Singular Value Decomposition in Image Noise Filtering and Reconstruction

The Singular Value Decomposition (SVD) has many applications in image processing. The SVD can be used to restore a corrupted image by separating significant information from the noise in the image data set. This thesis outlines broad applications that address current problems in digital image processing. In conjunction with SVD filtering, image compression using the SVD is discussed, including the process of reconstructing or estimating a rank reduced matrix representing the compressed image. Numerical plots and error measurement calculations are used to compare results of the two SVD image restoration techniques, as well as SVD image compression. The filtering methods assume that the images have been degraded by the application of a blurring function and the addition of noise. Finally, we present numerical experiments for the SVD restoration and compression to evaluate our computation

Birth Weight, Genetic Susceptibility, and Adulthood Risk of Type 2 Diabetes

OBJECTIVE Both stressful intrauterine milieus and genetic susceptibility have been linked to later-life diabetes risk. The current study aims to examine the interaction between low birth weight, a surrogate measure of stressful intrauterine milieus, and genetic susceptibility in relation to risk of type 2 diabetes in adulthood. RESEARCH DESIGN AND METHODS The analysis included two independent, nested case-control studies of 2,591 type 2 diabetic case subjects and 3,052 healthy control subjects. We developed two genotype scores: an obesity genotype score based on 32 BMI-predisposing variants and a diabetes genotype score based on 35 diabetes-predisposing variants. RESULTS Obesity genotype scores showed a stronger association with type 2 diabetes risk in individuals with low birth weight. In low–birth weight individuals, the multivariable-adjusted odds ratio (OR) was 2.55 (95% CI 1.34–4.84) by comparing extreme quartiles of the obesity genotype score, while the OR was 1.27 (1.04–1.55) among individuals with birth weight >2.5 kg (P for interaction = 0.017). We did not observe significant interaction between diabetes genotype scores and birth weight with regard to risk of type 2 diabetes. In a comparison of extreme quartiles of the diabetes gene score, the multivariable-adjusted OR was 3.80 (1.76–8.24) among individuals with low birth weight and 2.27 (1.82–2.83) among those with high birth weight (P for interaction = 0.16). CONCLUSIONS Our data suggest that low birth weight and genetic susceptibility to obesity may synergistically affect adulthood risk of type 2 diabetes

Genetic Determinants for Body Iron Store and Type 2 Diabetes Risk in US Men and Women

Background: High body iron store has been associated with an increased risk of type 2 diabetes (T2D); it remains unknown whether the genetic variants related to body iron status affect T2D risk. We aimed at comprehensively investigating the associations between the genetic variants related to body iron status and the T2D risk. Methodology/Principal Findings Six common SNPs related to body iron status from recent genome-wide association (GWA) studies were determined in the Nurses’ Health Study (NHS; 1,467 diabetic cases and 1,754 controls) and the Health Professionals Follow-up Study (HPFS; 1,124, diabetic cases and 1,298 controls). Plasma levels of ferritin, soluble transferrin receptor (sTfR), and transferrin were measured in NHS. Significant associations were observed for loci in TPMRSS6 with sTfR (P = 3.47×10−6), TF with transferrin (P = 0.0002 to 1.72×10−10); and HFE with ferritin (P = 0.017 to 1.6×10−8), sTfR (P = 0.007 to 7.9×10−6), and transferrin (P = 0.006 to 0.0007). The six SNPs together explained 5.7%, 2.7%, and 13.3% of the variation in plasma levels of ferritin, sTfR, and transferrin. After adjustment for the conventional risk factors, the T allele of SNP rs855791 in the TPMRSS6 gene was significantly associated with a 19% decreased risk of T2D (OR = 0.81; 95% CI = 0.66–0.98; P = 0.03) in men. Multiple tests attenuated this significant association to null. No associations were observed in women. SNPs at HFE and TF were not associated with diabetes risk in either sex. Dietary iron intake did not modify the associations of the newly identified loci with diabetes risk. Conclusions/Significance: The newly identified iron-related SNP rs855791 in TPMRSS6 was nominally associated with a decreased risk of T2D in men but not in women. The apparent differences by gender warrant further study

Influence of Fetal and Maternal Genetic Susceptibility to Obesity on Birthweight in African Ancestry Populations

Fetal and maternal genetic propensity to obesity can influence birthweight. We investigated the effects of fetal and maternal genetic risk of obesity on birthweight and evaluated whether these genetic influences modify the well-known association between maternal pre-pregnancy body mass index (BMI) and birthweight. In 950 mother-baby pairs of African ancestry, a genetic risk score for adulthood obesity was generated for mothers (mGRS) and their babies (bGRS) as the weighted sum of BMI-increasing alleles of 97 single nucleotide polymorphisms known to be associated with BMI. The median GRS value was used as a cut-off to define high or low bGRS and mGRS. High bGRS was significantly associated with 70 g lower birthweight (95% Confidence Interval [CI] = −127.4 to −12.4) compared to low bGRS. mGRS was positively correlated with birthweight but the association was not significant. mGRS modified the significant birthweight-increasing effect of maternal pre-pregnancy BMI (P-for-interaction = 0.03); among mothers with low mGRS, those who were overweight or obese had 127.7 g heavier babies (95% CI = 27.1 to 228.2) compared to those who had normal weight. In summary, fetal obesity genetic risk loci exert direct influence on birthweight, and maternal loci modify the effect of pre-pregnancy BMI on birthweight

Placental genetic variations in vitamin D metabolism and birthweight.

