Evaluation of methods using simulated data showed lack of power even at the conservative level of 0.05 for most of the causal genes on chromosome 3. Nevertheless, analysis of MAP4 produced good power for all tests at various levels of the tests for both DBP and SBP. Our results also confirmed that Fisher's method is not only robust but can also improve power over individual pooled linear and quadratic tests and is often better than other robust tests such as SKAT-O.The focus of our work is to evaluate several recently developed pooled association tests for rare variants and assess the impact of different gene annotation methods and binning strategies on the analyses of rare variants under Genetic Analysis Workshop 18 real and simulated data settings. We considered the sample of 103 unrelated individuals with sequence data, genotypes of rare variants from chromosome 3, real phenotype of hypertension status and simulated phenotypes of systolic blood pressure (SBP) and diastolic blood pressure (DBP), and covariates of age, sex, and the interaction between age and sex. In the analysis of real phenotype data, we did not obtain significant results for any binning strategy; however, we observed a slight deviation of the  Next-generation sequencing (NGS) technology provides rich data for the analysis of the role of rare variants in complex human diseases and traits. Because of the low power associated with analyzing one single rare variant, many pooled association tests have been proposed for joint analysis of a group of rare variants. The methods proposed so far include the earlier linear statistics that are powerful when most of the variants are causal and have the same direction of effect ,2, the qS = , whereWe considered 7 association tests recently developed for analyzing rare variants using a sample of unrelated individuals. All methods considered can be described through a vector of statistics, J is the total number of single-nucleotide polymorphisms (SNPs) in the bin or group of variants under study, i Yis the phenotype value of the ith individualij , i = 1,...,n, Gis the number of rare alleles for the ith individual at the jth SNP, and ith individual under the null hypothesis of no association between the phenotype Y and genotypes G of the group of variants under study. The fitted phenotype values can depend on a set of covariates X.and S, aggregating information across the J variants. They include two linear statistics of the formThe 7 "pooled" association test statistics are functions of the vector L1 Wuses equal weights [Lp Wuses minor allele frequency-  based weights [two quadratic statistics of the formwhere  weights , two quaA = I [HW, uses the inverse of the estimated covariance matrix of S under the null hypothesis [three robust hybrid statistics, SKAT-O [where the C-alpha statistic, es A = I , and thepothesis ,5, and t, SKAT-O , minimum, SKAT-O , which cand Fisher's statistic isL1 pand C pare p-values, respectively, of the linear L1 Wand quadratic C Wtests. The SKAT-O statistic is similar in nature to the minimum-p statistic [where tatistic ,7.For the purpose of this study, we focused on the 103 unrelated individuals with NGS data from the GAW18, rare variants from chromosome 3, the first of four measurements for systolic blood pressure (SBP), diastolic blood pressure (DBP) and hypertension status, and covariates age, sex, and the interaction between age and sex. Rare variants were defined as SNPs with MAF of 0.05 or less, estimated from the 103 unrelated sequenced individuals. The analysis of the real data focused only on the hypertension status; the analysis of the simulated response data studied premedicated SBP and DBP values  as well as hypertension status. Last, we analyzed the continuous and dichotomized values of simulated phenotype Q1, which is unrelated to the genotypes, to evaluate the type I error of the 7 statistics.et al [The selection of groups of rare variants for pooled association analyses is critical, in terms of statistical power, for all methods -7. We uset al .Based on the variant annotation, we considered a gene-based approach using three strategies to group/bin the variants within each gene. We grouped variants that belong to the same gene and are of the same annotation type, that is, (a) coding variants, (b) protein-changing variants, and (c) protein-damaging variants. We note that (c) is a subgroup of (b), and (b) is a subgroup of (a).p-values for each of the 7 statistics by parametric bootstrap [J SNPs are not associated with the continuous response variable (SBP or DBP) given the other covariates. Similarly, we fitted logistic regression models with the same set of covariates when hypertension status is considered as the response. We then generated bootstrap samples from the fitted models and calculated the 7 test statistics for each sample. Finally, we obtained the empirical p-value for each test as the proportion of bootstrap samples with statistics more extreme than the one calculated from the original data. For each statistic and a group of SNPs, we initially used 1000 bootstrap samples to estimate the p-value. If the p-value was less than 0.01, we used an additional 100,000 bootstrap samples to estimate the p-value more accurately. For the p-value of SKAT-O, we used the available SKAT-O package [We obtained ootstrap ,5. For t package .Based on the sequence data on chromosome 3, there were 7435 high-quality variants annotated as coding variants, 4099 as protein-changing variants, and 1791 as protein-damaging variants .We evaluated the type I error for all 7 methods using Q1 as the outcome. Because the −5 for coding, 0.05/720 = 6.94 × 10−5 for protein changing, and 0.05/460 = 1.1 × 10−4 for protein damaging). However, we observed that with a more refined selection of SNPs, such as the protein-damaging variants compared with the coding variants, there are deviations  from the expected Unif  distribution for p-values of all 7 tests, suggesting association. As an example, Figure reduced compared with the number of genes with at least one variant) based on each of the three binning strategies (a) to (c) in Table In the analysis of hypertension status based on the real data, none of the genes appeared to be statistically significant using a crude Bonferroni correction for multiple testing . Of the 31 causal genes on chromosome 3 influencing SBP or DBP, 25 genes had at least one rare variant annotated as coding and polymorphic in the sample of 103 unrelated individuals. For most of these causal genes, none of the 7 tests had reasonable power (10% ore more) for any of the three phenotypes even at the conservative type 1 error level of 0.05  between non-causal rare variants in genes with causal rare variants from other genes. For example, the non-causal rare variant with coordinate 127395914 in ABTB1 is highly correlated with the causal variant with coordinate 48040284 in MAP4. Similarly, we observed that the genotype of a single non-causal rare variant (169801953) in GPR160 is identical to the genotype of a causal rare variant (47913455) in MAP4 in the sample of 103 individuals considered.We first note that BTD gene, the estimated power for the two linear statistics was significantly larger than those for the two quadratic statistics (for both SBP and DBP), but the pattern is reversed for ABTB1 (for both SBP and DBP) and PPP2R3A (for DBP). In contrast, the three hybrid statistics, min, WF, Wand SKAT-OW, are robust: for each gene, they are comparable in terms of power to the method with the maximum power for that gene. We also observed that Fisher's method, which combines p-values from the linear and quadratic test statistics, has better power in most cases than the other two robust statistics, minimum-p and SKAT-O, and it is the best or the second best option in many cases.Results in Table MAP4, all tests have low power even at the 0.05 level; adjusting for multiple testing will further decrease power. Focusing on MAP4 and BTD, the two genes with the largest power for SBP, we investigated the relationship between power and level of the tests using a receiver operating characteristic (ROC) curve ; however, we did not observe statistically significant results after adjusting for multiple hypothesis testing. The strategies presented so far focused on coding variants. We also considered two other strategies that include non-coding variants but restricted to variants that belong to conservation tier 1 group (T1) (PhastCons score >0 and PhyloP score >1) and tier 2 group (T2) (PhastCons >400 and PhyloP score >1.5) . Most of the genes with causal variants had poor or no power even at the conservative level of 0.05. This is due to the small sample size, small effect sizes, and perhaps our binning strategy. Nevertheless, analysis of rinciple . The resrinciple . FinallyThe authors declare that they have no competing interests.AD, JFL, and LS created the overall study design. AD did the overall analysis. DM and ADP created the gene annotation design and did the analysis. AD, JFL, and LS drafted the manuscript. All authors read and approved the final manuscript.


