Recent developments in genomewide association scans: a workshop summary and review

Recent advances in the genetics of familial and sporadic ALS

ALS shows complex genetic inheritance patterns. In about 5% to 10% of cases, there is a family history of ALS or a related condition such as frontotemporal dementia in a first or second degree relative, and for about 80% of such people a pathogenic gene variant can be identified. Such variants are also seen in people with no family history because of factor influencing the expression of genes, such as age. Genetic susceptibility factors also contribute to risk, and the heritability of ALS is between 40% and 60%. The genetic variants influencing ALS risk include single base changes, repeat expansions, copy number variants, and others. Here we review what is known of the genetic landscape and architecture of ALS.

Application of multiple testing procedures for identifying relevant comorbidities, from a large set, in traumatic brain injury for research applications utilizing big health-administrative data

Background

Multiple testing procedures (MTP) are gaining increasing popularity in various fields of biostatistics, especially in statistical genetics. However, in injury surveillance research utilizing the growing amount and complexity of health-administrative data encoded in the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10), few studies involve MTP and discuss their applications and challenges.

Objective

We aimed to apply MTP in the population-wide context of comorbidity preceding traumatic brain injury (TBI), one of the most disabling injuries, to find a subset of comorbidity that can be targeted in primary injury prevention.

Methods

In total, 2,600 ICD-10 codes were used to assess the associations between TBI and comorbidity, with 235,003 TBI patients, on a matched data set of patients without TBI. McNemar tests were conducted on each 2,600 ICD-10 code, and appropriate multiple testing adjustments were applied using the Benjamini-Yekutieli procedure. To study the magnitude and direction of associations, odds ratios with 95% confidence intervals were constructed.

Results

Benjamini-Yekutieli procedure captured 684 ICD-10 codes, out of 2,600, as codes positively associated with a TBI event, reducing the effective number of codes for subsequent analysis and comprehension.

Conclusion

Our results illustrate the utility of MTP for data mining and dimension reduction in TBI research utilizing big health-administrative data to support injury surveillance research and generate ideas for injury prevention.

GWAS for genetics of complex quantitative traits: Genome to pangenome and SNPs to SVs and k-mers

The development of improved methods for genome-wide association studies (GWAS) for genetics of quantitative traits has been an active area of research during the last 25 years. This activity initially started with the use of mixed linear model (MLM), which was variously modified. During the last decade, however, with the availability of high throughput next generation sequencing (NGS) technology, development and use of pangenomes and novel markers including structural variations (SVs) and k-mers for GWAS has taken over as a new thrust area of research. Pangenomes and SVs are now available in humans, livestock, and a number of plant species, so that these resources along with k-mers are being used in GWAS for exploring additional genetic variation that was hitherto not available for analysis. These developments have resulted in significant improvement in GWAS methodology for detection of marker-trait associations (MTAs) that are relevant to human healthcare and crop improvement.

Quantitative genetics: pan-genomes, SVs, and k-mers for GWAS

For identification of marker-trait associations (MTAs) for complex traits in animals and plants, thousands of genome-wide association studies (GWAS) were conducted during the past two decades. This involved regular improvement in methodology. Initially, a reference genome and SNPs were used; more recently pan-genomes and the markers structural variations (SVs)/k-mers are also being used.

A Journey through Genetic Architecture and Predisposition of Coronary Artery Disease

Introduction

To halt the spread of coronary artery disease (CAD), the number one killer in the world, requires primary prevention. Fifty percent of all Americans are expected to experience a cardiac event; the challenge is identifying those at risk. 40 to 60% of predisposition to CAD is genetic. The first genetic risk variant, 9p21, was discovered in 2007. Genome-Wide Association Studies has since discovered hundreds of genetic risk variants. The genetic burden for CAD can be expressed as a single number, Genetic Risk Score (GRS). Assessment of GRS to risk stratify for CAD was superior to conventional risk factors in several large clinical trials assessing statin therapy, and more recently in a population of nearly 500,000 (UK Biobank). Studies were performed based on prospective genetic risk stratification for CAD. These studies showed that a favorable lifestyle was associated with a 46% reduction in cardiac events and programmed exercise, a 50% reduction in cardiac events. Genetic risk score is superior to conventional risk factors, and is markedly attenuated by lifestyle changes and drug therapy. Genetic risk can be determined at birth or any time thereafter.

Conclusion

Utilizing the GRS to risk stratify young, asymptomatic individuals could provide a paradigm shift in the primary prevention of CAD and significantly halt its spread.

Principals about principal components in statistical genetics

Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications.

Complex Phenotypes: Mechanisms Underlying Variation in Human Stature

PURPOSE OF REVIEW

The goal of the review is to provide a comprehensive overview of the current understanding of the mechanisms underlying variation in human stature.

RECENT FINDINGS

Human height is an anthropometric trait that varies considerably within human populations as well as across the globe. Historically, much research focus was placed on understanding the biology of growth plate chondrocytes and how modifications to core chondrocyte proliferation and differentiation pathways potentially shaped height attainment in normal as well as pathological contexts. Recently, much progress has been made to improve our understanding regarding the mechanisms underlying the normal and pathological range of height variation within as well as between human populations, and today, it is understood to reflect complex interactions among a myriad of genetic, environmental, and evolutionary factors. Indeed, recent improvements in genetics (e.g., GWAS) and breakthroughs in functional genomics (e.g., whole exome sequencing, DNA methylation analysis, ATAC-sequencing, and CRISPR) have shed light on previously unknown pathways/mechanisms governing pathological and common height variation. Additionally, the use of an evolutionary perspective has also revealed important mechanisms that have shaped height variation across the planet. This review provides an overview of the current knowledge of the biological mechanisms underlying height variation by highlighting new research findings on skeletal growth control with an emphasis on previously unknown pathways/mechanisms influencing pathological and common height variation. In this context, this review also discusses how evolutionary forces likely shaped the genomic architecture of height across the globe.

Extended methods for gene-environment-wide interaction scans in studies of admixed individuals with varying degrees of relationships

The etiology of many complex diseases involves both environmental exposures and inherited genetic predisposition as well as interactions between them. Gene-environment-wide interaction studies (GEWIS) provide a means to identify the interactions between genetic variation and environmental exposures that underlie disease risk. However, current GEWIS methods lack the capability to adjust for the potentially complex correlations in studies with varying degrees of relationships (both known and unknown) among individuals in admixed populations. We developed novel generalized estimating equation (GEE) based methods-GEE-adaptive and GEE-joint-to account for phenotypic correlations due to kinship while accounting for covariates, including, measures of genome-wide ancestry. In simulation studies of admixed individuals, both methods controlled family-wise error rates, an advantage over the case-only approach. They demonstrated higher power than traditional case-control methods across a wide range of underlying alternative hypotheses, especially where both marginal and interaction effects were present. We applied the proposed method to conduct a GEWIS of a known sarcoidosis risk factor (insecticide exposure) and risk of sarcoidosis in African Americans and identified two novel loci with suggestive evidence of G × E interaction.

