Real Genotype Research Articles

Genomic selection strategies for the German Merino sheep breeding programme - A simulation study.

Genomic selection is widely implemented in livestock breeding programmes across species. Its potential is also evident for sheep breeding; however, it has several limitations, particularly because of the high genetic diversity across and within sheep breeds. In Germany, the predominant sheep breed is the Merino sheep. Until now, there has been no use of genomic selection in the German Merino sheep breeding programme. In this simulation study, different genomic selection strategies were compared with a reference scenario with a breeding value estimation based on pedigree BLUP. A simplified version of the German Merino sheep breeding programme, including a health and a production trait in the breeding goal, was simulated via the R package Modular Breeding Program Simulator (MoBPS). Real genotype data were used to create a population specific simulation. The reference scenario was compared with several alternative scenarios in which selection was based on single-step GBLUP (ssGBLUP) breeding value estimation with varying genotyping strategies. In addition to scenarios in which all male and all male plus all female lambs were genotyped, scenarios with a preselection of lambs, that is only a certain proportion (top 25%, top 50%) genotyped, were simulated. The results revealed that genetic gain increased with increasing numbers of available genotypes. However, marginal gains decreased with increasing numbers of genotypes. Compared with the reference scenario, genotyping the top 25% of male lambs increased the genetic gain for the breeding ram population by 13% for both traits whereas genotyping the top 50% of male lambs or all male lambs led to increases of 18% (17%) or 26% (21%) for the health (production) trait, respectively. The potential of genotyping females in addition to male lambs was less evident on the male side with no significant differences between the scenarios with different proportions of genotyped females. The results have shown that genomic selection can be a valuable tool to increase genetic gain in the German Merino sheep population and that the genotyping of a certain proportion of animals might lead to substantial improvement over pedigree-based breeding value estimation. Nevertheless, further studies, especially economic evaluations, are needed before practical implementation.

#1358 The association between TNFRSF1B rs1061624 polymorphism and hypertension in end-stage kidney disease patients on dialysis

Abstract Background and Aims In chronic kidney disease (CKD), hypertension (HT) is commonly found during its development and is a leading cause for its progression. HT, dyslipidemia and visceral obesity are among the components of metabolic syndrome (MetS) and seem to associate with progression of CKD to end-stage kidney disease (ESKD). Enhanced levels of soluble tumor necrosis factor receptor 2 (sTNFR2) are known to associate with progressive CKD; and this biomarker was reported as an independent predictor of all-cause mortality in ESKD patients under dialysis. Concerning the TNFRSF1B gene, the polymorphism rs1061624 (A > G), located in the 3′-untranslated region (3′-UTR), was associated with the development of HT in men and with increased risk of MetS. The aim of this study was to determine the genotype frequencies of TNFRSF1B rs1061624 in ESKD patients with and without HT, and to evaluate its relationship with the circulating levels of some MetS biomarkers. Method We studied 277 ESKD patients on dialysis (recruited between 2016-2019), 170 diagnosed with HT and 107 without HT (no-HT group); age, sex, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP) and statin therapy were recorded. Real time PCR TaqMan SNP genotyping assay was used to assess genotype frequencies of TNFRSF1B rs1061624. We also evaluated the conventional lipid profile and the levels of oxidized low-density lipoprotein, adiponectin and leptin. Results The genotype frequencies in patients with and without HT were different (P = 0.002); the AA genotype was less frequent in HT than in no-HT group, while GG and AG genotypes were more frequent. Considering the AA genotype carriers, HT ESKD patients (n = 23), compared to the no-HT group (n = 33), presented lower adiponectin levels (P = 0.025); for the AG genotype carriers, HT group (n = 89) presented a trend towards lower adiponectin concentrations (P = 0.055), and were younger than no-HT patients (n = 48; P<0.001); in both comparisons, groups were matched for sex, BMI and number of patients under statins treatment, and in the first case also for age. Considering GG genotype, HT patients (n = 58) only differed from no-HT subjects (n = 26) in SBP, that was higher (P<0.001). Among HT patients, AG genotype carriers presented higher DBP (P = 0.004), lower values of leptin (P = 0.012) and leptin/adiponectin ratio (P = 0.022) and were younger (P<0.001) than GG genotype patients; compared to AA carriers, cholesterol levels were lower (P = 0.012). For the no-HT group, AG genotype patients presented lower DBP (P = 0.008) than AA genotype carriers. Conclusion The genotype frequencies were different in ESKD patients with and without HT, being G allele more frequent in HT. The HT ESKD patients with the AA or the AG genotypes seem to present lower adiponectin levels than the ESKD no-HT patients with the same genotypes. Acknowledgements This work was financed by CESPU, through the project SNPsCKD-GI2-CESPU-2022; FCT, in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences—UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy—i4HB.

