Case-control Genome-wide Association Studies Research Articles

Stochastic model search with binary outcomes for genome-wide association studies

ObjectiveThe spread of case–control genome-wide association studies (GWASs) has stimulated the development of new variable selection methods and predictive models. We introduce a novel Bayesian model search algorithm, Binary Outcome Stochastic Search (BOSS), which addresses the model selection problem when the number of predictors far exceeds the number of binary responses.Materials and methodsOur method is based on a latent variable model that links the observed outcomes to the underlying genetic variables. A Markov Chain Monte Carlo approach is used for model search and to evaluate the posterior probability of each predictor.ResultsBOSS is compared with three established methods (stepwise regression, logistic lasso, and elastic net) in a simulated benchmark. Two real case studies are also investigated: a GWAS on the genetic bases of longevity, and the type 2 diabetes study from the Wellcome Trust Case Control Consortium. Simulations show that BOSS achieves higher precisions than the reference methods while preserving good recall rates. In both experimental studies, BOSS successfully detects genetic polymorphisms previously reported to be associated with the analyzed phenotypes.DiscussionBOSS outperforms the other methods in terms of F-measure on simulated data. In the two real studies, BOSS successfully detects biologically relevant features, some of which are missed by univariate analysis and the three reference techniques.ConclusionThe proposed algorithm is an advance in the methodology for model selection with a large number of features. Our simulated and experimental results showed that BOSS proves effective in detecting relevant markers while providing a parsimonious model.

Abstract 1866: Genome-wide association study for irinotecan-related severe toxicities in patients with advanced non-small cell lung cancer

Abstract Purpose: We performed a case-control genome-wide association study (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with severe diarrhea and neutropenia in patients treated with irinotecan for advanced non-small cell lung cancer (NSCLC). Patients and Methods: We performed a case-control GWAS using the Affymetrix 5.0 array set in 103 patients enrolled in two phase II studies of with irinotecan and cisplatin. A subsequent replication study was conducted to validate major GWAS findings in an independent series of 146 patients enrolled in another phase II study of irinotecan-based chemotherapy. Cases were defined as patients with grade 3 diarrhea (G3D) or grade 4 neutropenia (G4N) according to the National Cancer institute's Common Terminology Criteria for adverse events v3.0. Results: The GWAS included 11 G3D (control 90) and 24 G4N (control 77). The replication set included 13 G3D (control 133) and 21 G4N (control 125). GWAS identified 49 and 32 SNPs putatively associated with G3D and G4N, respectively. In the replication series, SNPs, rs1517114 (C8orf34), rs1661167 (FLJ41856), and rs2745761 (PLCB1) were confirmed for G3D, and rs11128347 (PDZRN3), rs11979430 and rs7779029 (SEMAC3) were confirmed for G4N. Conclusion: This GWAS screen along with subsequent validation indentified novels SNPs associated with irinotecan-related severe toxicities. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1866. doi:1538-7445.AM2012-1866

A genome-wide association study to identify genetic markers associated with endometrial cancer grade

Endometrial cancer is the most commonly diagnosed gynaecological cancer. Although endometrioid endometrial cancer (80% of cases) generally carries a good prognosis, some patients with this tumour subtype relapse within two years. Identifying genetic variants associated with prognosis could inform clinical decision-making for management at diagnosis, and inform development of chemotherapeutic agents targeting aggressive disease. Genome-wide association studies (GWAS) have been successful in identifying common genetic variation involved in cancer susceptibility. Presently there are limited published studies using GWAS data to identify single nucleotide polymorphisms (SNPs) associated with tumour prognostic indicators, such as grade. We used case data from an endometrial cancer case-control GWAS to assess association of SNPs with endometrial cancer grade. Genome-wide genotyping of 1285 Australian and British women with endometrioid endometrial cancer and reporting Caucasian ethnicity was performed using the Illumina 610K BeadChip. After applying quality control measures, data on 583,366 SNPs for 1220 cases with grade information were used in the analysis. PLINK software was used to assess SNP association with grade (1, 2 or 3), adjusting for study group (Australian or British). Fifty-seven SNPs were found to be significant at <10–4. Two variants with evidence of association with higher endometrial cancer grade (p-trend<10–6) have been selected for validation in independent sample sets. These SNPs are located in or near genes not previously reported to be involved in cancer aetiology or prognosis and, if confirmed, would represent novel gene targets. Neither of these SNPs fall into the top 1500 SNPs prioritised for validation of association with risk. Results to date suggest that genetic alleles associated with prognostic features, such as cancer grade, may be distinct from those associated with predisposition. GWAS analysis of tumour prognostic features is thus likely to improve understanding of biological pathways influencing outcome for endometrial cancer patients.