Physical Confirmation and Comparative Genomics of the Rat Mammary carcinoma susceptibility 3 Quantitative Trait Locus

Human breast and rat mammary cancer susceptibility are complex phenotypes where complete sets of risk associated loci remain to be identified for both species. We tested multiple congenic rat strains to physically confirm and positionally map rat Mammary carcinoma susceptibility 3 (Mcs3)-a mammary cancer resistance allele previously predicted at Rattus norvegicus chromosome 1 (RNO1). The mammary cancer susceptible Wistar Furth (WF) strain was the recipient, and the mammary cancer resistant Copenhagen (Cop) strain was the RNO1-segment donor for congenics. Inbred WF females averaged 6.3 carcinogen-induced mammary carcinomas per rat. Two WF.Cop congenic strains averaged 2.8 and 3.4 mammary carcinomas per rat, which confirmed Mcs3 as an independently acting allele. Two other WF.Cop congenic strains averaged 6.6 and 8.1 mammary carcinomas per rat, and, thus, did not contain Mcs3 Rat Mcs3 was delimited to 27.8 Mb of RNO1 from rs8149408 to rs105131702 (RNO1:143700228-171517317 of RGSC 6.0/rn6). Human genetic variants with p values for association to breast cancer risk below 10^-7 had not been reported for Mcs3 orthologous loci; however, human variants located in Mcs3-orthologous regions with potential association to risk (10^-7 < p < 10^-3) were listed in some population-based studies. Further, rat Mcs3 contains sequence orthologous to human 11q13/14-a region frequently amplified in female breast cancer. We conclude that Mcs3 is an independently acting mammary carcinoma resistance allele. Human population-based, genome-targeted association studies interrogating Mcs3 orthologous loci may yield novel breast cancer risk associated variants and genes.

Accurate phenotyping: Reconciling approaches through Bayesian model averaging

Genetic research into complex diseases is frequently hindered by a lack of clear biomarkers for phenotype ascertainment. Phenotypes for such diseases are often identified on the basis of clinically defined criteria; however such criteria may not be suitable for understanding the genetic composition of the diseases. Various statistical approaches have been proposed for phenotype definition; however our previous studies have shown that differences in phenotypes estimated using different approaches have substantial impact on subsequent analyses. Instead of obtaining results based upon a single model, we propose a new method, using Bayesian model averaging to overcome problems associated with phenotype definition. Although Bayesian model averaging has been used in other fields of research, this is the first study that uses Bayesian model averaging to reconcile phenotypes obtained using multiple models. We illustrate the new method by applying it to simulated genetic and phenotypic data for Kofendred personality disorder-an imaginary disease with several sub-types. Two separate statistical methods were used to identify clusters of individuals with distinct phenotypes: latent class analysis and grade of membership. Bayesian model averaging was then used to combine the two clusterings for the purpose of subsequent linkage analyses. We found that causative genetic loci for the disease produced higher LOD scores using model averaging than under either individual model separately. We attribute this improvement to consolidation of the cores of phenotype clusters identified using each individual method.

Association of Agronomic Traits with SNP Markers in Durum Wheat (Triticum turgidum L. durum (Desf.))

Association mapping is a powerful approach to detect associations between traits of interest and genetic markers based on linkage disequilibrium (LD) in molecular plant breeding. In this study, 150 accessions of worldwide originated durum wheat germplasm (Triticum turgidum spp. durum) were genotyped using 1,366 SNP markers. The extent of LD on each chromosome was evaluated. Association of single nucleotide polymorphisms (SNP) markers with ten agronomic traits measured in four consecutive years was analyzed under a mix linear model (MLM). Two hundred and one significant association pairs were detected in the four years. Several markers were associated with one trait, and also some markers were associated with multiple traits. Some of the associated markers were in agreement with previous quantitative trait loci (QTL) analyses. The function and homology analyses of the corresponding ESTs of some SNP markers could explain many of the associations for plant height, length of main spike, number of spikelets on main spike, grain number per plant, and 1000-grain weight, etc. The SNP associations for the observed traits are generally clustered in specific chromosome regions of the wheat genome, mainly in 2A, 5A, 6A, 7A, 1B, and 6B chromosomes. This study demonstrates that association mapping can complement and enhance previous QTL analyses and provide additional information for marker-assisted selection.

From the era of genome analysis to the era of genomic drug discovery: a pioneering example of rheumatoid arthritis

Although we have obtained comprehensive catalogs of genetic risk loci that are linked to human diseases, little is known regarding how to devise a systematic strategy to integrate genetic study results with diverse biological resources. Such strategies will be crucial for providing novel insights into disease biology and for aiding drug discovery as an ultimate goal. Here we describe the current progress in this field using a pioneering example of large-scale genetic association studies on rheumatoid arthritis (RA), an autoimmune disease characterized by inflammation and destruction of joints. Through functional and bioinformatic annotations of risk single nucleotide polymorphisms (SNPs) and genes from >100 RA risk loci identified by genome-wide association study (GWAS) meta-analysis, we found novel biological insights into RA pathogenicity. Further, by integrating RA genetic findings with the complete catalog of approved drugs for RA and other diseases, we provide empirical data to indicate that human genetic-based approaches may be useful for supporting 'genetics-driven genomic drug discovery' efforts in complex human traits and suggest that further development of integrative approaches should be undertaken.

Applying genome-wide gene-based expression quantitative trait locus mapping to study population ancestry and pharmacogenetics

BACKGROUND

Gene-based analysis has become popular in genomic research because of its appealing biological and statistical properties compared with those of a single-locus analysis. However, only a few, if any, studies have discussed a mapping of expression quantitative trait loci (eQTL) in a gene-based framework. Neither study has discussed ancestry-informative eQTL nor investigated their roles in pharmacogenetics by integrating single nucleotide polymorphism (SNP)-based eQTL (s-eQTL) and gene-based eQTL (g-eQTL).

