Design Of Genome-wide Association Studies Research Articles

(222) POSSIBLE PATHOPHYSIOLOGICAL ROLES OF NEUROTRANSMITTER SYSTEMS IN MEN WITH LIFELONG PREMATURE EJACULATION: A SCOPING REVIEW

Abstract Introduction Lifelong premature ejaculation (LPE) is a subtype of premature ejaculation. Genetic research on LPE has primarily focused on neurotransmitters like serotonin, dopamine, and norepinephrine. LPE treatment studies have focused on drugs like serotonin reuptake inhibitors (SSRIs). However, findings from both genetic association and pharmacotherapeutic studies have been inconsistent, likely due to the lack of standardized definitions and measurement methods for LPE, such as the stopwatch method for clocking intravaginal ejaculation latency times (IELTs). In addition, some genetic studies in LPE have shown to be prone to genotyping errors which has led to inconclusive results. Objective To provide a quality assured overview of neurobiological targets that are potentially associated with LPE by investigating genetic and pharmacotherapeutic studies. Methods This scoping review was conducted using the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR tool). Five databases were searched in March 2023 without timeline- or language-related restrictions. We included pharmacological studies with multiple study designs: randomized clinical trials, cross-over studies, single arm trials or case reports. Studies were only eligible if pre- and posttreatment stopwatch measured IELTs were reported in order to measure a factor increase in IELTs. Studies that did not use the ISSM 2008 or 2014- or the DSM-5 definition for LPE or a cut-off IELT of approximately 1.5 minutes after vaginal penetration were excluded. Results After deduplication, 3949 records were obtained for reviewing. Following screening and full text review with citation tracking, 52 studies were included. Studies consisted of 18 genetic and 34 pharmacotherapy studies. Serotonergic targets, like the serotonin transporter and pre- and postsynaptic serotonergic receptors, were most often associated with LPE in both genetic and pharmacotherapeutic studies. Mixed results were found among polymorphisms within genetic studies. However, most serotonergic polymorphisms suggest up- or downregulation on serotonergic targets eventually leading to decreased synaptic levels of serotonin. This mechanism is in accordance with pharmacotherapeutic studies as the highest efficacy was found for potent serotonergic antidepressants such as paroxetine, sertraline, fluoxetine, dapoxetine, citalopram and clomipramine. Successful treatment was also observed with medication acting on phospoidesterase-5 enzyme (PDE5), such as tadalafil and vardenafil. Analyses of other genetic association studies did not yield any further evidence for associated targets. Conclusions This review is the first comprehensive scoping review on LPE, performed according to PRISMA-ScR guidelines. We found that serotonergic targets are most often associated with LPE, suggesting that the serotonergic pathway is a predisposing factor in LPE. Furthermore, there is some evidence for PDE5 which should be further investigated. Other previously investigated neurobiological targets appear less likely to contribute to LPE. However LPE is thought to be a complex disorder in which a subset of polymorphisms contribute to this phenotype. Therefore future studies should focus on multiple targets, ideally in a genome wide association study design. Disclosure Any of the authors act as a consultant, employee or shareholder of an industry for: In2, Virility, Israel.

