Real Genotype Research Articles

Lack of Association of BRCA1 and BRCA2 Variants with Breast Cancer in an Ethnic Population of Saudi Arabia, an Emerging High-Risk Area

Incidence of breast cancer shows geographical variation, even within areas of ethnic homogeneity. Saudi Arabia has witnessed an increase in occurrence of breast cancer in its unexplored ethnic populations over the past few years. We aimed at determining whether any association exists between single nucleotide polymorphisms in breast cancer associated gene 1 (BRCA1) and breast cancer associated gene 2 (BRCA2) and the risk of breast cancer. TaqMan based Real Time Polymerase chain reaction genotyping assays were used to determine the frequency of single nucleotide polymorphisms in BRCA1 (rs799917) and BRCA2 (rs144848) in a group of 100 breast cancer patients and unaffected age matched controls of Saudi Arabian origin. The present data revealed that neither BRCA1 nor the BRCA2 studied variant show any significant association with the disease. This study failed to find any role of the concerned variants in breast cancer either as risk or as prognostic factors. The small number of patients registered was one of the limitations of this study. In summary, comparison of mutation profile with other ethnic populations and regions reflected both differences and similarities indicating co-exposure to a unique set of risk factors. The differences could be due to exposure to particular environmental carcinogens; different lifestyle, reproductive pattern; dietary or cultural practices of Saudi Arabian women that need further investigations.

Dynamic interplay between CXCL levels in chronic hepatitis C patients treated by interferon.

BackgroundCombined pegylated interferon-α and ribavirin therapy has sustained virological response (SVR) rates of 54% to 61%. Pretreatment predictors of SVR to interferon therapy have not been fully investigated yet. The current study assesses a group of chemokines that may predict treatment response in Egyptian patients with chronic HCV infection.Patients and methodsCXCL5, CXCL9, CXCL11, CXCL12, CXCL 13, CXCL 16 chemokines and E-Cadherin were assayed in 57 chronic HCV patients’ sera using quantitative ELISA plate method. All studied patients were scheduled for combined pegylated interferon alpha and ribavirin therapy (32 patients received pegylated interferon α 2b, and 25 patients received pegylated interferon α 2a). Quantitative hepatitis C virus RNA was done by real time RT-PCR and HCV genotyping by INNOLIPAII.ResultsThere was no significant difference (p > 0.05) in baseline HCV RNA levels between responders and non-responders to interferon. A statistically significant difference in CXCL13 (p = 0.017) and E-Cadherin levels (P = 0.041) was reported between responders and nonresponders at week 12. Significant correlations were found between changes in the CXCL13 levels and CXCL9, CXCL16, E-cadherin levels as well as between changes in E-cadherin levels and both CXCL16 and ALT levels that were maintained during follow up. Also, significant changes have been found in the serum levels of CXCL5, CXCL13, and CXCL16 with time (before pegylated interferon α 2 a and α 2 b therapy, and at weeks 12 and 24) with no significant difference in relation to interferon type and response to treatment.ConclusionSerum levels of CXCL13 and E-Cadherin could be used as surrogate markers to predict response of combined PEG IFN-α/RBV therapy, especially at week 12. However, an extended study including larger number of patients is needed for validation of these findings.Clinical trial NoNCT01758939

The use of allelic imbalance to ascertain cis-regulation for human UGT2B7 in vivo

The use of allelic imbalance to ascertain cis-regulation for human UGT2B7 in vivo

Genetic Basis of Chronic Hepatitis C Virus and Autoimmune Hepatitis: A Comparative Study

Background: It is now well established that HCV is of global importance affecting all countries, leading to a major global health problem that requires widespread active interventions for its prevention and control. Chronic hepatitis C was linked to the development of cirrhosis and Hepatocellular Carcinoma (HCC) in many areas of the world. WHO reported that Egypt has the highest prevalence (22%) in the world? Objectives: Susceptibility to infection has been related to immunological disturbances.HCV and autoimmune hepatitis have been associated with HLA A1, A6, A8,-DRB∗01-∗15 alleles. Methods: HLA alleles were detected by Sequence Specific Oligoneucleotide Probe (SSOP) using INNO-LiPA based on reverse hybridization after amplification of target HLA allele sequence by Real Time Polymerase Chain Reaction (RT-PCR). The non-organ-specific Antibodies Include Antinuclear (ANA), Smooth Muscle (ASMA), Anti- Mitochondrial Antibody (AMA), Perinuclear Antineutrophil Cytoplasmic(pANCA), Liver-Kidney Microsomal Antibodies (LK/MA) and Anticardiolipn Antibodies (ACA) were all measured by commercial Indirect ImmunoFluorescence (IF). Clinical significance of these autoantibodies was analyzed in comparison to HCV load by Real Time Polymerase Chain Reaction (RT-PCR) and genotype by 4 byline probe assay INNO-LiPA. Conclusion: Current understanding of genetic basis (HLA alleles) of chronic HCV genotype4 cases and autoimmune hepatitis cases free from HCV will aid a lot in treating both types of hepatitis using successful line of therapy as early as possible to save money and side effects of wrong medications.

