A genome-wide association study to investigate genetic loci associated with primary glaucoma in American Cocker Spaniels.

With recent advances in biotechnology, genome-wide association study (GWAS) has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression (LR) based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression (PC-LR), partial least squares-based logistic regression (PLS-LR), have recently gained much attention in the analysis of high dimensional genomic data. However, the performance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism (SNP) set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.

Genome-wide association studies (GWASs) aim to detect genetic risk factors for complex human diseases by identifying disease-associated single-nucleotide polymorphisms (SNPs). The traditional SNP-wise approach along with multiple testing adjustment is over-conservative and lack of power in many GWASs. In this article, we proposed a model-based clustering method that transforms the challenging high-dimension-small-sample-size problem to low-dimension-large-sample-size problem and borrows information across SNPs by grouping SNPs into three clusters. We pre-specify the patterns of clusters by minor allele frequencies of SNPs between cases and controls, and enforce the patterns with prior distributions. In the simulation studies our proposed novel model outperforms traditional SNP-wise approach by showing better controls of false discovery rate (FDR) and higher sensitivity. We re-analyzed two real studies to identifying SNPs associated with severe bortezomib-induced peripheral neuropathy (BiPN) in patients with multiple myeloma (MM). The original analysis in the literature failed to identify SNPs after FDR adjustment. Our proposed method not only detected the reported SNPs after FDR adjustment but also discovered a novel BiPN-associated SNP rs4351714 that has been reported to be related to MM in another study.

Background: We have developed a new breast cancer risk assessment model (OncoVue) that integrates information from 22 single nucleotide polymorphisms (SNPs) and 5 personal risk factors. It has been shown to effectively stratify risk in three independent validation populations. Genome wide association studies (GWAS) have identified seven candidate SNPs independently associated with breast cancer risk. Using estimates of relative risks and allele frequencies derived from these studies, a theoretical examination of these SNPs has concluded that they have the potential to modestly improve clinical risk estimation. We have examined both the OncoVue model and these GWAS SNPs in the same case-control population to determine their utility in risk estimation.Materials and Methods: A randomly selected subset of participants (376 cases and 982 controls) that had enrolled in a larger case-control study conducted in six distinct geographic regions of the United States were examined. DNAs were genotyped for the 22 SNP variants in OncoVue and combined with personal factors to calculate the risk scores for the individual participants. DNAs were also genotyped for the following GWAS SNPs: rs2981582 (FGFR2), rs3817198 (LSP1), rs889312 (MAP3K), rs4415084 (MRPS30), rs13281615 (POU5F1P1), rs13387042 (TNP1), and rs3803662 (TOX3, formerly TNRCR9). Association of individual GWAS SNP genotypes with breast cancer risk was evaluated by calculating odds ratios (ORs). Assuming independent contribution of each SNP to risk, a combined GWAS risk score was calculated using a multiplicative model. Positive likelihood ratios (PLRs) were calculated using a risk threshold of 1.5-fold the control population mean risk to evaluate the proportion of individuals placed at elevated risk by OncoVue compared to the combined GWAS risk scores.Results: OncoVue exhibited significant ability to stratify risk at the 1.5-fold mean with a PLR of 2.2 and at a risk level of 2.5-fold mean the PLR increased to 5.0. In analyses of individual GWAS SNPs, statistically significant associations with breast cancer were identified for homozygous carriers of the rare allele for the FGFR2, MAP3K and TOX3 genes. However, the PLR for the combined GWAS risk scores at 1.5-fold mean was 1.0 indicating no ability to discriminate risk in this study population.Conclusions: In this case-control population OncoVue effectively stratified risk by accurately assigning elevated risk to breast cancer cases. In the same population, a combined risk score produced from seven GWAS SNPs did not effectively stratify risk. These results indicate that the OncoVue model has clinical utility for identifying elevated risk women who might benefit from additional screening and prevention. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 3177.