Evaluation of common genetic variants identified by GWAS for early onset and morbid obesity in population-based samples

Meta-analysis of case-control genome-wide association studies (GWAS) for early onset and morbid obesity identified four variants in/near the PRL, PTER, MAF and NPC1 genes. We aimed to validate association of these variants with obesity-related traits in population-based samples. Genotypes and anthropometric traits were available in up to 31 083 adults from the Fenland, EPIC-Norfolk, Whitehall II, Ely and Hertfordshire studies and in 2042 children and adolescents from the European Youth Heart Study. In each study, we tested associations of rs4712652 (near-PRL), rs10508503 (near-PTER), rs1424233 (near-MAF) and rs1805081 (NPC1), or proxy variants (r (2)>0.8), with the odds of being overweight and obese, as well as with body mass index (BMI), percentage body fat (%BF) and waist circumference (WC). Associations were adjusted for sex, age and age(2) in adults and for sex, age, age group, country and maturity in children and adolescents. Summary statistics were combined using fixed effects meta-analysis methods. We had 80% power to detect odds ratios of 1.046 to 1.092 for overweight and 1.067 to 1.136 for obesity. Variants near PRL, PTER and MAF were not associated with the odds of being overweight or obese, or with BMI, %BF or WC after meta-analysis (P>0.15). The NPC1 variant rs1805081 showed some evidence of association with %BF (β=0.013 s.d./allele, P=0.040), but not with any of the remaining obesity-related traits (P>0.3). Overall, these variants, which were identified in a GWAS for early onset and morbid obesity, do not seem to influence obesity-related traits in the general population.

Blindly Using Wald's Test Can Miss Rare Disease-Causal Variants in Case-Control Association Studies

There are four tests--the likelihood ratio (LR) test, Wald's test, the score test and the exact test--commonly employed in genetic association studies. On comparison of the four tests, we found that Wald's test, popular in genome-wide screens due to its low computational demands, exhibited a paradoxical behaviour in that the test statistic decreased as the effect size of the variant increased, resulting in a loss of power. The LR test always achieved the most significant P-values, followed by the exact test. We further examined the results in a real data set composed of high- and low-cholesterol subjects from the Dallas Heart Study (DHS). We also compared the single-variant LR test with two multi-variant analysis approaches--the burden test and the C-alpha test--in analysing the sequencing data by simulation. Our results call for caution in using Wald's test in genome-wide case-control association studies and suggest that the LR test is a better alternative in spite of its computational demands.

Correction for Founder Effects in Host-Viral Association Studies via Principal Components

Viruses such as HIV and Hepatitis C (HCV) replicate rapidly and with high transcription error rates, which may facilitate their escape from immune detection through the encoding of mutations at key positions within human leukocyte antigen (HLA)-specific peptides, thus impeding T-cell recognition. Large-scale population-based host-viral association studies are conducted as hypothesis-generating analyses which aim to determine the positions within the viral sequence at which host HLA immune pressure may have led to these viral escape mutations. When transmission of the virus to the host is HLA-associated, however, standard tests of association can be confounded by the viral relatedness of contemporarily circulating viral sequences, as viral sequences descended from a common ancestor may share inherited patterns of polymorphisms, termed 'founder effects'. Recognizing the correspondence between this problem and the confounding of case-control genome-wide association studies by population stratification, we adapt methods taken from that field to the analysis of host-viral associations. In particular, we consider methods based on principal components analysis within a logistic regression framework motivated by alternative formulations in the Frisch-Waugh-Lovell Theorem. We demonstrate via simulation their utility in detecting true host-viral associations whilst minimizing confounding by associations generated by founder effects. The proposed methods incorporate relatively robust, standard statistical procedures which can be easily implemented using widely available software, and provide alternatives to the more complex computer intensive methods often implemented in this area.

