Triticum aestivum L.) yield is inevitable. Simultaneously, maintaining high crop productivity and efficient use of nutrients, especially nitrogen use efficiency (NUE), are essential for sustainable agriculture. NUE and its components are inherently complex and highly influenced by environmental factors, nitrogen management practices and genotypic variation. Therefore, a better understanding of their genetic basis and regulation is fundamental. To investigate NUE-related traits and their genetic and environmental regulation, field trials were evaluated in a Central European wheat collection of 93 cultivars at two nitrogen input levels across three seasons. This elite germplasm collection was genotyped on DArTseq® genotypic platform to identify loci affecting N-related complex agronomic traits. To conduct robust genome-wide association mapping, the genetic diversity, population structure and linkage disequilibrium were examined. Population structure was investigated by various methods and two subpopulations were identified. Their separation is based on the breeding history of the cultivars, while analysis of linkage disequilibrium suggested that selective pressures had acted on genomic regions bearing loci with remarkable agronomic importance. Besides NUE, genetic basis for variation in agronomic traits indirectly affecting NUE and its components, moreover genetic loci underlying response to nitrogen fertilisation were also determined. Altogether, 183 marker-trait associations (MTA) were identified spreading over almost the entire genome. We found that most of the MTAs were environmental-dependent. The present study identified several associated markers in those genomic regions where previous reports had found genes or quantitative trait loci influencing the same traits, while most of the MTAs revealed new genomic regions. Our data provides an overview of the allele composition of bread wheat varieties anchored to DArTseq® markers, which will facilitate the understanding of the genetic basis of NUE and agronomically important traits.To satisfy future demands, the increase of wheat ( Wheat is grown on greater land area than any other commercial crops; its production has exceeded 700 million tons in the recent years, hence wheat became the most important food grain source for humans . ConsideNUE in agricultural systems is defined as GY per unit of available N (from the soil and/or fertiliser) , which cMoreover, the importance of the two NUE components, NUpE and N utilisation efficiency (NUtE) are variable, mostly depending on the amount of the available N. Previous studies revealed that NUpE contributes more to genetic variation in NUE, especially at low N levels, whereas at high N levels, variation in NUE is mainly due to differences in NUtE (reviewed by) –13. ConsSince NUE is determined by multiple genetic factors and influenced heavily by the environment, its genetic dissection is quite challenging . In addiNumerous studies have established that significant genetic variation exists for NUE-related traits in wheat. Components of NUE were investigated in bread wheat under five N levels, and depending on N the supply, a 24-42% difference was fund in NUtE between the elite wheat varieties . AnotherGenetic diversity is a fundamental aspect of crop improvement; therefore, the effective utilisation of genetic resources in breeding programs is essentialy as long as this diversity integrates positive and profitable genes . I has bMany genes or QTLs influencing NUE have been successfully mapped in wheat under various N supplies. Most of the studies utilised bi-parental populations to identify the genetic basis of NUE and its associated traits , 18–20.Association mapping exploits ancestral recombination events that occurred throughout the evolutionary history of the populations and takes into account all the segregating alleles present in the population. In contrast, due to the restricted number of meiotic events, the genetic resolution of the segregation based conventional bi-parental maps often remain insufficient. Moreover, only the alleles from the parental genotypes are scrutinised in these ‘classical’ bi-parental analyses. So, compared to the capabilities of bi-parental mapping association analysis is a more efficient approach to dissect complex quantitative traits . ConcernIn order to identify chromosomal regions involved in the determination of NUE and its related traits, a genome-wide association study was carried out in a European winter wheat collection under different conditions. The Marker-Trait-Associations (MTA) detected in commercially relevant genetic materials will facilitate the isolation of agronomically important genes, especially those related to NUE.Triticum aestivum L.) varieties  and mineral N (ammonium + nitrate) contents  and main soil properties were determined in accredited laboratory according to NAT-1-1093/2001 . Soil type at each location was chernozem, but they differed in their naturally available N content. The average Nmineral content  was 21, 494 and 78 kgha-1 in 2013, 2014 and 2015, respectively. Weather data  were also recorded in the Martonvásár region. Further information about soil parameters and meteorological data can be obtained from full by [full by  overlap N utilisation is the other important component of NUE, depending on both N assimilation and remobilisation . In our grain). Moreover, the efficiency of the plant to take up, translocate and incorporate N into grain protein was also examined: GNACE measures the overall efficiency with which plants extract N from the soil and accumulate it in the grain [grain, spreading over 8 and 9 chromosomes. In most cases these two traits showed significant association on the same markers. Furthermore, usually the same markers were also significant for N uptake. The wPt-1554 marker was in LD with chloroplastic/plastidic glutamine synthetase (GS2) gene on chromosome 2D [grain were identified in our study.In our study differences in the ability to increase the harvested grain N content was also investigated : the difference and the ratio between performance at 2 N input levels and the slope of joint regression.Several significant QTLs have been detected in wheat under low N stress and normal N growth conditions which helped to partially understand the genetic control of NUE and related traits ,20, althfull, NUpgrain and NUtE) were expressed as the ratio between N120 and N0. The genomic regions involved in RN included 38 MTAs spread over 14 chromosomes  was found [full_RN and NUpgrain_RN in the present study. In the same study two significant DArT markers (wPt-1250 and wPt-5153) were identified for response to N level on chromosome 5B and 7A; in this position MTAs for GPC_RN and NHI_RN (clone ID 1106769 and 1060475) were found in our study.The present study provides an opportunity for the identification of MTAs induced by response to N fertilisation (RN). For such a characterisation, 8 traits Click here for additional data file.S2 TableSignificant differences between the two treatments (A) and between the two subpopulations in the given traits (B) are indicated with asterisks.(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S1 FigMap information was provided by Triticarte Pty. Ltd.(TIF)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S4 Fig(DOCX)Click here for additional data file.