Study question Which single nucleotide variant (SNVs) are associated with aneuploid pregnancy loss? Summary answer We identified a SNV on MEIG1 gene, which are associated with meiosis/spermiogenesis. What is known already Recurrent pregnancy loss (RPL) refers to the loss of two or more pregnancies, with a frequency of 5%. Chromosomal abnormalities in embryos are found in 80% of first trimester miscarriages, 86% of which are aneuploid. Recently, embryonic aneuploidy was found to be the most common cause of RPL, with a frequency of 40-50%. Most trisomy miscarriages are of maternal origin, with errors occurring during meiosis of the oocyte. Chromosome segregation abnormalities in oocytes are thought to be an event associated with increasing maternal age, but in addition, maternal genetic causes are thought to contribute. Study design, size, duration A Genome wide association study (GWAS) was performed on a clinically well characterized cohort of 189 women with RPL whose previous aborted conceptus was ascertained to be an aneuploid embryo. Samples were mainly collected from 2007 to 2018 mainly at Nagoya City University Hospital. For control samples, we used 1157 samples from the population-based prospective cohorts that included fertile women. Participants/materials, setting, methods All patients underwent a systematic examination. Patients with antiphospholipid syndrome, an abnormal chromosome in either partner, or uterine anomaly were excluded. Patients whose previously miscarried POC exhibited triploidy or 45, X were excluded. DNA was isolated from stored EDTA-blood samples and genotyped by Axiom Japonica-array v2659,503 SNVs). For the GWAS, a chi-squared test was applied to a two-by-two contingency table in allele frequency model. Main results and the role of chance The mean (SD) ages and number of previous miscarriages of the patients were 36.8 (4.3) and 3.09 (1.13). GWAS data revealed 5 SNVs with suggestive significance (p < 9.46e-06). The SNVs that showed the most significant associations (P = 1.06E-06, OR = 1.72) was located on meiosis/spermiogenesis associated 1 (MEIG1) gene under an allelic model after Bonferroni correction considering the number of analyzed SNVs. The SNV rs7908491 was reported as a splicing QTL in the MEIG1 gene, which is a meiosis/meiosis-associated factor and is plausibly associated with chromosome aneuploidy. This is the first GWAS in patients with RPL caused by aneuploidy. Limitations, reasons for caution Since this study was conducted in a single center and had a small sample size, it needs to be replicated in different centers with more subjects and on an international scale. Whole genome imputation analysis will be performed to detect SNVs with more significant associations. Wider implications of the findings Our findings demonstrate that a specific genotype of MEIG1 gene can be a risk factor for aneuploid pregnancy loss. The establishment of clinically applicable maternal germ cell markers could identify groups for whom PGT would be more useful or provide patients with counseling that provides prognostic information about pregnancy. Trial registration number not applicable

See related article, pages 825–832. With a swiftly moving, highly technical field, it helps to have a reliable guide. In this issue of Stroke, Lanktree et al1 provide a timely overview of genomic analysis applied to stroke.1 Readers find a clear and concise synopsis of single-nucleotide polymorphisms, copy number variations, listings of the strengths and limitations of genome-wide association studies (GWAS), and a distillation of the findings from GWAS performed in 6 cohorts (5 of which were ischemic stroke cohorts). The review also covers a topic not usually found in clinical reviews, namely techniques to visually display quantitative information. Excellence in statistical graphics should, among other things, avoid distorting what the data have to say, present many numbers in a small space, and make large data sets coherent.2 The Manhattan and quantile–quantile plots are excellent examples of statistical graphics that have become invaluable for interpreting GWAS results. Along with visualization comes interpretation of data in the context of GWAS. Clinical investigators are well aware of the problem of multiple testing from such settings as interim and subgroup analyses in clinical trials, which can lead to wildly spurious conclusions, such as concluding that aspirin only helped individuals of certain astrological signs in the second International Study of Infarct Survival.3 GWAS simply escalates the problem of multiple testing by orders of magnitude. Because single-nucleotide polymorphism-based GWAS test hundreds of thousands …

IL28B single nucleotide polymorphisms in the treatment of hepatitis C

Second-generation antipsychotics (SGAs) are widely used to treat schizophrenia (SCZ), but they often induce metabolic side effects like dyslipidemia and obesity. We conducted genome-wide association studies (GWASs) to identify genetic variants associated with SGA-induced lipid and BMI changes in Chinese SCZ patients. A longitudinal cohort of Chinese SCZ receiving SGAs was followed for up to 18.7 years (mean = 5.7 years, SD = 3.3 years). We analysed the patients’ genotypes (N = 669), lipid profiles, and BMI using 19 316 prescription records and 3 917 to 7 596 metabolic measurements per outcome. Linear mixed models were employed to evaluate seven SGAs’ random effects on metabolic changes for each patient, followed by GWAS and gene set analyses with Bonferroni and FDR correction. Five SNPs achieved p-value < 5 × 10−08 before multiple testing correction: rs6532055 (ABCG2) linked to olanzapine-induced LDL changes, rs2644520 (near SORCS1) linked to aripiprazole-induced triglyceride changes, rs115843863 (near UPP2) linked to clozapine-induced HDL changes, rs2514895 (near KIRREL3) linked to paliperidone-induced LDL changes, and rs188405603 (SLC2A9) linked to quetiapine-induced triglyceride changes. These five SNPs passed FDR correction at 0.2 but not Bonferroni-corrected genome-wide significance threshold (p-value < 3.125 × 10−10) for 160 GWAS analyses. Gene-based analysis revealed six genome-wide significant genes after Bonferroni correction (p-value < 2.73 × 10−6): ABCG2, APOA5, ZPR1, GCNT4, MAST2, and CRTAC1. Four gene sets were significantly associated with SGA-induced metabolic side effects. In summary, this pharmacogenetic GWAS identified several genetic variants potentially associated with SGA-induced metabolic side effects, potentially informing personalized treatment strategies to minimize metabolic risk in SCZ patients. Given our limited sample size, further replications are required to confirm the findings.