PB1969: FUNCTIONAL CHARACTERIZATION OF SUSCEPTIBILITY LOCI TO MULTIPLE MYELOMA

Background: Multiple myeloma (MM) is the second most common blood malignancy, caused by an uncontrolled growth of plasma cells in the bone marrow, accounting for 20% of all newly diagnosed hematological cancers. Although the current 5-year survival rate is ranging between 40-60%, MM is still considered an incurable disease since most of the patients eventually relapse. While the causes of MM are incompletely understood, several genome-wide association studies (GWAS) have been conducted to identify germline variants that predispose to MM. Up to date, a total of 24 loci were found to be associated with MM risk, but very little information is available about their functional role. Aims: The principal goal is to explore in silico the function of the germline variants associated with MM risk. As we do not know if the GWAS-identified SNPs are the causal variants or just markers of risk, the functional characterization of the causal risk variants would lead to a better understanding of disease development. Methods: GWAS design takes advantage of the linkage disequilibrium (LD) structure of the human genome, thus the main GWAS findings are single-nucleotide polymorphisms (SNPs) that show the strongest association with MM risk (measured as the lowest p-values), but they are not necessarily the functionally causal variants. In this project, we used bioinformatics tools (GTEx, HaploReg v4.1, Roadmap, LDlinke, SNPnexus, RegulomeDB 2.0.3, SNP2TFBS, miRNASNP v3, GeneMANIA) to perform fine mapping of all GWAS-identified loci and to prioritize in each locus the polymorphism with the highest chance of being functionally relevant. In particular, we focused on the loci with the smallest number of SNPs in high LD (r2>0.8) in order to maximize the probability to capture the casual variant. Results: Four of the 24 MM risk loci had a relatively small number of SNPs in high LD and within them we found that the locus located at chromosome 16 contained the greatest number of functionally annotated SNPs. Of particular functional interest was rs3747481 (chr16:30666367 C/T) due to the following reasons: it is a missense variant (protein change: P359L), it is located in the PRR14 gene, that contributes to chromatin hierarchical organization and has a role in gene regulation, has a high CADD PHRED score (22.1). Additionally, according to GTEx portal, rs3747481 is associated with the expression level of the RNF40 gene (which plays a central role in histone code and gene regulation) in whole blood cells (p=3.02-12). It has a score of “1d” in RegulomeDB (eQTL+ TF binding + any motif + DNase peak), meaning that this variant has a high likelihood to affect binding of transcription factors. Some other SNPs (like rs35629860 and rs67128646) in the same LD block show the co-occurrence of H3K4me3 and H3K27me3 histone marks (associated with gene activation and repression, respectively) in promoters and enhancers in B lymphocytes. rs6565197 is predicted to affect the binding of the KLF4 and KLF5 transcription factors which play key roles in cell cycle regulation. Summary/Conclusion: Through a fine mapping of MM risk loci by bioinformatics tools, we found a variant in the locus 16p11.2 that shows in silico a very high probability to have biological role in the risk disease.

Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.

Ultra Low-Coverage Whole-Genome Sequencing as an Alternative to Genotyping Arrays in Genome-Wide Association Studies.

An array-based genotyping approach has been the standard practice for genome-wide association studies (GWASs); however, as sequencing costs plummet over the past years, ultra low-coverage whole-genome sequencing (ulcWGS <0.5× coverage) has emerged as a promising alternative that provides superior genomic coverage with substantial reduction of genotyping cost. To evaluate the potential utility of ulcWGS, we performed a whole-genome sequencing (WGS) of 72 European individuals to a target coverage of 0.4× and compared its performance with the widely used Infinium Global Screening Multi-Disease Array (GSA-MD). We showed that the number of variants captured by ulcWGS is comparable with imputed GSA-MD platform, particularly for low-frequency (95.5%) and common variants (99.9%), with high imputation R2 accuracy (mean 0.93 for SNPs and 0.86 for indels). Using deep-coverage 30× WGS as the “truth” genotypes, we found that ulcWGS has higher overall nonreference genotype concordance compared with imputed GSA-MD for both SNPs (0.90 vs. 0.88) and indels (0.86 vs. 0.83). In addition, ulcWGS proved to be as sensitive as the genotyping-based method in sex imputation and ancestry prediction producing similar principal component (PC) scores. Our findings provide important evidence that the cost efficient ulcWGS of <0.5× generates high genotype accuracy, outperforming the standard genotyping arrays, making it an attractive alternative to the array-based method in next-generation GWAS design.