Distribution of Hepatitis C Virus Genotypes in Kayseri Region, in Turkey: Unexpected Rate of Genotype 4

Hepatitis C virus (HCV) is an important cause of chronic liver disease. There are six genotypes and more than 80 subtypes of HCV. The aim of this study was to investigate the distribution of HCV genotypes in Middle Anatolia in Turkey, and the association of HCV genotypes with pre-treatment HCV RNA viral load, serum transaminase levels, and histopathological grade of liver fibrosis. A total of 160 patients (103 female, 57 male) with chronic hepatitis C were retrospectively evaluated. HCV RNA level was determined by commercial real time PCR method. HCV RNA positive sera were genotyped by the Abbott Real Time HCV Genotype II assay and sequenced by the ABI Prism 310 Genetic Analyzer. Gender, age, serum ALT, AST, HCV RNA viral load, and fibrosis staging of liver were determined in all patients. Genotype 1b was the most frequent (64.7%) followed by genotype 4d (28.3%), 2 (4.4%), and la (2.5%). The HCV genotype results were found consistent with both methods. The gender distribution of the 160 HCV infected patients was 57 male/103 female. Log HCVRNA was significantly higher in genotype 1b compared to genotype 4 and 1a. Stage of liver fibrosis, histology activity index, serum ALT and AST levels did not differ between groups depending on genotypes. Advanced liver fibrosis (Group 2) was found in 36 (76.6%) patients with genotype 1b and in 10 (21.3%) patients with genotype 4 and only in 1 patient (2.1%) with genotype 1a. HCV genotype 1b is the most frequent type (64.7%) in this region. Prevalence of genotype 4 in this region is higher than the national HCV genotype distribution. Serum transaminase levels and liver fibrosis scores are not associated with HCV genotypes.

Could Human Leukocyte Antigens (HLA) be Predictive Factors to Interferon Response among Chronic Hepatitis C Virus Hepatitis?

Prevalence of Hepatitis C virus (HCV) in Egypt is 22% as reported by World Health Organization (WHO) 2012. Interferon (IFN)-based treatments are currently the main therapeutic option. However, depending upon variations in their human leukocyte antigen (HLA), some patients do not respond well to IFN therapy. The current study evaluated some HLA class II alleles among 200 HCV positive individuals from Alexandria, Egypt, who were receiving standard IFN therapy. In this study, 30 patients (33.3 %) showed a sustained virological response (SVR) to IFN therapy, whereas 30 (33.3 %) did not and 30 (33.3%) cleared the virus spontaneously. 30 unrelated healthy volunteers served as controls. DNA was extracted by spin column method from lysed blood of all enrollees for HLA-DRB1 and HLADQB1 allele typing by sequence specific oligonucleotide probe (SSOP), whilst plasma was used for HCV quantitation by real time polymerase chain reaction (RT-PCR) and genotyping by line probe assay INNO LiPA (Innogenetics). HLA-DRB1*03 individually (p=0.025) or in combination HLA-DRB1*04 (p=0.035) revealed to be significant protective alleles against HCV infection. In patients on IFN therapy, HLA-DRB1*11 was significantly associated with viral clearance. In contrast, HLA-DRB1*07 (p=0.005) was associated with viral persistence. We can conclude that certain HLA class II alleles could predict response to IFN therapy as early as possible before starting treatment of chronic HCV cases and can be used as successful guide to clinicians in deciding the therapeutic regimen for Egyptian patients infected with HCV genotype 4.

