Genotyping Costs Research Articles

The International Mouse Phenotyping Consortium (IMPC): a functional catalogue of the mammalian genome that informs conservation

The International Mouse Phenotyping Consortium (IMPC) is building a catalogue of mammalian gene function by producing and phenotyping a knockout mouse line for every protein-coding gene. To date, the IMPC has generated and characterised 5186 mutant lines. One-third of the lines have been found to be non-viable and over 300 new mouse models of human disease have been identified thus far. While current bioinformatics efforts are focused on translating results to better understand human disease processes, IMPC data also aids understanding genetic function and processes in other species. Here we show, using gorilla genomic data, how genes essential to development in mice can be used to help assess the potentially deleterious impact of gene variants in other species. This type of analyses could be used to select optimal breeders in endangered species to maintain or increase fitness and avoid variants associated to impaired-health phenotypes or loss-of-function mutations in genes of critical importance. We also show, using selected examples from various mammal species, how IMPC data can aid in the identification of candidate genes for studying a condition of interest, deliver information about the mechanisms involved, or support predictions for the function of genes that may play a role in adaptation. With genotyping costs decreasing and the continued improvements of bioinformatics tools, the analyses we demonstrate can be routinely applied.

Identification of a Novel Polymorphism in Bovine lncRNA ADNCR Gene and Its Association with Growth Traits

ABSTRACTAdipocyte differentiation-associated long noncoding RNA (ADNCR) is a newly discovered lncRNA. It plays function by targeting miR-204 to significantly regulates the expression of the target SIRT1 gene in preadipocytes both at the level of mRNA and protein, thereby inhibiting adipogenesis. The tetra-primer amplification refractory mutation system PCR (T-ARMS-PCR) strategy is fast and accuracy at a negligible cost for SNP genotyping in large samples. In the study, a novel SNP g.1263T>A in intron 1 of bovine ADNCR gene was found. Herein, the T-ARMS-PCR assay was applied to detect the genotypes of the novel SNP of bovine ADNCR gene in 1017 individuals from seven cattle breeds and validated the accuracy by DNA sequencing assay of ninety animals representing three different genotypes. The concordance between two different methods was 100%. The association analysis indicated that this locus was significantly associated with the body weight (P = 0.010), chest girth (P = 0.014) and rump length (P = 0.038) in Jinnan cattle, hucklebone width (P = 0.032) in Qinchuan cattle, the cannon circumference (P = 0.019) in Jinjiang cattle, respectively. These novel findings may be used for marker-assisted selection (MAS) and contribute to the performance of beef cattle in the future.

Lessons learnt on the analysis of large sequence data in animal genomics.

The 'omics revolution has made a large amount of sequence data available to researchers and the industry. This has had a profound impact in the field of bioinformatics, stimulating unprecedented advancements in this discipline. Mostly, this is usually looked at from the perspective of human 'omics, in particular human genomics. Plant and animal genomics, however, have also been deeply influenced by next-generation sequencing technologies, with several genomics applications now popular among researchers and the breeding industry. Genomics tends to generate huge amounts of data, and genomic sequence data account for an increasing proportion of big data in biological sciences, due largely to decreasing sequencing and genotyping costs and to large-scale sequencing and resequencing projects. The analysis of big data poses a challenge to scientists, as data gathering currently takes place at a faster pace than does data processing and analysis, and the associated computational burden is increasingly taxing, making even simple manipulation, visualization and transferring of data a cumbersome operation. The time consumed by the processing and analysing of huge data sets may be at the expense of data quality assessment and critical interpretation. Additionally, when analysing lots of data, something is likely to go awry-the software may crash or stop-and it can be very frustrating to track the error. We herein review the most relevant issues related to tackling these challenges and problems, from the perspective of animal genomics, and provide researchers that lack extensive computing experience with guidelines that will help when processing large genomic data sets.

