To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency  non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals . The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D. Common  variants at over 100 loci have been robustly associated with disease risk, but most have not yet been translated to causal variants, effector transcripts, or disease mechanisms2. Because common variants from GWAS have modest effect sizes, and because those previously published explain in aggregate only 10–15% of the genetic basis of T2D3, it has been hypothesized that variants unexplored by GWAS might have a greater impact on efforts to understand or treat T2D5.Genome wide association studies (GWAS) have provided a valuable but incomplete window into the genetic basis of type 2 diabetes (T2D)3. First, to interrogate lower-frequency (MAF>0.5%) variation genome-wide, 2,657 Northern and Central European individuals were selected by GoT2D  and characterized with a combination of low-pass (~5x) whole-genome sequencing, deep (~82x) whole-exome sequencing, and high-density (2.5M) SNP genotyping. Genetic variants from these assays were incorporated into a phased integrated panel (WGS panel), capturing an estimated 99% of variants, genome-wide, present in more than 0.5% of individuals (WES panel), capturing an estimated 99.7% of coding variants present in more than 0.5% of the combined 12,940 individuals  reference panel, as well as a means to calibrate simulation-based models of population history9 or disease genetic architecture10.Collectively, these analyses suggest a limited role for low-frequency variation in the genetic basis of T2Det al.3.These methods are a modified version of the descriptions contained in Fuchsberger All human research was approved by the relevant institutional review boards and conducted according to the Declaration of Helsinki. All participants provided written informed consent.11. To decrease the likelihood of selecting T2D cases who in fact had type 1 diabetes (T1D) or monogenic forms of diabetes (such as Maturity Onset Diabetes of the Young), cases with an age of diagnosis below 35, testing positive for GAD antibodies, or with a first-degree relative known to have T1D were not included. Statistics of the 2,657 individuals ultimately included in the association analysis are provided in Individuals were sampled from four studies: the Finland-United States Investigation of NIDDM Genetics (FUSION) Study , KORA , the UKT2D Consortium , and the Malmö-Botnia Study . All individuals were of Northern or Central European ancestry. Cases were preferentially lean, had (relatively) early onset T2D, or had a familial history of T2D; controls by comparison were preferentially overweight or had low fasting glucose levelsMany of these individuals were measured for cardiometabolic phenotypes other than T2D, including glucose and insulin, anthropometrics, lipids, and blood pressure .De-identified DNA samples were sent to the Broad Institute in Cambridge, MA, USA , the Wellcome Trust Centre for Human Genetics in Oxford, UK (UKT2D), or the Helmholtz Zentrum München in Germany (KORA). DNA quantity was measured by Picogreen  to ensure sufficient total DNA and minimum concentrations for downstream experiments. Samples  were then genotyped on a Sequenom iPLEX assay for a set of 24 SNPs , with only samples with high-quality genotypes advanced for subsequent sequencing or genotyping.Genomic DNA was sheared, end repaired, ligated with barcoded Illumina sequencing adapters, amplified, size selected, and subjected to in-solution hybrid capture using the Agilent SureSelect Human All Exon v2.0  or v3.0 (KORA) bait set . Resulting Illumina exome sequencing libraries were qPCR quantified, pooled, and sequenced with 76-bp paired-end reads using Illumina GAII or HiSeq 2,000 sequencers to ~82-fold mean coverage.Whole-genome Illumina sequencing library construction was performed as described for exome sequencing above, except that genomic DNA was sheared to a larger target size and hybrid capture was not performed. The resulting libraries were size selected to contain fragment insert sizes of 380 bp±20%  and 420 bp±25% (UKT2D) using gel electrophoresis or the SAGE Pippin Prep . Libraries were qPCR quantified, pooled, and sequenced with 101-bp paired-end reads using Illumina GAII or HiSeq 2,000 sequencers to ∼5-fold mean coverage.P<10−6 were excluded from analysis.SNP array genotyping was performed by the Broad Genetic Analysis Platform. DNA samples were placed on 96-well plates and assayed using the Illumina HumanOmni2.5-4v1_B SNP array. Genotypes were then called using Illumina GenomeStudio v2010.3 with default clusters. SNPs with GenTrain score <0.6, cluster separation score <0.4, or call rate <97% were considered technical failures at the genotyping laboratory and excluded from further analysis. Next, 85 individuals with a genotype call rate below 98%, low genetic fingerprint (24-marker panel) concordance, or estimated gender discordance were excluded from further analysis. Finally, SNPs monomorphic across all individuals, failed by the 1000G Omni 2.5 QC filter, or with Hardy-Weinberg equilibrium http://broadinstitute.github.io/picard/), BWA12, and GATK14 software packages, following best-practice pipelines.Sequence data were processed and aligned to the human genome (build hg19) using the Picard  or average number of reads aligned across all bases genome-wide (genome sequencing). Based on these metrics, we excluded from further analysis exome sequence data  with coverage ≤20x in >20% of the