Introduction: As the most serious form of skin cancer, melanoma is a considerable public health burden. Genome wide association studies (GWAS) have successfully identified several loci important to melanoma susceptibility. Some of these loci have been associated with other cancers as well, similar to the pleiotropy previously demonstrated in the 8q24 and TERT regions. Such pleiotropy can highlight important regions and may help inform variant functionality. However, the potential pleiotropic effects of gene variants identified for other cancers have not been fully explored for melanoma. This study evaluates whether GWAS hits for melanoma, as well as hits for non-melanoma cancers, are associated with melanoma risk. Methods: We included 918 white melanoma cases and 4495 white controls in the Women's Health Initiative, which were genotyped as part of the Population Architecture using Genomics and Epidemiology (PAGE) study. We evaluated 8 single nucleotide polymorphisms (SNPs) previously shown to be associated with melanoma in GWAS as well as 175 SNPs previously shown to be associated with other cancers. These findings were mostly based on GWAS in Caucasian populations. We used logistic regression to evaluate the association between each SNP and melanoma risk, using an age-adjusted additive genetic model. Results: Of the 8 melanoma SNPs evaluated, 7 were statistically significantly associated with melanoma risk (p&amp;lt;0.05). These SNPs were rs1393350 (p=0.001), rs16891982 (p=3.34e-05), rs258322 (p=1.53e-9), rs4785763 (p=3.29e-4), rs7023329 (p=2.20e-5), rs910873 (p=2.38e-4), and rs4636294 (p=5.82e-5). For each of these significant SNPs, the association was in the same direction of risk as previously reported. The SNP that did not replicate was rs2284063 (p=0.38). Our analysis for pleotropic effects of other cancer GWAS hits did not yield any significant associations after adjustment for multiple comparisons (p=2.87e-4). The most significant SNP was rs710521 (p=0.006), previously associated with urinary bladder cancer. Conclusions: This preliminary analysis demonstrates that nearly all previously discovered melanoma SNPs replicated. Our analysis did not discover any non-melanoma cancer SNPs that are also associated with melanoma. The next step in this analysis will be to incorporate data from the other three PAGE studies: the Multiethnic Cohort (MEC), Epidemiologic Architecture of Genes Linked to Environment (EAGLE), and the CALiCo Consortium (see pagestudy.org). This will substantially improve the power of our pleiotropy analyses. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 868. doi:10.1158/1538-7445.AM2011-868

Genetic analysis has great potential as a tool to differentiate between different species and breeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism (SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus) breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198 chicken breeds, SNP markers specific to the target population were discovered through a case-control genome-wide association study (GWAS) and filtered out based on the linkage disequilibrium blocks. Significant SNP markers were selected by feature selection applying two machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machine learning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the Yeonsan Ogye chicken population demonstrated 100% accuracy. Hence, the GWAS and machine learning models used in this study can be efficiently utilized to identify the optimal combination of markers for discriminating target populations using multiple SNP markers.