Joint Genomic Prediction of Canine Hip Dysplasia in UK and US Labrador Retrievers

Canine hip dysplasia, a debilitating orthopedic disorder that leads to osteoarthritis and cartilage degeneration, is common in several large-sized dog breeds and shows moderate heritability suggesting that selection can reduce prevalence. Estimating genomic breeding values require large reference populations, which are expensive to genotype for development of genomic prediction tools. Combining datasets from different countries could be an option to help build larger reference datasets without incurring extra genotyping costs. Our objective was to evaluate genomic prediction based on a combination of UK and US datasets of genotyped dogs with records of Norberg angle scores, related to canine hip dysplasia. Prediction accuracies using a single population were 0.179 and 0.290 for 1,179 and 242 UK and US Labrador Retrievers, respectively. Prediction accuracies changed to 0.189 and 0.260, with an increased bias of genomic breeding values when using a joint training set (biased upwards for the US population and downwards for the UK population). Our results show that in this study of canine hip dysplasia, little or no benefit was gained from using a joint training set as compared to using a single population as training set. We attribute this to differences in the genetic background of the two populations as well as the small sample size of the US dataset.

Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population

BackgroundGenome-wide association studies and genomic predictions are thought to be optimized by using whole-genome sequence (WGS) data. However, sequencing thousands of individuals of interest is expensive. Imputation from SNP panels to WGS data is an attractive and less expensive approach to obtain WGS data. The aims of this study were to investigate the accuracy of imputation and to provide insight into the design and execution of genotype imputation.ResultsWe genotyped 450 chickens with a 600 K SNP array, and sequenced 24 key individuals by whole genome re-sequencing. Accuracy of imputation from putative 60 K and 600 K array data to WGS data was 0.620 and 0.812 for Beagle, and 0.810 and 0.914 for FImpute, respectively. By increasing the sequencing cost from 24X to 144X, the imputation accuracy increased from 0.525 to 0.698 for Beagle and from 0.654 to 0.823 for FImpute. With fixed sequence depth (12X), increasing the number of sequenced animals from 1 to 24, improved accuracy from 0.421 to 0.897 for FImpute and from 0.396 to 0.777 for Beagle. Using optimally selected key individuals resulted in a higher imputation accuracy compared with using randomly selected individuals as a reference population for re-sequencing. With fixed reference population size (24), imputation accuracy increased from 0.654 to 0.875 for FImpute and from 0.512 to 0.762 for Beagle as the sequencing depth increased from 1X to 12X. With a given total cost of genotyping, accuracy increased with the size of the reference population for FImpute, but the pattern was not valid for Beagle, which showed the highest accuracy at six fold coverage for the scenarios used in this study.ConclusionsIn conclusion, we comprehensively investigated the impacts of several key factors on genotype imputation. Generally, increasing sequencing cost gave a higher imputation accuracy. But with a fixed sequencing cost, the optimal imputation enhance the performance of WGP and GWAS. An optimal imputation strategy should take size of reference population, imputation algorithms, marker density, and population structure of the target population and methods to select key individuals into consideration comprehensively. This work sheds additional light on how to design and execute genotype imputation for livestock populations.

A Cost Analysis of Gyrase A Testing and Targeted Ciprofloxacin Therapy Versus Recommended 2-Drug Therapy for Neisseria gonorrhoeae Infection.

Novel approaches to combating drug-resistant Neisseria gonorrhoeae infections are urgently needed. Targeted therapy with ciprofloxacin has been made possible by a rapid assay for genotyping the gyrase A (gyrA) gene; a nonmutated gene reliably predicts susceptibility to ciprofloxacin. We determined the costs of running the gyrA assay, 500 mg of ciprofloxacin, 250 mg of ceftriaxone injection, and 1000 mg of azithromycin. Cost estimates for gyrA testing included assay reagents and labor. Cost estimates for ceftriaxone included medication, injection, administration, supplies, and equipment. We measured the cost of using the gyrA assay and treatment based on genotype using previously collected data over a 13-month period between November 2015 and November 2016 for all N. gonorrhoeae cases diagnosed at UCLA. We subsequently developed 3 cost models, varying the frequency of testing and prevalence of N. gonorrhoeae infections with ciprofloxacin-resistant or genotype-indeterminate results. We compared those estimates with the cost of recommended 2-drug therapy (ceftriaxone and azithromycin). Based on a 65.3% prevalence of cases with ciprofloxacin-resistant or genotype indeterminate N. gonorrhoeae infections when running an average of 1.7 tests per day, the per-case cost of gyrA genotyping and targeted therapy was US $197.19. The per-case cost was US $155.16 assuming a 52.6% prevalence of ciprofloxacin-resistant or genotype-indeterminate infections when running an average of 17 tests per day. The per-case cost of 2-drug therapy was US $142.75. Direct costs of gyrA genotyping and targeted ciprofloxacin therapy depend on the prevalence of ciprofloxacin-resistant or genotype-indeterminate infections and testing frequency.