target bases and genome sequence data  with average coverage ≤5x.15, either by direct comparison of sequence and HumanOmni2.5 array genotypes (where available) or by indirect estimates of contamination based on HumanOmni2.5 array allele frequencies. DNA samples with estimated contamination >2% using either method were excluded from further analysis . Uncontaminated DNA sample swaps were also detected via comparison of sequence and array data and corrected prior to variant calling.Possible DNA contamination of sequence data was assessed using verifyBamID16 and the GATK UnifiedGenotyper14. We merged unfiltered SNV calls across the two call sets and then processed the merged site list through the SVM and VQSR filtering algorithms implemented by those pipelines. SNVs that failed both filtering algorithms were excluded from further analysis. To identify SNVs from the whole-exome sequence data, we used the GATK UnifiedGenotyper best-practices pipeline14.To identify single nucleotide variants (SNVs) from the whole-genome sequence data, we used two independent SNV calling pipelines: GotCloud17, we applied more stringent criteria for indel QC than for SNV QC, excluding indels that failed either the SVM or VQSR filtering algorithms. To identify indels from the whole-exome sequence data, we used the GATK UnifiedGenotyper best-practices pipeline14.To identify short insertions and deletions (indels) from the whole-genome sequence data, we called variants using the GATK UnifiedGenotyper best-practices pipeline. Because indels are known to have high false positive rates18. To increase sensitivity after initial discovery of SVs, we merged the discovered sites with deletions identified in 1,092 sequenced individuals from the 1000G Project17 and then genotyped the merged site lists across the whole-genome sequenced individuals. After applying the default filtering implemented in GenomeSTRiP, pass-filtered sites variable in any of the individuals were identified as candidate variant sites. Among these candidate sites, we excluded variants in known immunoglobin loci to reduce the impact of possible cell-line artifacts. We did not call SVs from the whole-exome sequence data.To identify structural variants  from the whole-genome sequence data, we used GenomeSTRiP−6. If a site was not assayed by one of the three platforms, it was ignored in likelihood calculation for that platform.We merged variants discovered from the three experimental platforms into one site list. For individuals who had data from each of the three platforms, we then calculated genotype likelihoods across all sites separately by platform: for the whole-genome sequence data, we used GotCloud; for the exome sequence data, we used the GATK UnifiedGenotyper; and for the HumanOmni2.5 data, we converted hard genotype calls into genotype likelihoods assuming a genotype error rate of 1017, we then phased the integrated likelihoods using Beagle19  and refined phased genotypes using Thunder20 as implemented in GotCloud (with 400 states).We then calculated combined genotype likelihoods as the product of the genome, exome, and HumanOmni2.5 likelihoods, assuming independence across platforms. Following a strategy originally developed for the 1000G Phase 1 projectr2<0.20) set of MAF>0.01 autosomal variants (with variants in large high-LD regions22 or with Hardy-Weinberg P<10−6 removed). Inspecting the first ten PCs for each variant type, we identified 43 outlier individuals based on PCs from SNVs and indels and 136 additional outliers based on PCs from SVs; these 179 individuals were excluded from further analysis. Additionally, 38 individuals with close relationships with other study individuals  were excluded from further analysis.Using the genotypes from the integrated panel, we performed principal component analysis (PCA) separately for each of the three variant types , using EPACTS on an LD-pruned , the Wake Forest School of Medicine Study , the Korea Association Research Project , the Singapore Diabetes Cohort Study and Singapore Prospective Study Program , Ashkenazi , the Metabolic Syndrome in Men (METSIM) Study , the San Antonio Mexican American Family Studies , the Starr County Texas study , the London Life Sciences Population (LOLIPOP) Study , and the Singapore Indian Eye Study . Potential T1D or MODY cases were excluded via similar approaches as for the whole-genome sequencing experiment. Statistics of the individuals ultimately included in the association analysis are provided in As for the WGS panel, many individuals were measured for additional cardiometabolic phenotypes .DNA samples were obtained and sequenced in the same manner as described for the exome sequencing component of the WGS panel.12, and GATK14 software packages and best-practice pipelines. Genotype likelihoods were computed controlling for contamination. Hard calls  and dosages +2Pr(XX|data), where R is the reference and X the alternative allele) were computed for each individual at each variant site. Hard calls were used only for quality control, while dosages were used in downstream association analyses. Multi-allelic SNVs and indels were dichotomized by collapsing alternate alleles into one category.As for the exome sequence data within the WGS panel, sequence data for the WES panel were processed and aligned to the human genome (build hg19) using the Picard, BWAP<10−6 in any ancestry group), or differential call rate between T2D cases and controls (P<10−4 in any ancestry group).Individuals were excluded from analysis if they were outliers on one of