Genomic imprinting underlying growth and development traits has been recognized, with a focus on the form of absolute or pure growth. However, little is known about the effect of genomic imprinting on relative growth. In this study, we proposed a random regression model to estimate genome-wide imprinting effects on the relative growth of multiple tissues and organs to body weight in mice. Joint static allometry scaling equation as sub-model is nested within the genetic effects of markers and polygenic effects caused by a pedigree. Both chromosome-wide and genome-wide statistical tests were conducted to identify imprinted quantitative trait nucleotides (QTNs) associated with relative growth of individual tissues and organs to body weight. Real data analysis showed that three of six analysed tissues and organs are significantly associated with body weight in terms of phenotypic relative growth. At the chromosome-wide level, a total 122 QTNs were associated with allometries of kidney, spleen and liver weights to body weight, 36 of which were imprinted with different imprinting fashions. Further, only two imprinted QTNs responsible for relative growth of spleen and liver were verified by genome-wide test. Our approach provides a general framework for statistical inference of genomic imprinting underlying allometry scaling in animals. Genomic imprinting, an epigenetic phenomenon of parent-of-origin-specific gene expression, has been widely observed in plants234567810111314As a measure of relative growth, allometry scaling describes the relationship between the entire body size and partial body size or between certain two biological traits. A “simple equation of allometry” has been initially developed to quantify allometry scaling1819et al.et al.283031Methods for detecting imprinting loci have been adapted from methods of interval mapping for Mendelian quantitative trait loci (QTL). Imprinting effects can be estimated using either least squares9222392223Most current reports on genomic imprinting have been focused on brain and placenta in mammals33343637m partial body sizes and y be the entire body size. The joint static allometry modelLet β0 is an intercept, and jth partial component to the entire body size. Nonlinear least squares method is generally used to get unbiased estimators of the partial scaling exponents. Under the linear mixed model framework, a linear transformation of model (1) facilitates genetic analysis of allometric scalings. Taking the natural logarithm on both sides of model (1), we havewhere With the transformation, the joint static allometric scaling model can be optimized through a stepwise regression analysis.n individuals are genotyped for q markers and observed for m partial body sizes. Four possible genotypes, denoted as QQ, Qq, qQ and qq, are distinguished at each marker. The first allele of each genotype is inherited from the paternal parent when considering the parental origin of alleles. In addition to the additive and dominance effects, the two reciprocal heterozygotes Qq and qQ carry the genetic imprinting effect that reflects the difference in allele expression derived from the two parents. Polygenic effects can also be estimated using data from multiple full-sibling and half-sibling families. Following a simple animal model for a single trait, the relationship between the logarithm of entire body size and markers can be modelled asFor a family-based population, suppose that yi is the entire body size for the ith individual; bi represents the lth fixed effect in l systematic environments, such as sexes; aj, dj and ij are additive, dominance and imprinting genetic effects for the jth marker; hil, zij, wij and sij are indicator variables corresponding to bl, aj, dj and ij, where zij, wij and sij are defined in Mantey et al.gi is the polygenic genetic effect derived from the pedigree information, assuming that ei is the residual error with where To genetically analyse allometric scalings of multiple partial to entire body sizes, we embed model (2) into systematic environments, genetic effects of markers and polygenic effects in model (3), yielding the following random regression animal model41θ ∈ b, a, d, i or g. Let φ ∈ h, z, w or s and φ ∈ h, z, w or s, θ ∈ b, a, d or i and where A and G are the numerator-relationship matrix and polygenic genetic covariance matrix for multiple allometric scalings, respectively.In this model, genetic effects are fixed for all markers and polygenic effects are random for all individuals. Thus,t statistic is formulated to statistically infer significance of marker genetic effect, which is calculated asRestricted maximum likelihood methodt statistic is calculated asSimilarly, another to statistically infer imprinting patterns.p) (p is the probability of the test statistic greater than 1.96) at the significance level of 5%. Thus, the iQTLs can be identified at chromosome-wide and genome-wide levels: by chromosome-wide test, the markers with significant genetic effects are screened from each chromosome, and then by genome-wide test, iQTLs are detected from all the significant markers screened by chromosome-wide tests.The two test statistics asymptotically follow a standard normal distribution if the sample size is large enough so that the difference between the sample size and the number of estimated parameters exceeds 120. Under a standard normal distribution, the critical value is taken as 1.96 or 1.301 for −log, polar over-dominance (H0: a = 0 and d = i) and polar under-dominance (H0: a = 0 and d = −i). Following the definition of different imprinting types and the corresponding null hypothesis2627In fact, we need to test whether or not a QTL exists, in which fashion the detected QTL inherits and what imprinting pattern the iQTL carries. A marker is identified as a QTL if one of the genetic effects is significantly different from zero by formula (6). In the case of no significant imprinting effect, the detected QTL is defined as a Mendelian QTL; otherwise, it is defined as an iQTL. The imprinting pattern can be further classified as either additive imprinting or dominance imprinting. Classification of imprinting patterns depends on the value and sign of 2 intercross of large (LG/J) and small (SM/J) inbred mouse strains45461 hybrids. These F1 hybrids were intercrossed to produce 510 F2 animals. The F2 males and females were then reciprocally mated to produce 158 full-sibling F2 families with a total of 1,632 F3 progenies. Animals were sacrificed after 70 days of age. They were weighed to obtain an overall measure of body size (y). The length of the tail (x1) was measured with callipers. The mice were then immediately dissected by necropsy, and reproductive fat depot (x2), heart (x3), kidneys (x4), spleen (x5), and liver (x6) were weighed to the nearest 0.01 g with a digital scale.A set of reciprocal cross families for distinguishing the four genotypes considering parental origin was derived from an F2 animals and their F3 offspring. The F2 and F3 genotypes were used to reconstruct haplotypes using the “block-extension algorithm” in the PedPhase program3 population at each marker locus with the paternal allele listed first and the maternal allele second.A total of 353 single nucleotide polymorphism markers were chosen from the 4,200 polymorphic markers scored as part of the CTC/Oxford genotyping consortium. These makers were genotyped for all Fet al.Focusing on the form of absolute growth, Cheverud, Stepwise regression analysis showed that not all six tissues and organs are significantly associated with body weight in relative growth. Thus, we dropped non-significant partial allometry exponents for model (2), and reached the following phenotypic joint static allometry scaling model byx4, x5 and x6 refer to allometry scaling variables for kidney, spleen and liver, respectively. The joint static allometry scaling model of kidney, spleen and liver were chosen for a genome-wide random regression analysis to infer imprinting allometries; fatpad, tail and heart were excluded from the model due to their non-significant phenotypic partial allometry scalings effects.where −6.The gender variable was considered as fixed in the final random regression model of multiple static allometries. Each fixed effect and marker genetic effects were estimated for the partial allometry exponents, along with the covariance matrix for polygenic effects and residual variance in the random regression model were estimated using REML via the DMU package. The initial values were defaulted as zero for each fixed effect, as identity matrix for the additive genetic covariance matrix and as one for the residual variance. Convergence precision for REML was set to 10Results showed that a total of 122 QTNs were detected using the chromosome-wide tests. These QTNs were distributed on all chromosomes, and 48, 54 and 44 QTNs were associated with the relative growth of kidney, spleen and liver to body weight, respectively  in an under-dominance fashion, and allometry of spleen was regulated by SNP rs13480638 (on chromosome 10) with a complete maternal additive imprinting pattern. SNP rs13475748 (on chromosome 1) was inferred to be imprinted for the relative growth of liver to body weight, but its imprinting pattern was not defined since both the additive and dominant effects were significant.The purpose of simulation was to investigate the statistical behaviour of detecting QTNs inherited in different patterns with the genome-wide random regression analysis. Based on the results from real data analysis, five additive/dominant QTNs (on chromosome 2) and two imprinting QTNs (on chromosomes 12 and 15) were chosen for simulation analysis. In the simulation, the phenotypic values for kidney, spleen and liver were retained as well as the genotypes of genetic markers. Logarithm of body weights was generated by the estimated fix regression effects for sex Taking the joint static allometry scaling model3031It should be noted that our method can provide the estimates of iQTNs’ effects and polygenic effects but not heritabilities of iQTNs; that is, it cannot answer how much iQTNs contribute to phenotypic variation. If experimental individuals are sacrificed at different ages, the residual covariance matrix for multiple allometries can be estimated by nesting the joint static allometry model into permanent environmental effects caused by multiple ages at necropsy. A complete random regression model can therefore be constructed to identify genomic imprinting for joint allometries, denoted asxipi are random family and permanent environmental effect on multiple allometries. This allows us to successfully evaluate the genetic variation of multiple allometries, such as estimation of heritability for allometry scaling of each tissue and organ to body weight. In addition, a complete pedigree of many families and more recorded individuals are required to stably estimate the parameters in such a complex model. When separating real dataset with gender in case analysis, sub-dataset from male population is even not convergent in REML, this limits discussion about the effect of gender on imprinting status.where et al.With the