Gwid: an R package and Shiny application for Genome-Wide analysis of IBD data.

Genome-wide identity by descent (gwid) is an R package developed for the analysis of identity-by-descent (IBD) data pertaining to dichotomous traits. This package offers a set of tools to assess differential IBD levels for the two states of a binary trait, yielding informative and meaningful results. Furthermore, it provides convenient functions to visualize the outcomes of these analyses, enhancing the interpretability and accessibility of the results. To assess the performance of the package, we conducted an evaluation using real genotype data derived from the SNPs to investigate rheumatoid arthritis susceptibility from the Marshfield Clinic Personalized Medicine Research Project. gwid is available as an open-source R package. Release versions can be accessed on CRAN (https://cran.r-project.org/package=gwid) for all major operating systems. The development version is maintained on GitHub (https://github.com/soroushmdg/gwid) and full documentation with examples and workflow templates is provided via the package website (http://tinyurl.com/gwid-tutorial). An interactive R Shiny dashboard is also developed (https://tinyurl.com/gwid-shiny).

Evaluating the performance of genomic selection on purebred population by incorporating crossbred data in pigs

Genomic selection (GS) has been widely used in livestock, which greatly accelerated the genetic progress of complex traits. The population size was one of the significant factors affecting the prediction accuracy, while it was limited by the purebred population. Compared to directly combining two uncorrelated purebred populations to extend the reference population size, it might be more meaningful to incorporate the correlated crossbreds into reference population for genomic prediction. In this study, we simulated purebred offspring (PAS and PBS) and crossbred offspring (CAB) base on real genotype data of two base purebred populations (PA and PB), to evaluate the performance of genomic selection on purebred while incorporating crossbred information. The results showed that selecting key crossbred individuals via maximizing the expected genetic relationship (REL) was better than the other methods (individuals closet or farthest to the purebred population, CP/FP) in term of the prediction accuracy. Furthermore, the prediction accuracy of reference populations combining PA and CAB was significantly better only based on PA, which was similar to combine PA and PAS. Moreover, the rank correlation between the multiple of the increased relationship (MIR) and reliability improvement was 0.60–0.70. But for individuals with low correlation (Cor(Pi, PA or B), the reliability improvement was significantly lower than other individuals. Our findings suggested that incorporating crossbred into purebred population could improve the performance of genetic prediction compared with using the purebred population only. The genetic relationship between purebred and crossbred population is a key factor determining the increased reliability while incorporating crossbred population in the genomic prediction on pure bred individuals.

Developing flexible models for genetic evaluations in smallholder crossbred dairy farms

The productivity of smallholder dairy farms is very low in developing countries. Important genetic gains could be realized using genomic selection, but genetic evaluations need to be tailored for lack of pedigree information and very small farm sizes. To accommodate this situation, we propose a flexible Bayesian model for the genetic evaluation of milk yield, which allows us to simultaneously account for nongenetic random effects for farms and varying SNP variance (BayesR model). First, we used simulations based on real genotype data from Indian crossbred dairy cattle to demonstrate that the proposed model can separate the true genetic and nongenetic parameters even for small farm sizes (2 cows on average) although with high standard errors in scenarios with low heritability. The accuracy of genomic genetic evaluation increased until farm size was approximately 5. We then applied the model to real data from 4,655 crossbred cows with 106,109 monthly test day milk records and 689,750 autosomal SNPs. We estimated a heritability of 0.16 (0.04) for milk yield and using cross-validation, a genomic estimated breeding value (GEBV) accuracy of 0.45 and bias (regression of phenotype on GEBV) of 1.04 (0.26). Estimated genetic parameters were very similar using BayesR, BayesC, and genomic BLUP approaches. Candidate genes near the top variants, IMMP2L and ARHGEF2, have been previously associated with milk protein composition, mastitis resistance, and milk cholesterol content. The estimated heritability and GEBV accuracy for milk yield are much lower than those from intensive or pasture-based systems in many countries. Further increases in the number of phenotyped and genotyped animals in farms with at least 2 cows (preferably 3-5, to allow for dropout of cows) are needed to improve the estimation of genetic effects in these smallholder dairy farms.

