Patterns of co-expression for protein complexes by size in Saccharomyces cerevisiae

Many successful functional studies by gene expression profiling in the literature have led to the perception that profile similarity is likely to imply functional association. But how true is the converse of the above statement? Do functionally associated genes tend to be co-regulated at the transcription level? In this paper, we focus on a set of well-validated yeast protein complexes provided by Munich Information Center for Protein Sequences (MIPS). Using four well-known large-scale microarray expression data sets, we computed the correlations between genes from the same complex. We then analyzed the relationship between the distribution of correlations and the complex size (the number of genes in a protein complex). We found that except for a few large protein complexes, such as mitochondrial ribosomal and cytoplasmic ribosomal proteins, the correlations are on the average not much higher than that from a pair of randomly selected genes. The global impact of large complexes on the expression of other genes in the genome is also studied. Our result also showed that the expression of over 85% of the genes are affected by six large complexes: the cytoplasmic ribosomal complex, mitochondrial ribosomal complex, proteasome complex, F0/F1 ATP synthase (complex V) (size 18), rRNA splicing (size 24) and H+- transporting ATPase, vacular (size 15)

Patterns of co-expression for protein complexes by size in Saccharomyces cerevisiae

Many successful functional studies by gene expression profiling in the literature have led to the perception that profile similarity is likely to imply functional association. But how true is the converse of the above statement? Do functionally associated genes tend to be co-regulated at the transcription level? In this paper, we focus on a set of well-validated yeast protein complexes provided by Munich Information Center for Protein Sequences (MIPS). Using four well-known large-scale microarray expression data sets, we computed the correlations between genes from the same complex. We then analyzed the relationship between the distribution of correlations and the complex size (the number of genes in a protein complex). We found that except for a few large protein complexes, such as mitochondrial ribosomal and cytoplasmic ribosomal proteins, the correlations are on the average not much higher than that from a pair of randomly selected genes. The global impact of large complexes on the expression of other genes in the genome is also studied. Our result also showed that the expression of over 85% of the genes are affected by six large complexes: the cytoplasmic ribosomal complex, mitochondrial ribosomal complex, proteasome complex, F0/F1 ATP synthase (complex V) (size 18), rRNA splicing (size 24) and H+- transporting ATPase, vacular (size 15)

Leveraging Family History in Genetic Association Analyses of Binary Traits

BACKGROUND: Considering relatives\u27 health history in logistic regression for case-control genome-wide association studies (CC-GWAS) may provide new information that increases accuracy and power to detect disease associated genetic variants. We conducted simulations and analyzed type 2 diabetes (T2D) data from the Framingham Heart Study (FHS) to compare two methods, liability threshold model conditional on both case-control status and family history (LT-FH) and Fam-meta, which incorporate family history into CC-GWAS. RESULTS: In our simulation scenario of trait with modest T2D heritability (h CONCLUSIONS: Overall, LT-FH and Fam-meta had higher power than CC-GWAS in simulations, especially using phenotypes that were more prevalent in older age groups, and both methods detected known genetic variants with lower P-values in real data application, highlighting the benefits of including family history in genetic association studies

Girdling improved berry coloration in summer but suppressed return growth in the following spring in 'Kyoho' grapevines cultivated in the subtropical double cropping system

Improving berry skin coloration is one of the major challenges in the tropical and subtropical viticulture. In this paper we aimed to document the effects of girdling at veraison on berry coloration and quality in grapevines on different rootstocks and we assessed the seasonal variations of girdling effects in the subtropical double cropping system. In the first experiment, gird-ling at veraison was tested in 'Kyoho' on 5C rootstocks. In the second experiment, own-rooted 'Kyoho', 5C or 1202C rootstocks were compared. Vines were trained to a horizontal overhead trellis with a single trunk and two short arms. One arm of each vine was girdled at veraison and the other arm served as the control. Girdling at veraison significantly improved skin color of berries from own-rooted vines or 5C rootstocks in the summer cropping cycle but was less effective in the winter cropping cycle. Girdling improved total soluble solids in own-rooted vines in the summer cropping cycle and in vines on 1202C in the winter cropping cycle. Girdling made in the winter cropping cycle did not completely heal until post-bloom in the following spring and reduced length of the fruiting shoot, number of leaves per shoot, and length of inflorescences of the next summer cropping cycle. We concluded that girdling at veraison is a good practice to improve berry color and quality for the summer cropping cycle but is not recommended for the winter cropping cycle

Comparison of statistical approaches to rare variant analysis for quantitative traits

With recent advances in technology, deep sequencing data will be widely used to further the understanding of genetic influence on traits of interest. Therefore not only common variants but also rare variants need to be better used to exploit the new information provided by deep sequencing data. Recently, statistical approaches for analyzing rare variants in genetic association studies have been proposed, but many of them were designed only for dichotomous outcomes. We compare the type I error and power of several statistical approaches applicable to quantitative traits for collapsing and analyzing rare variant data within a defined gene region. In addition to comparing methods that consider only rare variants, such as indicator, count, and data-adaptive collapsing methods, we also compare methods that incorporate the analysis of common variants along with rare variants, such as CMC and LASSO regression. We find that the three methods used to collapse rare variants perform similarly in this simulation setting where all risk variants were simulated to have effects in the same direction. Further, we find that incorporating common variants is beneficial and using a LASSO regression to choose which common variants to include is most useful when there is are few common risk variants compared to the total number of risk variants

Exome Sequence association Study of Levels and Longitudinal Change of Cardiovascular Risk Factor Phenotypes in European americans and african americans From the atherosclerosis Risk in Communities Study