same dataset, Cheverud, If high density genetic markers are available that can distinguish the four genotypes, the method proposed here can be improved by doing an efficient marker selection, so that zero genetic effects can be first shrunk to zero by the least absolute shrinkage and selection operator55575960How to cite this article: Zhao, J. et al. Genome-wide random regression analysis for parent-of-origin effects of body composition allometries in mouse. Sci. Rep.7, 45191; doi: 10.1038/srep45191 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Breed-specific effects are observed when the same allele of a given genetic marker has a different effect depending on its breed origin, which results in different allele substitution effects across breeds. In such a case, single-breed breeding values may not be the most accurate predictors of crossbred performance. Our aim was to estimate the contribution of alleles from each parental breed to the genetic variance of traits that are measured in crossbred offspring, and to compare the prediction accuracies of estimated direct genomic values (DGV) from a traditional genomic selection model (GS) that are trained on purebred or crossbred data, with accuracies of DGV from a model that accounts for breed-specific effects (BS), trained on purebred or crossbred data. The final dataset was composed of 924 Large White, 924 Landrace and 924 two-way cross (F1) genotyped and phenotyped animals. The traits evaluated were litter size (LS) and gestation length (GL) in pigs.The genetic correlation between purebred and crossbred performance was higher than 0.88 for both LS and GL. For both traits, the additive genetic variance was larger for alleles inherited from the Large White breed compared to alleles inherited from the Landrace breed . The highest prediction accuracies of crossbred performance were obtained when training was done on crossbred data. For LS, prediction accuracies were the same for GS and BS DGV (0.23), while for GL, prediction accuracy for BS DGV was similar to the accuracy of GS DGV .In this study, training on crossbred data resulted in higher prediction accuracy than training on purebred data and evidence of breed-specific effects for LS and GL was demonstrated. However, when training was done on crossbred data, both GS and BS models resulted in similar prediction accuracies. In future studies, traits with a lower genetic correlation between purebred and crossbred performance should be included to further assess the value of the BS model in genomic predictions. In pig breeding, selection takes place in purebred lines and genetic evaluations are performed mainly with information that is collected on purebreds, in high-health environments, although the final product of the pig industry is a crossbred animal. This strategy may not be optimal when the objective is to improve crossbred performance. Genetic progress realized at the purebred level may not fully translate to improved crossbred performance (under field conditions) when the genetic correlation between purebred and crossbred performance is less than 1 . Low genA, of a given marker has a different effect on the crossbred phenotype depending on its breed origin. Breed-specific effects at genetic markers may occur when the linkage disequilibrium (LD) between markers and quantitative trait loci (QTL) differ between breeds or when the allele frequencies of the QTL vary across breeds [Using either real or simulated data, several studies have investigated the relevance of genotype-by-environment interactions and dominance effects for pig breeding –14. Howes breeds . When brWith the recent availability of high-density marker genotypes on both purebred and crossbred animals, we can now include crossbred data in genomic evaluations. Breed origin of alleles in crossbreds can also be determined and used to build breed-specific relationship matrices, as proposed by Christensen et al. . ReplaciIn simulation studies, Ibanez-Escriche et al.  concludeThe data used for this study were obtained as part of routine data recording in a commercial breeding program. Samples collected for DNA extraction were only used for routine diagnostic purposes of the breeding program. Data recording and sample collection were conducted strictly in line with the rules given by Dutch Animal Research Authorities.Phenotypic and genotypic data were available for pigs from two purebred populations: Large White (LW) and Landrace (LR), and from a two-way crossbred population (F1) that consisted of animals produced by reciprocal crosses of the purebred populations (LW♂ × LR♀ and LR♂ × LW♀). Phenotypic data were available for litter size  and gestation length . Both traits were recorded from parities 2 to 7. Records from the first parity were excluded because LS and GL measured in the first versus later parities have been described as different traits based on low genetic correlation , 19.Phenotypic data on both traits were available for 22,597 LW, 27,035 LR, and 29,847 F1 animals . SNPs located on the sex chromosomes and unmapped SNPs were also excluded. Positions of the SNPs were based on the Sscrofa10.2 assembly of the reference genome [Genotyping was performed mainly using the Illumina Porcine SNP60 Beadchip, but some animals from all populations were genotyped using the Illumina Porcine SNP60 v2 Beadchip. Genotypic data were available on 3723 LW, 3291 LR, and 1126 F1 animals , for which allele C was inherited with certainty from the father , the probabilities would be P1 = 0 and P2 = 1. When the phasing cannot be performed with certainty, these probabilities will have values between 0 and 1. Values of P1 or P2 between 0.1 and 0.9 were considered to have poor phasing. SNPs that were considered poorly phased in more than 95% of animals were excluded from the dataset. Then, animals that had more than 5% poorly phased genotypes were excluded. After this quality control, 924 F1 animals with genotypes for 31,930 SNPs were available to estimate variance components and SNP effects. The same set of SNPs was also used to estimate variance components and SNP effects for the purebred populations.To ensure the use of accurately phased haplotypes for determining breed origin of alleles, a threshold was applied to the F1 phased data. For each SNP genotype of each individual, AlphaImpute  generateAfter phasing of the genotype data, the breed origin of alleles was easily determined because the breeds of the parents of the F1 individuals were known. The final F1 population included 414 animals from the LW♂ × LR♀ cross and 510 animals from the LR♂ × LW♀ cross.The number of animals with both phenotypes and genotypes was larger in the purebred than in the crossbred population. Because the size of the training population influences the estimation of SNP effects and consequently prediction accuracy , we randy is a vector of phenotypes pre-adjusted for fixed effects; µ is the mean of the populations and 1 a vector of 1s; S is the design matrix for service sire effects; s is an unknown vector of service sire effects; P is the design matrix for permanent environmental effects; p is an unknown vector of permanent environmental effects; Z, ZLW and ZLR are design matrices for the additive genetic effects; uGS is an unknown vector of additive genetic effects ; uBS|LW and uBS|LR are unknown vectors of breed-specific additive genetic effects . Assumed distributions were I is an identity matrix, G is the traditional genomic additive relationship matrix, BLW and BLR are breed-specific genomic relationship matrices, and G matrix was built according to VanRaden [Variance components and SNP effects were estimated within each population using a traditional genomic selection model (GS model) and a model that accounts for breed-specific effects (BS model). The GS model was applied to both purebred and crossbred data, while the BS model was applied only to the crossbred data. These models were implemented in ASReml , as follVanRaden :pi and qi are the allele frequencies of the ith genetic marker, and M is a matrix of centered genotype codes . The BLR and BLW matrices were built according to the genomic gametic relationship matrices described by Christensen et al. [pi* and qi* are the frequencies of the allele codes from either LW (BLW) or LR (BLR) in the F1 population, and L is a matrix of centered allele codes  from either breed LW (BLW) or LR (BLR). After obtaining the estimated breeding values (EBV) from the GS model .A schematic representation of the steps involved in the prediction analyses is in Fig. jth parental purebred populations (LW or LR) and jth parental purebred populations, and In addition, within each replicate, two types of breed-specific (BS) DGV of the validation animals were estimated. The first type of BS DGV used SNP effects estimated within the parental purebred populations as: Prediction accuracy was defined as the correlation of the GS DGV, the BS DGV, or the total BS DGV with pre-adjusted phenotypes in the validation population. Prediction accuracies presented are averages over 40-fold random reference-training population replicates.The estimate of the pedigree-based genetic correlation between purebred LW and crossbred performance was 0.91 ± 0.04 for LS and 0.92 ± 0.02 for GL , which has been described as an efficient way of increasing genetic progress in both purebred and crossbred populations , 30. SucA CCPS approach to estimate BS DGV could be applied for both two-way and three-way crossbred populations. In the current study, we evaluated a two-way crossbred population and determination of the breed origin of alleles depended on pedigree information. In three-way crossbred populations, pedigree information is not commonly recorded, and thus, different strategies would be required to determine the breed origin of alleles. Recently, Bastiaansen et al.  proposedIn this study, we investigated the relevance of breed-specific effects when genomic selection is applied to real data for two reproductive traits in pigs. In future studies, traits that have a lower genetic correlation between purebred and crossbred performance should be included because benefits of BS models are expected to be larger in those cases. In addition to studying less correlated traits, investigating breed-specific effects in crosses of more distant purebred populations may result in larger benefits of the BS model. Furthermore, evaluation of larger datasets than those used in the current study is also required for more conclusive results and to quantify the benefits of accounting for breed-specific effects in prediction models.In this study, we provide evidence of breed-specific SNP effects for litter size and gestation length in a two-way crossbred population. Predicting performance of crossbred sows was shown to be more accurate when training was performed on crossbred instead of purebred data. However, when training was done on crossbred data, the GS and BS models resulted in similar prediction accuracies. In future studies, traits with lower genetic correlations between purebred and crossbred performance should be evaluated to confirm the potential benefit of BS models in genomic predictions.