On the Polymorphism Information Content (PIC) – A Practical Application for the DNA Sequencing Data

All traits of an organism are associated with the genes. Organisms carry genes all of their lifetimes and pass them to the next generations. In genetics, case-control linkage studies named Genome-wide association studies (GWAS) are conducted to establish an association between specific genes, and any medical conditions. It helps to understand the inheritance of traits. In GWAS, genetic markers help us to specify genes, and deliver the required information. The usefulness of a genetic marker depends on the amount of information it provides, which is calculated based on the heterozygosity it holds. To determine the informativity of genetic marker different measures are used. Polymorphism Information Content (PIC) is one of them. It can measure the strength of heterogeneity of a genetic marker that is associated with a gene. A simulated replica of human genome for 2000 individuals in a case-control setting was used in this study. The 16 genes were randomly selected with different features, and the genotype data were enumerated for demonstrating PIC. PIC values of some genes showed strong relation with the allele frequencies. A symmetric relationship between PIC and allele frequencies were observed for the selected genes. Similar association was also noticed for a gene having huge number of SNPs (1500). Although simulated data, this demonstration is expected to provide a significant practical insight for the introductory genetic researchers to whom the access and handling with the real genotype data is a big challenge.

#4713 TUMOR NECROSIS FACTOR RECEPTOR 2 POLYMORPHISM (RS1061622) AND INFLAMMATORY RESPONSE IN END-STAGE RENAL DISEASE PATIENTS UNDER DIALYSIS

Abstract Background and Aims Enhanced levels of soluble tumor necrosis factor receptor 2 (sTNFR2) are known to associate with progressive chronic kidney disease (CKD), and is pointed as a potential biomarker for early detection of CKD; moreover, it has been reported as an independent predictor of all-cause mortality in end-stage renal disease (ESRD) patients under dialysis. Despite the increase in TNFR2 and in other inflammatory markers, recognized as risk factors for mortality in dialysis patients, the hypothesis that genetic polymorphisms of those biomarkers might modulate the inflammatory response and, thereby, the patients’ survival predisposition, has been poorly studied. Concerning TNFR2 genetic variants, a single nucleotide polymorphism in TNFR2 (+ 676 T/G; rs1061622), that results in amino acid change at position 196 (Met/Arg), was associated with higher levels of sTNFR2 in inflammatory conditions. The aim of this study was to determine the allelic frequencies of TNFR2 in ESRD patients and controls, and to evaluate its relationship with the circulating levels of inflammatory biomarkers. Method We studied 277 ESRD patients on dialysis and 32 controls, matched for gender, body mass index, and, as far as possible, for age. Real time PCR TaqMan SNP genotyping assay was used to assess allelic frequencies of TNFR2 (rs1061622). We also evaluated the circulating levels of TNF-alpha, sTNFR2, ferritin, hepcidin, elastase and cell-free DNA (cfDNA). Deaths occurring along 1-year follow-up period were recorded and mortality rates were assessed. Results ESRD patients presented higher levels of all studied biomarkers, as compared to controls; their overall mortality rate was 10.5%. Allelic frequencies in ESRD patients and controls were similar for TNFR2 (rs1061622) considering the homozygous and heterozygous individuals (χ2, p = 0.518). Concerning sTNFR2 values, no significant differences were observed between patients with genotypes TT, GG or TG. The GG genotype patients, compared to TT genotype carriers, presented significantly lower ferritin (p = 0.048), hepcidin (p = 0.038), elastase (p = 0.006) and cfDNA levels (p = 0.016); and compared to TG genotype patients, showed significantly lower ferritin (p = 0.039) and a trend towards lower values of hepcidin (p = 0.097), elastase (p = 0.079) and cfDNA (p = 0.0164). TG genotype patients showed higher TNF-alpha (p = 0.049) and a trend towards lower elastase (p = 0.081) than TT genotype subjects. The GG genotype patients presented a trend towards lower mortality rate (6.3%, 7.5%, and 12.4% for GG, TG and TT, respectively). Conclusion No differences were found in the allelic frequencies between controls and ESRD patients. The GG genotype patients for TNFR2 rs1061622 polymorphism showed decreased levels of inflammation, suggesting a more favorable inflammatory response, which is usually associated to a lower mortality risk in these patients. In accordance with the scientific community, recommending studies on genetic survival predisposition in dialysis patients, the polymorphisms of TNFR2 and of other inflammatory biomarkers deserve further studies. Acknowledgements: This work was financed by CESPU, through the project SNPsCKD-GI2-CESPU-2022; FCT, through the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences—UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy—i4HB.