A Novel Kernel for Correcting Size Bias in the Logistic Kernel Machine Test with an Application to Rheumatoid Arthritis

Objectives: The logistic kernel machine test (LKMT) is a testing procedure tailored towards high-dimensional genetic data. Its use in pathway analyses of case-control genome-wide association studies results from its computational efficiency and flexibility in incorporating additional information via the kernel. The kernel can be any positive definite function; unfortunately, its form strongly influences the test's power and bias. Most authors have recommended the use of a simple linear kernel. We demonstrate via a simulation that the probability of rejecting the null hypothesis of no association just by chance increases with the number of SNPs or genes in the pathway when applying a simple linear kernel. Methods: We propose a novel kernel that includes an appropriate standardization in order to protect against any inflation of false positive results. Moreover, our novel kernel contains information on gene membership of SNPs in the pathway. Results: When applying the novel kernel to data from the North American Rheumatoid Arthritis Consortium, we find that even this basic genomic structure can improve the ability of the LKMT to identify meaningful associations. We also demonstrate that the standardization effectively eliminates problems of size bias. Conclusion: We recommend the use of our standardized kernel and urge caution when using non-adjusted kernels in the LKMT to conduct pathway analyses.

Bayesian Methods for Multivariate Modeling of Pleiotropic SNP Associations and Genetic Risk Prediction

Genome-wide association studies (GWAS) have identified numerous associations between genetic loci and individual phenotypes; however, relatively few GWAS have attempted to detect pleiotropic associations, in which loci are simultaneously associated with multiple distinct phenotypes. We show that pleiotropic associations can be directly modeled via the construction of simple Bayesian networks, and that these models can be applied to produce single or ensembles of Bayesian classifiers that leverage pleiotropy to improve genetic risk prediction. The proposed method includes two phases: (1) Bayesian model comparison, to identify Single-Nucleotide Polymorphisms (SNPs) associated with one or more traits; and (2) cross-validation feature selection, in which a final set of SNPs is selected to optimize prediction. To demonstrate the capabilities and limitations of the method, a total of 1600 case-control GWAS datasets with two dichotomous phenotypes were simulated under 16 scenarios, varying the association strengths of causal SNPs, the size of the discovery sets, the balance between cases and controls, and the number of pleiotropic causal SNPs. Across the 16 scenarios, prediction accuracy varied from 90 to 50%. In the 14 scenarios that included pleiotropically associated SNPs, the pleiotropic model search and prediction methods consistently outperformed the naive model search and prediction. In the two scenarios in which there were no true pleiotropic SNPs, the differences between the pleiotropic and naive model searches were minimal. To further evaluate the method on real data, a discovery set of 1071 sickle cell disease (SCD) patients was used to search for pleiotropic associations between cerebral vascular accidents and fetal hemoglobin level. Classification was performed on a smaller validation set of 352 SCD patients, and showed that the inclusion of pleiotropic SNPs may slightly improve prediction, although the difference was not statistically significant. The proposed method is robust, computationally efficient, and provides a powerful new approach for detecting and modeling pleiotropic disease loci.

Validation of prostate cancer risk-related loci identified from genome-wide association studies using family-based association analysis: evidence from the International Consortium for Prostate Cancer Genetics (ICPCG).

Multiple prostate cancer (PCa) risk-related loci have been discovered by genome-wide association studies (GWAS) based on case-control designs. However, GWAS findings may be confounded by population stratification if cases and controls are inadvertently drawn from different genetic backgrounds. In addition, since these loci were identified in cases with predominantly sporadic disease, little is known about their relationships with hereditary prostate cancer (HPC). The association between seventeen reported PCa susceptibility loci was evaluated with a family-based association test using 1,979 hereditary PCa families of European descent collected by members of the International Consortium for Prostate Cancer Genetics, with a total of 5,730 affected men. The risk alleles for 8 of the 17 loci were significantly over-transmitted from parents to affected offspring, including SNPs residing in 8q24 (regions 1, 2 and 3), 10q11, 11q13, 17q12 (region 1), 17q24 and Xp11. In subgroup analyses, three loci, at 8q24 (regions 1 and 2) plus 17q12, were significantly over-transmitted in hereditary PCa families with five or more affected members, while loci at 3p12, 8q24 (region 2), 11q13, 17q12 (region 1), 17q24 and Xp11 were significantly over-transmitted in HPC families with an average age of diagnosis at 65years or less. Our results indicate that at least a subset of PCa risk-related loci identified by case-control GWAS are also associated with disease risk in HPC families.