It was determined that the early observations taken on 14th and 28th DAS were reliable estimators to study ESV as compared to56th DAS. Further, 96 were selected from 629 genotypes by principal component (PCA) and discriminate function analyses. The selected genotypes were subjected to decipher the pattern of genetic diversity in terms of both phenotypic and genotypic by using ESV QTL linked simple sequence repeat (SSR) markers. To assess the genetic structure, model and distance based approaches were used. Genotyping of 96 rice lines using 39 polymorphic SSRs produced a total of 128 alleles with the phenotypic information content (PIC) value of 0.24. The model based population structure approach grouped the accession into two distinct populations, whereas unrooted tree grouped the genotypes into three clusters. Both model based and structure based approach had clearly distinguished the early vigor genotypes from non-early vigor genotypes. Association analysis revealed that 16 and 10 SSRs showed significant association with ESV traits by general linear model (GLM) and mixed linear model (MLM) approaches respectively. Marker alleles on chromosome 2 were associated with shoot dry weight on 28 DAS, vigor index on 14 and 28 DAS. Improvement in the rate of seedling growth will be useful for identifying rice genotypes acquiescent to direct seeded conditions through marker-assisted selection.Early seedling vigor (ESV) is the essential trait for direct seeded rice to dominate and smother the weed growth. In this regard, 629 rice genotypes were studied for their morphological and physiological responses in the field under direct seeded aerobic situation on 14 Oryza sativa L.) is widely cultivated under irrigated and rainfed conditions, with cultivated varieties and landraces that specifically adapt to these situations. In India, more than 50% of rice areas under rainfed conditions are cultivated as direct seeded rice (DSR). The existing rainfed upland varieties are of short duration type, drought tolerant and low yielding and possess early vigor trait. Direct seeding is also a common practice in low land rice of India and weed is the major problem in those situations. Early seedling vigor (ESV) trait of rice will help in competing with weed and this trait is needed in lowland rice. While, irrigated rice varieties are high yielding and relatively poorer in early vigor as compared to rainfed varieties. In rice growing areas of South and South East Asia, ESV trait has been exploited in rainfed upland cultivar development but not to the same extent in rainfed low land and irrigated situations, mainly due to the narrow genetic base of selecting attributes primarily for higher grain yields. Attempts were made to introgress ESV from upland landraces to high yielding cultivars; however, significant progress could not be achieved due to complex nature of the trait [Rice  are most commonly used, as they are hyper variable, co-dominant, robust, multi-allelic in nature, chromosome specific and greatly facilitated linkage map construction and use in plant breeding. SSR markers are widely employed to assess genetic diversity and genetic structure in rice and several other crops. However, limited information is available on classification of ESV in rice cultivars using DNA markers. Molecular markers-based genetic maps facilitate applied genetics and breeding program .ESV and its component traits being complex quantitative nature, their involvement with several physiological processes and influences of G x E interactions makes these traits to achieve slower progress for genetic enhancement. Therefore, integration of marker tools into breeding schemes provides an opportunity to handle such challenges and facilitates marker-assisted selection. Continuous variation could be observed for ESV within a segregating population and frequency of favorable alleles at many loci could also be achieved by selection. QTL mapping based on segregating population helps to identify loci for quantitative and qualitative traits with limited number of segregating alleles at any locus and low resolution typically in the range of 10-30cM . To overOryza sativa. L) genotypes  accessions cited in 3. Seeds of each of the genotype were direct sown in 4 m long row with 15 cm apart and 20 cm between rows in augumented block design. Pre-emergence herbicide Pendimethalin was sprayed at the recommended rate within 24 hr of irrigation and maintained weed-free through the growing period by hand weeding. Crop was raised with recommended fertilizers doses of 80 kg nitrogen ha-1, 40 kg phosphorus ha-1, and 40 kg potash ha-1. The field was maintained under non-saturated aerobic condition with supplemental surface irrigation.The total of 629 rice , six rice plants in the middle row from each genotype were selected to study the shoot length (cm), number of leaves, root length (cm) and shoot dry weight (DW) (g). The same set of traits was observed on 28 and 56 DAS with number of tillers per plant and leaves per tiller. Growth rate of seedlings were measured to assess ESV by vigor index (VI) as suggested by Maguire [n is the number of germinated seeds on the nth day and Yn is the number of days from the first experimental day.While, germination rate (GR) was calculated as1 and h2 are the plant height at times t1 and t2 respectively.Absolute growth rate (AGR) was calculated as-1day-1.1 and W2 are the plant dry weights at times t1 and t2 respectively.Relative growth rate (RGR) was determined by measuring plant dry weight periodically during growth and is represented as mg g-2 day-1.1 and w2 are the plant dry weights at times t1 and t2 respectively, P = spacing (m2).Crop growth rate (CGR) measured the plant dry weight of a particular ground area at a regular interval of time divided by land area and represented as g mAn initial descriptive statistics, including mean, standard deviation, minimum and maximum values, coefficient of variation (%), skewness, kurtosis, and distribution pattern was performed by box plot technique. PCA was performed to estimate Euclidean distance between two genotypes in the multivariate space and adaptation of genotypes to specific environment –7. TheseBased on the broad trend observed in the PCA and phenotypic data. Forward stepwise discriminate function analysis (DFA) was carried out to understand the combination of variables, which could best explain the grouping in STATISTICA ver.10 . In DFA, a larger eigen value indicates that the discriminant function is more useful in distinguishing between the groups.th and 28th DAS. By using these variables, RGR, AGR and CGR were derived to measure growth rate of seedlings as mentioned in experiment 1. PCA based clustering was performed to find out the pattern of phenotypic diversity and variables contributions to diversity using the Windostat 7.5 software.Ninety-six genotypes were selected representing high, medium and low vigor genotypes with almost equal proportions from each group were selected for the panel from the results of the experiment 1. Groups were defined by following the scale presented in 10E1 buffer  to obtain the final concentration of 15 ng/μl for amplification.The experimental material comprised of selected 96 genotypes that were direct sown in the pot and grown in net house at NRRI, Cuttack. After 20 days of sowing, young leaves were harvested and genomic DNA was isolated from the bulked leaf sample following CTAB method . The quapriori information was used to select ESV linked 52 SSR markers from the reports of earlier mapping population studies and the distribution of markers covered all the chromosomes to illustrate the diversity [A iversity .2, and 1U of Taq DNA polymerase . Template DNA initially denatured at 94°C for 5 min followed by 36 cycles of denaturation at 94°C for 1 min, annealing at 55–67°C (varies upon primers) for 1 min and extension at 72°C for 5 min followed by final extension at 72°C for 10 min. The amplification products were separated on 2.5% agarose gel containing ethidium bromide using 1x TBE buffer. DNA fragments were visualized under UV transillumination using Alpha Innotech gel documentation system .The PCR amplification was executed with 20 μl of PCR mixture contained 30 ng of genomic DNA, 1x PCR buffer, 200 μM dNTP mix, 4 picomoles of each forward and reverse primers, 2 mM of MgClo), expected heterozygosity (He), and PIC, for each SSR locus were determined by using Power marker 3.25[Amplicons were scored according to their product size for each genotype and primer combination. Number of alleles (N), major allele frequency (A), observed heterozygosity  General linear model (GLM) and ii) Mixed linear model (MLM) in TASSEL version 5.0. Signifith, 28th and 56th days after sowing (DAS), number of leaves and germination rate were negatively skewed (S <-1) on 14 DAS (th and 56th (52.14%), DW on 56 DAS (51.44%) and VI on 28 DAS (42.23%). Higher CV suggests that, the selected material in this experiment exhibited higher variability.In the present investigation, distribution pattern of ESV traits of 629 genotypes were presented in n 14 DAS , while dPrincipal component analysis (PCA) was performed to identify association between traits, responsible traits for ESV and grouping pattern of rice genotypes on the basis of the traits. The first two PCs accounted for about 71.47% of the total variability with >1.0 eigen value. The biplot in 1 accounted for about 60.71% and PC2 accounted for 21.22% of the total variance  under net house condition. From The selected 96 genotypes were raised under net house and field condition during dry season. PCA was executed for all the 96 genotypes under both situations to identify trends among low and high early seedling vigor genotypes and the traits responsible for source of the variability . The PC1o) ranged from 0.04(RM3839) to 0.97 (RM148) with an average heterozygosity across all 39 loci was 0.42. None of the loci exhibited heterozygosity as zero. The expected heterozygosity (gene diversity) (He) ranged from 0.04 to 0.50 with an average of 0.30. Major allele frequency was also calculated for all 39 loci, which ranged from 0.52 to 0.98 with an average of 0.79  was non-significant. The inbreeding co-efficient FIS (-0.419) and FIT (-0.396) were found to be 1.000. It indicated that individuals from different populations are genetically more closely related than within population. Nei’s genetic distance was estimated between assumed seven populations. The pairwise Nei’s genetic distance ranged from 0.0024 (lowest) between population of pop 6 and pop 7 to 0.0316 (highest) between population of pop 3 and pop 4.The assumed populations were subjected to AMOVA. AMOVA revealed that maximum variation was among individuals with greater variance (99%), while sub-populations were grouped together, the variance was only 1% and the Fjaponica, 11 improved lines, 26 ARC lines and seven landraces were grouped into one population with proportion of 47.91%. While, population 2 has the membership proportion of 52.08% consisted of 44 ARC lines, five landraces and one improved genotype IR72.By structure analysis, pure or admixture genotypes were categorized. The genotype with score >0.80 was considered as pure and <0.80 as admixture. Among the 96 genotypes, 27 genotypes were pure and 19 were admix in population 1, while 34 were pure and 16 were admix in population 2. In total, 61 were pure and 35 were identified as admixture and the same can be visualized in graphical representation (st) values of two population ranged between 0.0218 (population 1) and 0.3397 (population 2), while allele frequency divergence between two population was 0.057.STRUCTURE software was used to determine relationship among the genotypes studied and distinguished 96 genotypes into two populations with ΔK value of 31 . In popuentation . The fixThe results of association analysis using GLM and MLM (Q+K) are presented in the viz., Assam, Odisha, Uttar Pradesh, Karnataka, Tamil Nadu, West Bengal and Bihar. The early seedling vigor trait distribution pattern of the panel of genotype studied on 14th, 28th and 56th days after sowing (DAS), exhibited that germination rate, number of leaves on 14 DAS, DW on 56 DAS, VI on 56 DAS and CGR distribution was asymmetrical. VI and DW on 56 DAS and CGR between 28 and 56 DAS exhibited maximum variability and portrayed that the population was heterogeneous.Genetic variability is the key determinant for any breeding program. The sporadic reports on germplasm evaluation of landraces and genetics of ESV traits in rice encouraged us to take up this study. Traditional native landraces those grown under rainfed are competitive to weeds. By introduction of modern high yielding with low ESV semi dwarf varieties, weed competitive landraces are almost extinct and disappearing in farmers’ field. Therefore, the present experiment was executed to identify the suitable genotypes with high ESV and to find the loci responsible for those traits of the gene by association mapping. We began with a panel of 629 rice accessions . LandracIn this study, association between dry weight and shoot length or vigor index suggests that accumulation of dry weight is an important trait contributing for ESV. Cultivar-by-trait biplot analysis grouped the vectors of the variables of 14 and 28 DAS into a separate group (highly correlated). Similarly, variables of 56 DAS had formed another group (highly