RT-qPCR genotyping assays for differentiating Rift Valley fever phlebovirus strains

Rift Valley fever phlebovirus (RVFV) is an emerging, mosquito-borne, zoonotic pathogen. Real time RT-qPCR genotyping (GT) assays were developed to differentiate between two RVFV wild-type strains (128B-15 and SA01–1322) and a vaccine strain (MP-12). The GT assay uses a one-step RT-qPCR mix, with two different RVFV strain-specific primers (either forward or reverse) with long or short G/C tags and a common primer (either forward or reverse) for each of the 3 genomic segments. The GT assay produces PCR amplicons with unique melting temperatures that are resolved in a post PCR melt curve analysis for strain identification. Furthermore, a strain specific RT-qPCR (SS-PCR) assay was developed to allow for specific detection of low titer RVFV strains in mixed RVFV samples. Our data shows that the GT assays are capable of differentiating L, M, and S segments of RVFV strains 128B-15 versus MP-12, and 128B-15 versus SA01–1322. The SS-PCR assay results revealed that it can specifically amplify and detect a low titer MP-12 strain in mixed RVFV samples. Overall, these two novel assays are useful as screening tools for determining reassortment of the segmented RVFV genome during co-infections, and could be adapted and applied for other segmented pathogens of interest.

Thrombophilic mutations and risk of vascular complications in sickle cell disease

IntroductionSickle cell disease (SCD) is an inherited hemoglobinopathy that is characterized by hemolysis and hypercoagulability. The coinheritance of genetic variants causing thrombophilia may aggravate the hypercoagulable state in SCD patients. The identification of SCD patients with the risk factors for developing thrombosis may play an important role in the prevention and management of vaso-occlusive crises (VOCs) and vascular complications in these patients. The objective of the current case control study was to determine the relationship between thrombophilic variants, methylenetetrahydrofolate reductase (MTHFR) C677T, Factor V Leiden (FVLG1691A), prothrombin gene (PRT) G20210A variants and susceptibility to VOCs and vascular complications in Egyptian SCD patients. Patients and methodsEighty two patients (53 SS, 29 S/β thalassemia) and 70 healthy individuals were genotyped for these thrombophilic variants using PCR-RFLP. Sanger sequencing and real time PCR-based SNP genotyping assay were done for positive cases. ResultsMTHFR C677T was found in 40.2% of cases in heterozygous state (43.4% of SS, 34.5% of S/β thalassemia) with a highly significant difference in the prevalence between cases and healthy subjects (P = 0.001). Heterozygous FVLG1691A mutation was found in 13.4% of cases (15.1% of SS, 10.3% of S/β thalassemia). Seven cases carried both FVLG1691A and MTHFR C677T variants. There was a statistical significant difference between the frequency of acute painful crises and the heterozygosity for MTHFR C677T and FVLG1691A variants (P = 0.003). The heterozygote state of PRT G20210A mutation was present in one SS patient. ConclusionOur data suggested that the MTHFR C677T and FVLG1691A variants have a high impact as risk factors for VOCs in SCD.