multiple metrics: poor array genotype concordance (where available), high number of variant alleles or singletons, high or low allele balance , or excess mean heterozygosity or ratio of heterozygous to homozygous genotypes. Within this reduced set of individuals, we then further excluded variants based on hard call rate (<90% in any cohort), deviation from Hardy-Weinberg equilibrium  contains genotypes for 12,940 individuals at 2.89M SNVs and 110.2 K indels  and 5.3.We carried out genotype imputation, using existing SNP array data, from the WGS panel into 44,414 individuals  from 13 studies participating in the DIAGRAM consortium. Each study performed quality control independently. A more detailed description of the analyzed individuals is available elsewherehttp://www.illumina.com/Documents/products/technotes/technote_gencall_data_analysis_software.pdf ) or Birdseed23. Individuals were excluded if they had a low call rate (<99%), excess heterozygosity, high singleton counts, evidence of non-European ancestry, discrepancy between recorded and genotyped sex, or discordance with prior SNP array or genotyping platform fingerprint data (where available). Variants were excluded if they had a low call rate (<99%), deviation from Hardy-Weinberg equilibrium (P<10−6), GenTrain score <0.6, cluster separation score <0.4, or a suspect intensity plot based on manual inspection. After quality control, missing genotypes were re-called using zCall24, and additional quality control was performed to exclude poorly genotyped individuals  or variants . A more detailed description of the analyzed individuals is available elsewhere3.We considered 28,305 T2D cases and 51,549 controls from 13 studies of European ancestry, each genotyped with the Illumina exome array. Studies independently called genotypes using the Illumina GenCall algorithm  with the Firth bias-corrected likelihood ratio testhttps://genome.sph.umich.edu/wiki/EPACTS) or SNPTEST27. Residual population stratification for each study was accounted for using genomic control28. Association statistics were then combined across studies, using a fixed-effects sample-size weighted meta-analysis as implemented in METAL29.In each of the thirteen studies within which variants from the WGS panel were imputed, SNVs with minor allele count (MAC)≥1 were tested for T2D association under an additive genetic model. Association tests were adjusted for study-specific covariates and performed using either the Firth, likelihood ratio, or score tests as implemented in EPACTS  to account for residual population structure.For each variant in the WES panel, we tested for association between genotype and T2D in the 12,940 sequenced individuals. We computed separate association statistics for each ancestry group using EMMAXP-values from the EMMAX analysis and (ii) an inverse-variance weighting of effect size estimates from the Wald analysis. For the final results, P-values were taken from the EMMAX analysis, and effect size estimates from the Wald analysis.We subsequently performed a fixed-effects meta-analysis of ancestry-specific association summary statistics for each variant using (i) a sample-size weighting of P-values and standard errors were corrected based on the calculated genomic control inflation factor.In each study within which exome array genotyping was applied, variants were tested for association with T2D via both the EMMAX and Wald tests. For the Wald test, related individuals were excluded and statistics were adjusted for study-specific principal components. For each study, P-values were combined via a sample-size weighted analysis, and Wald effect sizes were combined via an inverse-variant weighted analysis. For the final results, P-values were taken from the EMMAX analysis, and effect sizes were estimated from the Wald analysis.Variants were then combined via a fixed-effects meta-analysis. EMMAX 31, and VEP v2.732. We then identified variants predicted by at least one of the three algorithms in at least one mapped transcript to be protein-truncating , denoted PTVs, or other protein-altering , denoted missense. We additionally used a previously described procedure33 to identify subsets of missense variants bioinformatically predicted to be deleterious: those annotated as damaging by each of Polyphen2-HumDiv, PolyPhen2-HumVar, LRT, Mutation Taster, and SIFT were considered to meet ‘strict’ criteria, while those annotated as damaging by one of these algorithms was considered to meet ‘broad’ criteria. We then calculated the MAF of each variant based on the highest frequency across each of the five ancestry groups. We finally combined these annotations to produce four masks: the PTV-only mask included PTVs, the PTV+NSstrict mask included variants in the PTV-only mask as well as those meeting ‘strict’ criteria for deleteriousness, the PTV+NSbroad mask included variants in the PTV-only mask as well as those with MAF<1% meeting ‘broad’ criteria for deleteriousness, and the PTV+missense mask included variants in the PTV+NSbroad mask as well as those with MAF<1% annotated as missense.We first generated four variant lists (‘masks’) based on functional annotations and observed allele frequencies. Annotations were computed based on transcripts in ENSEMBL 66 (GRCh37.66) using CHAoS v0.6.3, SnpEFF v3.134, employing the SKAT v0.93 library to perform a SKAT-O35 analysis within each ancestry group as well as across all ancestry groups via meta-analysis. Within each ancestry group, we assumed homogenous allele frequencies and genetic affects and