correlated). The reason might have been due to higher differences in the trait variability observed before 28 and 56 DAS. Further, it clarifies that the observation taken on 14 and 28 DAS was the reliable estimator to study ESV in rice than observed on 56 DAS. The position and perpendicular projection of genotypic points onto variable vectors grouped early vigor genotypes on 14 and 28 DAS in quadrant 2 and these genotypes were moderately vigorous on 56 DAS than genotypes captured in quadrant 1 and 4. Genotypes captured in quadrant 3, exhibited vigorous growth on 56 DAS with moderate early vigor than genotypes grouped in quadrant 1 and 4.viz., Varshadhan, Vandana, AC4387 and Pyari. In contrary, genotypes KB46, AC 38399 and ARC 10656 moved on the left side of the plot, where they exhibited reduced vigor index and dry weight.The PCA analysis of selected 96 genotypes under net house and field condition classified early vigor genotypes on right of the biplot and non-early vigorous genotypes on left side of the plot. This is evident from the genotypes like ‘Sabita’ (a known early vigor genotype)  positionThe genetic architecture of diverse rice accessions can be assessed by model based and distance based clustering approach using the SSRs or SNPs marker system –37. ThisThe 39 SSRs generated a total of 128 alleles with an average of 3.28 alleles per locus  and PIC Genetic relatedness among the accessions was assessed on the basis of their clustering pattern in the unrooted tree. The unrooted tree grouped varieties into three major clusters with varying number of genotypes per cluster. The clustering pattern of genotypes obtained in the present study was found to be trait based grouping and no geographical isolation clustering has been observed. Inclusion of large number of ARC collections might be the reason for lack of geographical isolation. Accessions of subgroup 1b possessing 11 ESV traits measured on 14 and 28 DAS were grouped together with higher crop growth rate (CGR). The genotypes were grouped together under the subgroup1b were tall and possessed high vigor index (VI) as preferred for ESV. While, cluster 3 positioned opposite to subgroup 1b grouped the genotypes of least vigor type and exhibited inferiority for 11 ESV traits including vigor index. Singh et al.  also repst indicates little divergence existing between subgroups of the population. FIT is the overall inbreeding coefficient of an individual within the total population. Negative value of FIT indicates higher proportion of heterozygote genotypes on the level of sub populations and all evaluated individuals as well. Negative FIS value also suggests that there is an excess of heterozygotes in these loci (out breeding) compared with Hardy Weinberg Equilibrium (HWE) expectations and reflects the genetic variability of general population. The reason for heterozygotes might be inclusion of landraces in the present study and these genotypes might have heterozygous allele for the studied trait.AMOVA study indicated higher partitioning of variation within individuals and lower portion existed among populations. Estimates of the fixation indices revealed that Fthand 28th DAS. Between the two sub-populations, second sub-population exhibited higher shoot length, root length, number of leaves, leaf length, dry weight and VI on both DAS than the sub-population-1. However, genotypes of admixtures were observed to have superior in early vigor trait than pure lines. Since, this study includes large set of landraces, which might have diverse ancestry still possessing heterozygosity for the trait studied in the present experiment.The model based approach by structure analysis revealed two subpopulations. The genetic structure of populations has been previously reported in rice to vary from 2 to 8 subpopulations , 41–44. The application of association mapping for ESV will be helpful in deciding the molecular markers to be used in stacking the multiple QTLs responsible for this trait. The significant SSR markers of GLM explained on an average of 5.0% and MLM demonstrated 5.2% of phenotypic variation. Maximum 10.3% and 8.4% phenotypic variation was explained by RM223 for leaf length on 28 DAS by GLM and MLM respectively. Similarly, Q-Q plot exhibits the significant association markers for leaf length on 28 DAS . A QTL fqRL-1 and qRL-2 of root length were reported on chromosome 1 and 2 respectively from RIL population of Xiushui 79/C-Bao [qRDW10-1was identified for root dry weight on chromosome of 10 with LOD score of 5.6 from RIL population of Zhenshan 97/Ming hui63 [SSR genomic region RM230 and RM125 mapped on chromosome 8 and 7 were significantly associated with QTL controlling leaf width variation on 14 DAS. Increasing leaf width by utilizing favorable allele at this locus would likely to be useful for increasing photosynthetic area of seedling and weed smothering ability. On the other hand, the same marker (RM230) allele was significantly associated with root length on 14 DAS. Previously, Zhang et al.  reported79/C-Bao . The roong hui63 .qSL-2for shoot length on chromosome 2 from back cross population of Vandana/Moroberekan. In our study, allele RM224 located on chromosome 11 was significantly associated with shoot dry weight on 28 DAS and another tightly linked marker allele (RM341) showed a stronger association with the same trait and detected by both GLM and MLM on chromosome 2. However, Zhou et al. [qFV-5-1 and qDW5 for shoot dry weight on chromosome 5 from Lemont/Teqing and IR64/Azucena mapping population respectively. Interestingly, RM341 coincided with QTLs for vigor index on chromosome 2 at 14 and 28 DAS and showed a stronger association detected by both GLM and MLM. Conversely Diwan et al. [qVI on chromosome 5 with LOD score of 4.4. Therefore, marker-assisted selection in favor of RM341 could increase the shoot length and vigor index and might also contribute favorably to grain yield and biomass under direct seeded conditions.Two significant QTLs (chromosome 1 and 3) were identified for shoot length on 14 DAS. Marker alleles RM13 on chromosome 1 and RM16 on chromosome 3 were positively associated with shoot length on 14 DAS. Moreover, previous report of Zhang et al.  supportsu et al.  and Cui u et al.  observedn et al.  observedth and 28th DAS. The genotypes utilized for studying diversity pattern by employing trait-linked markers have categorized early vigor and non-early vigor genotypes. The results of distance based approach and principal coordinate analysis were in accordance with model based structure analysis. Further, the study has identified significantly associated markers and markers with pleiotropic effects indicating that association mapping served as an effective tool. The putative QTLs, identified in this study, need to be validated before they could be applied for marker assisted breeding in rice. Genotypes identified from this study possessing favorable alleles can be utilized for improving early seedling vigor after functionally characterizing them.Screening large number of accessions in the present study for early seedling vigor brings out valuable information. We conclude from this study that the vigor index should be studied on 14S1 Fig(a-h). Distribution and differences for shoot length, number of leaves, root length, DW, VI, G. rate, AGR, CGR and RGR under direct seeded situation during 14, 28 and 56 DAS among 629 rice genotypes. Acronyms used: DW-shoot dry weight, VI-vigor index, G. rate-Germination rate, AGR-Absolute growth rate, CGR-Crop growth rate, RGR-Relative growth rate and DAS-days after sowing.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.


Most of the available methods are very useful in detecting association between common variants and complex diseases. Recently, methods to detect rare variants in association with complex diseases have been developed with the increasingly available sequencing data from next-generation sequencing. In this paper, we evaluate and compare several of these recent methods for performing statistical association using whole genome sequencing data in pedigrees. Specifically, functional principal component analysis (FPCA), extended combined multivariate and collapsing (CMC) method for families, a generalized T A similar extension of the CMC test is also developed and is provided by equation (15) in Zhu and Xiong [CMC Tis the CMC statistic for the population-based association test, and corr Pis the correction factor to adjust CMC Tstatistic so that it is valid for pedigree data. The FPCA statistic for the population-based association test in Luo et al [FPCA Tand corr Pare defined as in the previous test statistics for pedigree data. Also, CMCFT, and FPCAF Thave chi-square distributions [p-values from the individual chi-square tests for each genetic variant from a genomic region. The chi-square minimum statistic (Chi_min) adjusts for relatedness of pedigree members using corr P[An extension of the generalized T2 test  for famiT2 test . The testatistic , and Pcoc [Pcorr , p. 1030uo et al  also hasc [Pcorr , p. 1030c [Pcorr , p. 1030c [Pcorr , p. 1030We applied the above 4 methods to analyze the real data set from all odd-numbered chromosomes using hypertension status at exam 1 as the phenotype. The genotypes at each variant are coded as 0, 1, or 2 for aa, Aa, and AA, where allele A is the minor allele. The start and end boundary of all the human genes were obtained from hg19 genome assembly at NCBI. The genetic variants within 1 gene were analyzed together as each gene is 1 natural functional unit. For the FPCA method, if there are too few genetic variants, that is, less than 3, the estimate of the functional relation of the allele counts across the genetic variants will be far off. Consequently, genes with fewer than 3 genetic variants were not analyzed. The significant genes from the 4 methods were then compared with the findings from previous GWAS for genes associated with blood pressure.To examine the type I error rate and power of the 4methods, we applied these methods to the 200 replicates of the simulated data set and analyzed the data from chromosome 3. As in the real data analysis, we chose the hypertension status at exam 1 as the phenotype.2 finds more significant genes at lower significance levels . The number of genessignificant at the 4.7 × 10−6 level by the FPCA, Chi_min, CMC, and T2 methods are 0, 15, 598, and 1794, respectively. Figure 2 at 4.7 × 10−6 level. It is interesting to note that all 598 significant genes found by CMC overlap with those found by T2.Table The number of significant genes presented in Table We analyzed chromosome 3 of the simulated data set. There are a total of 1120 genes on chromosome 3, of which 30 were used for causal variants of hypertension in the simulation model. The remaining 1090 were assumed to be unrelated to the disease and are used only for calculating type I error rate. The linkage disequilibrium (LD) between the genetic variants from these groups of 1090 genes and 30 genes were analyzed with Haploview [The analysis of the 30 positive genes is used to calculate the power of the various methods. Table 2 seems to be a better method than the other 3 methods because it finds more significant genes at low significance levels. At the Bonferroni corrected p-value of 4.7 × 10−6 , T2 identified the genes CASZ1, ADAMTS8, NUCB2, ABCC8, SLC4A7, MAP4, CASR, EBF1, PLEKHA7, SOX6, ULK4, and MECOM. The last 4 genes were also identified by the CMC method. All the genes mentioned above were found to be associated with blood pressure, in particular ULK4 and PLEKAH7 by Levy et al [MAP4 by Wain et al [With the increasingly available sequence data from the next-generation sequencing technologies, it is important for a statistical association method to handle both common and rare genetic variants. It is also important for these methods to handle data from pedigrees because rare genetic variants are enriched in families with multiple affected individuals, which could confer more statistical power. From our analysis of the real data, Tin et al .2 and CMC are close to the nominal value. Also, T2 has better power than CMC, which is consistent with the result from the real data.As with GWAS, we need to keep a low significance level to account for multiple testing. We note from the analysis of the simulated data that FPCA has empirical type I error rate much less than the nominal value, making it very conservative. The Chi_min method has inflated type I error rate. The type I error rates by T2 is a better method, which is different from the findings of Zhu and Xiong [2. This mainly comes from the fact that CMC uses the T2 approach with common variants. There is also a tendency to pick up more genes with more variants for both CMC and T2 methods.From the analysis of the data sets we find that Tnd Xiong , which s2 is a preferable method for pedigree-based association studies with whole-genome sequencing data because it controls the false positive rate and is more powerful than the other two methods with similar type I error rates.From the analysis results of both real and simulated data, TThe authors declare that they have no competing interests.HX conceived of the study, performed the analysis, and helped to draft the manuscript. GM performed the analysis and helped to draft the manuscript. VG participated in the design and coordination of the study. All authors read and approved the manuscript.