GTQC: Automated Genotyping Array Quality Control and Report.

Genotyping array is the most economical approach for conducting large-scale genome-wide genetic association studies. Thorough quality control is key to generating high integrity genotyping data and robust results. Quality control of genotyping array is generally a complicated process, as it requires intensive manual labor in implementing the established protocols and curating a comprehensive quality report. There is an urgent need to reduce manual intervention via an automated quality control process. Based on previously established protocols and strategies, we developed an R package GTQC (GenoTyping Quality Control) to automate a majority of the quality control steps for general array genotyping data. GTQC covers a comprehensive spectrum of genotype data quality metrics and produces a detailed HTML report comprising tables and figures. Here, we describe the concepts underpinning GTQC and demonstrate its effectiveness using a real genotyping dataset. R package GTQC streamlines a majority of the quality control steps and produces a detailed HTML report on a plethora of quality control metrics, thus enabling a swift and rigorous data quality inspection prior to downstream GWAS and related analyses. By significantly cutting down on the time on genotyping quality control procedures, GTQC ensures maximum utilization of available resources and minimizes waste and inefficient allocation of manual efforts. GTQC tool can be accessed at https://github.com/slzhao/GTQC.

Identification and inference for subgroups with differential treatment efficacy from randomized controlled trials with survival outcomes through multiple testing.

With the uptake of targeted therapies, instead of the "one-fits-all" approach, modern randomized controlled trials (RCTs) often aim to develop treatments that target a subgroup of patients. Motivated by analyzing the Age-Related Eye Disease Study (AREDS) data, a large RCT to study the efficacy of nutritional supplements in delaying the progression of an eye disease, age-related macular degeneration (AMD), we develop a simultaneous inference procedure to identify and infer subgroups with differential treatment efficacy in RCTs with time-to-event outcomes. Specifically, we formulate the multiple testing problem through contrasts and construct their simultaneous confidence intervals, which appropriately control both within- and across-marker multiplicity. Realistic simulations are conducted using real genotype data to evaluate the method performance under various scenarios. The method is then applied to AREDS to assess the efficacy of antioxidants and zinc combination in delaying AMD progression. Multiple gene regions including ESRRB-VASH1 on chromosome 14 have been identified with subgroups showing differential efficacy. We further validate our findings in an independent subsequent RCT, AREDS2, by discovering consistent differential treatment responses in the targeted and non-targeted subgroups identified from AREDS. This multiple-testing-based simultaneous inference approach provides a step forward to confidently identify and infer subgroups in modern drug development.

Opportunities and limits of combining microbiome and genome data for complex trait prediction

BackgroundAnalysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: how useful can the microbiome be for complex trait prediction? Are estimates of microbiability reliable? Can the underlying biological links between the host’s genome, microbiome, and phenome be recovered?MethodsHere, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as inputs, and (ii) using variance-component approaches (Bayesian Reproducing Kernel Hilbert Space (RKHS) and Bayesian variable selection methods (Bayes C)) to quantify the proportion of phenotypic variance explained by the genome and the microbiome. The proposed simulation approach can mimic genetic links between the microbiome and genotype data by a permutation procedure that retains the distributional properties of the data.ResultsUsing real genotype and rumen microbiota abundances from dairy cattle, simulation results suggest that microbiome data can significantly improve the accuracy of phenotype predictions, regardless of whether some microbiota abundances are under direct genetic control by the host or not. This improvement depends logically on the microbiome being stable over time. Overall, random-effects linear methods appear robust for variance components estimation, in spite of the typically highly leptokurtic distribution of microbiota abundances. The predictive performance of Bayes C was higher but more sensitive to the number of causative effects than RKHS. Accuracy with Bayes C depended, in part, on the number of microorganisms’ taxa that influence the phenotype.ConclusionsWhile we conclude that, overall, genome-microbiome-links can be characterized using variance component estimates, we are less optimistic about the possibility of identifying the causative host genetic effects that affect microbiota abundances, which would require much larger sample sizes than are typically available for genome-microbiome-phenome studies. The R code to replicate the analyses is in https://github.com/miguelperezenciso/simubiome.