Assessment of cold stress tolerance in maize through quantitative trait locus, genome-wide association study and transcriptome analysis

Genome-wide association study (GWAS) has become a widely accepted strategy for decoding genotype phenotype associations in many species thanks to advances in next-generation sequencing (NGS) technologies. Maize is an ideal crop for GWAS and significant progress has been made in the last decade. This review summarizes current GWAS efforts in maize functional genomics research and discusses future prospects in the omics era. The general goal of GWAS is to link genotypic variations to corresponding differences in phenotype using the most appropriate statistical model in a given population. The current review also presents perspectives for optimizing GWAS design and analysis. GWAS analysis of data from RNA, protein, and metabolite-based omics studies is discussed, along with new models and new population designs that will identify causes of phenotypic variation that have been hidden to date. The detailed that low temperature in maize seedlings altogether restricts germination and seedlings' development and destabilizes the cancer prevention agent safeguard component. Cold pressure adversely influences root morphology, photosystem II (PS II) effectiveness, chlorophyll substance, and leaf region. A short scene of low temperature stress (for example, under 10 °C for 7 days) during the V6–V9 maize development stages can fundamentally defer the anthesis commencement. Among the morphological reactions by focused on maize plants, low temperature stress causes strange tuft development in maize, along these lines influencing the fertilization and grain filling measures. Hence, problematic temperatures can cause a genuine yield decrease if happening at basic conceptive stages, as plants allocate over half of their photosynthesis to foster grains during this stage until physiological development. Low temperature stress fundamentally diminishes the plant stature and absolute yield biomass of maize. Leaf improvement turns out to be delayed in chilly focused on plants because of a drawn-out cell cycle and diminished pace of mitosis. The joint and continuous efforts of the whole community will enhance our understanding of maize quantitative traits and boost crop molecular breeding designs.

Genomewide Association Studies in Pharmacogenomics.

The increasing availability of genotype data linked with information about drug‐response phenotypes has enabled genomewide association studies (GWAS) that uncover genetic determinants of drug response. GWAS have discovered associations between genetic variants and both drug efficacy and adverse drug reactions. Despite these successes, the design of GWAS in pharmacogenomics (PGx) faces unique challenges. In this review, we analyze the last decade of GWAS in PGx. We review trends in publications over time, including the drugs and drug classes studied and the clinical phenotypes used. Several data sharing consortia have contributed substantially to the PGx GWAS literature. We anticipate increased focus on biobanks and highlight phenotypes that would best enable future PGx discoveries.

Genomics of Endometriosis: From Genome Wide Association Studies to Exome Sequencing.

This review aims at better understanding the genetics of endometriosis. Endometriosis is a frequent feminine disease, affecting up to 10% of women, and characterized by pain and infertility. In the most accepted hypothesis, endometriosis is caused by the implantation of uterine tissue at ectopic abdominal places, originating from retrograde menses. Despite the obvious genetic complexity of the disease, analysis of sibs has allowed heritability estimation of endometriosis at ~50%. From 2010, large Genome Wide Association Studies (GWAS), aimed at identifying the genes and loci underlying this genetic determinism. Some of these loci were confirmed in other populations and replication studies, some new loci were also found through meta-analyses using pooled samples. For two loci on chromosomes 1 (near CCD42) and chromosome 9 (near CDKN2A), functional explanations of the SNP (Single Nucleotide Polymorphism) effects have been more thoroughly studied. While a handful of chromosome regions and genes have clearly been identified and statistically demonstrated as at-risk for the disease, only a small part of the heritability is explained (missing heritability). Some attempts of exome sequencing started to identify additional genes from families or populations, but are still scarce. The solution may reside inside a combined effort: increasing the size of the GWAS designs, better categorize the clinical forms of the disease before analyzing genome-wide polymorphisms, and generalizing exome sequencing ventures. We try here to provide a vision of what we have and what we should obtain to completely elucidate the genetics of this complex disease.

