Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

ADME pharmacogenetics: investigation of the pharmacokinetics of the antiretroviral agent lopinavir coformulated with ritonavir.

BACKGROUND: An ADME (absorption, distribution, metabolism and excretion)-pharmacogenetics association study may identify functional variants relevant to the pharmacokinetics of lopinavir co-formulated with ritonavir (LPV/r), a first-line anti-HIV agent. METHODS: An extensive search of literature and web resources helped select ADME genes and single nucleotide polymorphisms (SNPs, functional and HapMap tagging SNPs) with a proven or potentially relevant role in LPV/r pharmacokinetics. The study followed a two-stage design. Stage 1 (discovery) considered a Caucasian population (n=638) receiving LPV/r, where we selected 117 individuals with low LPV clearance (cases) and 90 individuals with high clearance (controls). Genotyping was performed by a 1536-SNP customized GoldenGate Illumina BeadArray. Stage 2 (confirmation) represented a replication study of candidate SNPs from the stage 1 in 148 individuals receiving LPV/r. The analysis led to formal population pharmacokinetic-pharmacogenetic modeling of demographic, environmental and candidate SNP effects. RESULTS: One thousand three hundred and eighty SNPs were successfully genotyped. Nine SNPs prioritized by the stage 1 analysis were brought to replication. Stage 2 confirmed the contribution of two functional SNPs in SLCO1B1, one functional SNP in ABCC2 and a tag SNP of the CYP3A locus in addition to body weight effect and ritonavir coadministration. According to the population pharmacokinetic-pharmacogenetic model, genetic variants explained 5% of LPV variability. Individuals homozygous rs11045819 (SLCO1B1*4) had a clearance of 12.6 l/h, compared with 5.4 l/h in the reference group, and 3.9 l/h in individuals with two or more variant alleles of rs4149056 (SLCO1B1*5), rs717620 (ABCC2) or rs6945984 (CYP3A). A subanalysis confirmed that although a significant part of the variance in LPV clearance was attributed to fluctuation in ritonavir levels, genetic variants had an additional effect on LPV clearance. CONCLUSION: The two-stage strategy successfully identified genetic variants affecting LPV/r pharmacokinetics. Such a general approach of ADME pharmacogenetics should be generalized to other drugs.

Gene-nutrient interactions in the metabolic syndrome: single nucleotide polymorphisms in ADIPOQ and ADIPOR1 interact with plasma saturated fatty acids to modulate insulin resistance

Background: Progression of the metabolic syndrome (MetS) is determined by genetic and environmental factors. Gene-environment interactions may be important in modulating the susceptibility to the development of MetS traits. Objective: Gene-nutrient interactions were examined in MetS subjects to determine interactions between single nucleotide polymorphisms (SNPs) in the adiponectin gene (ADIPOQ) and its receptors (ADIPOR1 and ADIPOR2) and plasma fatty acid composition and their effects on MetS characteristics. Design: Plasma fatty acid composition, insulin sensitivity, plasma adiponectin and lipid concentrations, and ADIPOQ, ADIPOR1, and ADIPOR2 SNP genotypes were determined in a cross-sectional analysis of 451 subjects with the MetS who participated in the LIPGENE (Diet, Genomics, and the Metabolic Syndrome: an Integrated Nutrition, Agro-food, Social, and Economic Analysis) dietary intervention study and were repeated in 1754 subjects from the LIPGENE-SU.VI.MAX (SUpplementation en VItamines et Mineraux AntioXydants) case-control study (http://www.ucd.ie/lipgene). Results: Single SNP effects were detected in the cohort. Triacylglycerols, nonesterified fatty acids, and waist circumference were significantly different between genotypes for 2 SNPs (rs266729 in ADIPOQ and rs10920533 in ADIPOR1). Minor allele homozygotes for both of these SNPs were identified as having degrees of insulin resistance, as measured by the homeostasis model assessment of insulin resistance, that were highly responsive to differences in plasma saturated fatty acids (SFAs). The SFA-dependent association between ADIPOR1 rs10920533 and insulin resistance was replicated in cases with MetS from a separate independent study, which was an association not present in controls. Conclusions: A reduction in plasma SFAs could be expected to lower insulin resistance in MetS subjects who are minor allele carriers of rs266729 in ADIPOQ and rs10920533 in ADIPOR1. Personalized dietary advice to decrease SFA consumption in these individuals may be recommended as a possible therapeutic measure to improve insulin sensitivity. This trial was registered at clinicaltrials.