Implementing a genomic rotational crossbreeding scheme to promote local dairy cattle breeds—A simulation study

In dairy cattle breeding, there is a clear trend toward the use of only a few high-yielding breeds. One main reason is that efficient breeding programs require a certain population size. Since some numerically small breeds are well known for their functional traits, they might be an interesting crossing partner for high-yielding breeds with the aim to utilize heterosis. This simulation study investigated the transition period of a small cattle population for the implementation of genomic selection and rotational crossbreeding with a high-yielding breed. Real SNP chip genotype data from the numerically small red dairy breed Angler and the high-yielding breed Holstein Friesian were used to simulate the base generations, from which rotational crossbreeding was conducted for 10 generations. A polygenic trait with many quantitative trait loci with additive and directional dominance effects was simulated. Different selection methods for Angler sires (purebred performance, crossbred performance, and weighted purebred-crossbred performance) and different sizes and structures of the reference population (Angler, crossbred animals, and Angler plus crossbred animals) were considered. The results showed that the implementation of a genomic rotational crossbreeding scheme is an appealing option to promote the numerically small Angler breed. The growing reference population consisting of Angler and crossbred individuals maximized the genetic gain for Angler and the performance level for the crossbred individuals. Selection for purebred performance, crossbred performance, or a weighted combination of both hardly affected the results, and differences between selection scenarios were observed only in the long term with decreasing purebred-crossbred correlations.

Molecular epidemiology and viral load analysis of hepatitis C virus genotypes from Sindh, Pakistan

The present study was aimed to assess the molecular epidemiology of Hepatitis C Virus (HCV) genotypes and viral load (VL) of HCV infected patients. A total of 261 anti-HCV positive patients were enrolled for VL analysis using COBAS AmpliPrep/COBAS TaqMan HCV and Abbott Real Time HCV Genotype II Assay Kit to determine the genotypes. The data demonstrated that 60.15% (n=157) samples were from females and 39.85% (n=104) from male patients. Serum markers such as Bilirubin, SGPT, PT, APTT, and α-fetoproteins were analyzed by commercial kits and Cobas-C 501, Sysmex Ca500 and Cobas-C 601 automatic analyzers as per manufacturer’s guidelines. All patients were above 15 years age and their mean age was 39 years. Male to female ratio was 0.66% (104/157). Age-wise ddistribution revealed that 31-45 years age group comprised majority of the patients, whereas lowest number belonged to age group 61 and above. HCV genotyping data revealed five different genotypes/subtypes. Genotype 3a was most frequent accounting 76.24% (n=199) followed by Genotype 1a (2.29%), 1b (2.68%), 2a (4.98%). Moreover, 13.79% (n=36) samples revealed untypable genotype. Subtype 1b had highest mean VL of 6.84 log10 IU/ml among all other genotypes. The data of serum markers analysis revealed a slight fluctuation but appeared under the normal range. However, Bilirubin was slightly lower in Genotype 1a as compared with other genotypes while elevated α-fetoprotein was found in patients infected with untypable genotypes. In summary, Genotype 3a was predominant genotype throughout Sindh and subtype 1b had highest viral load compared with subtypes 3a and 2a. Keywords: Genotyping; Hepatitis C; HCV; Prevalence; Serum markers http://dx.doi.org/10.19045/bspab.2021.100037

Confident identification of subgroups from SNP testing in RCTs with binary outcomes