adjusted for ethnic-specific axes of genetic variation after exclusion of 96 related individuals. For the meta-analysis, we used the MetaSKAT option to analyze genotype-level data, allowing for heterogeneity of allele frequencies and genetic effects between ancestry groups. All analyses were completed using the recommended ρ vector for SKAT-O: .We performed gene-level analysis using the MetaSKAT software package (v0.32)All analyses were performed using publically available software packages, using versions and parameters as described above.Genotypes and phenotypes from the WGS and WES panels are available at the European Genome-phenome Archive  and the database of Genotypes and Phenotypes .t2dgenes-got2d-dac@broadinstitute.org) to analyze data from all cohorts included in the study. The requester will receive an application packet that includes a project proposal document and a Data Transfer Agreement (DTA). The requester must then provide to the DAC a short description of their study, the proposed use of the data, an approval from the Institution's IRB, and a signed DTA. Assuming IRB approval and an executed DTA, the process for obtaining final approval from the DAC takes 4–6 weeks. Once approved, investigators can download either a single VCF file with genotypes from all individuals in the WGS panel (Data Citation 1) or a single VCF file with genotypes from all individuals in the WES panel (Data Citation 2).The data in EGA are covered under a single data use agreement, which complies with all of the cohort-specific data use restrictions. While this does limit data access according to the criteria of the most restrictive cohort, it is the only mechanism through which the entire WES and WGS panels are available to investigators. Additionally, the EGA contains data from one cohort that could not be released to a US-based repository. To download either the WGS or WES panel from the EGA, investigators must obtain approval from a data access committee  they can download cohort-specific data from dbGAP. Each cohort in dbGAP is subject to distinct data use restrictions, and investigators can obtain separate VCF files, as well as the raw sequence reads, for each of the cohorts.The WGS and WES panels are accompanied by exclusion lists of variants and individuals (Data Citation 1 and Data Citation 2). The WGS VCF file contains data from the full set of 2,874 individuals and 28.45M variants that passed QC, with additional lists provided containing the 2,657 individuals and 17.69M variants included in association analysis. The WES VCF file contains the full set of 13,008 individuals that passed QC, with additional lists provided containing the 3.04M variants that passed QC  and the 12,940 samples and 2.93M variants included in association analysis. Additionally, the WES dataset includes lists of variants included in gene-level analysis for each of the four analyzed masks.Five datasets of association statistics are also available for download. Association statistics for variants in the WGS panel are available from the whole-genome sequenced individuals or from those with imputed genotypes (Data Citation 1). Association statistics in the WES panel are available from the whole-exome sequenced individuals or from those genotyped on the exome array (Data Citation 2). Additionally, gene-level association statistics from the whole-exome sequenced individuals are available for each of the four variant masks (Data Citation 2).www.type2diabetesgenetics.org. Through the portal, users can construct queries to find variants satisfying specified annotations and association thresholds, both across the WES and WGS analyses as well as other GWAS datasets. Users can also dynamically construct a set of variants within a gene and obtain a P-value from aggregate association analysis within the WES individuals.A description of the datasets is available in We evaluated the variant sensitivity (fraction of true variant sites detected) of the WGS panel, based on the 2,538 individuals with data from all three experimental platforms . To assess the sensitivity of low-pass whole-genome sequencing alone, we computed the fraction of variants detected from whole-exome sequencing that were also detected by low-pass whole-genome sequencing. Sensitivity estimates were 99.8, 99.0, and 48.2% for common (MAF>5%), low-frequency (0.5%<MAF<5%), and rare (MAF<0.5%) SNVs, respectively, and >99.9, 93.8, and 17.9% for common, low-frequency, and rare short indels, respectively. We also assessed the coding SNV sensitivity of low-pass whole-genome sequence data combined with exome sequence data, based on the proportion of HumanOmni2.5 SNVs detected by either sequencing platform. Because HumanOmni2.5 SNVs are enriched for common variants, we calculated an averaged sensitivity at each allele count, weighted by the number of exome-detected variants given the allele count. Sensitivity estimates were 99.9, 99.7, and 83.9% for common, low-frequency, and rare variants, respectively. These sensitivity estimates provide lower bounds on the sensitivity of the full WGS panel, which combines HumanOmni2.5 SNP array data as well as the two types of sequence data .We further evaluated the genotype accuracy of the WGS panel for each of the three classes of variant . Across chromosome 20, concordance of low-pass whole-genome-sequence-based SNV genotypes with exome-sequence-based genotypes was 99.86%, with homozygous reference, heterozygous, and homozygous non-reference concordances of 99.97, 98.34, and 99.72%, respectively. Concordance between