A region-specific method, NTR (non-threshold rare) variant detection method, was developed—it does not use the threshold for defining rare variants and accounts for directions of effects. NTR also considers linkage disequilibrium within the region and accommodates common and rare variants simultaneously. NTR weighs variants according to minor allele frequency and odds ratio to combine the effects of common and rare variants on disease occurrence into a single score and provides a test statistic to assess the significance of the score. In the simulations, under different effect sizes, the power of NTR increased as the effect size increased, and the type I error of our method was controlled well. Moreover, NTR was compared with several other existing methods, including the combined multivariate and collapsing method (CMC), weighted sum statistic method (WSS), sequence kernel association test (SKAT), and its modification, SKAT-O. NTR yields comparable or better power in simulations, especially when the effects of linkage disequilibrium between variants were at least moderate. In an analysis of diabetic nephropathy data, NTR detected more confirmed disease-related genes than the other aforementioned methods. NTR can thus be used as a complementary tool to help in dissecting the etiology of complex diseases. Genome-wide association studies (GWAS) constitute a powerful means for analyzing common variations with minor allele frequency (MAF) greater than 1–5% , heart dAlthough rare variants have been proven to contribute to certain complex diseases, they have not been discovered by genomic searches such as those for common single-nucleotide polymorphisms (SNPs) . Many stSeveral methods can be used to detect rare variants within a gene, genomic region, or biochemical pathway, including the burden test and variance-component test, which assign weights to variants based on linear modeling of variant effects. Madsen and Browning  proposedSome disadvantages exist in these burden tests for detecting rare variants. The performance of existing and well-established methods depends on the MAF threshold used to define rare variants, which can result in the inclusion of neutral variants or exclusion of causal variants in the analysis . This prAccordingly, we developed a method, the NTR (non-threshold rare) variant detection method, that does not require arbitrary frequency thresholds for collapsing alleles and accounts for the directions of effects to detect the combined signal from rare and common variants within a genomic region while properly accounting for linkage disequilibrium (LD) between variants. We were particularly interested in five factors that might influence power and type I error of our method: (i) different ORs; (ii) different MAFs; (iii) different LD between variants in each region; (iv) variations in noise within a region, i.e., the number of non-causal variants in each region; and (v) variations in direction within a region, i.e., the number of positive and negative variants in each region. To evaluate the validity of NTR, we compared the results to those obtained from CMC, WSS, SKAT and SKAT-O. By evaluating results from simulations, we addressed the advantages and disadvantages of applying these methods to detect associated rare variants. Finally, we performed a rare-variant analysis using those methods and a publicly available dataset of diabetic nephropathy (DN) which was downloaded from the Database of Genotypes and Phenotypes (phs000389). We compared the results with current knowledge of DN. Our results reveal discrepancies among methods for rare-variant detection. The information from our results will assist researchers in identifying biological links to the etiology of complex diseases.Dim = him − piqm, him denotes the population proportion of haplotype AiBm for a two-locus haplotype consisting of alleles Ai and Bm, while pi = ∑mhim and qm = ∑ihim, the proportions of alleles Ai and Bm, respectively. Dmax isDim/Dmax|. The range of pi and qm, which makes cross-locus and cross-population comparisons uncomplicated [The common weaknesses and limitations of certain popular methods for detecting rare variants are their inability to account for LD within the region of interest, and the overestimation of the validity of any particular association for common variants. We therefore developed NTR, which integrates the effects of common and rare variants on disease occurrence into a single score and provides a test statistic for assessing its significance. NTR accounts for LD using Hedrick's multiallelic Dim′  (the rank (1987)  proposednsidered . Therefoplicated ,27.Xij be the number of minor alleles at the ith variant carried by the jth individual (both cases and controls) in a region , i = 1, 2, …, L, where L is the number of genotyped variants. We define the genetic score for the jth individual as follows:PR is the proportion of rare variants  among L variants and log2ORi is the logarithm of the corresponding OR (base 2) for the ith variant, and ith variant. The genetic effects of causal variants correlate inversely with their MAFs, and the ORs for causal variants have an exponential relationship with their MAFs [log2ORi for use in this study. We calculated three different weight Wim(k) values for interaction as follows.Wim values are the weights for the ith and mth variants, where ith and mth variants. We give lower weights to SNPs in high LD since they carry redundant information.For genome-wide data, such as whole-genome or whole-exon resequencing data, it is possible to categorize the data as “genes” or “genomic regions” and test the association of a specific variant with a disease/phenotype for each gene or region . Let Xijeir MAFs . Hence, lic Dim′ , which rWim(k) values were designed to maximize the capacity to detect SNPs, and we selected the one weight that yielded the smallest p-value. The first term of Wim(1) is the sum of two ORs, the first term of Wim(2) is the absolute value of the sum of two ORs, and the first term of Wim(3) is the sum of two absolute values of ORs. These three Wim(k) values yield the same magnitude but can have different signs, thus avoiding cancelation of the results if two variants have different directions.The three different Sj(k), most of Xs  equal zero, implying that most of Sj(k) are also equal to zero. We then use the permutation strategy to assess the power and type I error rate since many tied ranks  exist, which could make the distribution of scores severely skewed. We adopted approach used by Sanat K. Sarkar et al., 1997 and Yoav Benjamini et al., 2001[We use the sum of ranked scores from cases as the test statistic. In the formula for score al., 2001,31 that al., 2001 for the Step 1R(k) is sum of independently and identically distributed random variables, and this is thus approximately normally distributed according to the central limit theorem.All individuals (cases and controls together) are ranked according to their genetic scores, and the sum of the ranks for cases is calculated asStep 2n times for samples The affected/unaffected status is permuted among the individuals and repeated Step 3m(k) and sample standard deviations s(k) of Z(k) = (R(k) − m(k))/s(k). Under the null hypothesis, Z(k) follows an approximate standard normal distribution. k is chosen to produce the smallest p-value among three Z(k)′s. In other words,Z is a standard normal random variable. Thus, the p-value is 2 × P(Z > |Z(k*)|).The averages http://www.r-project.org). Different scenarios were considered in order to explore the efficiency of NTR versus other methods in terms of LD, OR, MAF, noise , and direction  in each region. First, we generated a latent vector from a multivariate normal distribution with a first-order autoregressive covariance structure, e.g. there was a correlation between any two latent components. We used the correlation coefficient for LE (ρ = 0) and LD  within each region. In each region, we simulated eight causal variants and five different numbers of non-causal variants . We compared regions with only rare variants