Cardiovascular disease (CVD) is responsible for 31% of all deaths worldwide. Among CVD risk factors are age, race, increased systolic blood pressure (BP), and dyslipidemia. Both BP and blood lipids levels change with age, with a dose-dependent relationship between the cumulative exposure to hyperlipidemia and the risk of CVD. We performed an exome sequence association study using longitudinal data with up to 7805 European Americans (EAs) and 3171 African Americans (AAs) from the Atherosclerosis Risk in Communities (ARIC) study. We assessed associations of common (minor allele frequency \u3e 5%) nonsynonymous and splice-site variants and gene-based sets of rare variants with levels and with longitudinal change of seven CVD risk factor phenotypes (BP traits: systolic BP, diastolic BP, pulse pressure; lipids traits: triglycerides, total cholesterol, high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C]). Furthermore, we investigated the relationship of the identified variants and genes with select CVD endpoints. We identified two novel genes: DCLK3 associated with the change of HDL-C levels in AAs and RAB7L1 associated with the change of LDL-C levels in EAs. RAB7L1 is further associated with an increased risk of heart failure in ARIC EAs. Investigation of the contribution of genetic factors to the longitudinal change of CVD risk factor phenotypes promotes our understanding of the etiology of CVD outcomes, stressing the importance of incorporating the longitudinal structure of the cohort data in future analyses

Genetic Effect on Body Mass Index and Cardiovascular Disease Across Generations

BACKGROUND: Whether genetics contribute to the rising prevalence of obesity or its cardiovascular consequences in today\u27s obesogenic environment remains unclear. We sought to determine whether the effects of a higher aggregate genetic burden of obesity risk on body mass index (BMI) or cardiovascular disease (CVD) differed by birth year. METHODS: We split the FHS (Framingham Heart Study) into 4 equally sized birth cohorts (birth year before 1932, 1932 to 1946, 1947 to 1959, and after 1960). We modeled a genetic predisposition to obesity using an additive genetic risk score (GRS) of 941 BMI-associated variants and tested for GRS-birth year interaction on log-BMI (outcome) when participants were around 50 years old (N=7693). We repeated the analysis using a GRS of 109 BMI-associated variants that increased CVD risk factors (type 2 diabetes, blood pressure, total cholesterol, and high-density lipoprotein) in addition to BMI. We then evaluated whether the effects of the BMI GRSs on CVD risk differed by birth cohort when participants were around 60 years old (N=5493). RESULTS: Compared with participants born before 1932 (mean age, 50.8 yrs [2.4]), those born after 1960 (mean age, 43.3 years [4.5]) had higher BMI (median, 25.4 [23.3-28.0] kg/m CONCLUSIONS: The significant GRS-birth year interactions indicate that common genetic variants have larger effects on middle-age BMI and CVD risk in people born more recently. These findings suggest that the increasingly obesogenic environment may amplify the impact of genetics on the risk of obesity and possibly its cardiovascular consequences

Incorporating biological knowledge in the search for gene × gene interaction in genome-wide association studies

We sought to find significant gene × gene interaction in a genome-wide association analysis of rheumatoid arthritis (RA) by performing pair-wise tests of interaction among collections of single-nucleotide polymorphisms (SNPs) obtained by one of two methods. The first method involved screening the results of the genome-wide association analysis for main effects p-values < 1 × 10-4. The second method used biological databases such as the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes to define gene collections that each contained one of four genes with known associations with RA: PTPN22, STAT4, TRAF1, and C5. We used a permutation approach to determine whether any of these SNP sets had empirical enrichment of significant interaction effects. We found that the SNP set obtained by the first method was significantly enriched with significant interaction effects (empirical p = 0.003). Additionally, we found that the "protein complex assembly" collection of genes from the Gene Ontology collection containing the TRAF1 gene was significantly enriched with interaction effects with p-values < 1 × 10-8 (empirical p = 0.012)

Multiethnic genome-wide meta-analysis of ectopic fat depots identifies loci associated with adipocyte development and differentiation

Variation in body fat distribution contributes to the metabolic sequelae of obesity. The genetic determinants of body fat distribution are poorly understood. The goal of this study was to gain new insights into the underlying genetics of body fat distribution by conducting sample-size weighted fixed-effects genome-wide association meta-analyses in up to 9,594 women and 8,738 men for six ectopic fat traits in European, African, Hispanic, and Chinese ancestry populations, with and without sex stratification. In total, 7 new loci were identified in association with ectopic fat traits (ATXN1, UBE2E2, EBF1, RREB1, GSDMB, GRAMD3 and ENSA; PATXN1 and UBE2E2 in primary mouse adipose progenitor cells impaired adipocyte differentiation, suggesting a physiological role for ATXN1 and UBE2E2 in adipogenesis. Future studies are necessary to further explore the mechanisms by which these genes impact adipocyte biology and how their perturbations contribute to systemic metabolic disease

Ankle-Brachial Index Is a Powerful Predictor of Renal Outcome and Cardiovascular Events in Patients with Chronic Kidney Disease

Ankle-brachial index (ABI) is an accurate tool to diagnose peripheral arterial disease. The aim of this study was to evaluate whether ABI is also a good predictor of renal outcome and cardiovascular events in patients with chronic kidney disease (CKD). We enrolled 436 patients with stage 3–5 CKD who had not been undergoing dialysis. Patients were stratified into two groups according to the ABI value with a cut point of 0.9. The composite renal outcome, including doubling of serum creatinine level and commencement of dialysis, and the incidence of cardiovascular events were compared between the two groups. After a median follow-up period of 13 months, the lower ABI group had a poorer composite renal outcome (OR = 2.719, P = 0.015) and a higher incidence of cardiovascular events (OR = 3.260, P = 0.001). Our findings illustrated that ABI is a powerful predictor of cardiovascular events and renal outcome in patients with CKD