RESULTS

In this g-eQTL mapping study, the transcript expression levels of genes (transcript-level genes; T-genes) were correlated with the SNPs of genes (sequence-level genes; S-genes) by using a method of gene-based partial least squares (PLS). Ancestry-informative transcripts were identified using a rank-score-based multivariate association test, and ancestry-informative eQTL were identified using Fisher's exact test. Furthermore, key ancestry-predictive eQTL were selected in a flexible discriminant analysis. We analyzed SNPs and gene expression of 210 independent people of African-, Asian- and European-descent. We identified numerous cis- and trans-acting g-eQTL and s-eQTL for each population by using PLS. We observed ancestry information enriched in eQTL. Furthermore, we identified 2 ancestry-informative eQTL associated with adverse drug reactions and/or drug response. Rs1045642, located on MDR1, is an ancestry-informative eQTL (P = 2.13E-13, using Fisher's exact test) associated with adverse drug reactions to amitriptyline and nortriptyline and drug responses to morphine. Rs20455, located in KIF6, is an ancestry-informative eQTL (P = 2.76E-23, using Fisher's exact test) associated with the response to statin drugs (e.g., pravastatin and atorvastatin). The ancestry-informative eQTL of drug biotransformation genes were also observed; cross-population cis-acting expression regulators included SPG7, TAP2, SLC7A7, and CYP4F2. Finally, we also identified key ancestry-predictive eQTL and established classification models with promising training and testing accuracies in separating samples from close populations.

CONCLUSIONS

In summary, we developed a gene-based PLS procedure and a SAS macro for identifying g-eQTL and s-eQTL. We established data archives of eQTL for global populations. The program and data archives are accessible at http://www.stat.sinica.edu.tw/hsinchou/genetics/eQTL/HapMapII.htm. Finally, the results from our investigations regarding the interrelationship between eQTL, ancestry information, and pharmacodynamics provide rich resources for future eQTL studies and practical applications in population genetics and medical genetics.

Genetic variants and non-genetic factors predict circulating vitamin D levels in Hispanic and non-Hispanic White women: the Breast Cancer Health Disparities Study

Genome-wide association studies (GWAS) have identified common polymorphisms in or near GC, CYP2R1, CYP24A1, and NADSYN1/DHCR7 genes to be associated with circulating levels of 25-hydroxyvitamin D [25(OH)D] in European populations. To replicate these GWAS findings, we examined six selected polymorphisms from these regions and their relation with circulating 25(OH)D levels in 1,605 Hispanic women (629 U.S. Hispanics and 976 Mexicans) and 354 non-Hispanic White (NHW) women. We also assessed the potential interactions between these variants and known non-genetic predictors of 25(OH)D levels, including body mass index (BMI), sunlight exposure and vitamin D intake from diet and supplements. The minor alleles of the two GC polymorphisms (rs7041 and rs2282679) were significantly associated with lower 25(OH)D levels in both Hispanic and NHW women. The CYP2R1 polymorphism, rs2060793, also was significantly associated with 25(OH)D levels in both groups. We found no significant associations for the polymorphisms in the CYP24A1. In Hispanic controls, 25(OH)D levels were significantly associated with the rs12785878T and rs1790349G haplotype in the NADSYN1/DHCR7 region. Significant interactions between GC rs2282679 and BMI and between rs12785878 and time spent in outdoor activities were observed. These results provide further support for the contribution of common genetic variants to individual variability in circulating 25(OH)D levels. The observed interactions between SNPs and non-genetic factors warrant confirmation.

Significant overlap between human genome-wide association-study nominated breast cancer risk alleles and rat mammary cancer susceptibility loci

Introduction

Human population-based genome-wide association (GWA) studies identify low penetrance breast cancer risk alleles; however, GWA studies alone do not definitively determine causative genes or mechanisms. Stringent genome- wide statistical significance level requirements, set to avoid false-positive associations, yield many false-negative associations. Laboratory rats (Rattus norvegicus) are useful to study many aspects of breast cancer, including genetic susceptibility. Several rat mammary cancer associated loci have been identified using genetic linkage and congenic strain based-approaches. Here, we sought to determine the amount of overlap between GWA study nominated human breast and rat mammary cancer susceptibility loci.

Methods

We queried published GWA studies to identify two groups of SNPs, one that reached genome-wide significance and one comprised of SNPs failing a validation step and not reaching genome- wide significance. Human genome locations of these SNPs were compared to known rat mammary carcinoma susceptibility loci to determine if risk alleles existed in both species. Rat genome regions not known to associate with mammary cancer risk were randomly selected as control regions.

Results

Significantly more human breast cancer risk GWA study nominated SNPs mapped at orthologs of rat mammary cancer loci than to regions not known to contain rat mammary cancer loci. The rat genome was useful to predict associations that had met human genome-wide significance criteria and weaker associations that had not.

Conclusions

Integration of human and rat comparative genomics may be useful to parse out false-negative associations in GWA studies of breast cancer risk.

Kernel canonical correlation analysis for assessing gene-gene interactions and application to ovarian cancer

Although single-locus approaches have been widely applied to identify disease-associated single-nucleotide polymorphisms (SNPs), complex diseases are thought to be the product of multiple interactions between loci. This has led to the recent development of statistical methods for detecting statistical interactions between two loci. Canonical correlation analysis (CCA) has previously been proposed to detect gene-gene coassociation. However, this approach is limited to detecting linear relations and can only be applied when the number of observations exceeds the number of SNPs in a gene. This limitation is particularly important for next-generation sequencing, which could yield a large number of novel variants on a limited number of subjects. To overcome these limitations, we propose an approach to detect gene-gene interactions on the basis of a kernelized version of CCA (KCCA). Our simulation studies showed that KCCA controls the Type-I error, and is more powerful than leading gene-based approaches under a disease model with negligible marginal effects. To demonstrate the utility of our approach, we also applied KCCA to assess interactions between 200 genes in the NF-κB pathway in relation to ovarian cancer risk in 3869 cases and 3276 controls. We identified 13 significant gene pairs relevant to ovarian cancer risk (local false discovery rate <0.05). Finally, we discuss the advantages of KCCA in gene-gene interaction analysis and its future role in genetic association studies.

Semi-supervised spectral clustering with application to detect population stratification

In genetic association studies, unaccounted population stratification can cause spurious associations in a discovery process of identifying disease-associated genetic markers. In such a situation, prior information is often available for some subjects' population identities. To leverage the additional information, we propose a semi-supervised clustering approach for detecting population stratification. This approach maintains the advantages of spectral clustering, while is integrated with the additional identity information, leading to sharper clustering performance. To demonstrate utility of our approach, we analyze a whole-genome sequencing dataset from the 1000 Genomes Project, consisting of the genotypes of 607 individuals sampled from three continental groups involving 10 subpopulations. This is compared against a semi-supervised spectral clustering method, in addition to a spectral clustering method, with the known subpopulation information by the Rand index and an adjusted Rand (ARand) index. The numerical results suggest that the proposed method outperforms its competitors in detecting population stratification.

