👤 Rachel Novotny

The healthy human brain is a mosaic of varied genomes. Long interspersed element-1 (LINE-1 or L1) retrotransposition is known to create mosaicism by inserting L1 sequences into new locations of somatic cell genomes. Using a machine learning-based, single-cell sequencing approach, we discovered that somatic L1-associated variants (SLAVs) are composed of two classes: L1 retrotransposition insertions and retrotransposition-independent L1-associated variants. We demonstrate that a subset of SLAVs comprises somatic deletions generated by L1 endonuclease cutting activity. Retrotransposition-independent rearrangements in inherited L1s resulted in the deletion of proximal genomic regions. These rearrangements were resolved by microhomology-mediated repair, which suggests that L1-associated genomic regions are hotspots for somatic copy number variants in the brain and therefore a heritable genetic contributor to somatic mosaicism. We demonstrate that SLAVs are present in crucial neural genes, such as DLG2 (also called PSD93), and affect 44-63% of cells of the cells in the healthy brain. Show less

The aim was to evaluate the relationships of the T-1131C (rs662799) polymorphism variants of apolipoprotein A5 (Apo A5) gene and variants of apolipoprotein E (Apo E) gene common polymorphism (rs429358, rs7412) to signs of metabolic syndrome (MetS). We examined 590 asymptomatic dyslipidemic patients divided into MetS+ (n=146) and MetS- (n=444) groups according to criteria of NCEP ATPIII Panel. We evaluated genotype frequencies and differences in MetS features between individual groups. Logistic regression analysis was used for the evaluation of Apo A5/Apo E variants as possible risk factors for MetS. We found no statistical differences between genotype and allele frequencies for both Apo A5 and Apo E polymorphisms between MetS+ and MetS- groups. In all subjects and MetS- group, we confirmed well-known association of the -1131C Apo A5 minor allele with elevated triglycerides (TG, p<0.001). The Apo E gene E2 and E4 variants were associated with higher levels of TG (p<0.01) in comparison to E33 common variant. However, no statistical differences were observed in MetS+ subjects, regardless of significantly higher TG levels in this group. Apo A5/Apo E variant analysis in all dyslipidemic patients revealed significant increase of TG levels in all subgroups in comparison to common -1131T/E3 variant carriers, the most in -1131C/E4 variant subgroup. Logistic regression analysis models showed no association of Apo A5, Apo E and all Apo A5/Apo E variants with metabolic syndrome, even after adjustment for age and sex. Our study refined the role of Apo A5 and Apo E genetic variants in the group of adult dyslipidemic patients. We demonstrate that except of TG, Apo A5 T-1131C (rs662799) and Apo E (rs429358, rs7412) polymorphisms have no remarkable effect on MetS characteristics. Show less

In a marker-trait association study we estimated the statistical significance of 65 single nucleotide polymorphisms (SNP) in 23 candidate genes on HDL levels of two independent Caucasian populations. Each population consisted of men and women and their HDL levels were adjusted for gender and body weight. We used a linear regression model. Selected genes corresponded to folate metabolism, vitamins B-12, A, and E, and cholesterol pathways or lipid metabolism. Extracted DNA from both the Sacramento and Beltsville populations was analyzed using an allele discrimination assay with a MALDI-TOF mass spectrometry platform. The adjusted phenotype, y, was HDL levels adjusted for gender and body weight only statistical analyses were performed using the genotype association and regression modules from the SNP Variation Suite v7. Statistically significant SNP (where P values were adjusted for false discovery rate) included: CETP (rs7499892 and rs5882); SLC46A1 (rs37514694; rs739439); SLC19A1 (rs3788199); CD36 (rs3211956); BCMO1 (rs6564851), APOA5 (rs662799), and ABCA1 (rs4149267). Many prior association trends of the SNP with HDL were replicated in our cross-validation study. Significantly, the association of SNP in folate transporters (SLC46A1 rs37514694 and rs739439; SLC19A1 rs3788199) with HDL was identified in our study. Given recent literature on the role of niacin in the biogenesis of HDL, focus on status and metabolism of B-vitamins and metabolites of eccentric cleavage of β-carotene with lipid metabolism is exciting for future study. Show less

Genome-wide association studies have identified common genetic variants that can contribute specifically to the risk of abdominal adiposity, as measured by waist circumference or waist-to-hip ratio. However, it is unknown whether these genetic risk factors affect relative body fat distribution in the abdominal visceral and subcutaneous compartments. The association between imaging-based abdominal fat mass and waist-size risk variants in the FTO, LEPR, LYPLAL1, MSRA, NRXN3, and TFAP2B genes was investigated. A cross-sectional sample of 60 women was selected among study participants of The Multiethnic Cohort, who were aged 60 to 65 years, of European or Japanese descent, and with a body mass index (calculated as kg/m(2)) between 18.5 and 40. Dual-energy x-ray absorptiometry and abdominal magnetic resonance imaging scans were used to measure adiposity. After adjustments for age, ethnicity, and total fat mass, the FTO variants showed an association with less abdominal subcutaneous fat and a higher visceral-to-subcutaneous abdominal fat ratio, with the variant rs9941349 showing significant associations most consistently (P=0.003 and 0.03, respectively). Similarly, the LEPR rs1137101 variant was associated with less subcutaneous fat (P=0.01) and a greater visceral-to-subcutaneous fat ratio (P=0.03) and percent liver fat (P=0.007). MSRA rs545854 variant carriers had a lower percent of leg fat. Our findings provide initial evidence that some of the genetic risk factors identified for larger waist size might also contribute to disproportionately greater intra-abdominal and liver fat distribution in postmenopausal women. If replicated, these genetic variants can be incorporated with other biomarkers to predict high-risk body fat distribution. Show less