exome-sequence-based SNV genotypes and HumanOmni2.5 genotypes was 99.4%, with homozygous reference, heterozygous, and homozygous non-reference concordances of 99.97, 99.69, and 99.88%, respectively. For indels genotyped with both low-pass whole-genome-sequence data and exome-sequence data, concordance was 99.4%, with homozygous reference, heterozygous, and homozygous non-reference concordances of 99.8, 95.8, and 98.6%, respectively.36. Taking the WTCCC data as a gold standard, we estimated genotype accuracy across 1,047 overlapping SVs  genome-wide. The overall genotype concordance was 99.8%, with homozygous reference, heterozygous, and homozygous non-reference concordances of 99.9, 99.6, and 99.7%, respectively.To evaluate the genotype accuracy of SVs detected from the low-pass whole-genome sequence data, we took advantage of the 181 individuals in our study who were previously included in the WTCCC array-CGH based structural variant detection experimentWe assessed the overall sequencing quality of individuals in the WES panel by computing distributions of global statistics, stratified by reported ancestry . After qWe also assessed bulk properties of indels within the WES panel. The length distribution of indels showed an excess of variants with lengths a multiple of three, as expected. Additionally, principal components computed from indels alone closely matched those computed from SNVs and indels together .2 values were in excess of 0.8 for Finnish individuals and in excess of 0.6 for British individuals , average rividuals .We assessed the overlap of variants present on the exome array with those observed in the WES panel. As the exome array primarily contains SNVs that are predicted to be protein altering, we focused on nonsense, essential splice site, and missense variants; only variants passing QC in both sequence and array data were included in the assessment. The fraction of variants in the WES panel on the exome array was highest for Europeans, at 81.6%, and lowest in African-Americans, at 49.0% .http://zzz.bwh.harvard.edu/gpc/) to estimate power to detect T2D association for each of the single variant analyses. All calculations assumed a T2D prevalence of 8%. 2=0.8) accuracy  and estimated power as a function of (i) the phenotypic variance, under a liability scale, explained by additive genetic effects and (ii) the percentage of variants that were causal (50% or 100%). As for single-variant power calculations, we considered variants of constant frequency across all five ancestry groups, as well as variants specific to one ancestry group. These calculations suggested3 that, even under optimistic scenarios, genes must explain >1% of genetic variance in order to achieve a moderately significant (P<10−3) association in the WES panel.For gene-based tests in the WES panel, we used a simulated haplotype data set  plots comparing observed statistics to those expected under the null distribution. The vast majority of statistics matched the expected distribution  with a deviation from the null for common variant associations from the WGS panel, imputed genotypes, and exome array genotypes (suggesting power to detect known positive control T2D-associated non-coding common variants).8 will provide greater imputation power for most use cases, the WGS panel is not restricted based on minor allele count and includes indels and SVs.The WGS and WES panels may be useful for simulation-based approaches that require individual-level genotypes and phenotypes. In this case the full list of ‘QC+’ variants  should be used, as the association analysis omitted very rare variants that might be useful in other settings. The WGS panel can also serve as a reference panel for genotype imputation, particularly in cases where an excess of haplotypes from T2D cases are required. Although more recent and larger efforts such as the Haplotype Reference Consortiumwww.type2diabetesgenetics.org as a first step, as its goal is to provide a simple and continuously updated means to query these and other association statistics. The portal is designed specifically for queries about individual variants or those within a single gene or genomic locus, as well as variant- or gene-level analyses for which investigators desire to adjust included variants, covariates, or individuals. Users should note that data from the T2D-GENES and GoT2D studies are included in, and thus should not be combined with data from, the Exome Aggregation Consortium (exac.broadinstitute.org).The most valuable data from the T2D-GENES and GoT2D studies are likely the catalogues of T2D association statistics for low-frequency and common variation. These statistics may prove useful for fine mapping or functional studies of T2D GWAS signals, in which enumeration of potential causal variants is required, or for ‘reverse genetic’ approaches, in which estimates of the phenotypic effects of variants with strong molecular effects are desired. For this usage, we advise investigators to query the T2D Knowledge Portal at 2>0.3). For gene-level analyses, investigators should first use the aggregate association statistics and then dissect results by examining variant-level statistics for each variant in the mask.Should investigators desire access to all association statistics, genome-wide, the files at EGA should be used . For single variant associations, a statistic in the largest available sample size should be used. Coding variant association statistics present in both the WES panel analysis and the exome chip analysis can be safely