Single nucleotide variation in the TP53 3' untranslated region in diffuse large B-cell lymphoma treated with rituximab-CHOP: a report from the International DLBCL Rituximab-CHOP Consortium Program

We identified multiple single nucleotide variants (SNVs) in the TP53 3' untranslated region (3'UTR) in tumor specimens from 244 patients with diffuse large B-cell lymphoma (DLBCL). Patients carrying a wild-type TP53 coding sequence (CDS) and 1 or more 3'UTR SNVs had a better 5-year survival rate than patients carrying a wild-type CDS and the reference 3'UTR, yet there is no statistically significance difference in overall survival (OS). In contrast, 3'UTR variation predicted poorer OS for patients with a mutant TP53 CDS. We then sequenced TP53 3'UTR in 247 additional DLBCL patients as a validation set. Altogether, we identified 187 novel SNVs; 36 occurred at least twice. Most of the newly identified 3'UTR SNVs were located at sites that are complementary to seed sequences of microRNAs (miRNAs) that are predicted or experimentally known to target TP53. Three SNVs disrupt the seed match between miR-125b and the TP53 3'UTR, thereby impeding suppression of p53 by this miRNA. In addition, a germline SNV (rs78378222) located in the TP53 polyadenylation signal resulted in downregulation of both p53 messenger RNA and protein levels and reduction of cellular apoptosis. This study is the first to demonstrate the prognostic value of the TP53 3'UTR in cancer.

Assessment of systematic effects of methodological characteristics on candidate genetic associations

Candidate genetic association studies have been found to have a low replication rate in the past. Here, we aimed to assess whether aspects of reported methodological characteristics in genetic association studies may be related to the magnitude of effects observed. An observational, literature-based investigation of 511 case-control studies of genetic association studies indexed in 2007, was undertaken. Meta-regression analyses were used to assess the relationship between 23 reported methodological characteristics and the magnitude of genetic associations. The 511 studies had been conducted in 52 countries and were published in 220 journals (median impact factor 5.1). The multivariate meta-regression model of methodological characteristics plus disease category accounted for 17.2 % of the between-study variance in the magnitude of the reported genetic associations. Our findings are consistent with the view that better conducted and better reported genetic association research may lead to less inflated results.

A short history of the genome-wide association study: where we were and where we are going

Recent rapid advances in genetic research are ushering us into the genome sequence era, where an individual's genome information is utilized for clinical practice. The most spectacular results of the human genome study have been provided by genome-wide association studies (GWASs). This is a review of the history of GWASs as related to my work. Further efforts are necessary to make full use of its potential power to medicine.

Correction for population stratification in random forest analysis

BACKGROUND

Population structure (PS), including population stratification and admixture, is a significant confounder in genome-wide association studies (GWAS), as it may produce spurious associations. Random forest (RF) has been increasingly applied in GWAS data analysis because of its advantage in analysing high dimensional genetic data. RF creates importance measures for single nucleotide polymorphisms (SNPs), which are helpful for feature selections. However, if PS is not appropriately corrected, RF tends to give high importance to disease-unrelated SNPs with different frequencies of allele or genotype among subpopulations, leading to inaccurate results.

METHODS

In this study, the authors propose to correct for the confounding effect of PS by including the information of PS in RF analysis. The correction procedure starts by extracting the information of PS using EIGENSTRAT or multi-dimensional scaling clustering procedure from a large number of structure inference SNPs. Phenotype and genotypes adjusted by the information of PS are then used as the outcome and predictors in RF analysis.

RESULTS

Extensive simulations indicate that the importance measure of the causal SNP is increased following the PS correction. By analysing a real dataset, the proposed correction removes the spurious association between the lactase gene and height.

CONCLUSION

The authors propose a simple method to correct for PS in RF analysis on GWAS data. Further studies in real GWAS datasets are required to validate the robustness of the proposed approach.

Analyzes of genome-wide association follow-up study for calving traits in dairy cattle

BACKGROUND

There is often a pronounced disagreement between results obtained from different genome-wide association studies in cattle. There are multiple reasons for this disagreement. Particularly the presence of false positives leads to a need to validate detected QTL before they are optimally incorporated or weighted in selection decisions or further studied for causal gene. In dairy cattle progeny testing scheme new data is routinely accumulated which can be used to validate previously discovered associations. However, the data is not an independent sample and the sample size may not be sufficient to have enough power to validate previous discoveries. Here we compared two strategies to validate previously detected QTL when new data is added from the same study population. We compare analyzing a combined dataset (COMB) including all data presently available to only analyzing a validation dataset (VAL) i.e. a new dataset not previously analyzed as an independent replication. Secondly, we confirm SNP detected in the Reference population (REF) (i.e. previously analyzed dataset consists of older bulls) in the VAL dataset.

RESULTS

Clearly the results from the combined (COMB) dataset which had nearly twice the sample size of other two subsets allowed the detection of far more significant associations than the two smaller subsets. The number of significant SNPs in REF (older bulls) was about four times higher compare to VAL (younger bulls) though both had similar sample sizes, 2,219 and 2,039 respectively. A total of 424 SNP-trait combinations on 22 chromosomes showed genome-wide significant association involving 284 unique SNPs in the COMB dataset. In the REF data set 101 associations (73 unique SNPs) and in the VAL 24 associations (18 unique SNPs) were found genome-wide significant. Sixty-eight percent of the SNPs in the REF dataset could be confirmed in the VAL dataset. Out of 469 unique SNPs showing chromosome-wide significant association with calving traits in the REF dataset 321 could be confirmed in the VAL dataset at P < 0.05.

CONCLUSIONS

The follow-up study for GWAS in cattle will depend on the aim of the study. If the aim is to discover novel QTL, analyses of the COMB dataset is recommended, while in case of identification of the causal mutation underlying a QTL, confirmation of the discovered SNPs are necessary to avoid following a false positive.

Power calculations in genetic studies

The power of a statistical test is the probability that it will yield a statistically significant result given that the null hypothesis is false. In other words, it represents the chance that the study will be successful in detecting a true effect and is dependent on a number of factors, including the magnitude of the effect, the sample size and study design, and the specified false-positive rate. Power calculations are primarily performed during the planning stages of a study, most typically in determining the sample size required. Consideration of statistical power can also sometimes shed light on the results of completed studies, particularly in the interpretation of negative results. In this article, we review the fundamentals of statistical power, discuss how power is calculated (using a genetic case/control study as an example), and consider the most pertinent factors that influence power in genetic studies. Finally, we focus on power in the context of modern whole-genome association studies, in which issues of coverage, multiple testing, and staged designs are paramount.

Common genetic factors for hematological traits in humans

Hematological traits are essential biomedical indicators that are widely used in clinical practice. The elucidation of the etiology that determines an individual's hematological traits would have a substantial impact. Hematological traits are known to be heritable, and it has been suggested that genetic factors contribute significantly to the inter-individual variance of these traits. Here, we review our current knowledge regarding the genetic architecture of hematological traits in humans, most of which has been obtained through recent developments in genome-wide association studies (GWAS). In addition to current knowledge, which is based on the hematological traits of the three major blood-cell lineages (white blood cells; WBC, red blood cells; RBC, and platelets; PLT), we propose future approaches that would be useful as a next step in the post-GWAS era.