The power of regional heritability analysis for rare and common variant detection: simulations and application to eye biometrical traits

Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of complex traits. However, they have explained relatively little trait heritability. Recently, we proposed a new analytical approach called regional heritability mapping (RHM) that captures more of the missing genetic variation. This method is applicable both to related and unrelated populations. Here, we demonstrate the power of RHM in comparison with single-SNP GWAS and gene-based association approaches under a wide range of scenarios with variable numbers of quantitative trait loci (QTL) with common and rare causal variants in a narrow genomic region. Simulations based on real genotype data were performed to assess power to capture QTL variance, and we demonstrate that RHM has greater power to detect rare variants and/or multiple alleles in a region than other approaches. In addition, we show that RHM can capture more accurately the QTL variance, when it is caused by multiple independent effects and/or rare variants. We applied RHM to analyze three biometrical eye traits for which single-SNP GWAS have been published or performed to evaluate the effectiveness of this method in real data analysis and detected some additional loci which were not detected by other GWAS methods. RHM has the potential to explain some of missing heritability by capturing variance caused by QTL with low MAF and multiple independent QTL in a region, not captured by other GWAS methods. RHM analyses can be implemented using the software REACTA (http://www.epcc.ed.ac.uk/projects-portfolio/reacta).

Comparison of methods to detect copy number alterations in cancer using simulated and real genotyping data

BackgroundThe detection of genomic copy number alterations (CNA) in cancer based on SNP arrays requires methods that take into account tumour specific factors such as normal cell contamination and tumour heterogeneity. A number of tools have been recently developed but their performance needs yet to be thoroughly assessed. To this aim, a comprehensive model that integrates the factors of normal cell contamination and intra-tumour heterogeneity and that can be translated to synthetic data on which to perform benchmarks is indispensable.ResultsWe propose such model and implement it in an R package called CnaGen to synthetically generate a wide range of alterations under different normal cell contamination levels. Six recently published methods for CNA and loss of heterozygosity (LOH) detection on tumour samples were assessed on this synthetic data and on a dilution series of a breast cancer cell-line: ASCAT, GAP, GenoCNA, GPHMM, MixHMM and OncoSNP. We report the recall rates in terms of normal cell contamination levels and alteration characteristics: length, copy number and LOH state, as well as the false discovery rate distribution for each copy number under different normal cell contamination levels.Assessed methods are in general better at detecting alterations with low copy number and under a little normal cell contamination levels. All methods except GPHMM, which failed to recognize the alteration pattern in the cell-line samples, provided similar results for the synthetic and cell-line sample sets. MixHMM and GenoCNA are the poorliest performing methods, while GAP generally performed better. This supports the viability of approaches other than the common hidden Markov model (HMM)-based.ConclusionsWe devised and implemented a comprehensive model to generate data that simulate tumoural samples genotyped using SNP arrays. The validity of the model is supported by the similarity of the results obtained with synthetic and real data. Based on these results and on the software implementation of the methods, we recommend GAP for advanced users and GPHMM for a fully driven analysis.

An accurate and reliable real time SNP genotyping assay for the HLA‐G +3142 bp C>G polymorphism

Human leukocyte antigen (HLA)-G is a non classical HLA class I antigen with immuno-modulatory functions. The HLA-G gene is characterized by a +3142C>G variant in the 3' untranslated region which is suggested to control protein production and to be associated with pathological conditions. DNAs form 221 randomly selected healthy subjects were genotyped for HLA-G +3142C>G polymorphism by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) (BaeGI), real-time PCR and sequencing. The 19% of the PCR-RFLP heterozygous samples were genotyped as 3142GG by real-time PCR and sequencing. This disagreement is caused by digestion efficiency in PCR-RFLP. This real-time PCR method will guarantee an accurate genotyping for future research and clinical purposes, where large cohorts should be tested.

VIPER: a visualisation tool for exploring inheritance inconsistencies in genotyped pedigrees