Evaluation of genotyping methods and costs for IL1α polymorphisms in Platelet Rich-Plasma (PRP); viewpoint for therapy on the diabetic foot ulcers.

Evaluation of genotyping methods and costs for IL1α polymorphisms in Platelet Rich-Plasma (PRP); viewpoint for therapy on the diabetic foot ulcers.

Optimal mating strategies to manage a heterozygous advantage major gene in sheep

Some mutations (or ‘major genes’) have a desirable effect in heterozygous carriers but an undesirable effect in homozygous carriers. When these mutations affect a trait of significant economic importance, their eradication, depending on their effect and frequency, may be counterproductive. This is especially the case of major genes affecting the ovulation rate and thus the prolificacy in meat sheep populations. To manage such situations, a mating design based on the major genotypes of reproducers has to be optimized. Both the effect of the major gene and the cost of genotyping candidates at this locus influence the expected genetic progress and profitability of the breeding plan. The aim of this study was to determine the optimal combination of matings that maximizes profitability at the level of the whole population (nucleus + commercial flocks). A deterministic model was developed and, using sequential quadratic programming methodology, the optimal strategy (optimal combination of matings) that maximized the economic gain achieved by the population across a range of genotype effects and genotyping costs was determined. The optimal strategy was compared with simpler and more practical strategies based on a limited number of parental genotype mating types. Depending on the genotype effect and genotyping costs, the optimal strategy varied, such that either the heterozygous frequency and/or polygenic gain was maximized with a large number of animals genotyped, or when genotyping costs were higher, the optimization led to lower heterozygous frequency and/or polygenic gain with fewer animals genotyped. Comparisons showed that some simpler strategies were close to the optimal strategy. An overlapping model was then derived as an application of the real case of the French Lacaune meat sheep OVI-TEST breeding program. Results showed that a practical strategy based on mating non-carriers to heterozygous carriers was only slightly less effective than the optimal strategy, with a reduction in efficiency from 3% to 8%, depending on the genotyping costs. Based on only two different parental genotype mating types, this strategy would be easy to implement.

Genotype imputation from various low-density SNP panels and its impact on accuracy of genomic breeding values in pigs

The uptake of genomic selection (GS) by the swine industry is still limited by the costs of genotyping. A feasible alternative to overcome this challenge is to genotype animals using an affordable low-density (LD) single nucleotide polymorphism (SNP) chip panel followed by accurate imputation to a high-density panel. Therefore, the main objective of this study was to screen incremental densities of LD panels in order to systematically identify one that balances the tradeoffs among imputation accuracy, prediction accuracy of genomic estimated breeding values (GEBVs), and genotype density (directly associated with genotyping costs). Genotypes using the Illumina Porcine60K BeadChip were available for 1378 Duroc (DU), 2361 Landrace (LA) and 3192 Yorkshire (YO) pigs. In addition, pseudo-phenotypes (de-regressed estimated breeding values) for five economically important traits were provided for the analysis. The reference population for genotyping imputation consisted of 931 DU, 1631 LA and 2103 YO animals and the remainder individuals were included in the validation population of each breed. A LD panel of 3000 evenly spaced SNPs (LD3K) yielded high imputation accuracy rates: 93.78% (DU), 97.07% (LA) and 97.00% (YO) and high correlations (>0.97) between the predicted GEBVs using the actual 60 K SNP genotypes and the imputed 60 K SNP genotypes for all traits and breeds. The imputation accuracy was influenced by the reference population size as well as the amount of parental genotype information available in the reference population. However, parental genotype information became less important when the LD panel had at least 3000 SNPs. The correlation of the GEBVs directly increased with an increase in imputation accuracy. When genotype information for both parents was available, a panel of 300 SNPs (imputed to 60 K) yielded GEBV predictions highly correlated (⩾0.90) with genomic predictions obtained based on the true 60 K panel, for all traits and breeds. For a small reference population size with no parents on reference population, it is recommended the use of a panel at least as dense as the LD3K and, when there are two parents in the reference population, a panel as small as the LD300 might be a feasible option. These findings are of great importance for the development of LD panels for swine in order to reduce genotyping costs, increase the uptake of GS and, therefore, optimize the profitability of the swine industry.