A Two-Stage Penalized Logistic Regression Approach to Case-Control Genome-Wide Association Studies

We propose a two-stage penalized logistic regression approach to case-control genome-wide association studies. This approach consists of a screening stage and a selection stage. In the screening stage, main-effect and interaction-effect features are screened by usingL1-penalized logistic like-lihoods. In the selection stage, the retained features are ranked by the logistic likelihood with the smoothly clipped absolute deviation (SCAD) penalty (Fan and Li, 2001) and Jeffrey’s Prior penalty (Firth, 1993), a sequence of nested candidate models are formed, and the models are assessed by a family of extended Bayesian information criteria (J. Chen and Z. Chen, 2008). The proposed approach is applied to the analysis of the prostate cancer data of the Cancer Genetic Markers of Susceptibility (CGEMS) project in the National Cancer Institute, USA. Simulation studies are carried out to compare the approach with the pair-wise multiple testing approach (Marchini et al. 2005) and the LASSO-patternsearch algorithm (Shi et al. 2007).

Replication of epistatic DNA loci in two case-control GWAS studies using OPE algorithm

Background One of the limiting factors of current genome-wide association studies (GWAS) is the inability of current methods to comprehensively examine SNP interactions for a reasonable sized dataset. It is hypothesised that this limitation is one of the reasons that GWAS studies have not been able to have a greater impact [1,2]. Many current methods for handling interactions are computationally expensive and do not scale to entire studies. Those methods that do scale often achieve this by pruning their datasets in some manner. This is commonly done by considering only those SNPs that show strong marginal effects, despite the fact that a strongly interacting pair may consist of SNPs with low effects individually.

Family-based analysis of susceptibility loci for polycystic ovary syndrome on chromosome 2p16.3, 2p21 and 9q33.3

Polycystic ovary syndrome (PCOS) is a complex endocrine-metabolic disorder. A previous genome-wide association study (GWAS) identified five single nucleotide polymorphisms (SNPs) which were independently associated with PCOS in Han Chinese. To overcome population stratification, a family-based analysis was conducted to validate whether these five SNPs are associated with PCOS. A total of 276 family trios (828 participants) having a proband with PCOS were included in the family-based study. The transmission disequilibrium test (TDT) was used to analyze the association between PCOS and five SNPs rs13429458, rs12478601, rs13405728, rs10818854 and rs2479106 in three susceptible loci 2p16.3, 2p21 and 9q33.3. A positive association was observed for the SNP rs13429458 (P= 3.74 × 10(-5)). TDT confirms that SNP rs13429458, in the THADA gene, is significantly associated with risk of PCOS. This family-based analysis enhances our previous case-control GWAS and provides further support for the role of susceptibility loci in PCOS.

Multilocus association mapping using generalized ridge logistic regression

BackgroundIn genome-wide association studies, it is widely accepted that multilocus methods are more powerful than testing single-nucleotide polymorphisms (SNPs) one at a time. Among statistical approaches considering many predictors simultaneously, scan statistics are an effective tool for detecting susceptibility genomic regions and mapping disease genes. In this study, inspired by the idea of scan statistics, we propose a novel sliding window-based method for identifying a parsimonious subset of contiguous SNPs that best predict disease status.ResultsWithin each sliding window, we apply a forward model selection procedure using generalized ridge logistic regression for model fitness in each step. In power simulations, we compare the performance of our method with that of five other methods in current use. Averaging power over all the conditions considered, our method dominates the others. We also present two published datasets where our method is useful in causal SNP identification.ConclusionsOur method can automatically combine genetic information in local genomic regions and allow for linkage disequilibrium between SNPs. It can overcome some defects of the scan statistics approach and will be very promising in genome-wide case-control association studies.