Molecular genetic studies of gene identification for sarcopenia

Sarcopenia, which is characterized by a progressive decrease of skeletal muscle mass and function with aging, is closely related to several common diseases (such as cardiovascular and airway diseases) and functional impairment/disability. Strong genetic determination has been reported for muscle mass and muscle strength, two most commonly recognized and studied risk phenotypes for sarcopenia, with heritability ranging from 30 to 85% for muscle strength and 45-90% for muscle mass. Sarcopenia has been the subject of increasing genetic research over the past decade. This review is designed to comprehensively summarize the most important and representative molecular genetic studies designed to identify genetic factors associated with sarcopenia. We have methodically reviewed whole-genome linkage studies in humans, quantitative trait loci mapping in animal models, candidate gene association studies, newly reported genome-wide association studies, DNA microarrays and microRNA studies of sarcopenia or related skeletal muscle phenotypes. The major results of each study are tabulated for easy comparison and reference. The findings of representative studies are discussed with respect to their influence on our present understanding of the genetics of sarcopenia. This is a comprehensive review of molecular genetic studies of gene identification for sarcopenia, and an overarching theme for this review is that the currently accumulating results are tentative and occasionally inconsistent and should be interpreted with caution pending further investigation. Consequently, this overview should enhance recognition of the need to validate/replicate the genetic variants underlying sarcopenia in large human cohorts and animal. We believe that further progress in understanding the genetic etiology of sarcopenia will provide valuable insights into important fundamental biological mechanisms underlying muscle physiology that will ultimately lead to improved ability to recognize individuals at risk for developing sarcopenia and our ability to treat this debilitating condition.

Physical confirmation and mapping of overlapping rat mammary carcinoma susceptibility QTLs, Mcs2 and Mcs6

Only a portion of the estimated heritability of breast cancer susceptibility has been explained by individual loci. Comparative genetic approaches that first use an experimental organism to map susceptibility QTLs are unbiased methods to identify human orthologs to target in human population-based genetic association studies. Here, overlapping rat mammary carcinoma susceptibility (Mcs) predicted QTLs, Mcs6 and Mcs2, were physically confirmed and mapped to identify the human orthologous region. To physically confirm Mcs6 and Mcs2, congenic lines were established using the Wistar-Furth (WF) rat strain, which is susceptible to developing mammary carcinomas, as the recipient (genetic background) and either Wistar-Kyoto (WKy, Mcs6) or Copenhagen (COP, Mcs2), which are resistant, as donor strains. By comparing Mcs phenotypes of WF.WKy congenic lines with distinct segments of WKy chromosome 7 we physically confirmed and mapped Mcs6 to ∼33 Mb between markers D7Rat171 and gUwm64-3. The predicted Mcs2 QTL was also physically confirmed using segments of COP chromosome 7 introgressed into a susceptible WF background. The Mcs6 and Mcs2 overlapping genomic regions contain multiple annotated genes, but none have a clear or well established link to breast cancer susceptibility. Igf1 and Socs2 are two of multiple potential candidate genes in Mcs6. The human genomic region orthologous to rat Mcs6 is on chromosome 12 from base positions 71,270,266 to 105,502,699. This region has not shown a genome-wide significant association to breast cancer risk in pun studies of breast cancer susceptibility.

Common alleles in candidate susceptibility genes associated with risk and development of epithelial ovarian cancer

Common germline genetic variation in the population is associated with susceptibility to epithelial ovarian cancer. Microcell-mediated chromosome transfer and expression microarray analysis identified nine genes associated with functional suppression of tumorogenicity in ovarian cancer cell lines; AIFM2, AKTIP, AXIN2, CASP5, FILIP1L, RBBP8, RGC32, RUVBL1 and STAG3. Sixty-three tagging single nucleotide polymorphisms (tSNPs) in these genes were genotyped in 1,799 invasive ovarian cancer cases and 3,045 controls to look for associations with disease risk. Two SNPs in RUVBL1, rs13063604 and rs7650365, were associated with increased risk of serous ovarian cancer [HetOR = 1.42 (1.15-1.74) and the HomOR = 1.63 (1.10-1.42), p-trend = 0.0002] and [HetOR = 0.97 (0.80-1.17), HomOR = 0.74 (0.58-0.93), p-trend = 0.009], respectively. We genotyped rs13063604 and rs7650365 in an additional 4,590 cases and 6,031 controls from ten sites from the United States, Europe and Australia; however, neither SNP was significant in Stage 2. We also evaluated the potential role of tSNPs in these nine genes in ovarian cancer development by testing for allele-specific loss of heterozygosity (LOH) in 286 primary ovarian tumours. We found frequent LOH for tSNPs in AXIN2, AKTIP and RGC32 (64, 46 and 34%, respectively) and one SNP, rs1637001, in STAG3 showed significant allele-specific LOH with loss of the common allele in 94% of informative tumours (p = 0.015). Array comparative genomic hybridisation indicated that this nonrandom allelic imbalance was due to amplification of the rare allele. In conclusion, we show evidence for the involvement of a common allele of STAG3 in the development of epithelial ovarian cancer.

Factors affecting the effective number of tests in genetic association studies: a comparative study of three PCA-based methods

The number of tested marker becomes numerous in genetic association studies (GAS) and one major challenge is to derive the multiple testing threshold. Some approaches calculating an effective number (M(eff)) of tests in GAS were developed and have been shown to be promising. As yet, there have been no comparisons of their robustness to influencing factors. We evaluated the performance of three principal component analysis (PCA)-based M(eff) estimation formulas (M(eff-C) in Cheverud (2001), M(eff-L) in Li and Ji (2005), and M(eff-G) in Galwey (2009)). Four influencing factors including LD measurements, marker density, population samples and the total number of tested markers were considered. We validated them by the Bonferroni's method and the permutation test with 10 000 random shuffles based on three real data sets. For each factor, M(eff-C) yielded conservative threshold except with D' coefficient, and M(eff-G) would be too liberal compared with the permutation test. Our results indicated that M(eff-L) based on r(2) coefficient achieve close approximation of the permutation threshold. As for a large number of markers, we recommended to use M(eff-L) with r(2) coefficient according to fixed-length separation, as well as fixed-number separation, to obtain accurate estimate of the multiple testing threshold and to save more computational time.