Warfarin is the most widely used oral anticoagulant worldwide, but it has a narrow therapeutic index which necessitates constant monitoring of anticoagulation response. Previous genome-wide studies have focused on identifying factors explaining variance in stable dose, but have not explored the initial patient response to warfarin, and a wider range of clinical and biochemical factors affecting both initial and stable dosing with warfarin.A prospective cohort of 711 patients starting warfarin was followed up for 6 months with analyses focusing on both non-genetic and genetic factors. The outcome measures used were mean weekly warfarin dose (MWD), stable mean weekly dose (SMWD) and international normalised ratio (INR) > 4 during the first week. Samples were genotyped on the Illumina Human610-Quad chip. Statistical analyses were performed using Plink and R.VKORC1 and CYP2C9 were the major genetic determinants of warfarin MWD and SMWD, with CYP4F2 having a smaller effect. Age, height, weight, cigarette smoking and interacting medications accounted for less than 20 % of the variance. Our multifactorial analysis explained 57.89 % and 56.97 % of the variation for MWD and SMWD, respectively. Genotypes for VKORC1 and CYP2C9*3, age, height and weight, as well as other clinical factors such as alcohol consumption, loading dose and concomitant drugs were important for the initial INR response to warfarin. In a small subset of patients for whom data were available, levels of the coagulation factors VII and IX (highly correlated) also played a role.Our multifactorial analysis in a prospectively recruited cohort has shown that multiple factors, genetic and clinical, are important in determining the response to warfarin. VKORC1 and CYP2C9 genetic polymorphisms are the most important determinants of warfarin dosing, and it is highly unlikely that other common variants of clinical importance influencing warfarin dosage will be found. Both VKORC1 and CYP2C9*3 are important determinants of the initial INR response to warfarin. Other novel variants, which did not reach genome-wide significance, were identified for the different outcome measures, but need replication.The online version of this article (doi:10.1186/s13073-015-0255-y) contains supplementary material, which is available to authorized users. Warfarin is the most widely used oral anticoagulant worldwide for the treatment of thromboembolic disorders . The widCYP2C9 and VKORC1, together with body mass index and age, account for about 50 % of the variation in warfarin daily dose requirements [Genetic factors are known to predict warfarin dose requirements - observational studies have shown that variation in irements . Many doirements  because irements  - this sirements , conductirements .We cannot exclude the possibility that other factors, either genetic, clinical or biochemical, could improve personalisation of dosing for warfarin, and thereby its efficacy and safety. In order to evaluate this, we have undertaken further analyses of our UK prospective cohort study , where tBlood samples, demographic and clinical data from patients initiating warfarin for venous thromboembolism or atrial fibrillation between November 2004 and March 2006 were collected, as described previously . UpdatedIn this prospective cohort study (EGA accession number EGAS00001001130), all patients received usual clinical care with doses being determined either by the anticoagulant clinic or attending physician. There were four fixed study visits for each patient, the first at the time of initiation of warfarin (index visit), then at 1 week, 8 weeks and 26 weeks of warfarin therapy. The index visit was before, or within 2 days of commencing warfarin, in 58 % of patients, 26 % of index visits were on the third day, and the remainder between 4 and 9 days after starting warfarin. Out of 160 patients for which baseline clotting factor and protein levels were measured, only eight had their index visit after starting warfarin . To confirm that timing of initiation relative to index visit did not significantly influence baseline clotting factor and protein levels, patients were stratified and these measurements compared between strata; trends in clotting factors relating to index day were also investigated. No significant trends were found, most probably because of the important natural population variation observed in the levels of clotting factors and the small number of patients for which data were collected after warfarin initiation (data available on request). Patients also attended anticoagulant clinic between these four fixed visits as per usual clinical practice, and the total number of INR measurements varied .At the index visit, patient demographics, smoking history, current medications and alcohol intake (assessed using the AUDIT questionnaire ) were coMean weekly dose (MWD): mean dose received weekly during a minimum follow-up time of 14 days post-loading; the loading period, that is, the first 3 days of treatment, was not included in the calculations.Stable mean weekly dose (SMWD): mean weekly dose for at least three consecutive visits where INRs were within the targeted range, spanning a minimum of 14 days and with at least 7 days separating the first and middle INR measurements, and the middle and last one.INR >4.0 in the first week on warfarin.For this GWAS, we used the following outcome measures:As the frequency distribution of stable warfarin dose was skewed, the data were normalised by taking square-root of stable dose.Samples were assayed on the Illumina Human610-Quad BeadChip using the Infinium HD Super Assay ; beadchips were scanned with an iScan. Intensity data, normalised according to the standard Illumina algorithm, was extracted and genotypes called using Illuminus . Sample Samples having a call rate of less than 95 % or having autosomal heterozygosity values in the tail of the distribution were excluded. Chromosome X heterozygosity was used to predict gender ; this was compared to the gender in the original documentation, and discrepancies resolved or samples excluded. A pairwise comparison was run for all samples using 400 well-spaced, common SNPs to identify duplicate samples. Genotypes for each sample were compared to the molecular fingerprint - a set of 26 markers typed using the Sequenom platform - to eliminate the possibility of arraying errors. Identity by descent (IBD) was calculated for all pairs of samples using PLINK , and oneImputation of genotypes was carried out using IMPUTE V2.1 , with thhttp://www.bioconductor.org/packages/release/bioc/html/CNVtools.html [A suite of Perl and R 2009) scripts were used as a framework to utilise the R package CNVtools, available from 09 scriptPrimer Allele FAM (G) AATCCCAGCACTTTGGGAGGC,Primer Allele HEX (T) GTAATCCCAGCACTTTGGGAGGA, andPrimer Common GGCTGGATTCGGACCCCTGGA.Genotyping of rs112942398 was performed with a custom KASP™ genotyping assay (LGC Genomics Ltd.) using the 68-62 °C touchdown thermal cycling conditions in accordance with the manufacturer’s instructions. Primer sequences are as follow:Approximately 30 ng genomic DNA was amplified in a 5 μL reaction mixture containing 1× high ROX KASP genotyping master mix and 0.07 μL of primer mix. To improve genotype clustering, the plate was thermally cycled for an extra 10 cycles with an annealing/elongation temperature of 64 °C. End-point FAM and HEX signals were read at 30 °C on an ABI 7900HT Fast Real-Time PCR System (Applied Biosystems). As part of quality control, negative controls (n = 2) containing water instead of DNA and 10 % duplicates were included in the run.et al. [P ≤0.05) were included as covariates in the linear or logistic regressions used to test for association between each SNP and outcome in turn, carried out in PLINK. When a SNP was found to be significantly associated with the outcome tested , it was added as a covariate to the multiple regression model and each SNP was then re-tested for association with the outcome using this updated model. This process was repeated until no further SNP reached genome-wide significance.Non-genetic variables used for testing univariately for association with each outcome were age, height, weight, BMI, gender, loading dose, total follow-up time, dosing method , mean target INR, blood count , potassium, bicarbonate, chloride, urea, creatinine, triglycerides, albumin, total protein, bilirubin, ALT, alkaline phosphate, gamma GT, fibrinogen, coagulation factors II, V, VII, IX and X, Proteins C and S, current smoking status, number of cigarette smoked per day, ex-smoker status, alcohol consumption, interacting co-medication (binary), non-interacting co-medication (binary), sum of effect of interacting co-medications. The coagulation factors were measured as described by Jorgensen et al. . For eacP value was adjusted for when testing for association with the SNPs.To avoid collinearity, all variables were checked for pairwise correlation using Pearson’s correlation test in R; pairs with a correlation over 0.7 were deemed highly correlated and, in the event that they were found significantly associated with in any of the investigated outcomes, only the one variable with the lowest VKORC1 rs9923231, CYP2C9*2 or *3, and CYP4F2 rs2108622) did not reach genome-wide significance, possibly through lack of power, they were added to the stepwise variable selection in order to determine if they would indeed improve the final model.For each outcome, all significantly associated SNPs at genome-wide level, as well as the non-genetic variables found significant univariately were then included together in a multiple regression model in R. Stepwise variable selection was applied to the model to establish a final model. When known variants influencing warfarin dosing (such as www.ingenuity.com). Canonical pathways analysis identified the pathways from the IPA library of canonical pathways that were most significant to the dataset. SNPs considered for canonical pathway analysis had a P value lower than 10−03.Manhattan and regional plots were prepared using in-house Python scripts. MWD results were further analysed through the use of IPA  and 55.6 % were men. Patients were treated for atrial fibrillation , venous thromboembolism , cardiovascular disease  or other conditions .Seven patients failed the genotyping rate threshold, nine failed the heterozygosity criteria, 15 were excluded based on ethnicity using principal component analysis with HapMap3 samples as they did not cluster with samples of European ancestry (data not