to regions with rare and common variants combined. Each rare variant had a MAF uniformly distributed between 0.001 and 0.01, and it was 0.01 to 0.5 for common variants. Second, the latent vector was dichotomized to yield a haplotype with MAFs selected randomly. Third, we combined two independent haplotypes and derived genotype data. Fourth, the disease status of the jth individual was generated from the logistic regression model. Fifth, as in any case-control design we sampled 3000 cases and 3001 controls in each dataset. Furthermore, we investigated additive genetic models and assumed two OR models: (1) only risk variants with OR in ; (2) risk and protective variants with OR in , ).We generated simulated data as in Basu and Pan (2011) ,33. A siTo evaluate power, 1000 permutations were performed under each scenario. In total, there were 1000 simulations for power evaluation for each scenario. We considered five different amounts of non-causal variants  and four correlation coefficients . As for OR, we considered two levels for a single direction  and two levels for two opposing directions , ). In our simulation, causal variants were not limited to being rare, as in reality, causal variants can be quite common. For these eight causal variants, in all scenarios and all simulated datasets, ~45% were rare (MAF < 1%), 62% were uncommon (MAF < 5%), and 38% were common (MAF > 5%).For one-direction scenarios , NTR hadFor two-direction scenarios , none ofIn summary, when the variants were independent, SKAT-O was more powerful; when the variants were correlated, NTR had the advantage of considering the effect of LD, thus making it more powerful. CMC and WSS were the least powerful methods regardless of scenario.® Pathway Analysis . We employed these 102 DN-related genes to confirm previous DN findings.DNA from 1726 individuals  in the UK-ROI collection (All Ireland and Warren 3 Genetics of Kidneys in Diabetes UK Collection) were genotyped using the Omni1-Quad array  downloaded from the Database of Genotypes and Phenotypes (phs000389.v1.p1). Chronic inflammation is a common contributor to progressive renal failure and leads to increased damage to mitochondrial DNA. Pathogenic mutations in mitochondrial DNA are an increasingly recognized cause of chronic morbidity, with mitochondrial mutations being implicated in a range of complex disorders, including kidney disease –36. Henc−4), and no cut-off allele frequency.A total of 341 SNPs were located in the eight mitochondrial genes, and all SNPs satisfied the following quality-control criteria: genotype call rate < 0.95, departure from Hardy-Weinberg equilibrium . This was consistent with previous studies [PACRG (PARK2 co-regulated), which was previously found to be deleted in clear-cell renal cell carcinomas [The results of the SNP analysis are shown in  studies  that reprcinomas .TOP1MT (topoisomerase (DNA) I, mitochondrial) was found by NTR (p = 0.02557), CMC (p = 0.02054) and WSS (p = 0.01967).By using CMC, WSS, SKAT, SKAT-O, and NTR, we assessed the association between rare variants for 7 mitochondrial genes (one of the 8 genes was excluded because it only contained one SNP). Among these 7 genes, CMC (3 genes) found more associated genes compared with NTR (1 genes), WSS (1 gene), SKAT (0 genes), and SKAT-O (0 genes) . Among tGSS , CTSH , and PPARG  was found only by NTR (see NR1H3 (nuclear receptor subfamily 1 group H member 3) was found by all the methods except CMC. NR1H3 belongs to the NR1 subfamily of the nuclear receptor superfamily and is highly expressed in visceral organs including liver, kidney, and intestine [NR1H3 also plays numerous roles in pathways involved in metabolic syndrome [PPARG has been implicated in the pathology of numerous diseases including obesity and diabetes [Of the 102 DN-related genes that were collected by IPA, NTR found considerably more genes (9 genes) than SKAT (7 genes), SKAT-O (8 genes), CMC (6 genes), or WSS (8 genes) see . Among t NTR see . Moreoventestine . NR1H3 asyndrome . Finallydiabetes .CTSH , and LD was smaller for ANGPT4 and yielded a non-significant p-value (p = 0.4252). Strong LD and small NTR p-values were also found for CR1, REN, NR1H3, GAS6, and GSS, whereas low LD and large NTR p-values were found for AKR1C3 and CXADR.Our research illustrates the important role of rare variants in DN and shows that NTR is useful for analyzing real datasets. Therefore, we also examined the LD structure in the DN data. We found that most of the 43 SNPs,  and ANGP44 SNPs, , LD for NTR was designed for detecting the combined signal from rare and common variants and does not require arbitrary frequency thresholds for collapsing alleles. Thus, the associations contributed by both common and rare variants are less likely to be overlooked. When using burden tests, it is usually necessary to remove all variants above a certain MAF threshold for the signal to not be overwhelmed by common variants. However, all MAF frequency thresholds are arbitrary. The signal from rare variants will be swamped by noise from common variants when the MAF threshold is too high. On the contrary, true causal variants may be neglected when the MAF threshold is too low. NTR utilizes information for both risk-associated and protective SNPs and considers LD among all variants within a genomic region. NTR efficiently weights all variants by combining the values of Hedrick's multiallelic notyping , have prnotyping . Therefor2 = 0.30 ± 0.32 has been observed for pairwise distances of <25 kb [In this study, we conducted extensive simulations to evaluate the performance of NTR for detecting rare variants. Simulation results demonstrated that the performance of NTR was superior to that of other methods over a wide range of scenarios, especially when the effects of LD between variants Figs  and 3 wef <25 kb ,45. For f <25 kb . As an ef <25 kb . In our f <25 kb ,21. HencR(k), i.e., the sum of score ranks from cases, and we used a permutation strategy to calculate the corresponding p-value. In this regard, we used the Mann–Whitney U test, but the performance was insufficient in that an excess number of OR. Too many tied ranks could affect the accuracy of the results. In addition, we found that the power of Mann–Whitney U test in our simulation was proportional to the number of non-causal SNPs; hence, a greater number of non-causal SNPs would increase the power, but this is counterintuitive. The reason could be that a greater number of SNPs diversifies the rank scores, so the Mann–Whitney U test tends to yield smaller p-values. However, in simulations we found that the permuted samples The statistic of NTR is Hedrick’s (1987)  Dim′ ranplicated . We alsoplicated . Accordiplicated , an analplicated .The computation time for NTR, however, grows exponentially as the number of markers in a genomic region increases. This is a limitation for considering pairwise LD of all markers. When using our Linux-based workstation (Intel Xeon X5690 3.47-GHz CPU) to calculate the association for rare variants, the computation time was ~1 min for 50 SNPs in a genomic region, 6 min for 100 SNPs, and 43 min for 200 SNPs. However, SKAT-O required ~11 and ~21 s for 100 and 200 SNPs, respectively, in a genomic region. Despite the longer computation time and slightly higher false-positive rate, NTR might identify a greater number of genuine rare variants that are associated with complex diseases.Our re-analysis of the DN dataset not only confirmed a landmark finding in genetic association studies but also discovered some potentially new candidate genes related to the disease. We caution, however, that the sample size in the DN dataset is relatively small, and hence these candidate genes require further investigation. Our research illustrates the important role of rare variants in DN and demonstrates that NTR is useful for analyzing real data. We conclude that different rare-variant association methods should complement each other toward the goal of dissecting possible risk factors for complex diseases.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.