Associação entre polimorfismos de base única e precocidade sexual em fêmeas da raça Nelore

Pós-graduação em Genética e Melhoramento Animal - FCAV

Uso das rotações de givens modificadas como um método direto para obtenção e atualização das soluções em sistemas com acumulação seqüencial de dados

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

The development of genomics applied to dairy breeding

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Genome-wide association study of reproduction traits in Nelore cattle, including additional phenotypic information from non-genotyped animals

Pós-graduação em Genética e Melhoramento Animal - FCAV

Genetic analysis of feet and legs in Nelore cattle

Pós-graduação em Genética e Melhoramento Animal - FCAV

Seleção e associação genômica para precocidade sexual em bovinos da raça Nelore

Pós-graduação em Genética e Melhoramento Animal - FCAV

Genome-wide association study reveals greater polygenic loading for schizophrenia in cases with a family history of illness

Genome-wide association studies (GWAS) of schizophrenia have yielded more than 100 common susceptibility variants, and strongly support a substantial polygenic contribution of a large number of small allelic effects. It has been hypothesized that familial schizophrenia is largely a consequence of inherited rather than environmental factors. We investigated the extent to which familiality of schizophrenia is associated with enrichment for common risk variants detectable in a large GWAS. We analyzed single nucleotide polymorphism (SNP) data for cases reporting a family history of psychotic illness (N = 978), cases reporting no such family history (N = 4,503), and unscreened controls (N = 8,285) from the Psychiatric Genomics Consortium (PGC1) study of schizophrenia. We used a multinomial logistic regression approach with model-fitting to detect allelic effects specific to either family history subgroup. We also considered a polygenic model, in which we tested whether family history positive subjects carried more schizophrenia risk alleles than family history negative subjects, on average. Several individual SNPs attained suggestive but not genome-wide significant association with either family history subgroup. Comparison of genome-wide polygenic risk scores based on GWAS summary statistics indicated a significant enrichment for SNP effects among family history positive compared to family history negative cases (Nagelkerke's R(2 ) = 0.0021; P = 0.00331; P-value threshold <0.4). Estimates of variability in disease liability attributable to the aggregate effect of genome-wide SNPs were significantly greater for family history positive compared to family history negative cases (0.32 and 0.22, respectively; P = 0.031). We found suggestive evidence of allelic effects detectable in large GWAS of schizophrenia that might be specific to particular family history subgroups. However, consideration of a polygenic risk score indicated a significant enrichment among family history positive cases for common allelic effects. Familial illness might, therefore, represent a more heritable form of schizophrenia, as suggested by previous epidemiological studies.

Increasing the power of genome wide association studies in natural populations using repeated measures : evaluation and implementation

1. Genomewide association studies (GWAS) enable detailed dissections of the genetic basis for organisms' ability to adapt to a changing environment. In long-term studies of natural populations, individuals are often marked at one point in their life and then repeatedly recaptured. It is therefore essential that a method for GWAS includes the process of repeated sampling. In a GWAS, the effects of thousands of single-nucleotide polymorphisms (SNPs) need to be fitted and any model development is constrained by the computational requirements. A method is therefore required that can fit a highly hierarchical model and at the same time is computationally fast enough to be useful. 2. Our method fits fixed SNP effects in a linear mixed model that can include both random polygenic effects and permanent environmental effects. In this way, the model can correct for population structure and model repeated measures. The covariance structure of the linear mixed model is first estimated and subsequently used in a generalized least squares setting to fit the SNP effects. The method was evaluated in a simulation study based on observed genotypes from a long-term study of collared flycatchers in Sweden. 3. The method we present here was successful in estimating permanent environmental effects from simulated repeated measures data. Additionally, we found that especially for variable phenotypes having large variation between years, the repeated measurements model has a substantial increase in power compared to a model using average phenotypes as a response. 4. The method is available in the R package RepeatABEL. It increases the power in GWAS having repeated measures, especially for long-term studies of natural populations, and the R implementation is expected to facilitate modelling of longitudinal data for studies of both animal and human populations.

Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs.

The aim of the present study was to propose and evaluate the use of factor analysis (FA) in obtaining latent variables (factors) that represent a set of pig traits simultaneously, for use in genome-wide selection (GWS) studies. We used crosses between outbred F2 populations of Brazilian Piau X commercial pigs. Data were obtained on 345 F2 pigs, genotyped for 237 SNPs, with 41 traits. FA allowed us to obtain four biologically interpretable factors: ?weight?, ?fat?, ?loin?, and ?performance?. These factors were used as dependent variables in multiple regression models of genomic selection (Bayes A, Bayes B, RR-BLUP, and Bayesian LASSO). The use of FA is presented as an interesting alternative to select individuals for multiple variables simultaneously in GWS studies; accuracy measurements of the factors were similar to those obtained when the original traits were considered individually. The similarities between the top 10% of individuals selected by the factor, and those selected by the individual traits, were also satisfactory. Moreover, the estimated markers effects for the traits were similar to those found for the relevant factor.