In modern drug development, genotype information becomes more frequently collected in randomized controlled trials (RCTs) for individualized risk prediction and personalized medicine development. Finding single nucleotide polymorphisms (SNPs) that are predictive of differential treatment efficacy, measured by a clinical outcome, is fundamentally different and more challenging than the traditional association test for a quantitative trait. With the objective to confidently identify and infer genetic subgroups with enhanced treatment efficacy from a large RCT for an eye disease, age-related macular degeneration (AMD), where the clinical endpoint is binary (progressed or not), we propose a novel SNP-testing procedure for binary clinical outcomes. Specifically, we formulate four contrasts to simultaneously assess all possible genetic effects on a logic-respecting efficacy measure, the relative risk (between treatment and control). Our method controls both within- and across-SNP multiplicity rigorously. We then use real genotype data to perform chromosome-wide simulations to evaluate our method performance and to provide practical recommendations. Finally, we apply the proposed method to perform a genome-wide SNP testing for the AMD trial and successfully identify multiple gene regions with genetic subgroups exhibiting enhanced efficacy in terms of decreasing the AMD progressionrate.

A novel workflow to improve genotyping of multigene families in wildlife species: An experimental set-up with a known model system.

Genotyping complex multigene families in novel systems is particularly challenging. Target primers frequently amplify simultaneously multiple loci leading to high PCR and sequencing artefacts such as chimeras and allele amplification bias. Most genotyping pipelines have been validated in nonmodel systems whereby the real genotype is unknown and the generation of artefacts may be highly repeatable. Further hindering accurate genotyping, the relationship between artefacts and genotype complexity (i.e. number of alleles per genotype) within a PCR remains poorly described. Here, we investigated the latter by experimentally combining multiple known major histocompatibility complex (MHC) haplotypes of a model organism (chicken, Gallus gallus, 43 artificial genotypes with 2-13 alleles per amplicon). In addition to well-defined 'optimal' primers, we simulated a nonmodel species situation by designing 'cross-species' primers based on sequence data from closely related Galliform species. We applied a novel open-source genotyping pipeline (ACACIA; https://gitlab.com/psc_santos/ACACIA), and compared its performance with another, previously published pipeline (AmpliSAS). Allele calling accuracy was higher when using ACACIA (98.5% versus 97% and 77.8% versus 75% for the 'optimal' and 'cross-species' data sets, respectively). Systematic allele dropout of three alleles owing to primer mismatch in the 'cross-species' data set explained high allele calling repeatability (100% when using ACACIA) despite low accuracy, demonstrating that repeatability can be misleading when evaluating genotyping workflows. Genotype complexity was positively associated with nonchimeric artefacts, chimeric artefacts (nonlinearly by levelling when amplifying more than 4-6 alleles) and allele amplification bias. Our study exemplifies and demonstrates pitfalls researchers should avoid to reliably genotype complex multigene families.

Robust Huber-LASSO for improved prediction of protein, metabolite and gene expression levels relying on individual genotype data.

Least absolute shrinkage and selection operator (LASSO) regression is often applied to select the most promising set of single nucleotide polymorphisms (SNPs) associated with a molecular phenotype of interest. While the penalization parameter λ restricts the number of selected SNPs and the potential model overfitting, the least-squares loss function of standard LASSO regression translates into a strong dependence of statistical results on a small number of individuals with phenotypes or genotypes divergent from the majority of the study population—typically comprised of outliers and high-leverage observations.Robust methods have been developed to constrain the influence of divergent observations and generate statistical results that apply to the bulk of study data, but they have rarely been applied to genetic association studies. In this article, we review, for newcomers to the field of robust statistics, a novel version of standard LASSO that utilizes the Huber loss function. We conduct comprehensive simulations and analyze real protein, metabolite, mRNA expression and genotype data to compare the stability of penalization, the cross-iteration concordance of the model, the false-positive and true-positive rates and the prediction accuracy of standard and robust Huber-LASSO.Although the two methods showed controlled false-positive rates ≤2.1% and similar true-positive rates, robust Huber-LASSO outperformed standard LASSO in the accuracy of predicted protein, metabolite and gene expression levels using individual SNP data. The conducted simulations and real-data analyses show that robust Huber-LASSO represents a valuable alternative to standard LASSO in genetic studies of molecular phenotypes.