shown), three had ambiguous gender, three were excluded because of IBD, and four patients decided not to participate in the study, leaving 711 available genotypes for analysis. One hundred patients stopped treatment within the first 2 weeks of treatment, not allowing their inclusion for mean weekly dose calculation, though 13 with appropriate available data were included in INR >4 calculations.2 = 0.73), as were gender and height (R2 = 0.83), current smoking status and number of cigarettes smoked (R2 = 0.79); therefore, only the most significant variable of each pair was kept as a covariate in the regression analyses.Out of 612 patients with dose and INR information fitting our SMWD outcome, only 326 (53.3 %) reached stability over the follow-up period, which covered up to 277 days. One hundred and nineteen (16.6 %) patients were current smokers . Out of 625, 116 (18.6 %) patients had an INR >4.0 during the first week of treatment. Out of the 711 patients retained for analysis, 160 had baseline data on full blood count, liver enzymes and coagulation factor levels. Weight and BMI were highly correlated (RP = 1.20 × 10−17), height (P = 5.10 × 10−07), weight (P = 5.02 × 10−10), total follow-up time (P = 9.41 × 10−03), number of cigarettes smoked per day (P = 5.34 × 10−06), ex-smoker status (P = 3.01 × 10−04), alcohol consumption (P = 2.00 × 10−04) as well as the use of interacting co-medications (P = 6.30 × 10−03), the sum of interactions (P = 1.38 × 10−04), and the use of medications not classified as interacting (P = 2.94 × 10−02) were found significant univariately and therefore adjusted for when testing for association with each SNP. MWD increased with height and weight, and decreased with age; it was higher for smokers and increased with the number of cigarettes smoked each day, while it appeared to be lower for ex-smokers in comparison to people who never smoked. MWD was lower in patients taking interacting co-medications, and decreased as the sum of interactions increased, but it also appeared to be lower in patients taking co-medications which are not on the interacting medications list.For MWD, age (P = 2.06 × 10−05), height (P = 2.43 × 10−03), weight (P = 3.58 × 10−04), haemoglobin (P = 3.64 × 10−02), basophils (P = 9.20 × 10−03), urea (P = 4.16 × 10−02), ALT (P = 4.71 × 10−02), Factor IX (P = 7.19 × 10−03) and current smoking status (P = 3.36 × 10−04) were found significant univariately and included as covariates in the linear regressions. Though not found univariately significant in this subset of patients, the use of interacting co-medications, the sum of interactions, and the use of medications not classified as interacting were also used as covariates in the linear regressions.In the subset of samples with extra baseline data, age (P = 6.20 × 10−09), height (P = 1.72 × 10−05), weight (P = 2.11 × 10−07) and the use of interacting co-medications (P = 6.62 × 10−03) were found significant univariately and adjusted for when testing for association with each SNP. SMWD increased with height and weight, and decreased with age, and was lower in patients taking interacting co-medications.For SMWD, age (P = 2.77 × 10−03), height (P = 1.53 × 10−02), weight (P = 2.98 × 10−04), loading dose (P = 7.28 × 10−07), alcohol consumption (P = 7.76 × 10−03) and use of medications (during first week of treatment) not classified as interacting (P = 4.28 × 10−02) were found significant univariately and included as covariates in the logistic regressions. In addition to these, in the subset of patient with extra baseline data, triglycerides (P = 4.97 × 10−02), albumin (P = 4.23 × 10−02) and Factor VII (P = 3.82 × 10−02) were also found significantly associated with INR4.For INR >4.0 during first week of treatment (INR4), age . In CYP2C9, the strongest signal, rs4917639 (P = 2.05 × 10−30) acts as a composite of CYP2C9*2 and *3 [P = 1.24 × 10−23) and *2  also reached genome-wide significance.For warfarin MWD Table , genome-2 and *3 ; neverthP = 7.70 × 10−03), explaining only a further 0.5 % of the MWD variance. Examination of genes at proximity of other signals of interest did not yield any obvious candidates based on gene function. Canonical pathway analysis in IPA, however, using all signals with a P value below 10−03, highlighted the thrombin signalling pathway, with 12 independent signals with P values ranging from 7.8 × 10−04 to 2.2 × 10−06 . One variant, rs2298978, appeared significantly associated with Factor IX levels .After adjustment for genetic and non-genetic covariates, no other SNP reached genome-wide significance Fig. . The preAfter stepwise regression including all significant non-genetic covariates as well as rs9923231, rs1057910 and rs1799853, ex-smoker status and alcohol consumption were not retained in the final model. Percentage of the MWD variance explained in the complete model was 57.4 % . Inclusion of rs2108622 (CYP4F2) brought the total variation explained to 57.89 %.2 = 0.61 with rs1057910, CYP2C9*3, in 1000 genome project pilot 1)  reached genome-wide significance, as well as an imputed SNP in the CYP2C9 locus  for VKORC1, and rs4917639  and rs1057910  for CYP2C9, while rs1799853  did not reach genome-wide significance. Despite not reaching genome-wide significance, rs1799853 was included in the multiple regression as it influences warfarin dose, along with rs9923231 and rs1057910. Three signals reached genome-wide significance after adjusting for genetic and non-genetic factors , 11–15383178 (P = 6.23 × 10−09) and rs112942398 (P = 2.40 × 10−08). All these SNPs are the result of imputation; there is close to no support from surrounding genotyped SNPs for 11–15383178 and 6–106244024 (data not shown), unlike for rs112942398  on chromosome 16, an imputed SNP in high LD with VKORC1’s rs9923231 (R2 = 0.98), followed by rs1072753 (P = 2.85 × 10−08), an imputed SNP on chromosome 10 in high LD with CYP2C9*3 . After accounting for rs2288004 and rs1072753 and non-genetic factors, no other signal reached genome-wide significance  is located in an intron in APLP2, which has been linked to haemostasis through its inhibitory effect on Factor XIa [P = 2.94 × 10−06) is located near TFRC, a gene implicated in the development of erythrocytes [Age, height, weight, loading dose, alcohol consumption and medications not known to interact with warfarin were significant in the univariate analysis and were adjusted for when testing for SNP associations. Two regions harbour signals reaching genome-wide significance .Non-genetic factors  and two VKORC1 and CYP2C9 *2 and *3 are the major genetic determinants of warfarin dose [VKORC1 rs9923231 explained between 25.6 % and 30.4 % of the dose variance , CYP2C9*3 between 8.1 % and 12.9 %, and CYP2C9*2 between 3.7 % and 3.8 %, all in accordance with previous findings [CYP4F2 rs2108622 did not reach genome-wide significance, but retaining it in the model explained 0.5 % of MWD and 1.7 % of SMWD. No other variants reached genome-wide significance in the MWD analysis. Pathway analysis of the top variants post-conditioning for MWD implicated multiple hits in the thrombin signalling pathway, but none of these variants remained in the model after stepwise regression, suggesting that they had little influence on the overall MWD. For SMWD, three variants remained significant after conditioning: while two of them are most probably imputation artefacts, the third signal, rs112942398, looks like a much more plausible signal based on its regional plot. However, this is likely to be an artefact given that its biological relevance is unclear, it has not been picked up by other studies [CYP2C9 and VKORC1 are unlikely to make a strong contribution towards the variance in warfarin dose.For both MWD and SMWD, we confirm previous findings that rin dose , 28–32. findings . Unlike findings , 23 CYP4 studies , and impIn terms of clinical factors for SMWD, these were similar to those previously described in the literature , 38, 39,rs2108622 (CYP4F2). The impact of warfarin on the various clotting factor levels, and how quickly they respond to warfarin, is not clear, and needs further investigation.In the subset of patients with extended clinical data, for the MWD, two clinical factors, on top of age and weight, were of importance and retained after stepwise regression: (a) smoking, rather than the more refined number of cigarettes per day, was highly significant and explained a large portion of the variance at about 10 %; and (b) baseline Factor IX levels explained approximately 4.4 % of the MWD variance, far more than the 0.5 % explained by VKORC1 rs9923231, with odds ratios of 7.9 and 2.23, for homozygous for the minor allele and heterozygous patients, respectively, and CYP2C9*3, with odds ratios of 24.22 and 2.88, respectively. The warfarin loading dose also seems to play an important role in this outcome measure with a univariate P value of 5.9 × 10−07 – within our cohort there was variability in loading doses used, ranging from 3 to 32 mg , adding to the inter-patient variability in warfarin response. Taken together, our data indicate the importance of taking VKORC1 and CYP2C9 genotype into account when determining the loading dose, consistent with our loading dose algorithm [For INR >4.0 during the first week of treatment, only two genetic factors were important: lgorithm , which wlgorithm  reduced lgorithm . It was lgorithm , possiblVKORC1 (rs9923231), CYP2C9 *3 and *2 are the most important genetic factors influencing warfarin dose, with CYP4F2 (rs2108622) having a minor effect, with age and BMI being important clinical covariates. Patients’ smoking habits and the totality of interacting co-medications, however, also seems to be important when determining warfarin dose. In relation to INR >4 after warfarin initiation, VKORC1 and CYP2C9*3 are important in determining the loading dose, together with alcohol consumption. Realistically, at the present time, it would not be possible to evaluate each of the clinical factors in trials to optimise warfarin dosing. Furthermore, in a randomised design, confounding clinical factors are likely to be balanced between two arms. Thus, the trials which have been undertaken, which take into account CYP2C9 and VKORC1 genotype, together with age and BMI in determining dosing algorithms, represent pragmatic designs in a Northern European population.In conclusion, our analysis shows that multiple factors, genetic and clinical, are important in determining the response to warfarin, which is perhaps not surprising given the pharmacology of warfarin.