Clustering by phenotype and genome-wide association study in autism

Autism spectrum disorder (ASD) has phenotypically and genetically heterogeneous characteristics. A simulation study demonstrated that attempts to categorize patients with a complex disease into more homogeneous subgroups could have more power to elucidate hidden heritability. We conducted cluster analyses using the k-means algorithm with a cluster number of 15 based on phenotypic variables from the Simons Simplex Collection (SSC). As a preliminary study, we conducted a conventional genome-wide association study (GWAS) with a data set of 597 ASD cases and 370 controls. In the second step, we divided cases based on the clustering results and conducted GWAS in each of the subgroups vs controls (cluster-based GWAS). We also conducted cluster-based GWAS on another SSC data set of 712 probands and 354 controls in the replication stage. In the preliminary study, which was conducted in conventional GWAS design, we observed no significant associations. In the second step of cluster-based GWASs, we identified 65 chromosomal loci, which included 30 intragenic loci located in 21 genes and 35 intergenic loci that satisfied the threshold of P < 5.0 × 10−8. Some of these loci were located within or near previously reported candidate genes for ASD: CDH5, CNTN5, CNTNAP5, DNAH17, DPP10, DSCAM, FOXK1, GABBR2, GRIN2A5, ITPR1, NTM, SDK1, SNCA, and SRRM4. Of these 65 significant chromosomal loci, rs11064685 located within the SRRM4 gene had a significantly different distribution in the cases vs controls in the replication cohort. These findings suggest that clustering may successfully identify subgroups with relatively homogeneous disease etiologies. Further cluster validation and replication studies are warranted in larger cohorts.

Sequencing and imputation in GWAS: Cost-effective strategies to increase power and genomic coverage across diverse populations.

A key aim for current genome‐wide association studies (GWAS) is to interrogate the full spectrum of genetic variation underlying human traits, including rare variants, across populations. Deep whole‐genome sequencing is the gold standard to fully capture genetic variation, but remains prohibitively expensive for large sample sizes. Array genotyping interrogates a sparser set of variants, which can be used as a scaffold for genotype imputation to capture a wider set of variants. However, imputation quality depends crucially on reference panel size and genetic distance from the target population. Here, we consider sequencing a subset of GWAS participants and imputing the rest using a reference panel that includes both sequenced GWAS participants and an external reference panel. We investigate how imputation quality and GWAS power are affected by the number of participants sequenced for admixed populations (African and Latino Americans) and European population isolates (Sardinians and Finns), and identify powerful, cost‐effective GWAS designs given current sequencing and array costs. For populations that are well‐represented in existing reference panels, we find that array genotyping alone is cost‐effective and well‐powered to detect common‐ and rare‐variant associations. For poorly represented populations, sequencing a subset of participants is often most cost‐effective, and can substantially increase imputation quality and GWAS power.

A genome-wide association study of deafness in three canine breeds.

Congenital deafness in the domestic dog is usually related to the presence of white pigmentation, which is controlled primarily by the piebald locus on chromosome 20 and also by merle on chromosome 10. Pigment-associated deafness is also seen in other species, including cats, mice, sheep, alpacas, horses, cows, pigs, and humans, but the genetic factors determining why some piebald or merle dogs develop deafness while others do not have yet to be determined. Here we perform a genome-wide association study (GWAS) to identify regions of the canine genome significantly associated with deafness in three dog breeds carrying piebald: Dalmatian, Australian cattle dog, and English setter. We include bilaterally deaf, unilaterally deaf, and matched control dogs from the same litter, phenotyped using the brainstem auditory evoked response (BAER) hearing test. Principal component analysis showed that we have different distributions of cases and controls in genetically distinct Dalmatian populations, therefore GWAS was performed separately for North American and UK samples. We identified one genome-wide significant association and 14 suggestive (chromosome-wide) associations using the GWAS design of bilaterally deaf vs. control Australian cattle dogs. However, these associations were not located on the same chromosome as the piebald locus, indicating the complexity of the genetics underlying this disease in the domestic dog. Because of this apparent complex genetic architecture, larger sample sizes may be needed to detect the genetic loci modulating risk in piebald dogs.

Variable prediction accuracy of polygenic scores within an ancestry group.

Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.