INTRODUCTION: Vitamin D has pleiotropic functions that regulate fetal growth and development. We investigated associations of common placental genetic variations in vitamin D metabolism with birthweight. METHODS: The study was conducted among participants (506 maternal-infant pairs) of a pregnancy cohort study. Data were collected using interviewer-administered questionnaires and post-delivery medical record abstraction. DNA, extracted from placental samples collected at delivery, was genotyped for eight single nucleotide polymorphisms (SNPs) in five vitamin D metabolism genes (CUBN, LRP2, VDR, GC, and CYP2R1). Linear and logistic regression models were used to evaluate associations of SNPs with birthweight and risk of low birthweight, respectively. Effect modification of associations by infant sex was examined using stratified analyses and interaction terms in regression models. RESULTS: Mean (standard-deviation) birthweight among all, male, and female infants was 3482.1 (549.9), 3544.6 (579.0) and 3419.2 (512.5) grams, respectively. Each copy of the minor allele of rs2282679 (GC) was associated with a 68.6 g (95%CI:3.1134.7 g) increase in birthweight overall. Sex-specific associations were observed for SNP rs4667591 (LRP2) (p-value for interaction \u3c 0.001). Each copy of the minor allele of rs4667591 was associated with a 124.7 g (95%CI:20.1229.0 g) increase in birthweight among female infants, and a suggested 81.6 g decrease in birthweight among male infants (95%CI:-183.7,20.5 g). DISCUSSION: Our study identified overall and sex-specific associations between placental genetic variations in vitamin D metabolism and birthweight. If confirmed by larger replication studies, observed associations may provide insight into mechanistic underpinnings of the relationships between placental vitamin D metabolism and birth size

DNA methylation loci in placenta associated with birthweight and expression of genes relevant for early development and adult diseases

Background Birthweight marks an important milestone of health across the lifespan, including cardiometabolic disease risk in later life. The placenta, a transient organ at the maternal-fetal interface, regulates fetal growth. Identifying genetic loci where DNA methylation in placenta is associated with birthweight can unravel genomic pathways that are dysregulated in aberrant fetal growth and cardiometabolic diseases in later life. Results We performed placental epigenome-wide association study (EWAS) of birthweight in an ethnic diverse cohort of pregnant women (n = 301). Methylation at 15 cytosine-(phosphate)-guanine sites (CpGs) was associated with birthweight (false discovery rate (FDR) < 0.05). Methylation at four (26.7%) CpG sites was associated with placental transcript levels of 15 genes (FDR < 0.05), including genes known to be associated with adult lipid traits, inflammation and oxidative stress. Increased methylation at cg06155341 was associated with higher birthweight and lower FOSL1 expression, and lower FOSL1 expression was correlated with higher birthweight. Given the role of the FOSL1 transcription factor in regulating developmental processes at the maternal-fetal interface, epigenetic mechanisms at this locus may regulate fetal development. We demonstrated trans-tissue portability of methylation at four genes (MLLT1, PDE9A, ASAP2, and SLC20A2) implicated in birthweight by a previous study in cord blood. We also found that methylation changes known to be related to maternal underweight, preeclampsia and adult type 2 diabetes were associated with lower birthweight in placenta. Conclusion We identified novel placental DNA methylation changes associated with birthweight. Placental epigenetic mechanisms may underlie dysregulated fetal development and early origins of adult cardiometabolic diseases. Clinical trial registration ClinicalTrials.gov, NCT0091213

Genome-wide and candidate gene association studies of placental abruption

Placental abruption (PA), a pregnancy-related vascular disorder, is a leading cause of maternal and perinatal morbidity and mortality. The success of identifying genetic susceptibility loci for PA, a multi-factorial heritable disorder, has been limited. We conducted a genome-wide association study (GWAS) and candidate gene association study using 470 PA cases and 473 controls from Lima, Peru. Genotyping for common genetic variations (single nucleotide polymorphisms, SNPs) was conducted using the Illumina Cardio-Metabo Chip platform. Common variations in 35 genes that participate in mitochondrial biogenesis (MB) and oxidative phosphorylation (OS) were selected for the candidate gene study. Regression models were fit to examine associations of each SNP with risk of PA. In pathway analyses, we examined functions and functional relationships of genes represented by the top GWAS hits. Genetic risk scores (GRS), based on top hits of the GWAS and candidate gene analyses, respectively, were computed using the risk allele counting method. The top hit in the GWAS analyses was rs1238566 (empirical P-value=1.04e-4 and FDR-adjusted P-value=5.65E-04) in FLI-1 gene, a megakaryocyte-specific transcription factor. Networks of genes involved in lipid metabolism and cell signaling were significantly enriched by the 51 genes whose SNPs were among the top 200 GWAS hits. SNPs known to regulate MB (e.g. CAMK2B, NR1H3, PPARG, PRKCA, and THRB) and OP (e.g., COX5A, and NDUF family of genes) were associated with PA risk. GRS was significantly associated with PA risk. Our study suggests that integrating multiple analytical strategies in genetic association studies can provide opportunities for identifying genetic risk factors and novel molecular mechanisms that underlie PA

Genetic correlations of psychiatric traits with body composition and glycemic traits are sex- and age-dependent

Peer reviewedPublisher PD

Quality control and conduct of genome-wide association meta-analyses

Rigorous organization and quality control (QC) are necessary to facilitate successful genome-wide association meta-analyses (GWAMAs) of statistics aggregated across multiple genome-wide association studies. This protocol provides guidelines for [1] organizational aspects of GWAMAs, and for [2] QC at the study file level, the meta-level across studies, and the meta-analysis output level. Real–world examples highlight issues experienced and solutions developed by the GIANT Consortium that has conducted meta-analyses including data from 125 studies comprising more than 330,000 individuals. We provide a general protocol for conducting GWAMAs and carrying out QC to minimize errors and to guarantee maximum use of the data. We also include details for use of a powerful and flexible software package called EasyQC. For consortia of comparable size to the GIANT consortium, the present protocol takes a minimum of about 10 months to complete