Genome wide association studies for milk production traits in Chinese Holstein population

Genome-wide association studies (GWAS) based on high throughput SNP genotyping technologies open a broad avenue for exploring genes associated with milk production traits in dairy cattle. Motivated by pinpointing novel quantitative trait nucleotide (QTN) across Bos Taurus genome, the present study is to perform GWAS to identify genes affecting milk production traits using current state-of-the-art SNP genotyping technology, i.e., the Illumina BovineSNP50 BeadChip. In the analyses, the five most commonly evaluated milk production traits are involved, including milk yield (MY), milk fat yield (FY), milk protein yield (PY), milk fat percentage (FP) and milk protein percentage (PP). Estimated breeding values (EBVs) of 2,093 daughters from 14 paternal half-sib families are considered as phenotypes within the framework of a daughter design. Association tests between each trait and the 54K SNPs are achieved via two different analysis approaches, a paternal transmission disequilibrium test (TDT)-based approach (L1-TDT) and a mixed model based regression analysis (MMRA). In total, 105 SNPs were detected to be significantly associated genome-wise with one or multiple milk production traits. Of the 105 SNPs, 38 were commonly detected by both methods, while four and 63 were solely detected by L1-TDT and MMRA, respectively. The majority (86 out of 105) of the significant SNPs is located within the reported QTL regions and some are within or close to the reported candidate genes. In particular, two SNPs, ARS-BFGL-NGS-4939 and BFGL-NGS-118998, are located close to the DGAT1 gene (160bp apart) and within the GHR gene, respectively. Our findings herein not only provide confirmatory evidences for previously findings, but also explore a suite of novel SNPs associated with milk production traits, and thus form a solid basis for eventually unraveling the causal mutations for milk production traits in dairy cattle.

Genetics and genomics of endometriosis

Endometriosis is a common cause of morbidity in women with an unknown etiology. Studies have demonstrated the familial nature of endometriosis and suggest that inheritance occurs in a polygenic/multifactorial fashion. Studies have attempted to define the gene or genes responsible for endometriosis through association or linkage studies with candidate genes or DNA mapping technology. A number of genomics studies have demonstrated significant alterations in gene expression in endometriosis. A more thorough understanding of the genetics and genomics of endometriosis will facilitate understanding the basic biology of the disease and open new inroads to diagnosis and treatment of this enigmatic condition.

Genome-wide association study for colorectal cancer identifies risk polymorphisms in German familial cases and implicates MAPK signalling pathways in disease susceptibility

Genetic susceptibility accounts for approximately 35% of all colorectal cancer (CRC). Ten common low-risk variants contributing to CRC risk have been identified through genome-wide association studies (GWASs). In our GWAS, 610 664 genotyped single-nucleotide polymorphisms (SNPs) passed the quality control filtering in 371 German familial CRC patients and 1263 controls, and replication studies were conducted in four additional case-control sets (4915 cases and 5607 controls). Known risk loci at 8q24.21 and 11q23 were confirmed, and a previously unreported association, rs12701937, located between the genes GLI3 (GLI family zinc finger 3) and INHBA (inhibin, beta A) [P = 1.1 x 10(-3), odds ratio (OR) 1.14, 95% confidence interval (CI) 1.05-1.23, dominant model in the combined cohort], was identified. The association was stronger in familial cases compared with unselected cases (P = 2.0 x 10(-4), OR 1.36, 95% CI 1.16-1.60, dominant model). Two other unreported SNPs, rs6038071, 40 kb upstream of CSNK2A1 (casein kinase 2, alpha 1 polypeptide) and an intronic marker in MYO3A (myosin IIIA), rs11014993, associated with CRC only in the familial CRC cases (P = 2.5 x 10(-3), recessive model, and P = 2.7 x 10(-4), dominant model). Three software tools successfully pointed to the overrepresentation of genes related to the mitogen-activated protein kinase (MAPK) signalling pathways among the 1340 most strongly associated markers from the GWAS (allelic P value < 10(-3)). The risk of CRC increased significantly with an increasing number of risk alleles in seven genes involved in MAPK signalling events (P(trend) = 2.2 x 10(-16), OR(per allele) = 1.34, 95% CI 1.11-1.61).

Genome-wide association studies and beyond

Genome-wide association studies (GWAS) provide an important avenue for undertaking an agnostic evaluation of the association between common genetic variants and risk of disease. Recent advances in our understanding of human genetic variation and the technology to measure such variation have made GWAS feasible. Over the past few years a multitude of GWAS have identified and replicated many associated variants. These findings are enriching our knowledge about the genetic basis of disease and leading some to advocate using GWA study results for genetic testing. For many of the GWA study results, however, the underlying mechanisms remain unclear and the findings explain only a limited amount of heritability. These issues may be clarified by more detailed investigations, including analyses of less common variants, sequence-level data, and environmental exposures. Such studies should help clarify the potential value of genetic testing to the public's health.

Gene signature is associated with early stage rectal cancer recurrence

BACKGROUND

Despite expected excellent outcomes of surgical resection for early stage rectal cancers, 20% of stage I and II rectal cancers recur. Identifying biologic factors that predict the subset prone to recur could allow more directed therapy. This study identifies a tumor gene expression profile that accurately predicts disease recurrence.

STUDY DESIGN

Stage I/II rectal cancer patients treated by surgery alone at a single institution were included and classified as having recurrent or nonrecurrent cancer. Tumor mRNA was isolated from frozen tissue and evaluated for total genome gene expression by microarray analysis. Background-corrected and normalized microarray data were analyzed using BAMarray software. Selected genes were further analyzed using unsupervised clustering and nearest-centroid classification. A balanced K-fold scoring-pair algorithm using 1,000 independent replications was used for gene signature development.

RESULTS

Sixty-nine patients with disease-free survival and 31 patients with recurrent disease were included at a median follow-up of 105 months (interquartile range 114 months) and 32 months (interquartile range 25 months), respectively. Demographics and tumor characteristics between groups were similar. Fifty-two genes from 43,148 probes were differentially expressed, and a 36-gene signature was found to be statistically associated with recurrence using a scoring-pair algorithm. Accuracy to identify recurrence as measured by area under the receiver operating characteristic curve was 0.803.

CONCLUSIONS

Differential gene expression within rectal cancers is associated with recurrence of early stage disease. A 36-gene signature correlates with an increased risk of more or less aggressive tumor behavior. This information obtainable at biopsy may assist in determining treatment decisions.

A robust test for two-stage design in genome-wide association studies

A two-stage design is cost-effective for genome-wide association studies (GWAS) testing hundreds of thousands of single nucleotide polymorphisms (SNPs). In this design, each SNP is genotyped in stage 1 using a fraction of case-control samples. Top-ranked SNPs are selected and genotyped in stage 2 using additional samples. A joint analysis, combining statistics from both stages, is applied in the second stage. Follow-up studies can be regarded as a two-stage design. Once some potential SNPs are identified, independent samples are further genotyped and analyzed separately or jointly with previous data to confirm the findings. When the underlying genetic model is known, an asymptotically optimal trend test (TT) can be used at each analysis. In practice, however, genetic models for SNPs with true associations are usually unknown. In this case, the existing methods for analysis of the two-stage design and follow-up studies are not robust across different genetic models. We propose a simple robust procedure with genetic model selection to the two-stage GWAS. Our results show that, if the optimal TT has about 80% power when the genetic model is known, then the existing methods for analysis of the two-stage design have minimum powers about 20% across the four common genetic models (when the true model is unknown), while our robust procedure has minimum powers about 70% across the same genetic models. The results can be also applied to follow-up and replication studies with a joint analysis.