M63 - IDENTIFICATION OF GENETIC VARIANTS FOR SUICIDAL BEHAVIORS IN PATIENTS WITH BIPOLAR OR MAJOR DEPRESSIVE DISORDERS

Background Suicide is the second leading cause of death in young adults, and is accounted for 1.4 percent of all deaths worldwide. More than 90% of suicide attempters or victims had been diagnosed with psychiatric disorders, particularly Major Depressive Disorder (MDD) and Bipolar Disorder (BD). Suicidal behaviour has substantial heritability, however, the underlying etiology of patients diagnosed with different mood disorders may not identical. In the current study, we adopted the Genome-Wide Association Study design (GWAS) to identify genetic variants associated with suicidal behaviors in patients with BD or MDD. Our goal is to identify possible common and unique genetic variants that might be associated to suicidal behavior between the two patient groups. We further test the predictive ability for suicide in the MDD group by using the calculated Polygenic Risk Score (PRS) for suicide in BD patients and vice versa. Methods We recruited 662 BD and 428 MDD patients in Taiwan, who were referred by psychiatrists based on DSM-IV criteria. BD patients were interviewed to collect information of demographic, clinical characteristics, and suicidal behaviors. In addition, the suicide item in Hamilton depression rating scale was used to obtain suicide information for MDD patients. Genotyping was conducted using Affymetrix CHB1 chip for BD, and Illumina Human Omni Express Exome bead chip for MDD with imputation for association analyses. Genetic association and PRS analyses were employed by Plink v1.07. The significance level was set at 5E-08 for adjustment of multiple comparisons, and a p-value less than 1E-06 was considered suggestive association. Results In our samples, 45% of BD and 30% of MDD subjects were male. The onset age for BP and MDD were 18.24 and 38.04 years respectively. The prevalence of suicide attempt was 35% in BD group and 43% in MDD group. There were no loci that reached the genome-wide significance level for suicidal behavior in BP patients. The most significant marker was rs34945506 (p=4.13E-06) located in RCSD1 gene on chromosome 1. In MDD group, marker rs76015177 (p=9.94E-07) showed suggestive association with suicidal behavior. This marker is located on chromosome 9 and is not mapped to any coding gene. There were no common SNPs associated with suicidal behavior in the MDD and BP groups. Discussion Genetic variants that showed suggestive signals with suicidal behavior in the present study were not reported in previous studies. However, due to limited sample size, the power of this study was only moderate, and large-scale replication studies are needed for further validation. One mapped gene, RCSD1, has been reported to regulate the ability of F-actin-capping protein to remodel actin filament assembly via stress-induced phosphorylation. Actin was found to be related to some neurodegeneration diseases, such as Alzheimer's disease. For MDD, two suggestive markers (rs78434258 and rs79070953) on chromosome 9 are found near to the most significant marker (rs76015177) were located on LOC105375976 gene, which is a non-coding RNA. However, the function of this gene is mostly unknown. Lastly, the non-overlapping genetic results for suicidal behaviors in the MDD and BP patients may indicate different pathogenesis and biological pathways for suicide by different diagnosis groups.

Genome-Wide Association Studies to Improve Wood Properties: Challenges and Prospects.

Wood formation is an excellent model system for quantitative trait analysis due to the strong associations between the transcriptional and metabolic traits that contribute to this complex process. Investigating the genetic architecture and regulatory mechanisms underlying wood formation will enhance our understanding of the quantitative genetics and genomics of complex phenotypic variation. Genome-wide association studies (GWASs) represent an ideal statistical strategy for dissecting the genetic basis of complex quantitative traits. However, elucidating the molecular mechanisms underlying many favorable loci that contribute to wood formation and optimizing GWAS design remain challenging in this omics era. In this review, we summarize the recent progress in GWAS-based functional genomics of wood property traits in major timber species such as Eucalyptus, Populus, and various coniferous species. We discuss several appropriate experimental designs for extensive GWAS in a given undomesticated tree population, such as omics-wide association studies and high-throughput phenotyping technologies. We also explain why more attention should be paid to rare allelic and major structural variation. Finally, we explore the potential use of GWAS for the molecular breeding of trees. Such studies will help provide an integrated understanding of complex quantitative traits and should enable the molecular design of new cultivars.

A Guide to Genome-Wide Association Mapping in Plants.

Genome-wide association studies (GWAS) have developed into a valuable approach for identifying the genetic basis of phenotypic variation. In this article, we provide an overview of the design, analysis, and interpretation of GWAS. First, we present results from simulations that explore key elements of experimental design as well as considerations for collecting the relevant genomic and phenotypic data. Next, we outline current statistical methods and tools used for GWA analyses and discuss the inclusion of covariates to account for population structure and the interpretation of results. Given that many false positive associations will occur in any GWA analysis, we highlight strategies for prioritizing GWA candidates for further statistical and empirical validation. While focused on plants, the material we cover is also applicable to other systems. © 2017 by John Wiley & Sons, Inc.

Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics.

Genome-wide Association Studies (GWAS) result in millions of summary statistics (“z-scores”) for single nucleotide polymorphism (SNP) associations with phenotypes. These rich datasets afford deep insights into the nature and extent of genetic contributions to complex phenotypes such as psychiatric disorders, which are understood to have substantial genetic components that arise from very large numbers of SNPs. The complexity of the datasets, however, poses a significant challenge to maximizing their utility. This is reflected in a need for better understanding the landscape of z-scores, as such knowledge would enhance causal SNP and gene discovery, help elucidate mechanistic pathways, and inform future study design. Here we present a parsimonious methodology for modeling effect sizes and replication probabilities, relying only on summary statistics from GWAS substudies, and a scheme allowing for direct empirical validation. We show that modeling z-scores as a mixture of Gaussians is conceptually appropriate, in particular taking into account ubiquitous non-null effects that are likely in the datasets due to weak linkage disequilibrium with causal SNPs. The four-parameter model allows for estimating the degree of polygenicity of the phenotype and predicting the proportion of chip heritability explainable by genome-wide significant SNPs in future studies with larger sample sizes. We apply the model to recent GWAS of schizophrenia (N = 82,315) and putamen volume (N = 12,596), with approximately 9.3 million SNP z-scores in both cases. We show that, over a broad range of z-scores and sample sizes, the model accurately predicts expectation estimates of true effect sizes and replication probabilities in multistage GWAS designs. We assess the degree to which effect sizes are over-estimated when based on linear-regression association coefficients. We estimate the polygenicity of schizophrenia to be 0.037 and the putamen to be 0.001, while the respective sample sizes required to approach fully explaining the chip heritability are 106 and 105. The model can be extended to incorporate prior knowledge such as pleiotropy and SNP annotation. The current findings suggest that the model is applicable to a broad array of complex phenotypes and will enhance understanding of their genetic architectures.

Genome-wide surveillance of mismatched alleles for graft-versus-host disease in stem cell transplantation

AbstractAcute graft-versus-host disease (aGVHD) represents one of the major complications in allogeneic stem cell transplantation and is primarily caused by genetic disparity between the donor and recipient. In HLA-matched transplants, the disparity is thought to be determined by loci encoding minor histocompatibility antigens (minor H antigens), which are presented by specific HLA molecules. We performed a genome-wide association study (GWAS) to identify minor H antigen loci associated with aGVHD. A total of 500 568 single nucleotide polymorphisms (SNPs) were genotyped for donors and recipients from 1589 unrelated bone marrow transplants matched for HLA-A, -B, -C, -DRB1, and -DQB1, followed by the imputation of unobserved SNPs. We interrogated SNPs whose disparity between the donor and recipient was significantly associated with aGVHD development. Without assuming HLA unrestriction, we successfully captured a known association between HLA-DPB1 disparity (P = 4.50 × 10−9) and grade II-IV aGVHD development, providing proof of concept for the GWAS design aimed at discovering genetic disparity associated with aGVHD. In HLA-restricted analyses, whereby association tests were confined to major subgroups sharing common HLA alleles to identify putative minor H antigen loci, we identified 3 novel loci significantly associated with grade III-IV aGVHD. Among these, rs17473423 (P = 1.20 × 10−11) at 12p12.1 within the KRAS locus showed the most significant association in the subgroup, sharing HLA-DQB1*06:01. Our result suggested that a GWAS can be successfully applied to identify allele mismatch associated with aGVHD development, contributing to the understanding of the genetic basis of aGVHD.

Indications for potential parent-of-origin effects within the FTO gene.