TOMM40 associated with rDR level at the genome-wide level (p = 5.0x10-08), an effect that replicated in an independent sample from the English Longitudinal Study on Ageing . Meta-analysis of rDR level confirmed the association (p = 5.0x10-11) and identified two others in TOMM40 , and one in APOE (rs769449 p = 3.1 x10-12). Meta-analysis of IR change identified associations with three of the same SNPs in TOMM40  and APOE (rs769449 p = 2.2x10-08). Conditional analyses indicate GWAS signals on rDR level were driven by APOE, whereas signals on IR change were driven by TOMM40. Additionally, we found that TOMM40 had effects independent of APOE e4 on both phenotypes. Findings from this first U.S. population-based GWAS study conducted on both age-related immediate and delayed verbal memory merit continued examination in other samples and additional measures of verbal memory.Verbal memory is typically studied using immediate recall (IR) and delayed recall (DR) scores, although DR is dependent on IR capability. Separating these components may be useful for deciphering the genetic variation in age-related memory abilities. This study was conducted to (a) construct individual trajectories in IR and independent aspects of delayed recall, or residualized-DR (rDR), across older adulthood; and (b) identify genetic markers that contribute to four estimated phenotypes: IR and rDR levels and changes after age 60. A cognitively intact sample  was drawn from the U.S. Health and Retirement Study, a nationally representative study of adults aged 50 and older. Mixed effects regression models were constructed using repeated measures from data collected every two years (1996–2012) to estimate level at age 60 and change in memory post-60 in IR and rDR. Genome-wide association scans (GWAS) were conducted in the genotypic subsample  using ~1.2 million single nucleotide polymorphisms (SNPs). One SNP (rs2075650) in  Memory impairment is the hallmark characteristic of mild cognitive impairment (MCI), Alzheimer disease (AD), and other types of dementia. It is estimated that currently in 2016), ~5.2 million, or 11.1% of the 47 million persons aged 65 or older in the United States will develop AD in their lifetimes 6, ~5.2 m. IdentifVerbal memory is one aspect of episodic memory that requires verbal processing for encoding or retrieval of words, sentences, or prose . It is tAPOE) e3/e3 and e3/e4 genotypes to assess the effect of the e4 risk allele. With regard to verbal memory, e3/e4 individuals exhibited significantly greater decline in delayed recall but not immediate recall [COMT) enzyme was evaluated for effects on prefrontal cognitive functions. While there were no associations found between COMT genotypes and verbal memory overall, upon testing differences in the association by gender, men who were homozygous for the Val allele  performed better on delayed recall only [Studies involving candidate genes have found learning and retrieval are partially distinct. For example, a study of community-dwelling older adults (ages 59 to 95) evaluated change in cognitive performance, comparing individuals with the Apolipoprotein E  factors contributing to loss of height in later life are the same as those contributing to stature in early adulthood. There has been increasing support of this age effect with episodic memory, and other cognitive aging phenotypes, as more studies have found that the effect of genes in older age  tends to be stronger [A related issue for studying trajectories of memory over age is whether the factors underlying stronger –30. In pstronger , 31–41. This study addresses three important limitations common in genetic research on memory decline in aging: (a) confounding of measurement of delayed memory with immediate memory and (b) reliance on candidate genes. We are unaware of any prior study that has used a genome-wide approach to identify unique genetic associations with immediate and delayed verbal recall.The first aim of this study was to characterize trajectories of four discrete components of verbal memory across older adulthood: level and decline in immediate recall, and level and change in independent aspects of delayed recall. This research employs data from a large population representative sample  of older individuals from the U.S. Health and Retirement Study (HRS), with repeated measures (up to eight assessments per person) of verbal memory.Our second aim was to identify genetic variants that associate with individual differences in the four components and to evaluate whether the associations are specific to each component. We employed genome wide association scans (GWAS) that had broad coverage of ~1.2 million SNP markers with the HRS genetic subsample , followed by GWAS in an independent replication sample from the English Longitudinal Study of Ageing .Discovery cohort. The Health and Retirement Study (HRS) is a nationally representative longitudinal study of U.S. households containing adults aged 50 years and older, with interviews conducted every two years, starting in 1992. Analyses of memory trajectories are based on data from a phenotypic sample comprising HRS participants who had completed at least two assessments of verbal recall between 1996 and 2012 and were at least 50 years of age during the study. Scores for some individuals were excluded if recorded prior to their turning age 50. We excluded scores of individuals who had a likely diagnosis of dementia, based on a question to the respondent on whether they had been diagnosed by a healthcare provider  or the respondent requiring a proxy to complete their interview. Memory scores for these individuals were excluded for the wave in which the diagnosis was reported, the wave prior to reporting the diagnosis, and all subsequent waves when the individual was interviewed. These criteria yielded a sample of N = 20,650, with individuals assessed up to 8 times over the 16 years , with an average of 6.1 assessments per person. This phenotypic sample is 57.9% female, and the self-reported racial/ethnic composition is 82.2% Non-Hispanic White (NHW), 14.1% African-American/Black, and 3.8% Hispanic/Latino.To reduce potential bias from population stratification bias and increase comparability with the ELSA replication sample, the GWAS was limited to individuals who identified as NHW . Other selection criteria were having verbal memory data on at least two occasions and providing a DNA sample, resulting in N = 7,486  with 65,937 observations.Replication cohort. The English Longitudinal Study of Ageing (ELSA) is a companion study of HRS, with a nationally representative cohort of individuals living in England aged 50 and older. The study began in 2002 with 11,391 participants and has since included interviews every two years, with measures collected for the purposes of harmonizing with HRS, including assessment of verbal memory . InclusIn HRS and ELSA, a verbal memory task was administered as part of a series of cognitive tests, based on the Modified Telephone Interview for Cognitive Status (TICS-m ). This aIn both HRS and ELSA, immediate recall (IR) and delayed recall (DR) were assessed as follows: Participants were alerted to pay attention and listen carefully, then asked to confirm readiness prior to being read a list of 10 nouns (administered by interviewer or taped recording) at the rate of one per second; then they were asked to recall the words immediately and again after a 5-minute delay  which waP-value < 10−4 in European samples), and sex differences in allelic frequency ≥ 0.2). Further detail is provided in HRS documentation [For HRS, genotype data were accessed from the National Center for Biotechnology Information Genotypes and Phenotypes Database (dbGaP ). DNA saentation . To reduhttp://www.elsa-project.ac.uk/). DNA was extracted from blood samples collected in 2004 and 2008, and samples were genotyped by University College London (UCL) Genomics Institute on the HumanOmni2.5–8 array from Illumina . Because the two studies were designed to be similar , the same quality control procedures used with HRS were applied to the ELSA data, except that UCL excluded SNPs with missing call rates >5% (rather than 2%), yielding available data on 1,204,603 SNPs for 7,404 individuals with European ancestry, of whom 6,898 had phenotypic data also.For ELSA, genotype data were accessed from the European Genome-phenome Archive  Phenotype construction. Mixed effects regression models were used to evaluate the age-based trajectories and between-subject variation in trajectories of IR and rDR . Four phenotypes were estimated for each individual: IR level, IR change, rDR level, and rDR change. (2) GWAS. Separate GWASs were run, first in the discovery cohort, to identify genetic loci associated with each of the four phenotypes. Replication GWASs were run in the independent cohort to evaluate replication. (3) SNP Comparison. We compared GWAS results across the phenotypes, and between the discovery and replication cohorts. (4) Meta-analysis. We conducted a meta-analysis of the discovery and replication cohorts to assess the robustness of the combined effects. (5) Functional genomic and pathway analysis. We evaluated the plausible biological pathways in which implicated genes play a role in memory outcomes.To isolate the component of delayed recall that is independent of immediate recall, we formed a residualized-delayed recall (rDR) score. Mixed effects regression models were used to regress DR on IR using data from all participants and observations. rDR is the residual value (difference between the observed DR score and predicted DR score based on IR). IR and rDR scores were used as dependent variables in spline modeling to obtain four estimates for each participant to be used as phenotypes in genetic analyses: immediate recall level (IR-L), immediate recall change (IR-C), residual delayed recall level (rDR-L), and residual delayed