BackgroundPedigree genotype datasets are used for analysing genetic inheritance and to map genetic markers and traits. Such datasets consist of hundreds of related animals genotyped for thousands of genetic markers and invariably contain multiple errors in both the pedigree structure and in the associated individual genotype data. These errors manifest as apparent inheritance inconsistencies in the pedigree, and invalidate analyses of marker inheritance patterns across the dataset. Cleaning raw datasets of bad data points (incorrect pedigree relationships, unreliable marker assays, suspect samples, bad genotype results etc.) requires expert exploration of the patterns of exposed inconsistencies in the context of the inheritance pedigree. In order to assist this process we are developing VIPER (Visual Pedigree Explorer), a software tool that integrates an inheritance-checking algorithm with a novel space-efficient pedigree visualisation, so that reported inheritance inconsistencies are overlaid on an interactive, navigable representation of the pedigree structure.Methods and resultsThis paper describes an evaluation of how VIPER displays the different scales and types of dataset that occur experimentally, with a description of how VIPER's display interface and functionality meet the challenges presented by such data. We examine a range of possible error types found in real and simulated pedigree genotype datasets, demonstrating how these errors are exposed and explored using the VIPER interface and we evaluate the utility and usability of the interface to the domain expert.Evaluation was performed as a two stage process with the assistance of domain experts (geneticists). The initial evaluation drove the iterative implementation of further features in the software prototype, as required by the users, prior to a final functional evaluation of the pedigree display for exploring the various error types, data scales and structures.ConclusionsThe VIPER display was shown to effectively expose the range of errors found in experimental genotyped pedigrees, allowing users to explore the underlying causes of reported inheritance inconsistencies. This interface will provide the basis for a full data cleaning tool that will allow the user to remove isolated bad data points, and reversibly test the effect of removing suspect genotypes and pedigree relationships.

Identification of polymorphic inversions from genotypes

BackgroundPolymorphic inversions are a source of genetic variability with a direct impact on recombination frequencies. Given the difficulty of their experimental study, computational methods have been developed to infer their existence in a large number of individuals using genome-wide data of nucleotide variation. Methods based on haplotype tagging of known inversions attempt to classify individuals as having a normal or inverted allele. Other methods that measure differences between linkage disequilibrium attempt to identify regions with inversions but unable to classify subjects accurately, an essential requirement for association studies.ResultsWe present a novel method to both identify polymorphic inversions from genome-wide genotype data and classify individuals as containing a normal or inverted allele. Our method, a generalization of a published method for haplotype data [1], utilizes linkage between groups of SNPs to partition a set of individuals into normal and inverted subpopulations. We employ a sliding window scan to identify regions likely to have an inversion, and accumulation of evidence from neighboring SNPs is used to accurately determine the inversion status of each subject. Further, our approach detects inversions directly from genotype data, thus increasing its usability to current genome-wide association studies (GWAS).ConclusionsWe demonstrate the accuracy of our method to detect inversions and classify individuals on principled-simulated genotypes, produced by the evolution of an inversion event within a coalescent model [2]. We applied our method to real genotype data from HapMap Phase III to characterize the inversion status of two known inversions within the regions 17q21 and 8p23 across 1184 individuals. Finally, we scan the full genomes of the European Origin (CEU) and Yoruba (YRI) HapMap samples. We find population-based evidence for 9 out of 15 well-established autosomic inversions, and for 52 regions previously predicted by independent experimental methods in ten (9+1) individuals [3,4]. We provide efficient implementations of both genotype and haplotype methods as a unified R package inveRsion.

P5-05-05: Human Papiloma Virus Identification in Intraductal Papilloma and Breast Cancer Using Broad-Spectrum Primers.

Abstract Background There is limited data about Human Papiloma Virus (HPV) presence in intraductal papilloma, and there are some conflicted studies about the effects of HPVs on breast cancer. Most of these studies have methodological pitfalls as well. We conducted a retrospective paraffin block study to understand the presence of HPV in papilloma and cancer tissues with broad-spectrum primers. Methods: Twenty seven intraductal papilloma and 18 breast cancer patients’ paraffin blocks were obtained from the patients who underwent ductoscopy aided papillomectomy or mastectomy. In totally 87 histopathological samples, HPV-DNA were identified by real time PCR and broad-spectrum genotyping was performed by in situ hybridization. The cancer patients were evaluated according to the status of Estrojen Reseptor (ER), Progesteron Reseptor (PR) and ErbB-2 positivity. Results: The mean age±SD was 49±16 in papilloma and 52±14 in breast cancer patients. We found that 29.6% (8 of 27) of intraductal papillomas and 44.4% (8 of 18) of breast cancer paraffin blocks had HPV-DNA positivity by PCR in situ hybridization with broad spectrum primers. Fifty percent (4 of 8) of HPV-DNA positive papillomas had only type 11; in one, only type 6; in one, mixture of type 6,11; in one, mixture of type 11, 39; and in one, mixture of type 11, 39, 82 was detected. Breast cancer lesions had only type 11 of HPV-DNA positivity in 62.5% (5 of 8) of cases; in one, mixture of type11, 39; in one, mixture of type 11, 82; in one, mixture of type 6, 11, 39 was detected. In seven HPV-DNA positive cancer patients, ER/PR and/or ErbB-2 was positive, but it was only positive in one triple negative patient. Conclusion: HPVs may have a role in developing papilloma and breast cancer. Validation of HPVs role in the etiology of breast cancer/papilloma, require further studies to understand whether the HPVs are episomal or integrated during the tumorigenesis. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P5-05-05.