Web-Based Genome-Wide Association Study Identifies Two Novel Loci and a Substantial Genetic Component for Parkinson's Disease

Although the causes of Parkinson's disease (PD) are thought to be primarily environmental, recent studies suggest that a number of genes influence susceptibility. Using targeted case recruitment and online survey instruments, we conducted the largest case-control genome-wide association study (GWAS) of PD based on a single collection of individuals to date (3,426 cases and 29,624 controls). We discovered two novel, genome-wide significant associations with PD–rs6812193 near SCARB2 (, ) and rs11868035 near SREBF1/RAI1 (, )—both replicated in an independent cohort. We also replicated 20 previously discovered genetic associations (including LRRK2, GBA, SNCA, MAPT, GAK, and the HLA region), providing support for our novel study design. Relying on a recently proposed method based on genome-wide sharing estimates between distantly related individuals, we estimated the heritability of PD to be at least 0.27. Finally, using sparse regression techniques, we constructed predictive models that account for 6%–7% of the total variance in liability and that suggest the presence of true associations just beyond genome-wide significance, as confirmed through both internal and external cross-validation. These results indicate a substantial, but by no means total, contribution of genetics underlying susceptibility to both early-onset and late-onset PD, suggesting that, despite the novel associations discovered here and elsewhere, the majority of the genetic component for Parkinson's disease remains to be discovered.

A genome-wide association study confirms APOE as the major gene influencing survival in long-lived individuals

We conducted a case–control genome-wide association study (GWAS) of human longevity, comparing 664,472 autosomal SNPs in 763 long-lived individuals (LLI; mean age: 99.7 years) and 1085 controls (mean age: 60.2 years) from Germany. Only one association, namely that of SNP rs4420638 near the APOC1 gene, achieved genome-wide significance (allele-based P = 1.8 × 10 −10). However, logistic regression analysis revealed that this association, which was replicated in an independent German sample, is fully explicable by linkage disequilibrium with the APOE allele ɛ4, the only variant hitherto established as a major genetic determinant of survival into old age. Our GWAS failed to identify any additional autosomal susceptibility genes. One explanation for this lack of success in our study would be that GWAS provide only limited statistical power for a polygenic phenotype with loci of small effect such as human longevity. A recent GWAS in Dutch LLI independently confirmed the APOE–longevity association, thus strengthening the conclusion that this locus is a very, if not the most, important genetic factor influencing longevity.

DASH: A Method for Identical-by-Descent Haplotype Mapping Uncovers Association with Recent Variation

Rare variants affecting phenotype pose a unique challenge for human genetics. Although genome-wide association studies have successfully detected many common causal variants, they are underpowered in identifying disease variants that are too rare or population-specific to be imputed from a general reference panel and thus are poorly represented on commercial SNP arrays. We set out to overcome these challenges and detect association between disease and rare alleles using SNP arrays by relying on long stretches of genomic sharing that are identical by descent. We have developed an algorithm, DASH, which builds upon pairwise identical-by-descent shared segments to infer clusters of individuals likely to be sharing a single haplotype. DASH constructs a graph with nodes representing individuals and links on the basis of such segments spanning a locus and uses an iterative minimum cut algorithm to identify densely connected components. We have applied DASH to simulated data and diverse GWAS data sets by constructing haplotype clusters and testing them for association. In simulations we show this approach to be significantly more powerful than single-marker testing in an isolated population that is from Kosrae, Federated States of Micronesia and has abundant IBD, and we provide orthogonal information for rare, recent variants in the outbred Wellcome Trust Case-Control Consortium (WTCCC) data. In both cohorts, we identified a number of haplotype associations, five such loci in the WTCCC data and ten in the isolated, that were conditionally significant beyond any individual nearby markers. We have replicated one of these loci in an independent European cohort and identified putative structural changes in low-pass whole-genome sequence of the cluster carriers.

SNPsyn: detection and exploration of SNP–SNP interactions

SNPsyn (http://snpsyn.biolab.si) is an interactive software tool for the discovery of synergistic pairs of single nucleotide polymorphisms (SNPs) from large genome-wide case-control association studies (GWAS) data on complex diseases. Synergy among SNPs is estimated using an information-theoretic approach called interaction analysis. SNPsyn is both a stand-alone C++/Flash application and a web server. The computationally intensive part is implemented in C++ and can run in parallel on a dedicated cluster or grid. The graphical user interface is written in Adobe Flash Builder 4 and can run in most web browsers or as a stand-alone application. The SNPsyn web server hosts the Flash application, receives GWAS data submissions, invokes the interaction analysis and serves result files. The user can explore details on identified synergistic pairs of SNPs, perform gene set enrichment analysis and interact with the constructed SNP synergy network.