Imputation of non-genotyped sheep from the genotypes of their mates and resulting progeny

Accurate genomic analyses are predicated on access to a large quantity of accurately genotyped and phenotyped animals. Because the cost of genotyping is often less than the cost of phenotyping, interest is increasing in generating genotypes for phenotyped animals. In some instances this may imply the requirement to genotype older animals with greater phenotypic information content. Biological material for these older informative animals may, however, no longer exist. The objective of the present study was to quantify the ability to impute 11 129 single nucleotide polymorphism (SNP) genotypes of non-genotyped animals (in this instance sires) from the genotypes of their progeny with or without including the genotypes of the progenys’ dams (i.e. mates of the sire to be imputed). The impact on the accuracy of genotype imputation by including more progeny (and their dams’) genotypes in the imputation reference population was also quantified. When genotypes of the dams were not available, genotypes of 41 sires with at least 15 genotyped progeny were used for the imputation; when genotypes of the dams were available, genotypes of 21 sires with at least 10 genotyped progeny were used for the imputation. Imputation was undertaken exploiting family and population level information. The mean and variability in the proportion of genotypes per individual that could not be imputed reduced as the number of progeny genotypes used per individual increased. Little improvement in the proportion of genotypes that could not be imputed was achieved once genotypes of seven progeny and their dams were used or genotypes of 11 progeny without their respective dam’s genotypes were used. Mean imputation accuracy per individual (depicted by both concordance rates and correlation between true and imputed) increased with increasing progeny group size. Moreover, the range in mean imputation accuracy per individual reduced as more progeny genotypes were used in the imputation. If the genotype of the mate of the sire was also used, high accuracy of imputation (mean genotype concordance rate per individual of 0.988), with little additional benefit thereafter, was achieved with seven genotyped progeny. In the absence of genotypes on the dam, similar imputation accuracy could not be achieved even using genotypes on up to 15 progeny. Results therefore suggest, at least for the SNP density used in the present study, that it is possible to accurately impute the genotypes of a non-genotyped parent from the genotypes of its progeny and there is a benefit of also including the genotype of the sire’s mate (i.e. dam of the progeny).

Estimating Realized Heritability in Panmictic Populations.

Narrow sense heritability [Formula: see text] is a key concept in quantitative genetics, as it expresses the proportion of the observed phenotypic variation that is transmissible from parents to offspring. [Formula: see text] determines the resemblance among relatives, and the rate of response to artificial and natural selection. Classical methods for estimating [Formula: see text] use random samples of individuals with known relatedness, as well as response to artificial selection, when it is called realized heritability. Here, we present a method for estimating realized [Formula: see text] based on a simple assessment of a random-mating population with no artificial manipulation of the population structure, and derive SE of the estimates. This method can be applied to arbitrary phenotypic segments of the population (for example, the top-ranking p parents and offspring), rather than random samples. It can thus be applied to nonpedigreed random mating populations, where relatedness is determined from molecular markers in the p selected parents and offspring, thus substantially saving on genotyping costs. Further, we assessed the method by stochastic simulations, and, as expected from the mathematical derivation, it provides unbiased estimates of [Formula: see text] We compared our approach to the regression and maximum-likelihood approaches utilizing Galton's dataset on human heights, and all three methods provided identical results.