Candidate-gene association analysis for a continuous phenotype with a spike at zero using parent-offspring trios

In this paper, we propose the class of generalized additive models for location, scale and shape in a test for the association of genetic markers with non-normally distributed phenotypes comprising a spike at zero. The resulting statistical test is a generalization of the quantitative transmission disequilibrium test with mating type indicator, which was originally designed for normally distributed quantitative traits and parent-offspring data. As a motivational example, we consider coronary artery calcification (CAC), which can accurately be identified by electron beam tomography. In the investigated regions, individuals will have a continuous measure of the extent of calcium found or they will be calcium-free. Hence, the resulting distribution is a mixed discrete-continuous distribution with spike at zero. We carry out parent-offspring simulations motivated by such CAC measurement values in a screening population to study statistical properties of the proposed test for genetic association. Furthermore, we apply the approach to data of the Genetic Analysis Workshop 16 that are based on real genotype and family data of the Framingham Heart Study, and test the association of selected genetic markers with simulated coronary artery calcification.

Changes on Hepatitis C virus genotype distribution in Western Turkey: Evaluation of twelve-year data.

Hepatitis C virus (HCV) prevalence is 1% in Turkey with genotype 1 being the predominant type traditionally. However unique geographical location of Turkey and increasing human migration in the region influences the epidemiology of the infection. The aim of this study was to determine the changes in distribution of HCV genotypes and risk factors. In this retrospective single-center study, HCV genotyping results of 558 patients were evaluated in between 2005 and 2016.Three different HCV genotyping assays were used during the 12-year study period;restriction fragment length polymorphism (RFLP), Abbott Real Time HCV Genotype II and Bosphore HCV genotyping kit. The most prevalent HCV genotype was genotype 1 detected in 88.4% of the patients followed by genotype 3 (5.2%),genotype 4 (2.9%),genotype 2 (2.1%), mixed genotypes (1.1%) and genotype 5 (0.3%).Genotype 1a showed an increasing prevalence.There were 19 patients (3.4%) either of foreign nationalities or Turkish citizens living abroad. Genotype 3 was the most common type among these patients which 10.3% had intravenous drug use history.Syrian migrant population differed in terms of HCV genotypes.Genotype 5 detected in two Syrian patients, which is the first report of HCV type 5 in Western Turkey. Among the HCV genotype 4 infected patients, 31.3% were Syrians. Our study showed that although genotype 1b dominance continues, the distribution and prevalence of HCV genotypes are changing in our region mainly due to migration and increase in the frequency of patients with non-traditional risk factors such as intravenous drug use. Monitoring the epidemiology of HCV genotypes may provide guidance in treatment decisions.

RAINBOW: Haplotype-based genome-wide association study using a novel SNP-set method.

Difficulty in detecting rare variants is one of the problems in conventional genome-wide association studies (GWAS). The problem is closely related to the complex gene compositions comprising multiple alleles, such as haplotypes. Several single nucleotide polymorphism (SNP) set approaches have been proposed to solve this problem. These methods, however, have been rarely discussed in connection with haplotypes. In this study, we developed a novel SNP-set method named "RAINBOW" and applied the method to haplotype-based GWAS by regarding a haplotype block as a SNP-set. Combining haplotype block estimation and SNP-set GWAS, haplotype-based GWAS can be conducted without prior information of haplotypes. We prepared 100 datasets of simulated phenotypic data and real marker genotype data of Oryza sativa subsp. indica, and performed GWAS of the datasets. We compared the power of our method, the conventional single-SNP GWAS, the conventional haplotype-based GWAS, and the conventional SNP-set GWAS. Our proposed method was shown to be superior to these in three aspects: (1) controlling false positives; (2) in detecting causal variants without relying on the linkage disequilibrium if causal variants were genotyped in the dataset; and (3) it showed greater power than the other methods, i.e., it was able to detect causal variants that were not detected by the others, primarily when the causal variants were located very close to each other, and the directions of their effects were opposite. By using the SNP-set approach as in this study, we expect that detecting not only rare variants but also genes with complex mechanisms, such as genes with multiple causal variants, can be realized. RAINBOW was implemented as an R package named "RAINBOWR" and is available from CRAN (https://cran.r-project.org/web/packages/RAINBOWR/index.html) and GitHub (https://github.com/KosukeHamazaki/RAINBOWR).