Shrinkage estimation for robust and efficient screening of single-SNP association from case-control genome-wide association studies

Population-based case-control design has become one of the most popular approaches for conducting genome-wide association scans for rare diseases like cancer. In this article, we propose a novel method for improving the power of the widely used single-single-nucleotide polymorphism (SNP) two-degrees-of-freedom (2 d.f.) association test for case-control studies by exploiting the common assumption of Hardy-Weinberg Equilibrium (HWE) for the underlying population. A key feature of the method is that it can relax the assumed model constraints via a completely data-adaptive shrinkage estimation approach so that the number of false-positive results due to the departure of HWE is controlled. The method is computationally simple and is easily scalable to association tests involving hundreds of thousands or millions of genetic markers. Simulation studies as well as an application involving data from a real genome-wide association study illustrate that the proposed method is very robust for large-scale association studies and can improve the power for detecting susceptibility SNPs with recessive effects, when compared to existing methods. Implications of the general estimation strategy beyond the simple 2 d.f. association test are discussed.

A modified forward multiple regression in high-density genome-wide association studies for complex traits

Genome-wide association studies (GWAS) have been widely used to identify genetic effects on complex diseases or traits. Most currently used methods are based on separate single-nucleotide polymorphism (SNP) analyses. Because this approach requires correction for multiple testing to avoid excessive false-positive results, it suffers from reduced power to detect weak genetic effects under limited sample size. To increase the power to detect multiple weak genetic factors and reduce false-positive results caused by multiple tests and dependence among test statistics, a modified forward multiple regression (MFMR) approach is proposed. Simulation studies show that MFMR has higher power than the Bonferroni and false discovery rate procedures for detecting moderate and weak genetic effects, and MFMR retains an acceptable-false positive rate even if causal SNPs are correlated with many SNPs due to population stratification or other unknown reasons.

Significance of genome-wide association studies in molecular anthropology

The successful advent of a genome-wide approach in association studies raises the hopes of human geneticists for solving a genetic maze of complex traits especially the disorders. This approach, which is replete with the application of cutting-edge technology and supported by big science projects (like Human Genome Project; and even more importantly the International HapMap Project) and various important databases (SNP database, CNV database, etc.), has had unprecedented success in rapidly uncovering many of the genetic determinants of complex disorders. The magnitude of this approach in the genetics of classical anthropological variables like height, skin color, eye color, and other genome diversity projects has certainly expanded the horizons of molecular anthropology. Therefore, in this article we have proposed a genome-wide association approach in molecular anthropological studies by providing lessons from the exemplary study of the Wellcome Trust Case Control Consortium. We have also highlighted the importance and uniqueness of Indian population groups in facilitating the design and finding optimum solutions for other genome-wide association-related challenges.

Bayesian variable and model selection methods for genetic association studies

Variable selection is growing in importance with the advent of high throughput genotyping methods requiring analysis of hundreds to thousands of single nucleotide polymorphisms (SNPs) and the increased interest in using these genetic studies to better understand common, complex diseases. Up to now, the standard approach has been to analyze the genotypes for each SNP individually to look for an association with a disease. Alternatively, combinations of SNPs or haplotypes are analyzed for association. Another added complication in studying complex diseases or phenotypes is that genetic risk for the disease is often due to multiple SNPs in various locations on the chromosome with small individual effects that may have a collectively large effect on the phenotype. Hence, multi-locus SNP models, as opposed to single SNP models, may better capture the true underlying genotypic-phenotypic relationship. Thus, innovative methods for determining which SNPs to include in the model are needed. The goal of this article is to describe several methods currently available for variable and model selection using Bayesian approaches and to illustrate their application for genetic association studies using both real and simulated candidate gene data for a complex disease. In particular, Bayesian model averaging (BMA), stochastic search variable selection (SSVS), and Bayesian variable selection (BVS) using a reversible jump Markov chain Monte Carlo (MCMC) for candidate gene association studies are illustrated using a study of age-related macular degeneration (AMD) and simulated data.

Association between common germline genetic variation in 94 candidate genes or regions and risks of invasive epithelial ovarian cancer

Background

Recent studies have identified several single nucleotide polymorphisms (SNPs) in the population that are associated with variations in the risks of many different diseases including cancers such as breast, prostate and colorectal. For ovarian cancer, the known highly penetrant susceptibility genes (BRCA1 and BRCA2) are probably responsible for only 40% of the excess familial ovarian cancer risks, suggesting that other susceptibility genes of lower penetrance exist.

Methods

We have taken a candidate approach to identifying moderate risk susceptibility alleles for ovarian cancer. To date, we have genotyped 340 SNPs from 94 candidate genes or regions, in up to 1,491 invasive epithelial ovarian cancer cases and 3,145 unaffected controls from three different population based studies from the UK, Denmark and USA.

Results

After adjusting for population stratification by genomic control, 18 SNPs (5.3%) were significant at the 5% level, and 5 SNPs (1.5%) were significant at the 1% level. The most significant association was for the SNP rs2107425, located on chromosome 11p15.5, which has previously been identified as a susceptibility allele for breast cancer from a genome wide association study (P-trend = 0.0012). When SNPs/genes were stratified into 7 different pathways or groups of validation SNPs, the breast cancer associated SNPs were the only group of SNPs that were significantly associated with ovarian cancer risk (P-heterogeneity = 0.0003; P-trend = 0.0028; adjusted (for population stratification) P-trend = 0.006). We did not find statistically significant associations when the combined data for all SNPs were analysed using an admixture maximum likelihood (AML) experiment-wise test for association (P-heterogeneity = 0.051; P-trend = 0.068).

Conclusion

These data suggest that a proportion of the SNPs we evaluated were associated with ovarian cancer risk, but that the effect sizes were too small to detect associations with individual SNPs.

Bayesian semiparametric multiple shrinkage

High-dimensional and highly correlated data leading to non- or weakly identified effects are commonplace. Maximum likelihood will typically fail in such situations and a variety of shrinkage methods have been proposed. Standard techniques, such as ridge regression or the lasso, shrink estimates toward zero, with some approaches allowing coefficients to be selected out of the model by achieving a value of zero. When substantive information is available, estimates can be shrunk to nonnull values; however, such information may not be available. We propose a Bayesian semiparametric approach that allows shrinkage to multiple locations. Coefficients are given a mixture of heavy-tailed double exponential priors, with location and scale parameters assigned Dirichlet process hyperpriors to allow groups of coefficients to be shrunk toward the same, possibly nonzero, mean. Our approach favors sparse, but flexible, structure by shrinking toward a small number of random locations. The methods are illustrated using a study of genetic polymorphisms and Parkinson's disease.