Dicentrarchus labrax) is one of the most important farmed species in Mediterranean aquaculture. The observed sexual growth and maturity dimorphism in favour of females adds value towards deciphering the sex determination system of this species. Current knowledge indicates the existence of a polygenic sex determining determination system that interacts with temperature. This was explored by restriction-site associated DNA (RAD) marker analysis in a test panel of 175 offspring that originated from a factorial cross between two dams and four sires from a single full-sib family.European sea bass (The first high-density single nucleotide polymorphism (SNP) based linkage map for sea bass was constructed, consisting of 6706 SNPs on 24 linkage groups. Indications for putative sex-determining QTL (quantitative trait loci) that were significant at the genome-wide threshold were detected on linkage groups 6, 11 and 18 to 21, although a genome-wide association study (GWAS) did not identify individual significant SNPs at a genome-wide threshold. A preliminary genomic prediction approach that tested the efficiency of SNP-based selection for female sea bass showed a slight advantage compared to traditional pedigree-based selection. However, when the same models were tested on the same animals for selection for greater length, a clear advantage of the SNP-based selection was observed.Overall, the results of this study provide additional support to the polygenic sex determination hypothesis in sea bass. In addition, identification of sex-ratio QTL may provide new opportunities for sex-ratio control in sea bass.The online version of this article (doi:10.1186/s12711-015-0148-y) contains supplementary material, which is available to authorized users. Both genetic and environmental factors are involved in the sex determination of various fish species, with sex in some species influenced by both factors , 2. BothDicentrarchus labrax (European sea bass) is a highly valuable commercial aquaculture species in Europe, with more than 148 000 t produced in 2014 [D. labrax is between 18 and 24 months, with a market size of about 350 to 400 g. Under aquaculture conditions, the percentage of males is usually very high (70 to 90 %), while current evidence suggests that, in wild populations, the sex-ratio is generally balanced [Menidia menidia [ in 2014 . The probalanced . Males tery high 0 to 90 %D. labrax sex determination system depends both on genetic factors and environmental effects, with temperature being considered as the most important environmental effect under hatchery conditions [D. labrax have indicated that the mechanism of sex determination is not explained by a simple monofactorial system with either male or female homogamety [The nditions , 11. Stumogamety . The sexmogamety . Howevermogamety  reportedD. labrax, and temperatures above 17 °C during early development (before 60 days post-fertilization) favour the development of males [D. labrax. In addition, Diaz et al. [Environmental temperature influences sex ratio in of males . Higher z et al.  reportedD. labrax as far as we are aware. Strong parental effects, as well as genotype-temperature interactions can modulate the sex ratio in D. labrax: the proportion of females resulting from individual crossings may range from 1 to 70 % [No major sex-determining gene or genetic markers associated with sex have been identified in  to 70 % , 16. Van to 70 %  providedDanio rerio (zebrafish), Oreochromis niloticus (Nile tilapia) and Hippoglossus hippoglossus  using RAD-seq [2 crosses of D. labrax that originated initially from an F0 cross between two families with divergent sex ratios. First, a high-density linkage map was constructed, then quantitative trait loci (QTL) mapping and a genome-wide association study (GWAS) were performed. We also carried out a preliminary genomic prediction approach to test the potential of SNP-based selection for increasing either female ratio or body length.Restriction-site associated DNA (RAD) sequencing is a reduced-representation sequencing platform that exploits high-throughput sequencing methodologies, while the use of barcodes allows multiplexing of samples . This te RAD-seq –22. In t2 cross population of hatchery-reared D. labrax. Three F0 males and three F0 females, which were the offspring of wild West-Mediterranean D. labrax, were mated by artificial fertilization in a factorial cross to simultaneously produce eight families (one family was lost), which were reared in common garden conditions; the overall sex ratio in the F1 offspring was 43.8 % females. F1 males and females were chosen from one family, which was a cross between the F0 female that produced the lowest proportion of females (26.6 %) and the F0 male that produced the highest proportion of females (58.2 %). Two F1 females (Dams 1 and 2) were each crossed to four F1 males  to produce eight families  plus the six F1 parents and the F0 female were used for RAD sequencing .The fish used in this study originated from an FDNA was extracted from fin samples using the REALPure genomic DNA extraction kit (Durviz S.L.) and treated with RNase. Each sample was quantified by spectrophotometry (Nanodrop), quality assessed by agarose gel electrophoresis, and diluted to a concentration of 50 ng/μL in 5 mmol/L Tris, pH 8.5.SbfI that recognises the CCTGCA|GG motif  using 6 U SbfI per μg genomic DNA in 1× Reaction Buffer 4 (NEB) at a final concentration of about 1 μg DNA per 50 μL reaction volume. The samples  were then heat-inactivated at 65 °C for 20 min. Individual specific P1 adapters, each with a unique 5 bp barcode  100 nmol/L P1 adapter, 0.45/0.15 μL 100 mmol/L rATP (Promega), 0.75/0.25 μL 10× Reaction Buffer 2 (NEB), 0.36/0.12 μL T4 ligase  and reaction volumes made up to 45/15 μL with nuclease-free water for each parental/offspring sample. After heat-inactivation at 65 °C for 20 min, the ligation reactions were slowly cooled to room temperature (over 1 h), then combined in appropriate multiplex pools , that lacked the restriction site or had ambiguous barcodes were discarded. Retained reads were sorted into loci and genotypes using Stacks software 1.02 described in Catchen et al.  The likeParentage assignment was performed with Vitassign V8-5.1  using 20P < 0.001) were excluded. Linkage groups were formed with a minimum LOD value of 10 using the SeparateChromosomes module of Lep-Map. SNPs within each linkage group were ordered by applying the OrderMarkers module. Map distances were estimated in centiMorgans (cM) using the Kosambi mapping function.The linkage map was constructed with Lep-Map . SNPs wii is the observed phenotype coded as a binary trait and li is the latent variable. The latent variable was modelled as follows:μ is the intercept, ui animal random effect that follows ~ N, with A the pedigree-based relationship matrix and σg2 the additive genetic variance, and ei the residual error. The additive genetic variance was estimated by Markov chain Monte Carlo (MCMC) estimation, using a prior following the χ2 (1 df) distribution .Heritability of phenotypic sex was estimated on the liability scale using R/MCMCglmm . The obsσg2 is the previous estimated additive genetic variance and σe2 is the residual variance that was fixed to 1 due to the identifiability issue with binary data [Heritability was estimated using the following formula:ary data .2 sib regression analysis model of the entire pedigree were used, allowing for the existence of one QTL at tested intervals that were 1 cM apart.Sex-determining QTL were identified by Haley-Knott regression mapping using GridQTL , 35. A hyij is the phenotypic sex of individual j belonging to full/half sib family i, with males coded as 1 and females as 2, mi is the F2 – half sib family specific intercept, μz is the effect of marker z, n is the number of possible QTL genotypes of the tested position, pz is the probability of the inferred genotype (gz) based on flanking markers (pz = Pr(gz |M), M flanking markers) and eij is the residual error. Confidence intervals (95 %) were estimated using bootstraps with resampling (10 000 iterations). Two levels of significance were calculated based on chromosome (α = 0.01) or genome-wide thresholds (α = 0.05) by performing 1000 permutations, with the detected QTL referred to as suggestive or significant, respectively [The models used had the following general form:ectively –37.y is the vector of the phenotypes, α is the vector of marker effects, u is the vector of animal random effects ∼ N and e is the vector of residuals. The matrix G represents the relationship matrix calculated from SNPs and σg2 the additive genetic variance estimated using REML. X and Z are incidence matrices relating y to α and u, respectively. The Bonferroni correction was used to correct for multiple testing (Type I error rate = 0.05).A GWAS was performed using R/rrBLUP  in orderA preliminary study was conducted to test whether breeding values that were estimated from the additive effects of SNPs, could be used as a predictive measure of phenotypic sex or length, respectively. SNPs with more than 5 % missing data were removed. Missing values of the remaining 4881 SNPs were imputed with R/synbreed . Additivy is the vector of phenotypic records, η the linear predictor and ε the vector of residuals. In the scenario for the prediction of phenotypic sex, the probit link function was used to connect the underlying latent variable with the linear predictor.The general form of the fitted models was the following:η in the case of RRBLUP, BayesA, BayesB, BayesC and Bayesian Lasso had the following general form:X1 and X2 are design matrices relating the phenotypes to the included fixed effects and markers, respectively, β1 is the vector of regression coefficients for the included fixed effects , and β2 is the vector of marker effects with corresponding priors depending on the model used.The linear predictor η in the case of pedigree-based BLUP had the following form:u is the animal random effect that follows ∼ N where V is the pedigree-based relationship matrix. Z is the incidence matrix relating y to u.The linear predictor The parameters of the above models were estimated by MCMC sampling . Convergence of the resulting posterior distributions was assessed both visually and analytically by the Geweke diagnostic using R/boa v1.1.7 .The dataset was randomly split into a training set  and a test set . This was repeated 100 times to record prediction accuracies for each model tested. The prediction of phenotypic sex was tested by the following naïve approach: animals in the test set that were predicted by the model to have a probability of being female greater than 0.5 were considered as females, while the rest were considered as males. The number of correctly assigned individuals for each model tested was recorded. Τ-tests (right-tailed) were performed to evaluate whether the mean number of correctly assigned individuals was significantly larger (α = 0.05) from that expected by chance alone.In total, 1 156 659 542 raw reads (100 bases long) were produced . After removing low-quality sequences , ambiguous barcodes, and orphaned paired-end reads, 76.7 % of the raw reads were retained (886 927 866 reads). Then, assembly and grouping of the sequences into the RAD loci for each individual were performed with the Stacks package  and 56 6P < 0.001) but the dam effect was not (P > 0.50). The sire-dam interaction was not significant (P > 0.50), which indicated that the genetic variation in sex-ratio was additive.All 175 progeny  were assigned to a unique parental pair using Vitassign , by alloD. labrax karyotype, with a total length of 4816 cM . In the F 21 Fig. , with F  21 Fig. .Fig. 2SeThe maternal half-sib regression model detected suggestive sex-determining QTL in four linkage groups. Two were detected in the first maternal half-sib panel . The SNPs with the lowest p-values (10−4 < P < 10−3) were located in linkage groups 6 , 12, and 16 . For prediction of phenotypic sex, all models led to a significantly larger number of correctly assigned animals than expected by chance Table . The appD. labrax is one of the most important species in Mediterranean aquaculture. The observed sexual dimorphism in growth and the need to control sex-ratio in selective breeding programmes are practical concerns that would benefit from a better understanding of the sex determination system in sea bass. Current evidence, based on the variance of sex-ratio between families, suggests the existence of a polygenic sex determination system [n system . Use of D. labrax, relatively rich genomic resources are available and a reference genome has recently been publicly released [D. labrax are mainly based on microsatellites and AFLP (amplification fragment length polymorphisms) and the most recent map consists of 190 microsatellites, 176 AFLP and two SNPs [D. labrax based on 6706 SNPs (2676 unique positions). The number of linkage groups corresponded to the number of chromosomes in the D. labrax karyoptype. The accurate grouping of markers was further confirmed by comparison to the reference genome  should help to improve future versions of the assembly.For released . Howevertwo SNPs , 49. In d 814 cM . The 856A = 0.5). Successive grading has been shown to produce dominantly female (larger fish) and dominantly male  populations [A moderate correlation of phenotypic sex with weight and length (r = 0.23) was found. In contrast, Vandeputte et al.  reportedulations , 50, whiulations  showed tulations , and 0.6ulations . Howeverulations , heritab2 sib regression analysis detected three genome-wide significant sex-determining QTL on linkage groups 11, 16 and 18. Among these, the best candidate QTL is in linkage group 16, since this region had the highest statistical significance both in the QTL mapping approach and the GWAS. However, since the supporting F-values only just exceeded the estimated genome-wide threshold and since QTL mapping studies tend to overestimate the QTL effect due to the Beavis effect [The Fs effect , these rD. labrax was obtained by testing the efficiency of predicting phenotypic sex based on estimated breeding values. The genomic-based model gave only slightly better predictions than the pedigree-based BLUP. This is most probably due to a combination of factors; primarily the analysis of a relatively small dataset and the response variable being a binary trait . The latter raises additional issues, e.g., the residual variance has to be fixed in order to achieve identifiable estimated parameters. In comparison, when the same models were tested for the prediction of a continuous trait , the genomic models were clearly more efficient than pedigree-based BLUP. Another issue is that some QTL may be homozygous in some of the parents and thus remain undetected. The fact that paternal half-sib regression does not detect any QTL while most of the variation in sex-ratio is between sire half-sib families would advocate this. In this case, homozygous QTL may contribute to variation between sires, but remain undetected since they do not segregate and thus do not contribute to genomic prediction. Finally, the fact that all genomic models gave similar prediction accuracies, a phenomenon often observed in the study of polygenic traits [D. labrax, sex is a polygenic trait, as hypothesised previously [Evidence that supports the hypothesis that a polygenic sex determination system exists in c traits , 53–55, eviously .Although firm conclusions cannot be drawn from this study, the fact that all tested models gave significantly better predictions than that expected by chance alone indicates that further improvement could be possible by increasing genotyping efforts.D. labrax. It is likely that if the unstable nature of the polygenic determinism of sex in D. labrax evolves to QTL with larger effects, as suggested by theory [This study presents the first high-density linkage map for the European sea bass. Based on the large number of SNPs 856) that are located in unanchored contigs of the recently published reference genome and thus, that could be positioned, this map will help to improve the existing genome assembly. Overall, the study supports the polygenic hypothesis of sex determination in sea bass of Vandeputte et al. . The fam that arey theory , this ev