Is the management of Rh-Rh incompatibility with noninvasive fetal Rh genotyping for targeted prophylaxis cost-effective in the Turkish population?

Background/aim: The aim of this study was to assess unnecessary immunization rates and compare the cost-effectiveness of targeted prophylaxis with fetal Rh genotyping with that of traditional management of Rh-Rh incompatibility in a virtual economic model. Materials and methods: This retrospective data analysis was conducted at two tertiary centers between 2011 and 2015. The data of 1135 pregnant women were analyzed. The main outcome measure was to determine the unnecessary immunization rate among the whole Rh-Rh incompatibility group. The second outcome measure was to compare the cost-effectiveness of universal immunization with that of targeted prophylaxis with fetal Rh genotyping in a virtual economic model. Results: Average cost per patient was found as $259.20 with universal prophylaxis and the total cost was $177,344, whereas if targeted prophylaxis had been applied to these patients the total cost would have been $263,392 and cost per patient would have been $385. Universal prophylaxis was more cost-effective than targeted prophylaxis in terms of both total cost and cost per patient (P < 0.0001). Conclusion: Unless the cost of noninvasive fetal Rh genotyping is reduced, a universal approach of anti-D immune globulin prophylaxis is more cost-effective than noninvasive determination of fetal Rh genotyping with targeted prophylaxis.

Methods and Tools in Genome-wide Association Studies.

Many traits, such as height, the response to a given drug, or the susceptibility to certain diseases are presumably co-determined by genetics. Especially in the field of medicine, it is of major interest to identify genetic aberrations that alter an individual's risk to develop a certain phenotypic trait. Addressing this question requires the availability of comprehensive, high-quality genetic datasets. The technological advancements and the decreasing cost of genotyping in the last decade led to an increase in such datasets. Parallel to and in line with this technological progress, an analysis framework under the name of genome-wide association studies was developed to properly collect and analyze these data. Genome-wide association studies aim at finding statistical dependencies-or associations-between a trait of interest and point-mutations in the DNA. The statistical models used to detect such associations are diverse, spanning the whole range from the frequentist to the Bayesian setting.Since genetic datasets are inherently high-dimensional, the search for associations poses not only a statistical but also a computational challenge. As a result, a variety of toolboxes and software packages have been developed, each implementing different statistical methods while using various optimizations and mathematical techniques to enhance the computations.This chapter is devoted to the discussion of widely used methods and tools in genome-wide association studies. We present the different statistical models and the assumptions on which they are based, explain peculiarities of the data that have to be accounted for and, most importantly, introduce commonly used tools and software packages for the different tasks in a genome-wide association study, complemented with examples for their application.

Parent-progeny imputation from pooled samples for cost-efficient genotyping in plant breeding.

The increased usage of whole-genome selection (WGS) and other molecular evaluation methods in plant breeding relies on the ability to genotype a very large number of untested individuals in each breeding cycle. Many plant breeding programs evaluate large biparental populations of homozygous individuals derived from homozygous parent inbred lines. This structure lends itself to parent-progeny imputation, which transfers the genotype scores of the parents to progeny individuals that are genotyped for a much smaller number of loci. Here we introduce a parent-progeny imputation method that infers individual genotypes from non-barcoded pooled samples of DNA of multiple individuals using a Hidden Markov Model (HMM). We demonstrate the method for pools of simulated maize double haploids (DH) from biparental populations, genotyped using a genotyping by sequencing (GBS) approach for 3,000 loci at 0.125x to 4x coverage. We observed high concordance between true and imputed marker scores and the HMM produced well-calibrated genotype probabilities that correctly reflected the uncertainty of the imputed scores. Genomic estimated breeding values (GEBV) calculated from the imputed scores closely matched GEBV calculated from the true marker scores. The within-population correlation between these sets of GEBV approached 0.95 at 1x and 4x coverage when pooling two or four individuals, respectively. Our approach can reduce the genotyping cost per individual by a factor up to the number of pooled individuals in GBS applications without the need for extra sequencing coverage, thereby enabling cost-effective large scale genotyping for applications such as WGS in plant breeding.