combined via meta-analysis, as can non-coding variant association statistics present in both the WGS panel analysis and the imputation-based analysis; statistics should not, however, be combined across the non-coding and coding variant analyses. Association results from the sequence or exome chip data should not need to be filtered, but it is advisable to filter results from the imputation-based analysis according to a threshold on imputation quality  conditional analyses, such as variant association controlling for additional individual phenotypes or genotypes at different variants, (b) association analyses with phenotypes other than T2D, and (c) novel statistical tests. In these cases, usage of the tests and inclusion of the covariates described in the methods section is recommended, and only variants and individuals present in the final ‘QC+’ analysis lists should be included.How to cite this article: Flannick, J. et al. Sequence data and association statistics from 12,940 type 2 diabetes cases and controls. Sci. Data 4:170179 doi: 10.1038/sdata.2017.179 (2017).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Herein, we describe the results of a Genome-wide association study for reactivity, assessed using REATEST® with a sample of 754 females from five dual-purpose (milk and meat production) Guzerat (Bos indicus) herds. Genotyping was performed using a 50k SNP chip and a two-step mixed model approach (Grammar-Gamma) with a one-by-one marker regression was used to identify QTLs. QTLs for reactivity were identified on chromosomes BTA1, BTA5, BTA14, and BTA25. Five intronic and two intergenic markers were significantly associated with reactivity. POU1F1, DRD3, VWA3A, ZBTB20, EPHA6, SNRPF and NTN4 were identified as candidate genes. Previous QTL reports for temperament traits, covering areas surrounding the SNPs/genes identified here, further corroborate these associations. The seven genes identified in the present study explain 20.5% of reactivity variance and give a better understanding of temperament biology.Temperament is fundamental to animal production due to its direct influence on the animal-herdsman relationship. When compared to calm animals, the aggressive, anxious or fearful ones exhibit less weight gain, lower reproductive efficiency, decreased milk production and higher herd maintenance costs, all of which contribute to reduced profits. However, temperament is a trait that is complex and difficult to assess. Recently, a new quantitative system, REATEST Temperament is a complex trait comprising many phenotypes including curiosity, exploration, aggressiveness, reactivity, passivity, physical movements, persistent habits, emotions, alertness and response to novelty . Like otCattle temperament describes “consistent behavioral and physiological differences observed between individuals in response to human interaction or environmental challenge” ,5. An obAs expected, the broad and subjective definition of temperament leads to difficulties in the robust assessment of this trait ,10. ManyAmong the restraint-based methods, the crush test and flight speed are frequently used. In the crush test, the animal is held in a crush and scores are subjectively assigned, considering the frequency and intensity of the movements, audible breathing and the frequency of bellowing and jumping . In flig®) was developed to evaluate animal reactivity [® were compared in the evaluation of the Nellore breed temperament and presented high positive correlation [® results correlated better with temperament scores from crush test, than flight speed [® in the Guzerat breed. REATEST® has several advantages: 1) it does not interfere in the daily management of farms and does not increase the number of activities with the animal, thus allowing the assessment of many animals in one day; 2) it eliminates the subjectivity of the evaluator; 3) it allows the detection of higher phenotypic variability, when compared to temperament scores; and, 4) the test does not expose the evaluator to additional accident risks and requires no changes in farm structure [In the last decade, a new and objective method  with docility [Despite its broad definition, several genes have already been associated with cattle temperament components –19, suchht speed  and DRD4docility . Howeverdocility ,23.In Brazil, most production systems are based on indicine breeds raised in pastures, a management system that implies less contact with humans. Due to this kind of management, but perhaps reflecting true biological differences, temperament issues have been reported for some indicine breeds . TherefoGuzerat is the third largest indicine purebreed in Brazil, exceeded in number only by Nellore and Gyr. In the first decades following its introduction in Brazil, Guzerat selection focused on meat but, over the last thirty years, breeders have been aiming at dual-purpose (meat and milk) selection. This breed has also been used frequently in crosses with both taurine and other indicine breeds to obtain animals with better performance for the tropical production systems.