Application of Multi-SNP Approaches Bayesian LASSO and AUC-RF to Detect Main Effects of Inflammatory-Gene Variants Associated with Bladder Cancer Risk

The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL), a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk.

A MULTILEVEL MODEL TO ADDRESS BATCH EFFECTS IN COPY NUMBER ESTIMATION USING SNP ARRAYS

Submicroscopic changes in chromosomal DNA copy number dosage are common and have been implicated in many heritable diseases and cancers. Recent high-throughput technologies have a resolution that permits the detection of segmental changes in DNA copy number that span thousands of basepairs across the genome. Genome-wide association studies (GWAS) may simultaneously screen for copy number-phenotype and SNP-phenotype associations as part of the analytic strategy. However, genome-wide array analyses are particularly susceptible to batch effects as the logistics of preparing DNA and processing thousands of arrays often involves multiple laboratories and technicians, or changes over calendar time to the reagents and laboratory equipment. Failure to adjust for batch effects can lead to incorrect inference and requires inefficient post-hoc quality control procedures that exclude regions that are associated with batch. Our work extends previous model-based approaches for copy number estimation by explicitly modeling batch effects and using shrinkage to improve locus-specific estimates of copy number uncertainty. Key features of this approach include the use of diallelic genotype calls from experimental data to estimate batch- and locus-specific parameters of background and signal without the requirement of training data. We illustrate these ideas using a study of bipolar disease and a study of chromosome 21 trisomy. The former has batch effects that dominate much of the observed variation in quantile-normalized intensities, while the latter illustrates the robustness of our approach to datasets where as many as 25% of the samples have altered copy number. Locus-specific estimates of copy number can be plotted on the copy-number scale to investigate mosaicism and guide the choice of appropriate downstream approaches for smoothing the copy number as a function of physical position. The software is open source and implemented in the R package CRLMM available at Bioconductor (http:www.bioconductor.org).

Effects of Perilipin (PLIN) Gene Variation on Metabolic Syndrome Risk and Weight Loss in Obese Children and Adolescents

Context: Genetic polymorphisms at the perilipin (PLIN) locus have been investigated for their potential utility as markers for obesity and metabolic syndrome (MS). We examined in obese children and adolescents (OCA) aged 7-14 yr the association of single-nucleotide polymorphisms (SNP) at the PLIN locus with anthropometric, metabolic traits, and weight loss after 20-wk multi-disciplinary behavioral and nutritional treatment without medication. Design: A total of 234 OCA [body mass index (BMI = 30.4 +/- 4.4 kg/m(2); BMI Z-score = 2.31 +/- 0.4) were evaluated at baseline and after intervention. We genotyped four SNPs (PLIN1 6209T -> C, PLIN4 11482G -> A, PLIN5 13041A -> G, and PLIN6 14995A -> T). Results: Allele frequencies were similar to other populations, PLIN1 and PLIN4 were in linkage disequilibrium (D` = 0.999; P < 0.001). At baseline, no anthropometric differences were observed, but minor allele A at PLIN4 was associated with higher triglycerides (111 +/- 49 vs. 94 +/- 42 mg/dl; P = 0.003), lower high-density lipoprotein cholesterol (40 +/- 9 vs. 44 +/- 10 mg/dl; P = 0.003) and higher homeostasis model assessment for insulin resistance (4.0 +/- 2.3 vs. 3.5 +/- 2.1; P +/- 0.015). Minor allele A at PLIN4 was associated with MS risk (age and sex adjusted) hazard ratio 2.4 (95% confidence interval = 1.1-4.9) for genotype GA and 3.5 (95% confidence interval = 1.2-9.9) for AA. After intervention, subjects carrying minor allele T at PLIN6 had increased weight loss (3.3 +/- 3.7 vs. 1.9 +/- 3.4 kg; P = 0.002) and increased loss of the BMI Z-score (0.23 +/- 0.18 vs. 0.18 +/- 0.15; P +/- 0.003). Due to group size, risk of by-chance findings cannot be excluded. Conclusion: The minor A allele at PLIN4 was associated with higher risk of MS at baseline, whereas the PLIN6 SNP was associated with better weight loss, suggesting that these polymorphisms may predict outcome strategies based on multidisciplinary treatment for OCA. (J Clin Endocrinol Metab 93: 4933-4940, 2008)