Noninvasive fetal RHD genotyping of RhD negative pregnant women for targeted anti-D therapy in Australia: A cost-effectiveness analysis.

To undertake a cost-effectiveness analysis of noninvasive fetal RHD genotyping to target pregnant women for antenatal anti-D prophylaxis therapy. A decision-analytic model was constructed to compare RHD testing and targeted anti-D prophylaxis, with current universal anti-D prophylaxis among pregnant women with RhD negative blood type. Model estimates were derived from national perinatal statistics, published literature, donor program records, and national cost sources. One-way sensitivity analyses addressed the uncertainty of variables on the main findings. The unit cost for RHD genotyping was estimated at AU$45.48 (US$31.84). The "mean cost per healthy baby" was AU$7495 (US$5247) for universal prophylaxis and AU$7471 (US$5230) for targeted prophylaxis. The findings were sensitive to the unit costs of anti-D 625IU (AU$59-AU$88) (US$41-US$62), the genetic test (AU$36-AU$55) (US$25-US$39), and packaging/transport costs of the samples for testing (AU$15-AU$40, US$11-US$28 per sample). With RHD genotyping, 13938 women would avoid antenatal anti-D prophylaxis at a total cost savings to the National Blood Authority of AU$2.1 million (US$1.5 million) per year. To the health system, net cost savings of AU$159701 (US$111791) per year (0.05%) were predicted for total health care costs. Given the vulnerable supply of donor plasma and other health concerns, RHD genotyping is an economically sound option for Australia.

Identifying the minimum number of microsatellite loci needed to assess population genetic structure: A case study in fly culturing

ABSTRACTSmall, isolated populations are constantly threatened by loss of genetic diversity due to drift. Such situations are found, for instance, in laboratory culturing. In guarding against diversity loss, monitoring of potential changes in population structure is paramount; this monitoring is most often achieved using microsatellite markers, which can be costly in terms of time and money when many loci are scored in large numbers of individuals. Here, we present a case study reducing the number of microsatellites to the minimum necessary to correctly detect the population structure of two Drosophila nigrosparsa populations. The number of loci was gradually reduced from 11 to 1, using the Allelic Richness (AR) and Private Allelic Richness (PAR) as criteria for locus removal. The effect of each reduction step was evaluated by the number of genetic clusters detectable from the data and by the allocation of individuals to the clusters; in the latter, excluding ambiguous individuals was tested to reduce the rate of incorrect assignments. We demonstrate that more than 95% of the individuals can still be correctly assigned when using eight loci and that the major population structure is still visible when using two highly polymorphic loci. The differences between sorting the loci by AR and PAR were negligible. The method presented here will most efficiently reduce genotyping costs when small sets of loci (“core sets”) for long-time use in large-scale population screenings are compiled.

A cost-benefit analysis to select the most effective method for positional cloning: genotyping by sequencing versus allele-specific PCR

Next-generation sequencing technologies have enabled the rapid generation of high-resolution genetic maps to enable the identification of genomic regions associated with traits of interest. In certain cases these regions of interest need to be narrowed down via fine mapping to increase resolution to the level of the individual gene. In those cases, there is a choice of genotyping options, whereby cost, time, and information content need to be considered. We developed detailed, customizable models to compare the cost of genotyping by sequencing and allele-specific PCR for fine-mapping genomic regions of interest. The models were validated experimentally with data from a fine-mapping experiment designed to identify candidate genes within a 3.2-Mb disease resistance locus in the cereal crop sorghum (Sorghum bicolor (L.). Moench), which has a sequenced genome. The size of the mapping population, genetic diversity of the parental lines, choice of reagents, and labor cost were shown to influence the overall cost of the two methods, and, consequently, which method would be most cost-effective for a given experiment. The model can be easily customized to reflect experiments with different species, genetic populations, experimental and personnel costs to determine the most cost-effective procedure for a new positional cloning project.

Application of genomic selection in farm animal breeding.