Evaluation of genome-wide association study results through development of ontology fingerprints

MOTIVATION

Genome-wide association (GWA) studies may identify multiple variants that are associated with a disease or trait. To narrow down candidates for further validation, quantitatively assessing how identified genes relate to a phenotype of interest is important.

RESULTS

We describe an approach to characterize genes or biological concepts (phenotypes, pathways, diseases, etc.) by ontology fingerprint--the set of Gene Ontology (GO) terms that are overrepresented among the PubMed abstracts discussing the gene or biological concept together with the enrichment p-value of these terms generated from a hypergeometric enrichment test. We then quantify the relevance of genes to the trait from a GWA study by calculating similarity scores between their ontology fingerprints using enrichment p-values. We validate this approach by correctly identifying corresponding genes for biological pathways with a 90% average area under the ROC curve (AUC). We applied this approach to rank genes identified through a GWA study that are associated with the lipid concentrations in plasma as well as to prioritize genes within linkage disequilibrium (LD) block. We found that the genes with highest scores were: ABCA1, lipoprotein lipase (LPL) and cholesterol ester transfer protein, plasma for high-density lipoprotein; low-density lipoprotein receptor, APOE and APOB for low-density lipoprotein; and LPL, APOA1 and APOB for triglyceride. In addition, we identified genes relevant to lipid metabolism from the literature even in cases where such knowledge was not reflected in current annotation of these genes. These results demonstrate that ontology fingerprints can be used effectively to prioritize genes from GWA studies for experimental validation.

Tagging single-nucleotide polymorphisms in candidate oncogenes and susceptibility to ovarian cancer

Low-moderate risk alleles that are relatively common in the population may explain a significant proportion of the excess familial risk of ovarian cancer (OC) not attributed to highly penetrant genes. In this study, we evaluated the risks of OC associated with common germline variants in five oncogenes (BRAF, ERBB2, KRAS, NMI and PIK3CA) known to be involved in OC development. Thirty-four tagging SNPs in these genes were genotyped in approximately 1800 invasive OC cases and 3000 controls from population-based studies in Denmark, the United Kingdom and the United States. We found no evidence of disease association for SNPs in BRAF, KRAS, ERBB2 and PIK3CA when OC was considered as a single disease phenotype; but after stratification by histological subtype, we found borderline evidence of association for SNPs in KRAS and BRAF with mucinous OC and in ERBB2 and PIK3CA with endometrioid OC. For NMI, we identified a SNP (rs11683487) that was associated with a decreased risk of OC (unadjusted P(dominant)=0.004). We then genotyped rs11683487 in another 1097 cases and 1792 controls from an additional three case-control studies from the United States. The combined odds ratio was 0.89 (95% confidence interval (CI): 0.80-0.99) and remained statistically significant (P(dominant)=0.032). We also identified two haplotypes in ERBB2 associated with an increased OC risk (P(global)=0.034) and a haplotype in BRAF that had a protective effect (P(global)=0.005). In conclusion, these data provide borderline evidence of association for common allelic variation in the NMI with risk of epithelial OC.

Functional complementation studies identify candidate genes and common genetic variants associated with ovarian cancer survival

Common germline genetic variation and/or somatic alterations in tumours may be associated with survival in women diagnosed with ovarian cancer. The successful identification of genetic associations relies on a suitable strategy for identifying and testing candidate genes. We used microcell-mediated chromosome transfer approach and expression microarray analysis to identify genes that were associated with neoplastic suppression in ovarian cancer cell lines. Sixty-five tagging single nucleotide polymorphisms (tSNPs) in nine candidate genes were genotyped in approximately 1700 invasive ovarian cancer cases to look for associations with survival. For two of these genes, loss of heterozygosity (LOH) analysis of tSNPs in 314 ovarian tumours was used to identify associations between somatic gene deletions and survival. We identified significant associations with survival for a tSNP in caspase 5 (CASP5) [hazard ratio (HR) = 1.13 (95% CI: 1.00-1.27), P = 0.042] and two tSNPs in the retinoblastoma binding protein (RBBP8) gene [HR = 0.85 (95% CI: 0.75-0.95), P = 0.007 and HR = 0.83 (95% CI: 0.71-0.95), P = 0.009]. After adjusting for multiple prognostic factors in a multivariate Cox regression analysis, both associations in RBBP8 remained significant (P = 0.028 and 0.036). We then genotyped 314 ovarian tumours for several tSNPs in CASP5 and RBBP8 to identify gene deletions by LOH. For RBBP8, 35% of tumours in 101 informative cases showed somatic allelic deletion; LOH of RBBP8 was associated with a significantly worse prognosis [HR = 2.19 (95% CI: 1.36-3.54), P = 0.001]. In summary, a novel in vitro functional approach in ovarian cancer cells has identified RBBP8 as a gene for which both germline genetic variation and somatic alterations in tumours are associated with survival in ovarian cancer patients.

Genes, cognition, and communication: insights from neurodevelopmental disorders

Twin and family studies have demonstrated that most cognitive traits are moderately to highly heritable. Neurodevelopmental disorders such as dyslexia, autism, and specific language impairment (SLI) also show strong genetic influence. Nevertheless, it has proved difficult for researchers to identify genes that would explain substantial amounts of variance in cognitive traits or disorders. Although this observation may seem paradoxical, it fits with a multifactorial model of how complex human traits are influenced by numerous genes that interact with one another, and with the environment, to produce a specific phenotype. Such a model can also explain why genetic influences on cognition have not vanished in the course of human evolution. Recent linkage and association studies of SLI and dyslexia are reviewed to illustrate these points. The role of nonheritable genetic mutations (sporadic copy number variants) in causing autism is also discussed. Finally, research on phenotypic correlates of allelic variation in the genes ASPM and microcephalin is considered; initial interest in these as genes for brain size or intelligence has been dampened by a failure to find phenotypic differences in people with different versions of these genes. There is a current vogue for investigators to include measures of allelic variants in studies of cognition and cognitive disorders. It is important to be aware that the effect sizes associated with these variants are typically small and hard to detect without extremely large sample sizes.

Normal tissue reactions to radiotherapy: towards tailoring treatment dose by genotype

A key challenge in radiotherapy is to maximize radiation doses to cancer cells while minimizing damage to surrounding healthy tissue. As severe toxicity in a minority of patients limits the doses that can be safely given to the majority, there is interest in developing a test to measure an individual's radiosensitivity before treatment. Variation in sensitivity to radiation is an inherited genetic trait and recent progress in genotyping raises the possibility of genome-wide studies to characterize genetic profiles that predict patient response to radiotherapy.