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Abstract Several studies have shown association of single nucleotide polymorphisms (SNPs) of hepcidin regulatory pathways genes with impaired iron status. The most common is in the TMPRSS6 gene. In Africa, very few studies have been reported. We aimed to investigate the correlation between the common SNPs in the transmembrane protease, serine 6 (TMPRSS6) gene and iron indicators in a sample of Egyptian children for identifying the suitable candidate for iron supplementation. Patients and methods One hundred and sixty children aged 5–13 years were included & classified into iron deficient, iron deficient anemia and normal healthy controls. All were subjected to assessment of serum iron, serum ferritin, total iron binding capacity, complete blood count, reticulocyte count, serum soluble transferrin receptor and serum hepcidin. Molecular study of TMPRSS6 genotyping polymorphisms (rs4820268, rs855791 and rs11704654) were also evaluated. Results There was an association of iron deficiency with AG of rs855791 SNP, (P = 0.01). The minor allele frequency for included children were 0.43, 0.45 & 0.17 for rs4820268, rs855791 & rs11704654 respectively. Genotype GG of rs4820268 expressed the highest hepcidin gene expression fold, the lowest serum ferroportin & iron store compared to AA and AG genotypes (p = 0.05, p = 0.05, p = 0.03 respectively). GG of rs855791 had lower serum ferritin than AA (p = 0.04), lowest iron store & highest serum hepcidin compared to AA and AG genotypes (p = 0.04, p = 0.01 respectively). Children having CC of rs11704654 had lower level of hemoglobin, serum ferritin and serum hepcidin compared with CT genotype (p = 0.01, p = 0.01, p = 0.02) respectively. Conclusion Possible contribution of SNPs (rs855791, rs4820268 and rs11704654) to low iron status.

Several studies have shown association of single nucleotide polymorphisms (SNPs) of hepcidin regulatory pathways genes with impaired iron status. The most common is in the TMPRSS6 gene. In Africa, very few studies have been reported. We aimed to investigate the correlation between the common SNPs in the transmembrane protease, serine 6 (TMPRSS6) gene and iron indicators in a sample of Egyptian children for identifying the suitable candidate for iron supplementation. Patients and methods One hundred and sixty children aged 5–13 years were included & classified into iron deficient, iron deficient anemia and normal healthy controls. All were subjected to assessment of serum iron, serum ferritin, total iron binding capacity, complete blood count, reticulocyte count, serum soluble transferrin receptor and serum hepcidin. Molecular study of TMPRSS6 genotyping polymorphisms (rs4820268, rs855791 and rs11704654) were also evaluated. Results There was an association of iron deficiency with AG of rs855791 SNP, (P = 0.01). The minor allele frequency for included children were 0.43, 0.45 & 0.17 for rs4820268, rs855791 & rs11704654 respectively. Genotype GG of rs4820268 expressed the highest hepcidin gene expression fold, the lowest serum ferroportin & iron store compared to AA and AG genotypes (p = 0.05, p = 0.05, p = 0.03 respectively). GG of rs855791 had lower serum ferritin than AA (p = 0.04), lowest iron store & highest serum hepcidin compared to AA and AG genotypes (p = 0.04, p = 0.01 respectively). Children having CC of rs11704654 had lower level of hemoglobin, serum ferritin and serum hepcidin compared with CT genotype (p = 0.01, p = 0.01, p = 0.02) respectively. Conclusion Possible contribution of SNPs (rs855791, rs4820268 and rs11704654) to low iron status. [ABSTRACT FROM AUTHOR]

Molecular phylogenies are a cornerstone of modern comparative biology and are commonly employed to investigate a range of biological phenomena, such as diversification rates, patterns in trait evolution, biogeography, and community assembly. Recent work has demonstrated that significant biases may be introduced into downstream phylogenetic analyses from processing genomic data; however, it remains unclear whether there are interactions among bioinformatic parameters or biases introduced through the choice of reference genome for sequence alignment and variant calling. We address these knowledge gaps by employing a combination of simulated and empirical data sets to investigate the extent to which the choice of reference genome in upstream bioinformatic processing of genomic data influences phylogenetic inference, as well as the way that reference genome choice interacts with bioinformatic filtering choices and phylogenetic inference method. We demonstrate that more stringent minor allele filters bias inferred trees away from the true species tree topology, and that these biased trees tend to be more imbalanced and have a higher center of gravity than the true trees. We find the greatest topological accuracy when filtering sites for minor allele count (MAC) >3–4 in our 51-taxa data sets, while tree center of gravity was closest to the true value when filtering for sites with MAC >1–2. In contrast, filtering for missing data increased accuracy in the inferred topologies; however, this effect was small in comparison to the effect of minor allele filters and may be undesirable due to a subsequent mutation spectrum distortion. The bias introduced by these filters differs based on the reference genome used in short read alignment, providing further support that choosing a reference genome for alignment is an important bioinformatic decision with implications for downstream analyses. These results demonstrate that attributes of the study system and dataset (and their interaction) add important nuance for how best to assemble and filter short-read genomic data for phylogenetic inference. [ABSTRACT FROM AUTHOR]