The genetic polymorphism of Plasmodium vivax genes in endemic regions of Thailand

Objective To investigate the genetic polymorphism of Plasmodium vivax ( P. vivax) PvCSP and PvMSP1 genes from field isolates at four endemic regions (North, East, West and South) of Thailand. Methods The 152 P. vivax infected cases from dried blood spots were DNA extracted and confirmed by species-specific primer sets using multiplex PCR method. PvMSP1 fragments F2 and F3; PvCSP were genotyped using RFLP-PCR method. Results Totally amplified DNA which was multiple genotypes for PvMSP1 F2 and PvMSP1 F3 were 12.50% and 8.55%, respectively while PvCSP was 3.95%. The overall frequency of multiple genotypes was 25%. There were 12 allele types of PvMSP1 F2 using AluI enzyme digestion and 8 size variations were found in PvMSP1 F3. The isolates from western region was highly genetic diverse when compare among all isolates. The predominant variant type of PvCSP gene was VK210 type. Conclusions The multiple genotypes are common found in Thailand and it might hide the real genotype. PvCSP does not have extensive genetic diversity in this study. However, PvMSP1 marker due to multiple genotypes is difficult to be analyzed. The multiple genotypes findings might stem from population migration and vector species findings.

Methods for Identifying SNP Interactions: A Review on Variations of Logic Regression, Random Forest and Bayesian Logistic Regression

Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.

Identity‐by‐descent‐based phasing and imputation in founder populations using graphical models

Accurate knowledge of haplotypes, the combination of alleles co-residing on a single copy of a chromosome, enables powerful gene mapping and sequence imputation methods. Since humans are diploid, haplotypes must be derived from genotypes by a phasing process. In this study, we present a new computational model for haplotype phasing based on pairwise sharing of haplotypes inferred to be Identical-By-Descent (IBD). We apply the Bayesian network based model in a new phasing algorithm, called systematic long-range phasing (SLRP), that can capitalize on the close genetic relationships in isolated founder populations, and show with simulated and real genome-wide genotype data that SLRP substantially reduces the rate of phasing errors compared to previous phasing algorithms. Furthermore, the method accurately identifies regions of IBD, enabling linkage-like studies without pedigrees, and can be used to impute most genotypes with very low error rate. Genet. Epidemiol. 2011. © 2011 Wiley Periodicals, Inc.35:853-860, 2011

Visualising Errors in Animal Pedigree Genotype Data

AbstractGenetic analysis of a breeding animal population involves determining the inheritance pattern of genotypes for multiple genetic markers across the individuals in the population pedigree structure. However, experimental pedigree genotype data invariably contains errors in both the pedigree structure and in the associated individual genotypes, introducing inconsistencies into the dataset, rendering them useless for further analysis. The resolution of these errors requires consideration of genotype inheritance patterns in the context of the pedigree structure. Existing pedigree visualisations are typically more suited to human pedigrees and are less suitable for large complex animal pedigrees which may exhibit cross generational inbreeding. Similarly, table‐based viewers of genotype marker data can highlight where errors become apparent but lack the functionality and interactive visual feedback to allow users to locate the origin of errors within the pedigree. In this paper, we detail a design study steered by biologists who work with pedigree data, and describe successive iterations through approaches and prototypes for viewing genotyping errors in the context of a displayed pedigree. We describe how each approach performs with real pedigree genotype data and why eventually we deemed them unsuitable. Finally, a novel prototype visualisation for pedigrees, which we term the ‘sandwich view’, is detailed and we demonstrate how the approach effectively communicates errors in the pedigree context, supporting the biologist in the error identification task.