Genome-Wide Association Studies (GWAS) were successfully applied to discover associations with obesity. However, the GWAS design is usually based on unrelated individuals and inheritance information on the parental origin of the alleles is missing. Taking into account parent-of-origin may provide further insights into the genetic mechanisms contributing to obesity. We hypothesized that there may be variants within the robustly replicated fat mass and obesity associated (FTO) gene that may confer different risk for obesity depending on transmission from mother or father. Genome-wide genotypes and pedigree information from the Sorbs population were used. Phased genotypes among 525 individuals were generated by AlphaImpute. Subsequently, 22 SNPs within FTO introns 1 to 3 were selected and parent-of-origin specific association analyses were performed using PLINK. Interestingly, we identified several SNPs conferring different genetic effects (P≤0.05) depending on parental origin—among them, rs1861868, rs1121980 and rs9939973 (all in intron 1). To confirm our findings, we investigated the selected variants in 705 German trios comprising an (extremely) obese child or adolescent and both parents. Again, we observed evidence for POE effects in intron 2 and 3 (P≤0.05) as indicated by the parental asymmetry test. Our results suggest that the obesity risk transmitted by several FTO variants may depend on the parental origin of the allele. Larger family-based studies are warranted to replicate our findings.

Statistical power for identifying nucleotide markers associated with quantitative traits in genome-wide association analysis using a mixed model

Use of mixed models is in the spotlight as an emerging method for genome-wide association studies (GWASs). This study investigated the statistical power for identifying nucleotide variants associated with quantitative traits using the mixed model methodology. Quantitative traits were simulated through design of heritability, the number of causal variants (NCV), the number of polygenic variants, and genetic variance ratio of causal to polygenic variants (VRCTP). Statistical power estimates were influenced not only by individual factors of heritability, NCV, and VRCTP, but also by their interactions (P<0.05). As the genetic variance ratio decreased, the difference in power between heritabilities of 0.3 and 0.5 increased with the use of 20 causal variants, but decreased when there were 100 causal variants (P<0.05). The power empirically estimated from the simulation study would be applicable to the design of GWAS for quantitative traits with known genetic parameters by predicting the degree of false negative associations.

A novel colorectal cancer risk locus at 4q32.2 identified from an international genome-wide association study.

Only a fraction of colorectal cancer heritability is explained by known risk-conferring genetic variation. This study was designed to identify novel risk alleles in Europeans. We conducted a genome-wide association study (GWAS) meta-analysis of colorectal cancer in participants from a population-based case-control study in Israel (n = 1616 cases, 1329 controls) and a consortium study from the Colon Cancer Family Registry (n = 1977 cases, 999 controls). We used a two-stage (discovery-replication) GWAS design, followed by a joint meta-analysis. A combined analysis identified a novel susceptibility locus that reached genome-wide significance on chromosome 4q32.2 [rs35509282, risk allele = A (minor allele frequency = 0.09); odds ratio (OR) per risk allele = 1.53; P value = 8.2 × 10(-9); nearest gene = FSTL5]. The direction of the association was consistent across studies. In addition, we confirmed that 14 of 29 previously identified susceptibility variants were significantly associated with risk of colorectal cancer in this study. Genetic variation on chromosome 4q32.2 is significantly associated with risk of colorectal cancer in Ashkenazi Jews and Europeans in this study.

Genome-wide association studies: applications and insights gained in Ophthalmology

Genome-wide association studies (GWAS) use high-throughput genotyping technologies to genotype thousands of single-nucleotide polymorphisms (SNPs) and relate them to the development of clinical and quantitative traits. Their use has been highly successful in the field of ophthalmology, and since the advent of GWAS in 2005, many genes not previously suspected of having a role in disease have been identified and the findings replicated. We conducted an extensive literature review and describe the concept, design, advantages, and limitations of GWAS and provide a detailed description of the applications and discoveries of GWAS in the field of eye disease to date. There have been many novel findings revealing previously unknown biological insights in a diverse range of common ocular conditions. GWAS have been a highly successful modality for investigating the pathogenesis of a wide variety of ophthalmic conditions. The insights gained into the pathogenesis of disease provide not only a better understanding of underlying disease mechanism but also offer a rationale for targeted treatment and preventative strategies. Expansive international collaboration and standardised phenotyping will permit the continued success of this investigative technique.