Genomic selection (GS) has become a widely accepted method in animal breeding to genetically improve economic traits. With the declining costs of high-density SNP chips and next-generation sequencing, GS has been applied in dairy cattle, swine, poultry and other animals and gained varying degrees of success. Currently, major challenges in GS studies include further reducing the cost of genome-wide SNP genotyping and improving the predictive accuracy of genomic estimated breeding value (GEBV). In this review, we summarize various methods for genome-wide SNP genotyping and GEBV prediction, and give a brief introduction of GS in livestock and poultry breeding. This review will provide a reference for further implementation of GS in farm animal breeding.

Assessment of Pharmacogenomic Panel Assay for Prediction of Taxane Toxicities: Preliminary Results.

Backbone: Paclitaxel and docetaxel are the primary taxane anticancer drugs regularly used to treat, breast, gastric, ovarian, head/neck, lung, and genitourinary neoplasm. Suspension of taxane treatments compromising patient benefits is more frequently caused by peripheral neuropathy and allergy, than to tumor progression. Several strategies for preventing toxicity have been investigated so far. Recently, findings on the genetic variants associated with toxicity and resistance to taxane-based chemotherapy have been reported.Methods: An extensive panel of five polymorphisms on four candidate genes (ABCB1, CYP2C8*3, CYP3A4*1B, XRCC3), previously validated as significant markers related to paclitaxel and Docetaxel toxicity, are analyzed and discussed. We genotyped 76 cancer patients, and 35 of them received paclitaxel or docetaxel-based therapy. What is more, an early outline evaluation of the genotyping costs and benefit was assessed.Results: Out of 35 patients treated with a taxane, six (17.1%) had adverse neuropathy events. Pharmacogenomics analysis showed no correlation between candidate gene polymorphisms and toxicity, except for the XRCC3 AG+GG allele [OR 2.61 (95% CI: 0.91–7.61)] which showed a weak significant trend of risk of neurotoxicities vs. the AG allele [OR 1.52 (95% CI: 0.51–4.91)] P = 0.03.Summary: Based on our experimental results and data from the literature, we propose a useful and low-cost genotyping panel assay for the prevention of toxicity in patients undergoing taxane-based therapy. With the individual pharmacogenomics profile, clinicians will have additional information to plan the better treatment for their patients to minimize toxicity and maximize benefits, including determining cost-effectiveness for national healthcare sustainability.

Replication confirms the association of loci in FOXE1, PDE8B, CAPZB and PDE10A with thyroid traits: a Genetics of Diabetes Audit and Research Tayside study (GoDARTS).

Replication of associations in genome-wide association studies is desirable to ensure that such signals are potentially clinically meaningful. This study aimed to replicate associations of selected single-nucleotide polymorphisms (SNPs) with hypothyroidism and serum thyroid-stimulating hormone (TSH) using electronic medical records (EMRs). A cross-sectional study was carried out among patients of European Caucasian ethnicity from the Genetics of Diabetes Audit and Research Tayside recruited in Tayside (Scotland, UK). EMRs (biochemistry, prescribing, hospital admissions and demographics) were used to ascertain patients with hypothyroidism and their controls as well as average serum TSH concentration, and linked to genetic biobank data. Genetic tests of association were performed using logistic and linear regression models. We analysed 1703 cases of hypothyroidism and 9457 controls. All four SNPs located on chromosome 9 at FOXE1 were associated with hypothyroidism with similar effect estimates (odds ratio=0.75-0.76, P<5e-08). Also, loci on chromosomes 1 (PTPN22), six (HLA-E/HLA-C) and 12 (SH2B3) were replicated. For serum TSH, we confirmed 12 SNPs previously reported at PDE8B, CAPZB, PDE10A, LOC105371356, NR3C2, VEGFA, IGFBP5, INSR, PRDM11, NFIA, ITPK1 and ABO. Overall, these SNPs accounted for 6.8% of the serum TSH variation (P<1e-04). EMRs linked to genomic data in large populations enable validation of genome-wide association studies discoveries without additional genotyping costs. Our replication confirmed at genome-wide significance the association of loci at FOXE1 with hypothyroidism, and PDE8B, CAPZB and PDE10A with serum TSH. A total of 12 SNPs seemed to explain nearly 7% of the serum TSH variation.