In the past decade, many genome-wide association studies (GWASs) have been conducted to explore association of single nucleotide polymorphisms (SNPs) with complex diseases using a case-control design. These GWASs not only collect information on the disease status (primary phenotype, D) and the SNPs (genotypes, X), but also collect extensive data on several risk factors and traits. Recent literature and grant proposals point toward a trend in reusing existing large case-control data for exploring genetic associations of some additional traits (secondary phenotypes, Y) collected during the study. These secondary phenotypes may be correlated, and a proper analysis warrants a multivariate approach. Commonly used multivariate methods are not equipped to properly account for the non-random sampling scheme. Current ad hoc practices include analyses without any adjustment, and analyses with D adjusted as a covariate. Our theoretical and empirical studies suggest that the type I error for testing genetic association of secondary traits can be substantial when X as well as Y are associated with D, even when there is no association between X and Y in the underlying (target) population. Whether using D as a covariate helps maintain type I error depends heavily on the disease mechanism and the underlying causal structure (which is often unknown). To avoid grossly incorrect inference, we have proposed proportional odds model adjusted for propensity score (POM-PS). It uses a proportional odds logistic regression of X on Y and adjusts estimated conditional probability of being diseased as a covariate. We demonstrate the validity and advantage of POM-PS, and compare to some existing methods in extensive simulation experiments mimicking plausible scenarios of dependency among Y, X, and D. Finally, we use POM-PS to jointly analyze four adiposity traits using a type 2 diabetes (T2D) case-control sample from the population-based Metabolic Syndrome in Men (METSIM) study. Only POM-PS analysis of the T2D case-control sample seems to provide valid association signals.

Genome-wide association studies (GWAS) have served as primary methods for the past decade for identifying associations between genetic variants and traits or diseases. Many software packages have been developed for GWAS analysis based on different statistical models. One key factor influencing the statistical reliability of GWAS is the amount of input data used. Few studies have been conducted to investigate this effect by comparing the performance of GWAS programs using varied amounts of experimental data, especially in the context of plants and plant genomes. In this paper, we investigate how input data quantity influences output of four widely used GWAS programs, PLINK, TASSEL, GAPIT, and FaST-LMM. Both synthetic and real data are used. Standard GWAS output includes single nucleotide polymorphisms (SNPs) and their p-values. To evaluate the programs, p-values and q-values of SNPs, and Kendall rank correlation between output SNP lists, are used. Results show that with the same GWAS program, different Arabidopsis thaliana datasets demonstrate similar trends of rank correlation with varied input quantity, but differentiate on the numbers of SNPs passing a given p- or q-value threshold. In practice, experimental datasets may have samples containing varied numbers of biological replicates. We show that this variation in replicates influences the p-values of SNPs, but does not strongly affect the rank correlation. When comparing synthetic and real data, the output SNPs from synthetic data have similar rank correlation trends across all four GWAS programs, but the same measure from real data is diverse across the programs. In addition, the real data results in a linear-like increase in the numbers of significant SNPs with more input data, but the synthetic data does not follow this trend. This study provides guidance on selecting GWAS programs when varied experimental data is present and on selecting significant SNPs for subsequent study. It contributes to understanding how much input data is necessary to yield satisfying GWAS results.

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Introduction Chronic lymphocytic leukemia (CLL) is a B-cell malignancy that is known to have a familial component to disease risk. Although 31 loci have been found to be associated with CLL risk, the functional variant(s) driving these associations is mostly unknown. Here we set out to identify rare, highly-penetrant, cosegregating, susceptibility variants within the known GWAS discovered loci using whole exome sequencing (WES) data in CLL families from the Mayo Clinic family study of B-cell malignancies. Methods We performed WES on germ line DNA of 93 CLL families with two or more members with CLL, using Agilent capture kits and Illumina HiSeq2000. Bioinformatics analyses leveraged the following software packages: Novoalign, Picard, The Genome Analysis Toolkit (GATK), and the Biological Reference Repository (bioR). Quality control filters were implemented; subjects with mis-specified relationships were removed, as were variants with &amp;lt;75% call rate, &amp;lt;8X coverage, and those identified as sequencing artifacts. Each GWAS locus was defined by +/- 1Mb of the top GWAS hit within the locus. Linkage disequilibrium (LD) was calculated among the single nucleotide variants (SNVs) located within each locus. Potentially functional SNVs were identified based on: a) uncommon in public databases (&amp;lt; 5%), b) cosegregating in at least two CLL families, c) being highly conserved and in coding regions, and d) functional prediction status of deleterious (SIFT Score), damaging (PolyPhen Score), and a moderate, or high variant impact (SNP Effect). Results In our 93 CLL families, we sequenced 443 individuals: 160 with CLL, 73 with monoclonal B cell lymphocytosis (MBL), and 210 relatives that were not diagnosed with CLL or MBL at the time of sequencing. Median age of CLL diagnosis was 59 years (range 34-87), and 56% were male. Among the MBL individuals and relatives, the median age at recruitment was 55 years (range 18-93), and 40% were male. A total of 317,666 SNVs passed our sequencing quality control filters of which 10,731 were within +/- 1 Mb of known GWAS hits from 31 loci. Of these SNVs, 91% were in coding regions, 18% were reported to have high or moderate impact, 6% were estimated to be damaging and 6% were predicted to be deleterious. From these SNVs, we identified 76 putatively functional variants distributed across 25 GWAS loci that were cosegregating in the individuals with CLL or MBL in multiple CLL pedigrees. These SNVs were all located in coding regions with high or moderate impact and were predicted to be damaging and deleterious. Of these 76 variants, 56 had a frequency of &amp;lt;0.005 in 1000 Genomes’ European population while the remaining 20 had a frequency of 1%. Conclusions Through WES, we identified a number of rare, penetrant and potentially predisposing SNVs located within 25 of the 31 CLL GWAS-discovered loci. These segregating variants provide a list for future validation and functional studies. Citation Format: Sara Beiggi, Daniel R. O'Brien, Sara J. Achenbach, Kari G. Chaffee, Timothy G. Call, Neil E. Kay, Tait D. Shanafelt, Julie Cunningham, James R. Cerhan, Celine M. Vachon, Susan L. Slager. Cosegregating variants in chronic lymphocytic leukemia (CLL) families that are located in loci discovered by genome wide association studies (GWAS). [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2764. doi:10.1158/1538-7445.AM2015-2764