The traits used in this study were the occurrence of early pregnancy (EP) and scrotal circumference (SC). Data from 72,675 females and 83,911 males with phenotypes were used; of these, 1,770 females and 1,680 males were genotyped. The SNP effects were estimated with a single-step procedure (WssGBLUP) and the observed phenotypes were used as dependent variables. All animals with available genotypes and phenotypes, in addition to those with only phenotypic information, were used. A single-trait animal model was applied to predict breeding values and the solutions of SNP effects were obtained from these breeding values. The results of GWAS are reported as the proportion of variance explained by windows with 150 adjacent SNPs. The 10 windows that explained the highest proportion of variance were identified. The results of this study indicate the polygenic nature of EP and SC, demonstrating that the indicator traits of sexual precocity studied here are probably controlled by many genes, including some of moderate effect. The 10 windows with large effects obtained for EP are located on chromosomes 5, 6, 7, 14, 18, 21 and 27, and together explained 7.91% of the total genetic variance. For SC, these windows are located on chromosomes 4, 8, 11, 13, 14, 19, 22 and 23, explaining 6.78% of total variance. GWAS permitted to identify chromosome regions associated with EP and SC. The identification of these regions contributes to a better understanding and evaluation of these traits, and permits to indicate candidate genes for future investigation of causal mutations.The objective of this study was to perform a genome-wide association study (GWAS) to detect chromosome regions associated with indicator traits of sexual precocity in Nellore cattle. Data from Nellore animals belonging to farms which participate in the DeltaGen Bos taurus indicus animals that are sexually less precocious than Bos taurus taurus  de deOVOL2 HILS1 gene that remodels chromatin during gamete production  fo foIGFBP4anestrus , evaluatIt should be noted that part of the region identified here on chromosome 14 associated with EP has been described in a study investigating meat tenderness in Nellore cattle . FurtherITGA3 –[DLX3 –[FAM117A –[DERL1 –[E2F1 –[MAP1LC3A –[LRFN2 –[TSSK6 –[The windows with large effects found for the two traits were not overlapping, although a moderate genetic correlation between these traits exists in Nellore cattle . This faITGA3 –; DLX3 –[ –[DLX3 –; FAM117AFAM117A –; DERL1 ––[DERL1 –; E2F1 –[ –[E2F1 –; MAP1LC3AP1LC3A –; LRFN2 ––[LRFN2 –), and th–[TSSK6 –).GWAS permitted to identify chromosome regions associated with EP and SC. The identification of these regions contributes to a better understanding and evaluation of these traits, and permits to indicate candidate genes for future investigation of causal mutations.S1 FileResults of WssGBLUP for occurrence of early pregnancy and scrotal circumference.(XLSX)Click here for additional data file.


Correlations between self-reports and HM were not significant, due largely to increased measurement error. These results indicate that treating HM as a nuisance covariate in neuroimaging studies of ADHD will likely reduce power to detect between-group effects, as the implicit assumption of independence between HM and Inattention or Hyperactivity-Impulsivity is not warranted. The implications of this finding are problematic for fMRI studies of ADHD, as failing to apply HM correction is known to increase the likelihood of false positives. We discuss two ways to circumvent this problem: censoring the motion contaminated frames of the RS-fMRI scan or explicitly modeling the relationship between HM and Inattention or Hyperactivity-Impulsivity.Head motion (HM) is a well known confound in analyses of functional MRI (fMRI) data. Neuroimaging researchers therefore typically treat HM as a nuisance covariate in their analyses. Even so, it is possible that HM shares a common genetic influence with the trait of interest. Here we investigate the extent to which this relationship is due to shared genetic factors, using HM extracted from resting-state fMRI and maternal and self report measures of Inattention and Hyperactivity-Impulsivity from the Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour (SWAN) scales. Our sample consisted of healthy young adult twins  including 95 MZ and 144 DZ twin pairs, mean age 22, who had mother-reported SWAN; N = 725  including 101 MZ and 156 DZ pairs, mean age 25, with self reported SWAN). This design enabled us to distinguish genetic from environmental factors in the association between head movement and ADHD scales. HM was moderately correlated with maternal reports of Inattention  and Hyperactivity-Impulsivity , and these associations were mainly due to pleiotropic genetic factors with genetic correlations [95% CIs] of r Head motion (HM) is a well known confound for functional and structural neuroimaging studies –9. Movem(but see ) by reducing the number of false positive results. Importantly, both these methods assume that HM is independent of the trait of interest ; thus the HM contribution to the fMRI signal is considered to be solely noise. However, in groups of patients with diseases such as multiple sclerosis or stroke there is increased HM compared to healthy controls  (Supplementary Table 4 and Supplementary Appendix 3).Heritability of Inattention was between 0 and 43% for maternal report with a dominant genetic effect explaining another 32 to 83% of the variance. For mother reported Hyperactivity-Impulsivity we found the additive sources to explain between 88 and 94% of the variance in females. The A component was only explaining 63 to 87% in males. The general sex limitation model outperformed standard ACE confirming tests from the hypothesis testing (supplementary Table 3). Genetic correlation (rranging from 0.09 to 0.20 (Table 2). All phenotypic correlations were significant, as suggested by the 95% CI .The phenotypic correlation between HM and the SWAN scores was fairly homogeneous, with correlation coefficients Δdf = 1, p-value = 3.0E-2 for Inattention, 3.9E-3 for Hyperactivity-Impulsivity). The correlation of HM with self report Inattention or Hyperactivity-Impulsivity did not reach significance as indicated by the CI . Correction for age and age difference between ADHD scoring and MRI scanning did not significantly improve the fit of the models , and left the correlation estimates unchanged.Most of this correlation arose from common genetic factors, with the genetic correlation (0.19 to 0.40) being greater than the environmental correlation (0.03 to 0.12). Genetic correlation coefficients with HM were significantly different from 0 for maternal scoring  between HM and the SWAN sub-scales. Therefore, treating HM as a nuisance covariate in an RS-fMRI study of ADHD would likely remove some of the signal of interest.Our findings align with the (phenotypic) association of HM with (self reported) impulsivity in a similar age group (r = 0.10) and in children (r = 0.34) . We alsoFig 2, HM regression would limit the detection of common factors  that we estimated to represent about a quarter of the ADHD genetic factors. HM regression could also reduce heritability of processed brain phenotypes that are genetically correlated with HM. A similar criticism can be made about HM regression in downstream analyses that typically include HM as a covariate in across subjects analysis [The genetic nature of the association between HM and maternal reports of Hyperactivity-Impulsivity raises concerns about the way HM is handled during the image processing pipeline and to a lesser extent during downstream analyses of neuroimaging studies. Indeed, our results indicate HM appears to contain meaningful information for the study of hyperactivity-impulsivity. Regressing out HM when studying ADHD (or Inattention or Hyperactivity-Impulsivity) using neuroimaging would result in a loss of information in the analysis, with a direct increase of the false negative rate. For example, in an analysis of brain changes associated with ADHD, factors contributing to ADHD, brain phenotype and HM would suffer from diminished power. For example in analysis ). Howeveanalysis –6, we mianalysis , 85, 86 analysis  might haanalysis . Indeed,analysis .A similar limitation may apply to Global Signal Regression (GRS) whose independence of the trait/disease of interest has been debated. Factors influencing the global signal in RS-fMRI are only partially known: cardiac pulsation and breathing , 90, heaTable A in S1 File). We were limited by a very small sample . However, if exclusions were found to be associated with levels of Inattention of Hyperactivity-Impulsivity, researchers would have to reconsider gross motion exclusion in rs-fMRI processing.In addition, we presented only weak evidence that the exclusion of scans based on motion could cause a sampling bias . SEM more generally applies to all study RS-fMRI data in the presence of dependency between a confounding factor  and a trait of interest. It is also compatible with twin and family data for which the variance can be further decomposed into genetic and environmental factors [Some already available preprocessing methods might be employed to avoid gross motion exclusion and regression of HM (or other confounds), such as within-subject image volume censoring , 10. Thi factors . FinallyAnother alternative to motion regression or censoring could be regression of principal components of the voxel-wise nuisance signals from WM and CSF (aCompCor) . Indeed,Finally, beyond considerations of RS-fMRI image processing, the genetic correlations measured here suggest that HM could be a valuable endophenotype  and a riWe showed some of the correlations to be reliable across scores , despite observations acquired at different ages. Even so, the association between self report scores and HM was weaker, which could be explained by noisier measurement  and perhS1 Fig(TIFF)Click here for additional data file.S2 FigWave 1 corresponds to mother report acquired during the clinical visits of the twins at age 14. Wave 2 was collected using an online questionnaire several years after the clinical visits. If the twins were older than 20 years old when mothers were contacted, they were asked to rate current symptoms (prospective design). If twins were not 20, mothers were instructed to “think back to when he/she was in primary school” (retrospective design).(TIFF)Click here for additional data file.S3 FigThe first 2 waves of self report SWAN scores were acquired from computer assisted telephone interviews between February and December 2009 (wave 1) and between December 2009 and November 2011 (wave 2). The third wave started in August 2012 using an online questionnaire, recruitment is still ongoing. Wave 1 was retrospective (“think back when you were in primary school”) while the 2 following were prospective.(TIFF)Click here for additional data file.S4 FigTwo individuals scored 0 for Inattention and Hyperactivity/Impulsivity, which explains that there are only 3 vertical bars.(TIFF)Click here for additional data file.S5 Fig(TIFF)Click here for additional data file.S6 FigFMZ: females monozygotic group, MMZ: male monozygotic, FDZ: females from same sex dizygotic pairs, MDZ: males from same sex dizygotic pairs, FOSDZ: females from opposite sex DZ pairs, MOSDZ: males from opposite-sex DZ pairs.(TIFF)Click here for additional data file.S1 File† The models used on the SWAN scores are 2 groups (MZ and DZ) ACE or ADE models for, except for MR Hyperactivity/ Impulsivity where a general sex limitation model was used. As a result, we estimated 7 parameters instead of 3: sex specific A, C/D and E and genetic correlation between sexes. The genetic correlation was exactly 1 in the models leaving the fit unchanged when set to constant. We concluded that the same genetic sources of variance influence mother reported hyperactivity-Impulsivity for males and females, with however differences in intensity. Head motion twin model included age at scan as covariate and allowed the variance to vary between same sex and opposite sex zygosity groups. Best univariate models were AE models for Hyperactivity-Impulsivity (self and mother report), as well as for motion. For the two Inattention scores, ADE models had a significant better fit to the data than AE as the heritability of the trait appears to come mostly from dominant sources of variance .(DOCX)Click here for additional data file.