Background: Peroxisome proliferator-activated receptors (PPAR) α and γ genes play an important role in dyslipidaemia of T2DM. Aims: To estimate the frequency distribution of PPAR α and γ gene polymorphisms in South Indian T2DM patients with dyslipidaemia compared to healthy controls. Normative frequencies of SNPs were established and compared with data for 1000 genome populations. Methods: Eligible 382 cases and 336 age and sex-matched controls were enrolled. Six SNPs in PPARα [rs1800206 C>G (Leu162Val), rs4253778 G>C, rs135542 T>C] and PPARγ [rs3856806 (C>T), rs10865710 (C>G), rs1805192 C>G (Pro12Ala)] genes were selected for genotyping. Results: The allele and gene frequencies did not significantly differ between the diabetic dyslipidaemia cases and healthy controls. However, they were significantly different from that of 1000 genome populations except for rs1800206 C>G (Leu162Val) and rs1805192 C>G (Pro12Ala). Conclusion: The studied polymorphisms in PPARα and PPARγ genes are not associated with diabetic dyslipidaemia among South Indian patients.

Determining variant TPMT alleles to predict patient response to thiopurine therapy represents one of the first successful implementations of pharmacogenomics in clinical practice. However, despite the TPMT -adjusted thiopurine dosing, some TPMT wild-type patients still exhibit toxicity at standard doses. Over the past decade, the pharmacogene NUDT15 has emerged as a significant co-modulator of thiopurine therapy. Initially, NUDT15 was considered important predominantly in Asian populations, but recent studies have highlighted its relevance in European populations as well.To evaluate the pharmacogenetic significance of NUDT15 in the Slovenian population, we sequenced extended regions of exon 1 and exon 3 in 109 healthy individuals and 37 patients with acute lymphoblastic leukemia.We identified eight variants, including one with established clinical significance (allele *3) and one extremely rare variant (Chr13 at 48045861; GRCh38, NC_000013.11). The frequencies of most previously described variants in both the general population and in the ALL cohort were consistent with those reported in other European populations, except for rs45465203, which was less frequent in the Slovenian population. None of the variants, except for NUDT15*3 , were associated with cumulative thiopurine doses in ALL patients. However, these variants warrant further investigation in larger ALL cohorts.

This study aimed to investigate the prediction ability for growth and maternal traits using different low-density customized SNP arrays selected by informativeness and distribution of markers across the genome employing single-step genomic BLUP (ssGBLUP). Phenotypic records for adjusted weight at 210 and 450 days of age were utilized. A total of 945 animals were genotyped with high-density chip, and 267 individuals born after 2008 were selected as validation population. We evaluated 11 scenarios using five customized density arrays (40 k, 20 k, 10 k, 5 k and 2 k) and the HD array was used as desirable scenario. The GEBV predictions and BIF (Beef Improvement Federation) accuracy were obtained with BLUPF90 family programs. Linear regression was used to evaluate the prediction ability, inflation, and bias of GEBV of each customized array. An overestimation of partial GEBVs in contrast with complete GEBVs and increase of BIF accuracy with the density arrays diminished were observed. For all traits, the prediction ability was higher as the array density increased and it was similar with customized arrays higher than 10 k SNPs. Level of inflation was lower as the density array increased of and was higher for MW210 effect. The bias was susceptible to overestimation of GEBVs when the density customized arrays decreased. These results revealed that the BIF accuracy is sensible to overestimation using low-density customized arrays while the prediction ability with least 10,000 informative SNPs obtained from the Illumina BovineHD BeadChip shows accurate and less biased predictions. Low-density customized arrays under ssGBLUP method could be feasible and cost-effective in genomic selection. [ABSTRACT FROM AUTHOR]

BackgroundOsteoporosis is a major public health problem associated with excess disability and mortality. It is estimated that 50-70% of the variation in osteoporotic fracture risk is attributable to genetic factors. The purpose of this hypothesis-generating study was to identify possible genetic determinants of fracture among African American (AA) women in a GWAS meta-analysis.MethodsData on clinical fractures (all fractures except fingers, toes, face, skull or sternum) were analyzed among AA female participants in the Women's Health Initiative (WHI) (N = 8155), Cardiovascular Health Study (CHS) (N = 504), BioVU (N = 704), Health ABC (N = 651), and the Johnston County Osteoarthritis Project (JoCoOA) (N = 291). Affymetrix (WHI) and Illumina (Health ABC, JoCoOA, BioVU, CHS) GWAS panels were used for genotyping, and a 1:1 ratio of YRI:CEU HapMap haplotypes was used as an imputation reference panel. We used Cox proportional hazard models or logistic regression to evaluate the association of ~ 2.5 million SNPs with fracture risk, adjusting for ancestry, age, and geographic region where applicable. We conducted a fixed-effects, inverse variance-weighted meta-analysis. Genome-wide significance was set at P

Imputation may be used to rescue genomic data from animals that would otherwise be eliminated due to a lower than desired call rate. The aim of this study was to compare the accuracy of genotype imputation for Afrikaner, Brahman, and Brangus cattle of South Africa using within- and multiple-breed reference populations. A total of 373, 309, and 101 Afrikaner, Brahman, and Brangus cattle, respectively, were genotyped using the GeneSeek Genomic Profiler 150 K panel that contained 141,746 markers. Markers with MAF ≤ 0.02 and call rates ≤ 0.95 or that deviated from Hardy Weinberg Equilibrium frequency with a probability of ≤ 0.0001 were excluded from the data as were animals with a call rate ≤ 0.90. The remaining data included 99,086 SNPs and 360 Afrikaner, 75,291 SNPs and 288 animals Brahman, and 97,897 SNPs and 99 Brangus animals. A total of 7986, 7002, and 7000 SNP from 50 Afrikaner and Brahman and 30 Brangus cattle, respectively, were masked and then imputed using BEAGLE v3 and FImpute v2. The within-breed imputation yielded accuracies ranging from 89.9 to 96.6% for the three breeds. The multiple-breed imputation yielded corresponding accuracies from 69.21 to 88.35%. The results showed that population homogeneity and numerical representation for within and across breed strategies, respectively, are crucial components for improving imputation accuracies. [ABSTRACT FROM AUTHOR]

Abstract Background Due to the advent of SNP array technology, a genome-wide analysis of genetic differences between populations and breeds has become possible at a previously unattainable level. The Wright’s fixation index (Fst) and the principal component analysis (PCA) are widely used methods in animal genetics studies. In paper we compared the power of these methods, their complementing each other and which of them is the most powerful. Results Comparative analysis of the power Principal Components Analysis (PCA) and Fst were carried out to reveal genetic differences between herds of Holsteinized cows. Totally, 803 BovineSNP50 genotypes of cows from 13 herds were used in current study. Obtained Fst values were in the range of 0.002–0.012 (mean 0.0049) while for rare SNPs with MAF 0.0001–0.005 they were even smaller in the range of 0.001–0.01 (mean 0.0027). Genetic relatedness of the cows in the herds was the cause of such small Fst values. The contribution of rare alleles with MAF 0.0001–0.01 to the Fst values was much less than common alleles and this effect depends on linkage disequilibrium (LD). Despite of substantial change in the MAF spectrum and the number of SNPs we observed small effect size of LD - based pruning on Fst data. PCA analysis confirmed the mutual admixture and small genetic difference between herds. Moreover, PCA analysis of the herds based on the visualization the results of a single eigenvector cannot be used to significantly differentiate herds. Only summed eigenvectors should be used to realize full power of PCA to differentiate small between herds genetic difference. Finally, we presented evidences that the significance of Fst data far exceeds the significance of PCA data when these methods are used to reveal genetic differences between herds. Conclusions LD - based pruning had a small effect on findings of Fst and PCA analyzes. Therefore, for weakly structured populations the LD - based pruning is not effective. In addition, our results show that the significance of genetic differences between herds obtained by Fst analysis exceeds the values of PCA. Proposed, to differentiate herds or low structured populations we recommend primarily using the Fst approach and only then PCA.

Abstract Background PLINK is probably the most used program for analyzing SNP genotypes and runs of homozygosity (ROH), both in human and in animal populations. The last decade, ROH analyses have become the state-of-the-art method for inbreeding assessment. In PLINK, the --homozyg function is used to perform ROH analyses and relies on several input settings. These settings can have a large impact on the outcome and default values are not always appropriate for medium density SNP array data. Guidelines for a robust and uniform ROH analysis in PLINK using medium density data are lacking, albeit these guidelines are vital for comparing different ROH studies. In this study, 8 populations of different livestock and pet species are used to demonstrate the importance of PLINK input settings. Moreover, the effects of pruning SNPs for low minor allele frequencies and linkage disequilibrium on ROH detection are shown. Results We introduce the genome coverage parameter to appropriately estimate FROH and to check the validity of ROH analyses. The effect of pruning for linkage disequilibrium and low minor allele frequencies on ROH analyses is highly population dependent and such pruning may result in missed ROH. PLINK’s minimal density requirement is crucial for medium density genotypes and if set too low, genome coverage of the ROH analysis is limited. Finally, we provide recommendations for the maximal gap, scanning window length and threshold settings. Conclusions In this study, we present guidelines for an adequate and robust ROH analysis in PLINK on medium density SNP data. Furthermore, we advise to report parameter settings in publications, and to validate them prior to analysis. Moreover, we encourage authors to report genome coverage to reflect the ROH analysis’ validity. Implementing these guidelines will substantially improve the overall quality and uniformity of ROH analyses.

Estimating the phenotypic correlations between complex traits and diseases based on their genome-wide association summary statistics has been a useful technique in genetic epidemiology and statistical genetics inference. Two state-of-the-art strategies, Z-score correlation across null-effect single nucleotide polymorphisms (SNPs) and LD score regression intercept, were widely applied to estimate phenotypic correlations. Here, we propose an improved Z-score correlation strategy based on SNPs with low minor allele frequencies (MAFs), and show how this simple strategy can correct the bias generated by the current methods. The low MAF estimator improves phenotypic correlation estimation, thus it is beneficial for methods and applications using phenotypic correlations inferred from summary association statistics. [ABSTRACT FROM AUTHOR]

Estimating the phenotypic correlations between complex traits and diseases based on their genome-wide association summary statistics has been a useful technique in genetic epidemiology and statistical genetics inference. Two state-of-the-art strategies, Z-score correlation across null-effect single nucleotide polymorphisms (SNPs) and LD score regression intercept, were widely applied to estimate phenotypic correlations. Here, we propose an improved Z-score correlation strategy based on SNPs with low minor allele frequencies (MAFs), and show how this simple strategy can correct the bias generated by the current methods. The low MAF estimator improves phenotypic correlation estimation, thus it is beneficial for methods and applications using phenotypic correlations inferred from summary association statistics.

Subgroups of patients with breast cancer may be at greater risk for cytokine-induced changes in cognitive function after diagnosis and during treatment. The purposes of this study were to identify subgroups of patients with distinct trajectories of attentional function and evaluate for phenotypic and genotypic (i.e., cytokine gene polymorphisms) predictors of subgroup membership. Self-reported attentional function was evaluated in 397 patients with breast cancer using the Attentional Function Index before surgery and for six months after surgery (i.e., seven time points). Using growth mixture modeling, three attentional function latent classes were identified: High (41.6%), Moderate (25.4%), and Low-moderate (33.0%). Patients in the Low-moderate class were significantly younger than those in the High class, with more comorbidities and lower functional status than the other two classes. No differences were found among the classes in years of education, race/ethnicity, or other clinical characteristics. DNA was recovered from 302 patients' samples. Eighty-two single nucleotide polymorphisms among 15 candidate genes were included in the genetic association analyses. After controlling for age, comorbidities, functional status, and population stratification due to race/ethnicity, IL1R1 rs949963 remained a significant genotypic predictor of class membership in the multivariable model. Carrying the rare "A" allele (i.e., GA+AA) was associated with a twofold increase in the odds of belonging to a lower attentional function class (OR: 1.98; 95% CI: 1.18, 3.30; p=.009). Findings provide evidence of subgroups of women with breast cancer who report distinct trajectories of attentional function and of a genetic association between subgroup membership and an IL1R1 promoter polymorphism.

BackgroundThe primary rescue medication to treat acute asthma exacerbation is the short-acting β₂-adrenergic receptor agonist; however, there is variation in how well a patient responds to treatment. Although these differences might be due to environmental factors, there is mounting evidence for a genetic contribution to variability in bronchodilator response (BDR).ObjectiveTo identify genetic variation associated with bronchodilator drug response in Latino children with asthma.MethodsWe performed a genome-wide association study (GWAS) for BDR in 1782 Latino children with asthma using standard linear regression, adjusting for genetic ancestry and ethnicity, and performed replication studies in an additional 531 Latinos. We also performed admixture mapping across the genome by testing for an association between local European, African, and Native American ancestry and BDR, adjusting for genomic ancestry and ethnicity.ResultsWe identified 7 genetic variants associated with BDR at a genome-wide significant threshold (P < 5 × 10(-8)), all of which had frequencies of less than 5%. Furthermore, we observed an excess of small P values driven by rare variants (frequency,

PurposeWe determined whether variants in genes associated with genital tubercle (the anlage for the penis) and early urethral development were associated with hypospadias in humans.Materials and methodsWe examined 293 relatively common tag single nucleotide polymorphisms in BMP4, BMP7, FGF8, FGF10, FGFR2, HOXA13, HOXD13, HOXA4, HOXB6, SRY, WT1, WTAP, SHH, GLI1, GLI2 and GLI3. The analysis included 624 cases (81 mild, 319 moderate, 209 severe, 15 undetermined severity) and 844 population based nonmalformed male controls born in California from 1990 to 2003.ResultsThere were 28 single nucleotide polymorphisms for which any of the comparisons (ie overall or for a specific severity) had a p value of less than 0.01. The homozygous variant genotypes for 4 single nucleotide polymorphisms in BMP7 were associated with at least a twofold increased risk of hypospadias regardless of severity. Five single nucleotide polymorphisms for FGF10 were associated with threefold to fourfold increased risks, regardless of severity. For 4 of them the results were restricted to whites. For GLI1, GLI2 and GLI3 there were 12 associated single nucleotide polymorphisms but results were inconsistent by severity and race/ethnicity. For SHH 1 single nucleotide polymorphism was associated with a 2.4-fold increased risk of moderate hypospadias. For WT1 6 single nucleotide polymorphisms were associated with approximately a twofold increased risk, primarily for severe hypospadias.ConclusionsThis study provides evidence that single nucleotide polymorphisms in several genes that contribute to genital tubercle and early urethral development are associated with hypospadias risk.

In recent decades, developing countries with traditionally strong links with aquaculture have adopted genetic improvement programs for newly domesticated and captively bred aquaculture species. Accurate genealogical and pedigree information is an essential tool for efficient and successful genetic improvement programs and traditionally this type of information was obtained by separate rearing of families and physical tagging of the animals. More recently, molecular based pedigrees (e.g. using SNPs) is becoming a potential option, which also may permit the communal rearing of families at a stage earlier than usual, prior to physical tagging. Here we consider the utility of molecular based pedigrees in a multi-generation breeding and selection program for the Portuguese oyster (Crassostrea angulata) and assess parameters for parentage assignment.Using 18,849 SNP markers obtained from 489 sequenced oysters including parents and their progeny, our results indicated that approximately 400 SNPs with minor allele frequency ≥ 0.3 were capable of assigning offspring to their 33 parents (18 sires and 15 dams).

We propose an extended Gaussian mixture model for the distribution of causal effects of common single nucleotide polymorphisms (SNPs) for human complex phenotypes that depends on linkage disequilibrium (LD) and heterozygosity (H), while also allowing for independent components for small and large effects. Using a precise methodology showing how genome-wide association studies (GWASs) summary statistics (z-scores) arise through LD with underlying causal SNPs, we applied the model to GWAS of multiple human phenotypes. Our findings indicated that causal effects are distributed with dependence on total LD and H, whereby SNPs with lower total LD and H are more likely to be causal with larger effects; this dependence is consistent with models of the influence of negative pressure from natural selection. Compared with the basic Gaussian mixture model it is built on, the extended model--primarily through quantification of selection pressure--reproduces with greater accuracy the empirical distributions of z-scores, thus providing better estimates of genetic quantities, such as polygenicity and heritability, that arise from the distribution of causal effects. [ABSTRACT FROM AUTHOR]

Single nucleotide polymorphisms (SNPs) have now replaced microsatellite markers in several species for various genetic investigations like parentage assignment, genetic breed composition, assessment for individuality and, most popularly, as a useful tool in genomic selection. However, such a resource, which can offer to assist breed identification in a cost-effective manner is still not explored in cattle breeding programs. In our study, we have tried to describe methods for reducing the number of SNPs to develop a breed-specific panel. We have used SNP data from Dryad open public access repository. Starting from a global dataset of 178 animals belonging to 10 different breeds, we selected five panels each comprising of similar number of SNPs using different methods i.e., Delta, Pairwise Wright's FST, informativeness for assignment, frequent item feature selection (FIFS) and minor allele frequency–linkage disequilibrium (MAF-LD) based method. MAF-LD based method has been recently developed by us for construction of breed-specific SNP panels. The STRUCTURE software analysis of MAF-LD based method showed appropriate clustering in comparison to other panels. Later, the panel of 591 breed-specific SNPs was called to their respective breeds using Venny 2.1.0 and UGent web tools software. Breed-specific SNPs were later annotated by using various Bioinformatics softwares. [ABSTRACT FROM AUTHOR]

Background: Due to the advent of SNP array technology, a genome-wide analysis of genetic differences between populations and breeds has become possible at a previously unattainable level. The Wright's fixation index (Fst) and the principal component analysis (PCA) are widely used methods in animal genetics studies. In paper we compared the power of these methods, their complementing each other and which of them is the most powerful. Results: Comparative analysis of the power Principal Components Analysis (PCA) and Fst were carried out to reveal genetic differences between herds of Holsteinized cows. Totally, 803 BovineSNP50 genotypes of cows from 13 herds were used in current study. Obtained Fst values were in the range of 0.002–0.012 (mean 0.0049) while for rare SNPs with MAF 0.0001–0.005 they were even smaller in the range of 0.001–0.01 (mean 0.0027). Genetic relatedness of the cows in the herds was the cause of such small Fst values. The contribution of rare alleles with MAF 0.0001–0.01 to the Fst values was much less than common alleles and this effect depends on linkage disequilibrium (LD). Despite of substantial change in the MAF spectrum and the number of SNPs we observed small effect size of LD - based pruning on Fst data. PCA analysis confirmed the mutual admixture and small genetic difference between herds. Moreover, PCA analysis of the herds based on the visualization the results of a single eigenvector cannot be used to significantly differentiate herds. Only summed eigenvectors should be used to realize full power of PCA to differentiate small between herds genetic difference. Finally, we presented evidences that the significance of Fst data far exceeds the significance of PCA data when these methods are used to reveal genetic differences between herds. Conclusions: LD - based pruning had a small effect on findings of Fst and PCA analyzes. Therefore, for weakly structured populations the LD - based pruning is not effective. In addition, our results show that the significance of genetic differences between herds obtained by Fst analysis exceeds the values of PCA. Proposed, to differentiate herds or low structured populations we recommend primarily using the Fst approach and only then PCA. [ABSTRACT FROM AUTHOR]

Background: PLINK is probably the most used program for analyzing SNP genotypes and runs of homozygosity (ROH), both in human and in animal populations. The last decade, ROH analyses have become the state-of-the-art method for inbreeding assessment. In PLINK, the --homozyg function is used to perform ROH analyses and relies on several input settings. These settings can have a large impact on the outcome and default values are not always appropriate for medium density SNP array data. Guidelines for a robust and uniform ROH analysis in PLINK using medium density data are lacking, albeit these guidelines are vital for comparing different ROH studies. In this study, 8 populations of different livestock and pet species are used to demonstrate the importance of PLINK input settings. Moreover, the effects of pruning SNPs for low minor allele frequencies and linkage disequilibrium on ROH detection are shown. Results: We introduce the genome coverage parameter to appropriately estimate FROH and to check the validity of ROH analyses. The effect of pruning for linkage disequilibrium and low minor allele frequencies on ROH analyses is highly population dependent and such pruning may result in missed ROH. PLINK's minimal density requirement is crucial for medium density genotypes and if set too low, genome coverage of the ROH analysis is limited. Finally, we provide recommendations for the maximal gap, scanning window length and threshold settings. Conclusions: In this study, we present guidelines for an adequate and robust ROH analysis in PLINK on medium density SNP data. Furthermore, we advise to report parameter settings in publications, and to validate them prior to analysis. Moreover, we encourage authors to report genome coverage to reflect the ROH analysis' validity. Implementing these guidelines will substantially improve the overall quality and uniformity of ROH analyses. [ABSTRACT FROM AUTHOR]

Prospects of high throughput technology in animal genetics makes easy to investigate hidden genetic variation in farm animal's genetic resources. However, many SNPs technologies are currently practicing in animal genetics. In this study, we investigated genome wide SNPs variations and its distribution across the indigenous cattle population in Pakistan using Illumina Bovine HD (777K) SNPs bead chip. A total of 136 individuals from ten different breeds were genotyped and after filtration 500, 939 SNPs markers were used for further analysis. The mean minor allele frequency (MAF) was 0.23, 0.20, 0.22, 0.22, 0.20, 0.18, 0.20, 0.22, 0.21 and 0.18 observed for Achi, Bhagnari, Cholistani, Dhanni, Dajal, Kankraj, Lohani, Red sindi, Sahiwal and Tharparkar cattle, respectively. Significant difference (P0.05) within breeds and remaining 36% were considered as monomorphic markers. Average observed (Ho) and expected (HE) heterozygosity values 0.662 and 0.640 were estimated among these breeds. In conclusion, this preliminary study results revealed that these SNPs variation level could potentially be used for genetic characterization of zebu cattle breeds and could also be used to estimate genetic potential of these cattle breeds for livestock improvement in country.

In previous studies, we demonstrated that some sites in the first intron likely regulate gene expression. In the present work, we sought to further confirm the functional relevance of first intron sites by estimating the quantity of rare alleles in the first intron. A basic hypothesis posited herein is that genomic regions carrying more functionally important sites will have a higher proportion of rare alleles. We estimated the proportions of rare single nucleotide polymorphisms with a minor allele frequency < 0.01 located in several histone marks in the first introns of various genes, and compared them with those in other introns and those in 2-kb upstream regions. As expected, rare alleles were found to be significantly enriched in most of the regulatory sites located in the first introns. Meanwhile, transcription factor binding sites were significantly more enriched in the 2-kb upstream regions (i.e., the regions of putative promoters of genes) than in the first introns. These results strongly support our proposal that the first intron sites of genes may have important regulatory functions in gene expression independent of promoters.

Pemphigus vulgaris (PV) is a potentially fatal autoimmune disease characterized by blistering of the skin, mucous membranes, and oral cavity. Genetics are implicated in its etiology, with the ST18 gene identified as a potential risk factor for pemphigus in certain populations, suggesting its role as a novel molecular target for therapeutic intervention. This study aimed to detect single nucleotide polymorphisms (SNPs) rs17315309 A/G and rs2304365 C/G in the ST18 gene among Iraqi/Arabic patients with PV. A total of 90 Iraqi subjects participated in this study, including 45 patients diagnosed with PV and 45 healthy controls. SNP analysis was performed using High-Resolution Melt Analysis (HRMA) with Eva Green I Dye. For SNP rs17315309 A/G, the distribution of heterozygous genotypes showed highly significant differences between the patient and healthy groups ( P = 0.005), with the mutant G-allele being significantly more prevalent in patients than in the healthy group ( P = 0.001). In contrast, for SNP rs2304365 C/G, the distribution of heterozygous and mutant genotypes did not differ significantly between patients and healthy individuals ( P = 0.8 and P = 0.3, respectively), with the mutant G-allele also showing no significant difference ( P = 0.4). Our data indicate a significant association between PV and the rs17315309 A/G SNP in the ST18 gene among the Iraqi population of Arabic origin. However, no association was found between patients with PV and the rs2304365 C/G SNP in the same gene., Competing Interests: The authors declare no conflict of interest., (© 2024 by the authors.)

Growth hormone gene belongs to a highly polymorphic gene family whose members contain regions of very similar conserved sequences making this gene a challenging candidate to analyze. Almost 90% of the sequence of growth hormone gene family members is similar and prone to single nucleotide variations. The sequence variation occurring in the non-coding region of the GH-1 gene leads to altered transcriptional regulation causing various developmental defects in humans. In this report, we have analyzed a short sequence of ~200-240 bp spanning the proximal promoter and exon 1 (proximal portion) of the GH-1 gene to analyze the SNPs and allele frequencies of respective SNPs. Genomic DNA from blood was extracted, and the selected region was amplified using PCR. Sequencing of the amplified product was performed using Sanger sequencing, and the resulting sequence was analyzed. We have recorded 11 SNPs in total that occur in the VDRE region, 5' UTR and untranslated region of exon 1. A greater number of the SNPs were observed to coincide with the reported data from various sources. Two SNPs at position -62 and -56 (relative to transcription start site) are novel and not reported elsewhere up to the best of our knowledge. The minor allele frequencies of SNPs at position -57, -50, -48, -40, -34, -1 and +58, respectively are higher than other populations. All the observed SNPs are located in the region involved in transcription factor binding. This study is the first report regarding the allele frequency data and SNPs of the GH promoter region and exon 1 in the Local population. [ABSTRACT FROM AUTHOR]

A common method of minimizing errors in large DNA sequence data sets is to drop variable sites with a minor allele frequency (MAF) below some specified threshold. Although widespread, this procedure has the potential to alter downstream population genetic inferences and has received relatively little rigorous analysis. Here we use simulations and an empirical single nucleotide polymorphism data set to demonstrate the impacts of MAF thresholds on inference of population structure—often the first step in analysis of population genomic data. We find that model‐based inference of population structure is confounded when singletons are included in the alignment, and that both model‐based and multivariate analyses infer less distinct clusters when more stringent MAF cutoffs are applied. We propose that this behaviour is caused by the combination of a drop in the total size of the data matrix and by correlations between allele frequencies and mutational age. We recommend a set of best practices for applying MAF filters in studies seeking to describe population structure with genomic data. [ABSTRACT FROM AUTHOR]

The objective of the present study was to quantify the accuracy of imputing medium-density single nucleotide polymorphism (SNP) genotypes from lower-density panels (384 to 12,000 SNPs) derived using alternative selection methods to select the most informative SNPs. Four different selection methods were used to select SNPs based on genomic characteristics (i.e. minor allele frequency (MAF) and linkage disequilibrium (LD)) within five sheep breeds (642 Belclare, 645 Charollais, 715 Suffolk, 440 Texel, and 620 Vendeen) separately. Selection methods evaluated included (i) random, (ii) splitting the genome into blocks of equal length and selecting SNPs within block based on MAF and LD patterns, (iii) equidistant location while optimizing MAF, (iv) a combination of MAF, distance from already selected SNPs, and weak LD with the SNP(s) already selected. All animals were genotyped on the Illumina OvineSNP50 Beadchip containing 51,135 SNPs of which 44,040 remained after edits. Within each breed separately, the youngest 100 animals were assumed to represent the validation population; the remaining animals represented the reference population. Imputation was undertaken under three different conditions: (i) SNPs were selected within a given breed and imputed for all breeds individually, (ii) all breeds were collectively used to select SNPs and were included as the reference population, and (iii) the SNPs were selected for each breed separately and imputation was undertaken for all breeds but excluding from the reference population, the breed from which the SNPs were selected. Regardless of SNP selection method, mean animal allele concordance rate improved at a diminishing rate while the variability in mean animal allele concordance rate reduced as the panel density increased. The SNP selection method impacted the accuracy of imputation although the effect reduced as the density of the panel increased. Overall, the most accurate SNP selection method for panels with <9,000 SNPs was that based on MAF and LD pattern within genomic blocks. The mean animal allele concordance rate varied from 0.89 in Texel to 0.97 in Vendeen. Greater imputation accuracy was achieved when SNPs were selected and imputed within each breed individually compared with when SNPs were selected across all breeds and imputed using a multi-breed reference population. In all, results indicate that accurate genotype imputation to medium density is achievable with low-density genotype panels with at least 6,000 SNPs. [ABSTRACT FROM AUTHOR]

An important and broadly used tool for selection purposes and to increase yield and genetic gain in plant breeding programs is genomic prediction (GP). Genomic prediction is a technique where molecular marker information and phenotypic data are used to predict the phenotype (eg, yield) of individuals for which only marker data are available. Higher prediction accuracy can be achieved not only by using efficient models but also by using quality molecular marker and phenotypic data. The steps of a typical quality control (QC) of marker data include the elimination of markers with certain level of minor allele frequency (MAF) and missing marker values and the imputation of missing marker values. In this article, we evaluated how the prediction accuracy is influenced by the combination of 12 MAF values, 27 different percentages of missing marker values, and 2 imputation techniques (IT; naïve and Random Forest (RF)). We constructed a response surface of prediction accuracy values for the two ITs as a function of MAF and percentage of missing marker values using soybean data from the University of Nebraska–Lincoln Soybean Breeding Program. We found that both the genetic architecture of the trait and the IT affect the prediction accuracy implying that we have to be careful how we perform QC on the marker data. For the corresponding combinations MAF-percentage of missing values we observed that implementing the RF imputation increased the number of markers by 2 to 5 times than the simple naïve imputation method that is based on the mean allele dosage of the non-missing values at each loci. We conclude that there is not a unique strategy (combination of the QCs and imputation method) that outperforms the results of the others for all traits. [ABSTRACT FROM AUTHOR]

Objective: To establish the normative frequency of POR *28 genetic variant in South Indian population. Methods: This study is a genetic epidemiological study conducted on healthy volunteers belonging to south India. A total of 108 healthy volunteers who satisfied the eligibility criteria were included in the study. The DNA isolation was done using the QIAGEN Blood DNA isolation kit (Qiagen). The genotyping for POR *28 polymorphism was done using TaqMan genotyping master mix kits (Applied Biosystems) in real-time polymerase chain reaction platform. The data were analyzed using Fisher's exact test. P < 0.05 was considered statistically significant. Hardy–Weinberg principle was tested using the Chi-square test. Results: The genotyping of the POR *28 showed that the wild-type (homozygous) in the south Indian population was 41.7% and heterozygous and mutant type as 47.2% and 11.1%, respectively. The allele frequency showed that the minor allele frequency (MAF) was 34.7% in the south Indian population. This distribution of allele frequency was found to be significantly different from that of African–Americans 0.191 (95% confidence interval 0.157, 0.231). It was not statistically significant when compared to Caucasians 0.264 (0.228, 0.304); Chinese 0.367 (0.319, 0.419); Mexicans 0.310 (0.265, 0.360); and Japanese 0.40 (0.339, 0.464). Conclusion: The frequency distribution of POR *28 genetic variant was established in the south Indian population. The MAF was found to be 34.7% which was significantly different from other major ethnic groups. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) is a strategy to predict the genetic merits of individuals using genome-wide markers. However, GS prediction accuracy is affected by many factors, including missing rate and minor allele frequency (MAF) of genotypic data, GS models, trait features, etc. In this study, we used one wheat population to investigate prediction accuracies of various GS models on yield and yield-related traits from various quality control (QC) scenarios, missing genotype imputation, and genome-wide association studies (GWAS)-derived markers. Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two major factors in QC. Five missing rate levels (0%, 20%, 40%, 60%, and 80%) and three MAF levels (0%, 5%, and 10%) were considered and the five-fold cross validation was used to estimate the prediction accuracy. The results indicated that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided better prediction accuracy. Under this QC scenario, prediction accuracies were further calculated for imputed and GWAS-derived markers. It was observed that the accuracies of the six traits were related to their heritability and genetic architecture, as well as the GS prediction model. Moore−Penrose generalized inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) resulted in higher prediction accuracies than other GS models across traits. Imputation of missing genotypic data had marginal effect on prediction accuracy, while GWAS-derived markers improved the prediction accuracy in most cases. These results demonstrate that QC on missing rate and MAF had positive impact on the predictability of GS models. We failed to identify one single combination of QC scenarios that could outperform the others for all traits and GS models. However, the balance between marker number and marker quality is important for the deployment of GS in wheat breeding. GWAS is able to select markers which are mostly related to traits, and therefore can be used to improve the prediction accuracy of GS.