Postoperative delirium is the most common postoperative complication in older individuals. Genome-wide association studies (GWAS) can provide insights into how genetic factors influence postoperative risk. We examined the genetic architecture of postoperative delirium after major surgery and its relationship with related cognitive conditions (delirium of any type and Alzheimer's disease, including the APOE ε4 allele). A case-control GWAS was performed in the UK Biobank to identify genetic variants associated with postoperative delirium, adjusted for age, sex, genetic chip, and the first 10 principal components. These results were then used in genetic correlation and polygenic risk score analyses to investigate shared genetic risk between postoperative delirium and a) delirium of all causes, and b) Alzheimer's disease. The GWAS (1,016 cases, 139,148 controls) identified seven Single Nucleotide Polymorphisms (SNPs) that mapped to four genes (APOE, TOMM40, APOC1, and PVRL2); p < 5 x 10-8. Five SNPs remained significant after excluding pre-existing dementia, and two after excluding subsequent dementia. The lead SNP was rs429358, a missense variant of APOE. Genetic correlation and polygenic risk score analyses revealed evidence of shared genetic architecture and risk between postoperative delirium and Alzheimer's disease (rho 0.68, 95% CI [0.46, 0.81]; p < 0.001). After adjustment for age and sex, the APOE ε4 isoform had a dose-response effect on risk (odds ratios for one and two copies: 1.75, 95% CI [1.53, 2.0], and 4.19, 95% CI [3.25, 5.41], respectively; p < 0.001). The main limitations of the study include the reliance upon clinical coding for outcome definition and limited statistical power to detect small or modest genetic effects. We identified genetic variants associated with increased risk of postoperative delirium. We also found evidence of shared genetic liability with Alzheimer's disease via APOE, complementing recent large-scale studies in all-cause delirium. If validated, the findings have potential clinical applications, including preoperative risk stratification and early identification of pre-clinical Alzheimer's disease risk.

Soybean is a primary meal protein for human consumption, poultry, and livestock feed. In this study, quantitative trait locus (QTL) controlling protein content was explored via genome-wide association studies (GWAS) and linkage mapping approaches based on 284 soybean accessions and 180 recombinant inbred lines (RILs), respectively, which were evaluated for protein content for 4 years. A total of 22 single nucleotide polymorphisms (SNPs) associated with protein content were detected using mixed linear model (MLM) and general linear model (GLM) methods in Tassel and 5 QTLs using Bayesian interval mapping (IM), single-trait multiple interval mapping (SMIM), single-trait composite interval mapping maximum likelihood estimation (SMLE), and single marker regression (SMR) models in Q-Gene and IciMapping. Major QTLs were detected on chromosomes 6 and 20 in both populations. The new QTL genomic region on chromosome 6 (Chr6_18844283–19315351) included 7 candidate genes and the Hap.XAA at the Chr6_19172961 position was associated with high protein content. Genomic selection (GS) of protein content was performed using Bayesian Lasso (BL) and ridge regression best linear unbiased prediction (rrBULP) based on all the SNPs and the SNPs significantly associated with protein content resulted from GWAS. The results showed that BL and rrBLUP performed similarly; GS accuracy was dependent on the SNP set and training population size. GS efficiency was higher for the SNPs derived from GWAS than random SNPs and reached a plateau when the number of markers was >2,000. The SNP markers identified in this study and other information were essential in establishing an efficient marker-assisted selection (MAS) and GS pipelines for improving soybean protein content.