recall change (rDR-C).Splines are piecewise segments that fit linear trends, but each spline has its own intercept and slope to reflect the changing trends of the score over age. We conducted preliminary analyses to identify the number of splines needed to summarize the overall trajectories in the data and to identify the knot points (the dividing point between splines). The two-spline model was selected as the most parsimonious for characterizing overall trajectories for both IR and rDR. Based on prior literature , 24–26 aAll spline models were conducted as mixed effects analyses using PROC MIXED in SAS 9.4  to estimr’s<0.02) with each of the four phenotypes. As a check for non-linear associations, we examined graphs in which level of immediate recall was divided into quartiles and plotted against the first three principal components (PCs) , adjusting for sex and population stratification . As with any statistical analysis of association, if the correlation between dependent and independent variables differs for subpopulations, spurious associations may result in GWAS . To reduTo estimate the fraction of phenotypic variation explained by common SNPs  for each phenotype, we implemented genomic-relationship-matrix restricted maximum likelihood (GREML) in unrelated individuals from all genotyped SNPs in the data set. We included only individuals falling within 1 SD of all self-identified non-Hispanic Whites for eigenvectors 1 and 2 in the principal components analysis of all unrelated participants. More information on the process for screening and selection of the sample by ancestral homogeneity and relatedness is provided in HRS documentation . SNP herp ≤ 5.0x10-08 [p ≤ 5.0x10-06 because we prefer not to miss potential new findings (Type II error) at the cost of an overly stringent Type I cutoff. To compare SNP associations from discovery and replication data sets we: 1) assessed the independent contribution of SNPs meeting genome-wide significance in the discovery scan; and 2) compared effect sizes and p-values to those estimated for these SNPs in the replication scans. For visualization of the SNP plots, we used R  and LocusZoom v1.1 [We evaluated SNP associations in the GWASs by p-value, using the genome-wide significance level of .0x10-08 . We alsooom v1.1 .http://www.sph.umich.edu/csg/abecasis/Metal [We conducted a meta-analysis of the discovery and replication cohorts using the inverse variance weighted model approach implemented in the METAL package .http://www.scandb.org/newinterface/index.html). Gene functions were identified using the literature search feature of the “Gene Expression Analysis” (GEA) tool (http://chrisarm.com/gea/lsearch/). Gene network analysis was conducted using the Ingenuity Knowledge Base and Ingenuity Pathway Analysis . Additional interactions were added sourced from the Qinsight Biomedical Search Engine  and the BaseSpace Correlation Engine , which summarizes evidence from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus .Genes and flanking genes were identified using dbSNP and the “SNP and CNV Annotation Database” (SCAN) (ß = 0.02).In the full phenotypic HRS sample, IR was strongly correlated with DR (r = .77), but (as expected) not with residualized delayed recall , reflecting our goal of isolating the immediate and delayed components of verbal recall. Aim 1 of the study was to characterize the longitudinal trajectories of the phenotypes: IR level (IR-L), IR change (IR-C), residual DR level (rDR-L) and residual DR change (rDR-C). To achieve this, we calculated and compared the estimated parameters from spline models for the four phenotypes for the HRS full phenotypic sample. The first slope component (s1) is a linear function of the scores obtained prior to age 60 and a second slope component (s2) is a linear function of scores obtained after age 60. There turned out to be negligible variation in slope prior to age 60 . Because there was more between-person variation after age 60 for IR and DR , the key model parameters are subject-specific intercepts (random effects) for memory level at age 60  and subject-specific estimates of slope (change) after age 60, representing IR-C or rDR-C. However, variance in rDR-C post-60 was considerably smaller than for IR-C and DR-C.r = -.62) and DR (r = -.64), indicating that individuals with initially better recall had more rapid decline, whereas those who began lower did not decline as much across subsequent measurements. In contrast, rDR level was only weakly associated with decline (r = -.18), suggesting that change on recall is independent of ability.As shown in Quantile-quantile (QQ) plots for the HRS sample were constructed to show the ratio of observed to expected p-values for each SNP in the four GWASs, each adjusted for three PCs , and several other SNPs in all four scans met our criterion for suggestive association (p ≤ 5.0x10-06). Corresponding Manhattan plots for the ELSA sample are provided in the Supporting Information  and rs71352238  on chromosome 19  and the. Two SNPs on chromosome 19 in the PVRL2  and TOMM40  genes had suggestive association with both IR-C and rDR-L. Effect sizes for these two SNPs replicated in ELSA for rDR-L , but not IR-C . Interpretation of the size of genetic effects is provided in the Supporting Information  approached the genome-wide cutoff for an association with rDR-L in HRS but that effect size did not replicate in ELSA . Similarly, a SNP on chromosome 20, rs6025261  exceeded the suggestive level for an association with IR-L, but the effects did not replicate in ELSA . Several other SNPs met the criterion for suggestive association with IR-C (7 SNPs) or rDR-C (6 SNPs), but effects did not replicate in ELSA.One SNP on chromosome 20, rs6125934  in the HRS cohort was estimated for each phenotype based on the combined genetic effect across all SNPs included in the GWAS, adjusted for sex and three PCs. Estimates were significantly greater than 0 for IR-L (h2 = 22.4%), IR-C (h2 = 12.0%) and DR-L (h2 = 13.4%), but not for rDR-L (h2 = 3.4%), rDR-C (h2 = 6.7%) or DR-C (h2 = 8.6%).SNP heritability , we focus on SNPs that surpassed genome-wide significance and use the single top SNP to represent each of the TOMM40 and APOE effects.Meta-analyses of the HRS and ELSA cohorts identified four SNPs that surpassed the genome-wide significance cut-off for rDR-L, three of which were also significantly associated with IR-C, and none of which significantly associated with rDR-C or IR-L . All SNPEvaluation of meta-analysis results for rDR-L. In the meta-analysis of rDR-L, the top two SNPs in the APOE (rs769449) and TOMM40 (rs2075650) are in LD with at r2 ≥ 0.6. These are represented by the top diamond and circle in TOMM40 on nonpathological cognitive decline is not independent of APOE effects [TOMM40, rs2075650 (APOE SNP diminished (rs769449 p = 5.9x10-03). Second, we performed a meta-analysis of the GWASs that were run conditioned on the top genome-wide significant SNP in APOE, rs769449 (TOMM40 SNP diminished (rs2075650 p = 0.23) to a non-significant effect. APOE rs79449 and s2075650 , and fours769449 , and fouAPOE e4. SNP rs769449 resides in APOE, and is in high pairwise LD with the SNPs that determines the APOE e4 isoform, rs429358 (r2 = 0.779), as calculated using the Phase 3 of the 1000 Genomes Project and CEU reference panel [APOE e4, and thus APOE genotype, was not determined from directly genotyped SNPs in HRS or ELSA, and thus it was not included in the primary analyses of this study. However, because the two non-synonymous SNPs that characterize the isoforms (rs429358 and rs7412) were inferred through imputation with estimated certainties ≥.997 in HRS [TOMM40 rs2075650 on rDR-L, with APOE e4 in the model, and covariates , we found that the effect of TOMM40 remained  with effect of e4 also significant .Third, we analyzed rDR-L, conditioned on ce panel . The pre7 in HRS , 76, we 8 r2 = 0.9, as cal7 in HRS  and summTOMM40 on rDR-L in other APOE genotypes to examine effects beyond e4. Results of each model predicting rDR-L are provided in the Supporting Information  and APOE (rs769449), and in LD at r2 ≥ 0.8. APOE SNP (rs769449), to examine the change in effect of TOMM40 SNP rs157582 (-04), as did the effect of another TOMM40 SNP (rs71352238 p = 8.5x10-04). Second, we performed a meta-analysis of the GWASs, conditioned on the top genome-wide significant TOMM40 SNP (rs157582), to examine the change in effect of APOE SNP rs769449 .rs157582 . Comparers157582 , the conrs769449 . The assTOMM40 SNP, rs157582  and APOE e4 , the effect of TOMM40 remained while the effect of APOE was reduced and no longer significant.Third, we constructed linear regression models using the HRS cohort, predicting IR-C from the e4 allele, adjusted for covariates, and found a significant but small effect of e4 . Next, in a model predicting IR-C from both the TOMM40 rs157582 A (risk) allele within each of the APOE genotypes. The models showed that there were significant effects of the rs157582 A allele on IR-C within the e3/e3  and e2/e3 strata . Results of each model predicting IR-C are provided in the Supporting Information ; in older age decades, there were no differences in effects of the A allele on IR change  and from the current study, as associated with rDR-L and IR-C, in order to elucidate relationships between genes and the variants involved in AD-related processes. The network  in the pathway of influence on verbal memory, episodic memory, AD, cognitive impairment, memory deficits, and reduced hippocampal volume. Some of these pathways likely involve other genes  with upstream influence from F2. The network also shows interactions between APOE and other genes in nearly every pathway to a memory and cognition-related outcome.We evaluated the networks and molecular pathways including the relationships between  network , shows tTOMM40 region, associated at the genome-wide significant level with the phenotype of delayed recall level that is independent of immediate recall. This finding replicated in a completely independent sample and remained in our subsequent meta-analysis. The meta-analysis on delayed recall level also identified two other TOMM40 SNPs  and an APOE SNP (rs769449) with genome-wide significant associations. Additional evaluation of the TOMM40 and APOE SNPs indicated that the GWAS signal for delayed recall level was mostly attributed to APOE; however, there were effects of the TOMM40 variant independent of e4. The meta-analysis on the separate phenotype of immediate recall change identified genome-wide significant SNPs in both genes . Additional evaluation indicated that TOMM40 had not only stronger effects than APOE on immediate recall change, but also effects independent of e4. Several other SNPs that reached the suggestive association level in the present study reside within genes associated with memory phenotypes in other studies. More critically, our results underscore the importance for specificity in phenotypes when assessing the genetic influence on components of memory abilities.In this study, we used a population-based sample repeatedly assessed on verbal memory to characterize individual trajectories of change over age, giving greater precision in assessing level and decline of memory ability than available in most other studies. Trajectories were constructed to reflect immediate and delayed recall after age 60, an age at which other longitudinal studies suggest consistent decline in memory ability occurs. One SNP (rs2075650) on chromosome 19, in the An innovation of this study was to separate the components involved in different aspects of recall. Whereas immediate recall reflects initial encoding and rehearsal, our residualized-delayed recall score, created by partialling the immediate score from the delayed score, is a distinct phenotype reflecting the “pure” retrieval component of memory  and late-onset Alzheimer disease (LOAD) in other studies, that effect on verbal information retrieval detected through GWAS is primarily driven by linked APOE variants [TOMM40 plays a larger role, specifically, in decline of verbal learning after age 60. Further, our analysis showed that there are unique effects of TOMM40 beyond APOE e4 effects on both level of delayed recall prior to age 60 and decline in immediate recall after 60.The overarching goal of this study was to examine the complexity of verbal memory abilities over age, and whether genetic variants associate uniquely with specific components of verbal memory, represented by four phenotypes. Our finding with regard to rDR level supports work from prior cognitive aging studies. Taken together, although rs2075650, in the TOMM40 SNP (rs157582) indicates that it has significant effects outside of e4. Upon cursory assessment with TOMM40 being 16 kb upstream of e4, conclusions could be drawn that the TOMM40 SNP simply serves as a marker for the e4 effect; however, several critical pieces of information suggest otherwise. First, the APOE SNP rs769449 resides 2 kb from e4 and is in high LD with it (r2 = .799) such that if the effect of TOMM40 on IR-C were attributable solely to APOE, we would have observed the effect of TOMM40 disappear when it was included in a model with rs769449 predicting IR-C. Yet, the effect of TOMM40 remained statistically significant whereas the effect of rs769449 did not. Second, if the signal were attributable solely to APOE, when the effect of the TOMM40 SNP was conditioned on the e4 allele (inferred from rs429358), the effect size of TOMM40 would have diminished. Conversely, the effect size of TOMM40 remained the same while the effect of e4 diminished to non-significance. Several additional observations point towards an independent TOMM40 effect. One relates to linkage disequilibrium (LD) and that the TOMM40 SNP (rs157582) is in imperfect LD with the APOE SNPs  and rs429358 were directly genotyped and tested in separate additive logit models for associations with LOAD, they showed different degrees of effect . Therefectively ). Third,APOE-TOMM40 haplotype associations [TOMM40 variants influence memory performance and dementia risk beyond that conferred by the APOE e4 variant [APOE e3-TOMM40 VL haplotype developed LOAD an average of seven years earlier than those with the APOE e3-TOMM40 S haplotype  have been characterized to test the association between APOE e3/e3-TOMM40 variant haplotypes with delayed verbal recall level and immediate recall change could provide further understanding of these findings.Our findings are in line with several recent studies that have implemented newer methods to evaluate ciations  and foun variant –94. For p = 0.02 ). This ic memory , 94. OurTOMM40/APOE region is associated with level of ability in the retrieval component of verbal memory and change in initial encoding and rehearsal. Given the key importance of delayed recall in discriminating degrees of cognitive impairment, partialling out the phenotype to reflect true retrieval provides a more refined measure with which to study and potentially link underlying genetic associations with impairment. The interpretation of our findings would be clarified further by following up these results in a sample that has genotyping data and assessment of memory in non-verbal domains and probing of encoding by recognition as well as recall.Second, and more relevant to genetic specificity for verbal memory, prior GWAS studies use phenotypes  that aggregate multiple cognitive domains, including crystallized ability, fluid ability, speed, and episodic memory. Since these processes differ in their phenotypic trajectories over age , 97 and TOMM40 and APOE associate with level of delayed recall and change in immediate recall have plausible biological mechanisms. An examination of gene interactions and protein expression through gene network analyses identified possible pathways from both genes to AD and memory-related conditions. For example, cognitively intact, older APOE e4 carriers have been shown to exhibit greater age-related reductions in hippocampal volume [APOE e4 carriers in combination with the TOMM40 rs11556505 ‘T’  and rs2075650 (the SNP in our study) ‘G’  “risk” alleles [TOMM40 “risk” alleles on recall abilities, beyond effects of APOE, may be mediated via brain morphology.Our findings that l volume  and redul volume . In anot alleles . This suAlthough verbal memory components may share some genetic underpinnings and biologic pathways, prior research and our findings support the notion that phenotypic specificity remains critically important. In a prior study, left hippocampal body volume was shown to be associated with delayed verbal memory , but not immediate memory among healthy older adults, ages 55 to 83 . In anotThis study addressed several limitations of prior research aimed at identifying genetic loci associated with memory decline, including the use of cross-sectional data to estimate age-related change and using an index of delayed memory that is confounded with immediate. We harnessed repeated measures from a large population-based study and created distinct phenotypes that better reflect the complexity of verbal memory ability and change over time. Results from longitudinal studies of episodic memory can be limited by practice effects , but thiTOMM40 and APOE on rDR-L and IR-C, we were not able to directly account for the effects of APOE e4, nor were we able to examine haplotype effects. Thus, our analyses cannot confirm causality between TOMM40 and phenotypes, nor can we confirm the independence of the effect of TOMM40 given the proximity to other susceptibility genes. For further confirmation, the finding warrants replication with a sample in which the genotyped APOE alleles are available, as well as the TOMM40 poly-T variant.This study had several limitations. First, our measure of verbal memory was based on a single presentation of a word list administered by a lay-interviewer and likely contains greater measurement error than other approaches using repeated presentations of a word list or multiple memory measures. However, this would be expected to introduce noise rather than false positive results. This illustrates a significant challenge for GWAS study designs: achieving samples sufficiently large to detect the expected effect sizes often means a trade-off in measurement intensity. By using a repeated measures design in a large sample we were able to characterize individual trajectories of age-related change and increased power compared to cross-sectional designs , but we TOMM40 region, has biological plausibility for its effect on delayed recall beyond its association with APOE e4 risk because of findings linking TOMM40, hippocampal formation and volume, and delayed recall. With regard to change in immediate recall ability, there is evidence for the genetic effects of both TOMM40 and APOE, as well as independent effects of TOMM40 among individuals who do not carry the APOE e4 allele. These findings need confirmation in other samples, but support the existence of differential effects of TOMM40 and APOE on specific components of verbal memory.The current study provides evidence of genetic underpinnings for specific components of aging-related verbal memory performance, delayed recall at age 60 that is unconfounded with attention or memory span, and decline in immediate recall after age 60. The results underscore the importance of studying phenotypes that represent distinct components of memory, or other aspects of cognition, when assessing genetic influences. One identified genetic variant, a SNP in the S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.S7 Fig(PDF)Click here for additional data file.S1 File(PDF)Click here for additional data file.S2 File(PDF)Click here for additional data file.S3 File(PDF)Click here for additional data file.