Lack of Association of CD36 SNPs With Early Onset Obesity: A Meta‐Analysis in 9,973 European Subjects

A recent study suggested that four CD36 polymorphisms (namely rs3211867, rs3211883, rs3211908, and rs1527483) were associated with an increased risk of obesity, an increased BMI and percentage of body fat in European adolescents. We first attempted to confirm these results in three independent case-control genome-wide association studies (GWAS) data totaling 3,509 subjects of French and German origin, but we were unable to find any association of these variants with early onset obesity risk. We then genotyped the four CD36 single-nucleotide polymorphisms (SNPs) in a large population-based study of 4,667 Finnish subjects and we did not replicate any of the recently reported associations with BMI. By combining all available data in a meta-analysis (N = 9,973), we found no evidence for an association of the reported four variants in CD36 with increased obesity risk or increased BMI (0.07 ≤ P values ≤ 0.93). Finally, we assessed the contribution of the full CD36 locus gene variation to obesity risk in 3,509 subjects and we did not detect any significant association with obesity after correction for multiple testing. In summary, we were unable to confirm the recently reported association of variants in CD36 with early onset obesity in populations of European ancestry.

Cyclin-G-associated kinase modifies -synuclein expression levels and toxicity in Parkinson's disease: results from the GenePD Study

Although family history is a well-established risk factor for Parkinson's disease (PD), fewer than 5% of PD cases can be attributed to known genetic mutations. The etiology for the remainder of PD cases is unclear; however, neuronal accumulation of the protein α-synuclein is common to nearly all patients, implicating pathways that influence α-synuclein in PD pathogenesis. We report a genome-wide significant association (P = 3.97 × 10(-8)) between a polymorphism, rs1564282, in the cyclin-G-associated kinase (GAK) gene and increased PD risk, with a meta-analysis odds ratio of 1.48. This association result is based on the meta-analysis of three publicly available PD case-control genome-wide association study and genotyping from a new, independent Italian cohort. Microarray expression analysis of post-mortem frontal cortex from PD and control brains demonstrates a significant association between rs1564282 and higher α-synuclein expression, a known cause of early onset PD. Functional knockdown of GAK in cell culture causes a significant increase in toxicity when α-synuclein is over-expressed. Furthermore, knockdown of GAK in rat primary neurons expressing the A53T mutation of α-synuclein, a well-established model for PD, decreases cell viability. These observations provide evidence that GAK is associated with PD risk and suggest that GAK and α-synuclein interact in a pathway involved in PD pathogenesis. The GAK protein, a serine/threonine kinase, belongs to a family of proteins commonly targeted for drug development. This, combined with GAK's observed relationship to the levels of α-synuclein expression and toxicity, suggests that the protein is an attractive therapeutic target for the treatment of PD.

The Influence of Errors Inherent in Genome Wide Association Studies(GWAS) in Relation To Single Gene Models

Nearly one thousand human genome wide association studies (GWAS) have examined over 210 diseases and traits and found over 1,200 SNP associations. With improved genotyping technologies and the growing number of available markers, case-control Genome Wide Association Studies (GWAS) have become a key tool for investigating complex diseases. This study assesses the influence of genotype and diagnosis errors present in GWAS by analyzing a synthetic gene dataset incorporating factors known to influence association measurement. Monte Carlo methods were used to generate the synthetic gene data, which incorporated factors including gene inheritance, relative risk levels, disease penetrance, genotype distribution, sample size, as well as the two error factors that are the focus of this study. The resulting dataset provides a truth set for assessing statistical method performance and association sensitivity. While previously understood, these results quantify and document the extent of the relationship between genotype and diagnosis error measures and statistical power loss. Our results also demonstrate that for low risk non-recessive loci, sample sizes in the range of 1,000 - 2,000 cases will achieve 80% power thresholds for error type I error levels of 10-8 even with realistic genotype and phenotype error assumptions. Nevertheless, compensating for power loss due to the presence of genotype and diagnosis errors by increasing sample size should not be underestimated. Our estimates indicate that sample size increase requirements are in the range of 20% to 40%, depending on the gene inheritance model assumed.