Longitudinal phenotypic data provides a rich potential resource for genetic studies which may allow for greater understanding of variants and their covariates over time. Herein, we review 3 longitudinal analytical approaches from the Genetic Analysis Workshop 19 (GAW19). These contributions investigated both genome-wide association (GWA) and whole genome sequence (WGS) data from odd numbered chromosomes on up to 4 time points for blood pressure–related phenotypes. The statistical models used included generalized estimating equations (GEEs), latent class growth modeling (LCGM), linear mixed-effect (LME), and variance components (VC). The goal of these analyses was to test statistical approaches that use repeat measurements to increase genetic signal for variant identification.GRM7, which passed multiple test corrections for 2 hypertension-derived traits. The second GWA approach employed the LME to estimate genetic associations with systolic blood pressure (SBP) change trajectories identified using LCGM. This LCGM method identified 5 SBP trajectories and association analyses identified a genome-wide significant locus, near ATOX1 (p = 1.0E−8). Finally, a third VC-based model using WGS and simulated SBP phenotypes that constrained the β coefficient for a genetic variant across each time point was calculated and compared to an unconstrained approach. This constrained VC approach demonstrated increased power for WGS variants of moderate effect, but when larger genetic effects were present, averaging across time points was as effective.Two analytical methods were applied to the GAW19: GWA using real phenotypic data, and one approach to WGS using 200 simulated replicates. The first GWA approach applied a GEE-based model to identify gene-based associations with 4 derived hypertension phenotypes. This GEE model identified 1 significant locus, In this paper, we summarize 3 GAW19 contributions applying novel statistical methods and testing previously proposed techniques under alternative conditions for longitudinal genetic association. We conclude that these approaches when appropriately applied have the potential to: (a) increase statistical power; (b) decrease trait heterogeneity and standard error; (c) decrease computational burden in WGS; and (d) have the potential to identify genetic variants influencing subphenotypes important for understanding disease progression. Analysis of longitudinal measurements in genetic epidemiology provides a methodological strategy for the understanding of changes affecting long-term averages and changes in complex disease phenotypes over time. The design of these longitudinal studies may provide additional phenotypic information regarding age of onset, allowing for more precise definition of complex disease status. Inclusion of repeat measurements potentially allows for an increased understanding of the trajectory of traits and disease progression. Finally, longitudinal studies permit the perspective measurement of time-varying covariates and potential interactions that are not accounted for in traditional genetic studies focused on an individual time point .In addition to these advantages, there are challenges in the appropriate statistical methodology and interpretation of longitudinal data. Repeat measurements from the same subject are often correlated and those statistical models that assume independent measurements are inappropriate. In family-based genetic studies, correlation among family members also needs to be considered . The incA number of recent genetic epidemiological studies have used longitudinal measurements in order to take advantage of repeat measurements of time-varying variables –6. TheseTwo GAW18 groups investigated longitudinal measurements, with one group focused on methods using genome-wide association (GWA) SNPs and a second group focused on whole genome sequence (WGS) data or a combination of GWA and WGS. Both of these groups used blood pressure phenotypes from the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples (T2D-GENES) consortium. In addition, for GAW18, there was a limited sample of unrelated individuals . The GAWIn this paper, we summarize 3 GAW19 contributions  for 8,348,674 variants from odd-numbered autosomes and did not include structural variants. The 200 simulated replicates included phenotypic measurements from 3 time points generated using the same pedigree structures and the same 849 individuals who had both phenotype and imputed sequence data in the real data set. For individuals not examined at all-time points in the real data set, missing ages at exam were filled in by adding or subtracting 3.9 years between exams 1 and 2 and/or 6.9 years between exams 1 and 3. Information regarding SBP, DBP, hypertension diagnosis, medication use, and tobacco smoking were generated for each simulation replicate.(Ever); (b) incidence event with status changed from unaffected to affected (Progression); (c) first available visit as baseline only (Baseline); and (d) all available time points . They compared the estimates of the disease locus positions, their standard errors, the genetic effect estimate at the disease loci, and their significance for the 4 phenotypes to examine the efficiency gained from using repeatedly measured phenotypes. In this approach, genotypes are treated as random. Given the phenotype, these missing GWA SNPs are presumably independent of the phenotype, making missing completely at random (MCAR) a legitimate assumption.In the first GWA approach, Chiu et al.  developeith individual are independent. Each LCGM trajectory group was ranked based on health risk, defined as the average number of cumulative years as hypertensive . Pairwise GWA analyses between groups, with group 1 as the referent group, were conducted using the MMAP  computing package [In the second GWA approach, Justice et al.  aimed to package . MMAP esMAP4 region on chromosome 3 and a randomly ascertained equivalent region on chromosome 1. The gene-based test applies gene-specific relationship matrices to determine the proportion of the trait’s variance explained by an individual gene as a result of the departure of its localized empirical kinship estimate from the pedigree-derived theoretical kinship estimates. All analyses included the covariates baseline age, sex, their interactions, smoking status, and were performed using the computer package SOLAR  [In the third approach, Melton et al.  conducteoutines) .Ever; (b) Progression; (c) Baseline; and (d) Longitudinal. Of 1095 genes tested, 119 (Ever), 79 (Progression), and 42  were significantly associated (P <4.57 × 10−5), using a Bonferroni multiple correction for number of genes, with hypertension status. A total of 49 genes were identified in association with Baseline hypertension. Several gene regions identified in this study were near previously implicated hypertension genes . Also, the results of the Ever and Progression phenotypes were very similar, with 21 overlapping significant genes and similar standard errors. There was 1 overlapping locus, GRM7, which passed multiple test corrections for both Baseline and Longitudinal. Additionally, of those gene regions with nominal significance (p <0.05) for both Longitudinal and Baseline phenotypes, 64 % of these regions exhibited lower standard error for the Longitudinal phenotype as compared to Baseline [In the first GWA approach, Chiu et al.  used mulp <1.6E-6) in 1 or more pairwise-association tests, and including one that reached genome-wide significance (p <1.3E-7) near the ATOX1 gene  for the pairwise association analysis of trajectory group 2. The greatest number of suggestive loci (4) were identified in trajectory class 5, the class with the greatest perceived health risk and the smallest sample size (N = 136), including rs4756864 (p = 1.7E-7) within a previously implicated SBP associated region [In the second GWA approach, Justice et al.  found thd region .MAP4 variants 3_48040283 and 3_47957996 as being genome-wide significant  in 99.5 % of the 200 replicates for the unconstrained and 100 % of the replicates for the constrained method. For the remaining 7 variants tested, there was some variation across the models, but overall the constrained approach showed increased power. Although when larger genetic effects were present, collapsing, using the average across measures, was equally effective. For the gene-based test, averaging all repeat measurements displayed the greatest power in detecting the simulated true-positive effects for the MAP4 region. The gene-based test using only baseline SBP had greater power to detect true-positive effects than did either the constrained or unconstrained models, but this was likely because the authors used all available variants in each region tested rather than prioritizing variants based on functional annotation [MAP4 region as compared to the individual variant association tests.In the third approach for WGS, Melton et al.  successfnotation . OverallA direct comparison of results between each contribution summarized herein is made difficult by the variability in phenotype, genotype, and analytical method implemented . There are other potential variables that may be of interest when adjusting SBP and hypertension status, and that may be of particular concern for longitudinal data. For example, behavioral recommendations that accompany medical prescriptions could be important mediators or effect modifiers for the genetic influence of changes in blood pressure between visits at the same scale as medication use  , 23. HowAs noted in the GAW18 longitudinal WGS and GWA summary paper, computational burden, total analysis time, and analyst burden may be limiting factors, especially for WGS . However and SBP-associated loci (PLEKHA7) from cross-sectional analyses with much larger sample sizes, provide strong evidence that these statistical models are robust. Future work will be required to definitively show increased power using either method.One of the greatest possible benefits for longitudinal data is in the use of correlations between repeat measurements within subjects, which increases power to detect genetic effects by decreasing trait heterogeneity. Chiu et al.  and MeltRecent studies that have partitioned heterogeneous phenotypes into meaningful subphenotypes have proven useful for the identification of novel genetic susceptibilities and allowed for the estimation of previously missing heritability in complex disease traits  –27. ThesEver, Progression, and Longitudinal) decreased the standard error of effect estimates over Baseline-only phenotype analyses. This method may prove useful for analyzing other incident diseases across time .Chiu et al.  showed tWGS poses an increased challenge for longitudinal data, because of rare-variant identification and the computational burden of analyzing millions of markers simultaneously using complex, multilevel models. Family-based studies are useful for conducting rare-variant identification because of their ability to distinguish true variants from sequencing errors, reduce genetic heterogeneity, and increase the number of allelic copies. Therefore, the GAW19 family data set is ideal for improving statistical models incorporating repeat measurements using WGS. Additionally, Melton et al.  were ablIn this paper, we summarize 3 GAW19 contributions applying novel statistical methods and testing of previously proposed techniques under alternative conditions for longitudinal genetic association. Although these statistical techniques varied, each evaluated the potential benefits of using phenotype collapsing and noncollapsing approaches to analyze longitudinal phenotypes. These longitudinal approaches may: (a) increase power to detect genetic effects; (b) decrease trait heterogeneity and standard error of effect estimates; (c) decrease computational burden for WGS; and (d) have the potential to identify subphenotypes important for understanding disease progression. Disease progression is a complicated process; however, genetic variants and biological pathways will offer new means to identify disease susceptibility, predict progression, and highlight effective public health interventions.