A Novel Adaptive Method for the Analysis of Next-Generation Sequencing Data to Detect Complex Trait Associations with Rare Variants Due to Gene Main Effects and Interactions

There is solid evidence that rare variants contribute to complex disease etiology. Next-generation sequencing technologies make it possible to uncover rare variants within candidate genes, exomes, and genomes. Working in a novel framework, the kernel-based adaptive cluster (KBAC) was developed to perform powerful gene/locus based rare variant association testing. The KBAC combines variant classification and association testing in a coherent framework. Covariates can also be incorporated in the analysis to control for potential confounders including age, sex, and population substructure. To evaluate the power of KBAC: 1) variant data was simulated using rigorous population genetic models for both Europeans and Africans, with parameters estimated from sequence data, and 2) phenotypes were generated using models motivated by complex diseases including breast cancer and Hirschsprung's disease. It is demonstrated that the KBAC has superior power compared to other rare variant analysis methods, such as the combined multivariate and collapsing and weight sum statistic. In the presence of variant misclassification and gene interaction, association testing using KBAC is particularly advantageous. The KBAC method was also applied to test for associations, using sequence data from the Dallas Heart Study, between energy metabolism traits and rare variants in ANGPTL 3,4,5 and 6 genes. A number of novel associations were identified, including the associations of high density lipoprotein and very low density lipoprotein with ANGPTL4. The KBAC method is implemented in a user-friendly R package

Serum paraoxonase and arylesterase activities in patients with lung cancer in a Turkish population

BACKGROUND: Lung cancer (LC) is the leading cause of cancer-related deaths. Oxidative DNA damage may contribute to the cancer risk. The antioxidant paraoxonase (PON1) is an endogenous free radical scavenger in the human body. The aim of this study was to determine serum PON1 and arylesterase (ARE) activities in patients with newly diagnosed LC. METHODS: This case control study involved a total of 39 patients with newly diagnosed LC (untreated) and same number of age- and sex-matched healthy individuals. Serum PON1 and ARE activities in addition to lipid parameters were measured in both groups. RESULTS: Serum PON1 and ARE activities were found to be lower in patients with LC compared to the controls (p = 0.001 and p = 0.018, respectively). The ratio of PON1/high density lipoprotein (HDL) was significantly lower in the LC group compared to the control one (p = 0.009). There were positive correlations between the serum levels of HDL and PON1 in both the control (r = 0.415, p = 0.009) and the LC groups (r = 0.496, p = 0.001), respectively. PON1 enzyme activity was calculated as three different phenotypes in both groups. In regard to lipid parameters, total cholesterol levels were significantly lower (p = 0.014) in the LC group whereas the other lipid parameters such as HDL, LDL, and triglyceride levels were not significantly different among groups. CONCLUSION: Serum PON1 activity is significantly low in the LC group compared with the healthy controls. Metastasis status and cigarette smoking do not affect serum PON1 activity in the LC patients

Paraoxonase 1 (PON1) Polymorphisms, Haplotypes and Activity in Predicting CAD Risk in North-West Indian Punjabis

Human serum paraoxonase-1 (PON1) prevents oxidation of low density lipoprotein cholesterol (LDL-C) and hydrolyzes the oxidized form, therefore preventing the development of atherosclerosis. The polymorphisms of PON1 gene are known to affect the PON1 activity and thereby coronary artery disease (CAD) risk. As studies are lacking in North-West Indian Punjabi's, a distinct ethnic group with high incidence of CAD, we determined PON1 activity, genotypes and haplotypes in this population and correlated them with the risk of CAD.350 angiographically proven (≥ 70% stenosis) CAD patients and 300 healthy controls were investigated. PON1 activity was determined towards paraoxon (Paraoxonase; PONase) and phenylacetate (Arylesterase; AREase) substrates. In addition, genotyping was carried out by using multiplex PCR, allele specific oligonucleotide -PCR and PCR-RFLP methods and haplotyping was determined by PHASE software. The serum PONase and AREase activities were significantly lower in CAD patients as compared to the controls. All studied polymorphisms except L55M had significant effect on PONase activity. However AREase activity was not affected by them. In a logistic regression model, after adjustment for the conventional risk factors for CAD, QR (OR: 2.73 (1.57-4.72)) and RR (OR, 16.24 (6.41-41.14)) genotypes of Q192R polymorphism and GG (OR: 2.07 (1.02-4.21)) genotype of -162A/G polymorphism had significantly higher CAD risk. Haplotypes L-T-G-Q-C (OR: 3.25 (1.72-6.16)) and L-T-G-R-G (OR: 2.82 (1.01-7.80)) were also significantly associated with CAD.In conclusion this study shows that CAD patients had lower PONase and AREase activities as compared to the controls. The coding Q192R polymorphism, promoter -162A/G polymorphism and L-T-G-Q-C and L-T-G-R-G haplotypes are all independently associated with CAD

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta