R2 And P Value In Ld Explains Pdf

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Association Between Polygenic Risk Score and Risk of Myopia

Either your web browser doesn't support Javascript or it is currently turned off. In the latter case, please turn on Javascript support in your web browser and reload this page. Prolonged hyperuricemia is a cause of gout and an independent risk factor for chronic health conditions including diabetes and chronic kidney diseases.

In addition, there is still much heritability to be explained. Three loci on chromosome 11 were distributed within a distance of 1. In a subsequent association analysis on the GWAS loci of chromosome 11 using closely positioned markers derived from whole genome sequencing data, the most significant variant to be linked with the nearby GWAS signal was rs c. This variant has been found only in Korean and Japanese subjects and is known to lower the SUA concentration in the general population.

Thus, this low-frequency variant, rs, known to regulate SUA concentrations in previous studies, is responsible for the nearby GWAS signals. Uric acid is the final product of purine metabolism produced from xanthine and hypoxanthine by the action of xanthine oxidase. Uric acid is mainly present as urate under normal physiological conditions. Prolonged elevation of SUA concentrations—hyperuricemia—accelerates the formation of monosodium urate crystals and causes gout.

Hyperuricemia is independently associated with hypertension, diabetes, chronic kidney diseases and cardiovascular mortality [ 1 — 3 ]. In addition, recent clinical or experimental studies have suggested that hyperuria contributes to the development of metabolic syndrome [ 4 ].

To identify underlying genetic alterations involved in the regulation of SUA concentration, genome-wide association studies GWASs have been conducted in many populations, and genetic loci covering ABCG2 and SLC9A2 have been found most repeatedly and significantly [ 6 — 10 ]. However, except for rs in ABCG2 , most single nucleotide polymorphisms SNPs that have been reported to be significant so far are located in intronic or intergenic regions, and causative variants have not been identified for most loci.

Thus, much heritability is yet to be explained and many genetic variants remain to be identified. While GWASs to date have provided valuable clues about the genetic background for SUA concentrations, additional GWASs using common variants with low effect are unlikely to be able to explain the residual heritability robustly. One hypothesis is that common variants with weak effects might have been found in GWASs because of their linkage disequilibrium LD relationships with causative variants with low-frequencies and large effects.

To find such low-frequency causative variants responsible for GWAS signals, resequencing of target sites or whole genome sequencing WGS is required. Here, we identify such a low-frequency causative variant and its relationship with common variants in GWAS signals. The baseline characteristics for study participants are described in S1 Table.

The median SUA concentration was 6. The genomic inflation factor was calculated as 1. The most significant genetic association was found on chromosome 11, in a 65 Mb region Fig 1B.

Rs is located on the intronic region of FRMD8. The other five SNPs are located on noncoding or intergenic regions of different genes and the areas in which they were located contain many genes. The third strongest signal was from chromosome 11, in a 64 Mb region. The horizontal red line represents the genome-wide significance level.

The red circle indicates the most significant SNP in the locus, and the circle size is proportional to the strength of LD r 2 with the most significant SNP. Rs and rs were genotyped and analyzed as an independent group Table 1 ; they were validated and had statistical significance.

Because only males were included in the GWAS stage and both males and females were included in the validation study stage, we further tested the effect of these two SNPs rs and rs on the SUA concentration of each male and female subject. In both males and females, these two SNPs were significantly associated with SUA concentration and the direction of their effect was also the same S4 Table. Although rs and rs have been validated successfully with statistical significance, it is difficult to regard these SNPs as causative variants because they are located in noncoding regions and the functions of their adjacent genes are insufficient to explain a causal relationship with SUA concentration.

To identify more influential variants, we performed additional association analysis using closely spaced markers around the GWAS signal on chromosome 11, 63—67 Mb in extent, extracted from the WGS data that formed part of the validation samples S2 Fig.

W into a stop codon p. Interestingly, the degree of LD estimated as r 2 between rs and surrounding SNPs and the negative logarithm of the P -value —log 10 p of their association showed positive correlation, which was also observed in the relationship with rs, rs and rs Fig 2A. To confirm the association between rs and SUA concentration in larger samples, rs was genotyped and analyzed in an entire sample including discovery GWAS and validation samples.

Among a total of individuals that were successfully genotyped, had a heterozygous p. The mean SUA concentrations of those with or without p. The circle size is proportional to the strength of LD r 2 with rs red circle. C , D Regional plots for the results of the association analysis for SUA concentration using SNPs derived from whole genome sequencing data before C and after D the conditional analysis incorporating rs First, in our GWAS, we found four association signals reaching genome-wide significance, two previously established common SNPs rs and rs and two new low-frequency SNPs rs and rs Although the latter two were validated using a subsequent study with increased sample size, because of their location in a noncoding region and weak cogency of their surrounding genes, we anticipated that there might be a causative variant nearby, and conducted further association analysis using more closely spaced markers derived from WGS data.

The SNP rs—the most significant variant found in this analysis—shows a LD relationship with surrounding variants including the three SNPs that reached genome-wide significance in the initial GWAS in chromosome 11 at 64—65 Mb rs, rs and rs The degrees of LD between rs and each surrounding SNP and the significance of their associations with SUA concentrations were closely related.

In addition, in the conditional analysis on rs, the associations of these SNPs with SUA concentrations disappeared. The SLC22A12 gene encodes a urate transporter in renal proximal tubules serving to reabsorb urate [ 14 ]. A SLC22A12 p. In addition to patients with this Mendelian inherited disorder, the effects of p. The Suita study targeted a community-based general population in Japan and sequenced the SLC22A12 gene of 24 subjects with low SUA concentrations; it found that those with a homozygous or heterozygous p.

They suggested that loss-of-function variants of SLC22A12 might not be harmful in the general population. Another study examined the genotype distribution of p. Five individuals with a homozygous p. The SLC22A12 p. Its allele frequency in Japanese and Korean general populations has been reported to be 0. In our study, subjects with the heterozygous p. Thus, the role of SLC22A12 p.

Targeted sequencing of the SLC22A12 gene has identified low-frequency variants greatly affecting SUA concentrations in the general population. In addition to p. Among these, p. R90H and p. RH showed statistically significant associations with hypouricemia; a deletion of p. D—P was identified in only one subject, but it was proved experimentally that it abolished the urate transport activity of SLC22A12 [ 17 ]. In addition, a subject with the p.

RE variant had a low SUA concentration, although this missense variant was not proved experimentally and was not statistically significant because it was found in only one subject. Direct sequencing of exons 7 and 9 of the SLC22A12 gene revealed a deletion p.

TM in 1. Although that study did not directly address the association of these variants with SUA concentration, these variants were initially found in patients with primary renal hypouricemia in the same geographic area.

R90H, p. RH and p. QL and one splicing site variant c. No subject had a homozygotic or a compound heterozygotic form of these variants; instead, all variant-positive subjects had only one of them in a heterozygous form and also showed low SUA concentrations S7 Table.

The p. QL variant was reported in a Japanese patient with renal hypouricemia as a compound heterozygotic form of p. QL [ 16 ]. The splicing site variant c. These variants inevitably show a population-specific distribution, because they are recent [ 24 ]. In addition to the genome projects involving 26 populations from all over the world, efforts are under way to create reference panels using WGS approaches in particular sample sets including the British UK10K , Dutch Genome of the Netherlands [ 25 ], Sardinian [ 26 ] and Icelandic deCODE Genetics [ 27 ] populations.

These have shown that population-based panels are particularly effective in discovering genotypes with a low population frequency. In these studies, some low-frequency variants strongly associated with specific phenotypes have been identified, as summarized recently [ 28 ].

The role of the common noncoding variant found in GWASs has been revealed through subsequent studies in some cases. Surakka et al. Some of these low-frequency variants explain the association of common variants in the overlapping loci [ 31 ]. In our study, rs explained the association of surrounding common or low-frequency variants located over 1 Mb and at—least in Koreans—it might explain the signals found at this locus in previous GWASs for SUA concentration.

This study linked the previously found GWAS signals and a variant that causes Mendelian-inherited disorders. The biggest weakness of this study is that rs is found only in Koreans and Japanese, so the results cannot be applied to other populations. Because this genetic locus has been repeatedly found in GWASs of other populations, there might be other low-frequency variants such as rs However, this study was conducted only in Koreans, so we could not find such variations in other ethnic groups.

In addition, for many GWAS results for common variants lacking known molecular mechanisms, it will be necessary to use multifaceted approaches such as deep sequencing or functional experiments to discover their mechanism of action or the actual functioning variants surrounding them.

The design of this study has been described previously [ 32 ]. A schema of this study is shown in S2 Fig. This study was originally designed to investigate the genetic background of abdominal obesity and metabolic syndrome and only male participants were recruited during that period.

Participants with conditions that might influence body weight were excluded: individuals who had 1 been diagnosed with thyroid diseases or taken thyroid medication; 2 taken medication or undergone a procedure or operative treatment for obesity within 3 months of enrollment; 3 a medical history of stroke, cardiovascular diseases, diabetes mellitus treated with medication, cancer, or abdominal surgery.

Repeated examinees and those with insufficient blood for testing were also excluded. In addition, individuals taking medications that could influence the SUA concentration, including allopurinol and antituberculotics, were excluded. After application of the exclusion criteria, 1, males were recruited for the GWAS.

Since , it has expanded to include both male and female participants. In this phase, 2, individuals 1, males and 1, females were enrolled for the validation study. This study was approved by the institutional review board of SNUH C and all subjects gave written informed consent.

Association Between Polygenic Risk Score and Risk of Myopia

The proper understanding and use of statistical tools are essential to the scientific enterprise. This is true both at the level of designing one's own experiments as well as for critically evaluating studies carried out by others. Unfortunately, many researchers who are otherwise rigorous and thoughtful in their scientific approach lack sufficient knowledge of this field. This methods chapter is written with such individuals in mind. Although the majority of examples are drawn from the field of Caenorhabditis elegans biology, the concepts and practical applications are also relevant to those who work in the disciplines of molecular genetics and cell and developmental biology. Our intent has been to limit theoretical considerations to a necessary minimum and to use common examples as illustrations for statistical analysis. Our chapter includes a description of basic terms and central concepts and also contains in-depth discussions on the analysis of means, proportions, ratios, probabilities, and correlations.

Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population stratification. More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution.

Metrics details. Genome-wide association studies GWAS based on linkage disequilibrium LD provide a promising tool for the detection and fine mapping of quantitative trait loci QTL underlying complex agronomic traits. In this study we explored the genetic basis of variation for the traits heading date, plant height, thousand grain weight, starch content and crude protein content in a diverse collection of spring barleys of worldwide origin. The whole panel was genotyped with a customized oligonucleotide pool assay containing SNPs using Illumina's GoldenGate technology resulting in successful SNPs covering all chromosomes. The morphological trait "row type" two-rowed spike vs.

phenotypic variation each score explained using semi-partial-R2 (with genotyped data and no LD trimming at a p-value threshold for SNP.

Association Between Polygenic Risk Score and Risk of Myopia

Jfet Oscillator Circuit. The output oscillations are generated because of the phase shift between the stages of the RC oscillator. The FET offers good. N Channel junction FET 51 9.

Genome-wide association studies for agronomical traits in a world wide spring barley collection

Association Between Polygenic Risk Score and Risk of Myopia

The low predictive power of single SNPs, coupled with the high significance threshold needed to correct for multiple testing, greatly decreases the power of GWAS. Results: We propose a procedure in which all the SNPs are analyzed in a multiple generalized linear model, and we show its use for extremely high-dimensional datasets. Thus, our method tests whether or not a SNP carries any additional information about the phenotype beyond that available by all the other SNPs. In addition, the method offers a data driven approach to identifying and refining groups of SNPs that jointly contain informative signals about the phenotype. We show, in particular, that our method is also capable of finding significant SNPs that were not identified in the original WTCCC study, but were replicated in other independent studies. Availability and implementation: Reproducibility of our research is supported by the open-source Bioconductor package hierGWAS. Contact: peter.

Автобус тронулся, а Беккер бежал за ним в черном облаке окиси углерода. - Espera! - крикнул он ему вдогонку. Его туфли кордовской кожи стучали по асфальту, но его обычная реакция теннисиста ему изменила: он чувствовал, что теряет равновесие. Мозг как бы не поспевал за ногами. Беккер в очередной раз послал бармену проклятие за коктейль, выбивший его из колеи.

Убийца шагнул к. Беккер поднялся над безжизненным телом девушки. Шаги приближались. Он услышал дыхание. Щелчок взведенного курка.

converges to the heritability explained by the SNPs as sample size grows pruning with r2 threshold and subsequently applying p value thresholding, where dspace/bitstream///1/wifusion.org Daetwyler, H.D.

1 Introduction

 Настали не лучшие времена, - вздохнул Стратмор. Не сомневаюсь, - подумала. Сьюзан никогда еще не видела шефа столь подавленным. Его редеющие седые волосы спутались, и даже несмотря на прохладу, создаваемую мощным кондиционером, на лбу у него выступили капельки пота. Его костюм выглядел так, будто он в нем спал. Стратмор сидел за современным письменным столом с двумя клавиатурами и монитором в расположенной сбоку нише. Стол был завален компьютерными распечатками и выглядел каким-то чужеродным в этом задернутом шторами помещении.




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ClГ­o T.


calculating PRS – e.g. LD adjustment via clumping, beta shrinkage using lasso explained (R2), area under the curve (AUC), and P-value.



Genome-wide association studies GWAS have become a powerful tool for analyzing complex traits in crop plants.

Danielle Q.


Each PRS included information from approximately 1.

Uriel L.


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