalence found in NHANES for NHW participants. ALT was higher in men than women, and lower among NHB participants compared to other racial/ethnic groups, consistent with previous findings. Our data suggest consistency of AoU with national averages related to obesity and suggest this resource is likely to be a major source of scientific inquiry and discovery in diverse populations. [ABSTRACT FROM AUTHOR]

Introduction: Pseudogenes are paralogues of functional genes historically viewed as defunct due to either the lack of regulatory elements or the presence of frameshift mutations. Recent evidence, however, suggests that pseudogenes may regulate gene expression, although the functional role of pseudogenes remains largely unknown. We previously reported that MYLKP1, the pseudogene of MYLK that encodes myosin light chain kinase (MLCK), is highly expressed in lung and colon cancer cell lines and tissues but not in normal lung or colon. The MYLKP1 promoter is minimally active in normal bronchial epithelial cells but highly active in lung adenocarcinoma cells. In this study, we further validate MYLKP1 as an oncogene via elucidation of the functional role of MYLKP1 genetic variants in colon cancer risk. Methods: Proliferation and migration assays were performed in MYLKP1-transfected colon and lung cancer cell lines (H441, A549) and commercially-available normal lung and colon cells. Fourteen MYLKP1 SNPs (MAFs >0.01) residing within the 4 kb MYLKP1 promoter region, the core 1.4 kb of MYLKP1 gene, and a 4 kb enhancer region were selected and genotyped in a colorectal cancer cohort. MYLKP1 SNP influences on activity of MYLKP1 promoter (2kb) was assessed by dual luciferase reporter assay. Results: Cancer cell lines, H441 and A549, exhibited increased MYLKP1 expression, increased MYLKP1 luciferase promoter activity, increased proliferation and migration. Genotyping studies identified two MYLKP1 SNPs (rs12490683; rs12497343) that significantly increase risk of colon cancer in African Americans compared to African American controls. Rs12490683 and rs12497343 further increase MYLKP1 promoter activity compared to the wild type MYLKP1 promoter. Conclusion: MYLKP1 is a cancer-promoting pseudogene whose genetic variants differentially enhance cancer risk in African American populations. [ABSTRACT FROM AUTHOR]

Imputation of human leukocyte antigen (HLA) alleles from SNP-level data is attractive due to importance of HLA alleles in human disease, widespread availability of genome-wide association study (GWAS) data, and expertise required for HLA sequencing. However, comprehensive evaluations of HLA imputations programs are limited. We compared HLA imputation results of HIBAG, SNP2HLA, and HLA*IMP:02 to sequenced HLA alleles in 3,265 samples from BioVU, a de-identified electronic health record database coupled to a DNA biorepository. We performed four-digit HLA sequencing for HLA-A, -B, -C, -DRB1, -DPB1, and -DQB1 using long-read 454 FLX sequencing. All samples were genotyped using both the Illumina HumanExome BeadChip platform and a GWAS platform. Call rates and concordance rates were compared by platform, frequency of allele, and race/ethnicity. Overall concordance rates were similar between programs in European Americans (EA) (0.975 [SNP2HLA]; 0.939 [HLA*IMP:02]; 0.976 [HIBAG]). SNP2HLA provided a significant advantage in terms of call rate and the number of alleles imputed. Concordance rates were lower overall for African Americans (AAs). These observations were consistent when accuracy was compared across HLA loci. All imputation programs performed similarly for low frequency HLA alleles. Higher concordance rates were observed when HLA alleles were imputed from GWAS platforms versus the HumanExome BeadChip, suggesting that high genomic coverage is preferred as input for HLA allelic imputation. These findings provide guidance on the best use of HLA imputation methods and elucidate their limitations. [ABSTRACT FROM AUTHOR]

A single mutation can alter cellular and global homeostatic mechanisms and give rise to multiple clinical diseases. We hypothesized that these disease mechanisms could be identified using low minor allele frequency (MAF<0.1) non-synonymous SNPs (nsSNPs) associated with “mechanistic phenotypes”, comprised of collections of related diagnoses. We studied two mechanistic phenotypes: (1) thrombosis, evaluated in a population of 1,655 African Americans; and (2) four groupings of cancer diagnoses, evaluated in 3,009 white European Americans. We tested associations between nsSNPs represented on GWAS platforms and mechanistic phenotypes ascertained from electronic medical records (EMRs), and sought enrichment in functional ontologies across the top-ranked associations. We used a two-step analytic approach whereby nsSNPs were first sorted by the strength of their association with a phenotype. We tested associations using two reverse genetic models and standard additive and recessive models. In the second step, we employed a hypothesis-free ontological enrichment analysis using the sorted nsSNPs to identify functional mechanisms underlying the diagnoses comprising the mechanistic phenotypes. The thrombosis phenotype was solely associated with ontologies related to blood coagulation (Fisher's p = 0.0001, FDR p = 0.03), driven by the F5, P2RY12 and F2RL2 genes. For the cancer phenotypes, the reverse genetics models were enriched in DNA repair functions (p = 2×10−5, FDR p = 0.03) (POLG/FANCI, SLX4/FANCP, XRCC1, BRCA1, FANCA, CHD1L) while the additive model showed enrichment related to chromatid segregation (p = 4×10−6, FDR p = 0.005) (KIF25, PINX1). We were able to replicate nsSNP associations for POLG/FANCI, BRCA1, FANCA and CHD1L in independent data sets. Mechanism-oriented phenotyping using collections of EMR-derived diagnoses can elucidate fundamental disease mechanisms. [ABSTRACT FROM AUTHOR]