Significant association of genetic polymorphism of human nonmetastatic clone 23 type 1 gene with an increased risk of endometrial cancer

Objective To investigate the association of single nucleotide polymorphisms (SNPs) of nonmetastatic clone 23 type 1 (nm23-H1) gene with endometrial cancer and their implication in clinicopathologic characteristics of women in Taiwan. Methods Three hundred and fifty-nine blood samples were collected from 268 healthy women and 91 patients with endometrial cancer to analyze SNPs rs16949649 and rs2302254 of nm23-H1 promoter using real time polymerase chain reaction and genotyping. The association of genotype and allele differences of nm23-H1 SNPs with endometrial cancer and their implication in some clinicopathologic variables were analyzed using Pearson's Chi-square or Fisher exact tests. Results Women with heterozygous genotypes TC in rs16949649 or CT in rs2302254 exhibited higher risk to develop endometrial cancer as compared to those with their wild-type or homozygous genotypes (odds ratio 3.30 and 1.86; 1.84 and 1.90 for respective SNP). Individuals with CC genotype were at less risk (OR: 0.08; P = 0.037) to have non-endometrioid type as compared to those with TT genotype in rs16949649. However, a trend of increased risk (OR: 26.67; P = 0.01) of advanced stage endometrial cancer (stage III–IV) was observed in patients with TT genotype as compared to those with CC genotype in rs2302254. Conclusions Heterozygous genotypes TC in rs16949649 and CT in rs2302254 of nm23-H1 promoter are potential susceptibility factors for endometrial cancer in Taiwan women. Once having the endometrial cancer, Taiwan women with variant homozygote CC in rs1694964 were at less risk to have non-endometrioid type, while women with variant homozygote TT in rs2302254 tended to have advanced stage cancer.

GWAF: an R package for genome-wide association analyses with family data

GWAF, Genome-Wide Association analyses with Family, is an R package designed for GWAF. It implements association tests between a batch of genotyped or imputed single nucleotide polymorphisms (SNPs) and a binary or continuous trait with user specified genetic model, and generates informative results from the analyses. In addition, GWAF provides functions to visualize results. We evaluated GWAF using a simulated continuous trait and a binary trait dichotomized from the simulated continuous trait with real genotype data from the Framingham Heart Study's SNP Health Association Resource project.

The Accuracy of Genomic Selection in Norwegian Red Cattle Assessed by Cross-Validation

Genomic Selection (GS) is a newly developed tool for the estimation of breeding values for quantitative traits through the use of dense markers covering the whole genome. For a successful application of GS, accuracy of the prediction of genomewide breeding value (GW-EBV) is a key issue to consider. Here we investigated the accuracy and possible bias of GW-EBV prediction, using real bovine SNP genotyping (18,991 SNPs) and phenotypic data of 500 Norwegian Red bulls. The study was performed on milk yield, fat yield, protein yield, first lactation mastitis traits, and calving ease. Three methods, best linear unbiased prediction (G-BLUP), Bayesian statistics (BayesB), and a mixture model approach (MIXTURE), were used to estimate marker effects, and their accuracy and bias were estimated by using cross-validation. The accuracies of the GW-EBV prediction were found to vary widely between 0.12 and 0.62. G-BLUP gave overall the highest accuracy. We observed a strong relationship between the accuracy of the prediction and the heritability of the trait. GW-EBV prediction for production traits with high heritability achieved higher accuracy and also lower bias than health traits with low heritability. To achieve a similar accuracy for the health traits probably more records will be needed.

Power analysis of principal components regression in genetic association studies

Association analysis provides an opportunity to find genetic variants underlying complex traits. A principal components regression (PCR)-based approach was shown to outperform some competing approaches. However, a limitation of this method is that the principal components (PCs) selected from single nucleotide polymorphisms (SNPs) may be unrelated to the phenotype. In this article, we investigate the theoretical properties of such a method in more detail. We first derive the exact power function of the test based on PCR, and hence clarify the relationship between the test power and the degrees of freedom (DF). Next, we extend the PCR test to a general weighted PCs test, which provides a unified framework for understanding the properties of some related statistics. We then compare the performance of these tests. We also introduce several data-driven adaptive alternatives to overcome difficulties in the PCR approach. Finally, we illustrate our results using simulations based on real genotype data. Simulation study shows the risk of using the unsupervised rule to determine the number of PCs, and demonstrates that there is no single uniformly powerful method for detecting genetic variants.