® with a large sample of the Guzerat breed in Brazil.Allegedly, Guzerat brings resistance to endo- and ectoparasites, endurance in adverse environments, particularly in the dry period or in the semiarid regions, satisfactory weight gain and growth, even consuming gross forage, and maternal ability . HoweverThis study was performed following approval of the Embrapa Dairy Cattle Ethical Committee of Animal Use (CEUA-EGL) under the protocol number 09/2014.The data used in the present study is a part of the data collected and published by Peixoto et al (2016), where the reactivity of all females of five farms was evaluated . The cur®. This test uses an electronic device containing an accelerometer that is positioned under the chute to capture the cow’s movements for 20 seconds during the routine weighting. A computer program converts the frequency and intensity of movements to a value between 0 and 9,999 [The reactivity of all females, from weaning to advanced ages (19–202 months) whether lactating or not, was measured, using REATESTnd 9,999 , herein nd 9,999 . Here weYiklmn = dependable variable reactivity of the female iklmn, Hi = fixed effect of the herd i, PSk = fixed effect of the physiological status k, EOl = fixed effect of weight l, Am = fixed effect of age m; and εiklmn = residual random term in the observation n. This variable will be referred to as adjusted reactivity (REACTadj). The adjusted reactivity data was used in all the association tests performed in the following analyses  v9.2 , was useanalyses .DNA was extracted from peripheral blood as described elsewhere . Animalscheck.marker implemented in the package GenABEL [−6) were excluded. Second, samples showing low mean call rates, high mean heterozygosity (FDR<1%) and high identity by state (IBS>95%) across a random sample of markers were excluded. This procedure was repeated until there were no more inconsistencies in the sample. Details on markers and animals excluded in each step are provided on Tables A and B in The R open source software  was used GenABEL . Missingh2). Therefore, it calculates the estimates of h2 in the first step and applies the resulting matrix to the association test. Additionally, the association results are corrected for genomic control and for deviations in the estimates of effects. Markers are fitted as a linear covariate in the statistical model and the genotypes are represented as the number of copies of the less frequent allele. The genomic relationship matrix used to obtain the heritability estimates was calculated through the ibs function implemented in the GenABEL package. The GRAMMAR-Gamma test accounts for population stratification and familial relationship in the sample. No additional fixed effects were included in the second step of the analysis, as the adjusted reactivity (REACTadj) were used in the score test. Values of inflation factor (λ) between 0.9 and 1.1 were considered acceptable and the test statistics were divided by λ to adjust for the remaining inflation [In the GWAS, the GRAMMAR-Gamma method implemented in the GenABEL package was used . GRAMMARnflation .A false discovery rate (FDR) threshold set to 5% was used to correct the results obtained in the GWAS for multiple testing.http://www.ncbi.nlm.nih.gov/) and Ensembl (http://www.ensembl.org/index.html) databases. When the markers map to intergenic regions, analyses of evolutionary conservation of a SNP nucleotide position and a search for evolutionary conserved regions (ECR) around them were performed using the software Multi Pip Maker [Bos taurus herein . These species were selected based on the availability of their pre-computed alignment in the ECR Browser software . When an ECR was encountered, a search for transcription factor binding sites (TFBS) inside the ECR was performed using rVista 2.0 [adj. Information on each gene of the list was obtained from different sources, such as articles published in PUBMED (http://www.ncbi.nlm.nih.gov/pubmed/), UNIPROT (http://www.uniprot.org/), GeneCards (http://www.genecards.org/), and STRING (http://string-db.org/).Map positions of the SNPs were based on UMD3.1. Information about the markers  were obtained using the SNPchiMp tool . When thip Maker  and the ip Maker , respectista 2.0  in orderadj>0.2857. Therefore, the Generalized Linear Mixed Model GLINMIX was used to obtain a skew-normal distribution for data analysis. Mean, minimum, maximum and the heritability of both REACT and REACTadj are presented in REACT presented a skewed distribution distribution, with most animals presenting low reactivity scores; only 189 individuals, 25% of sample, showed REACT>1010 or REACTMean values of marker distribution ranged from 10 to 14 markers/Mb for all the autosomes. Mean heterozygosity by sample was 0.26. GRAMMAR-Gamma was used, returning an inflation factor for the test (λ = 1.047) within the usually accepted limits of 0.9 and 1.1 Fig A in . In addiadj, mapping to chromosomes BTA1, BTA5, BTA14 and BTA25 (−7), was significantly associated with REACTadj even after a Bonferroni correction (α = 5%) . No significant repercussion in splicing or miRNA recognition sites were predicted as a consequence of these allele substitutions. The other two SNPs map to intergenic regions. rs29002595 maps 141 kb upstream to netrin 4 (NTN4) and rs41965198 maps to over 250 kb upstream to ephrin receptor A6 (EPHA6). For these SNPs, analyses of evolutionary conservation were conducted. No significant results were for rs29002595. rs41965198 affects a nucleotide position which is conserved in humans, dogs, pigs and chimpanzees, but not in chickens and mice.After an FDR correction (α = 5%), seven markers were associated with REACTnd BTA25 . One of (α = 5%) . rs10900EPHA6), which included the SNP, was aligned in all these species. Three ECRs were identified around the rs41965198 position. ECR1 (chr1:40280223–40281010) is conserved in mammals and in chicken. ECR2 (chr1:40221973–40222560) is conserved in chicken and in mammals, except for opossum. ECR3 (chr1:40018913–40019884) is conserved in mammals, chicken and frogs. Analyses of TFBS in the three deeply conserved ECRs returned a large number of predicted sites inside the ECRs, indicating that they could be involved in regulatory processes.In the search for ECRs, eight species were used in the multi-alignment: fugu, tetraodon, frog, chicken, opossum, mouse, chimpanzee and human. An 800kb fragment upstream from the closest gene  has been associated with docility. GLRB and GRIA2 (both in BTA17) and QKI (BTA9) have been associated with flight speed [Several association studies for cattle temperament have already been published and reviewed by Haskell and colaborators in 2014 . QTLs onht speed ,22. Threht speed ,43. In Bht speed . Below, In BTA1, significant results were obtained for three markers distributed over a 25Mb interval . It is aPOU1F1 mutations cause anterior hypopituitarism  [POU1F1 has already been identified as QTL for production traits in bovines. In cattle, POU1F1 polymorphisms have been associated with a wide range of production traits such as: milk production; birth weight; weight at 90, 270 and 450 days; weaning weight; and, pre- and post-weaning average daily weight gains [In humans, hormone) ,46 and ahormone) . POU1F1 ht gains ,49.POU1F1 mutations on production traits, there are, nevertheless, negative impacts on reproduction phenotypes. These negative effects may implicate POU1F1 as a candidate for the negative hitchhiking effects observed in milk selection programs. However, an association with reactivity has not yet been described.On the other hand, some studies have already described an association between the selection for high production efficiency in farm animals and undesirable side effects such as loss of homeostatic balance, resulting in pathologies and affecting animal welfare ,51. DespPOU1F1. This marker explains 2.6% of the reactivity variance, with an allele substitution effect of 0.823 points in REACTadj. The genes closest to this marker are LOC782966  and EPHA6, mapping over 200 kb downstream from rs41965198. LOC782966  is a retrogene and there is no information in the literature or in databases confirming that it is expressed.The second significantly associated marker, rs41965198, maps to an intergenic region 5Mb downstream from EPHA6 encodes for Ephrin type-A receptor 6. In mouse embryos, this receptor is highly expressed during the development of the central nervous system; and, in adult animals, in the hypothalamus, thalamus and amygdala. EPHA6 is a tyrosine kinase receptor which binds promiscuously the GPI-anchored ephrin-A family ligands to adjacent cell membranes, leading to contact-dependent bidirectional signaling between neighboring cells. The signaling pathway activated by the ephrin-A-EPHA6 ligation mediates processes such as axon guidance and axon growth repulsion [EphA6 KO mice present abnormally low results in tests evaluating learning and retrieval of the fear conditioning stimulus [epulsion , indispeepulsion . In addistimulus .The existence of three deeply conserved ECRs suggests that the region around rs41965198 may contain long distance regulatory elements for the nearby genes. Alternatively, rs41965198 may be in linkage disequilibrium with the truly functional variant, since this polymorphism does not map to these ECRs. To test this hypothesis, an analysis of TFBS was performed on these three ECRs and the results suggest that this SNP does, in fact, map close to a probable long-range regulatory element ,56.ZBTB20 (zinc finger and BTB domain containing 20) 25Mb downstream from POU1F1. This gene encodes for a transcription factor that has been implicated in hematopoiesis, oncogenesis, and immune response in mammals. Diseases associated with ZBTB20 in humans include Primrose syndrome and juvenile pilocytic astrocytoma. This gene is more abundantly expressed in tissues from the nervous, secretory and reproductive systems in humans (http://www.uniprot.org/uniprot/Q9HC78). In the brain of transgenic mice, ectopic Zbtb20 expression produces cortex lamination defects resulting in behavioral abnormalities, suggesting impaired processing of visual and spatial memory [Zbtb20 directly impacts on the development of different parts of the hippocampus [adj. Therefore, ZBTB20 is a new, highly pleiotropic and interesting candidate for reactivity.rs108944043 is located within the first intron of the gene l memory . Zbtb20 pocampus , affectipocampus . This maZBTB20 neighborhood, another important functional candidate gene emerges, dopamine receptor 3 (DRD3), 70kb upstream from ZBTB20. DRD3 is highly expressed in the ventral striatum, a region associated with behavioral traits, and poorly expressed in other regions of the central nervous system [DRD3 has been associated with high impulsiveness in violent individuals and also with sensory sensitivity . Furthermore, DRD3 blockers induce cognition-enhancing and hyperactivity-dampening effects [On the other hand, analyzing the s system . DRD3 ha effects –63.adj at FDR 5%, is located within intron 18 of ABCC1, a membrane-associated protein, member of the ATP-binding cassette (ABC) superfamily, highly expressed in the brain and involved in multidrug resistance. ABCC1 is a component of the blood-brain barrier. In addition, it exports corticosteroids, which have been involved in stress response, from the adipose tissue [ABCC1 explains 2.8% of the REACTadj variance, with an allele substitution effect of 0.441 points.On BTA25, significant results were obtained for two markers mapping 5Mb from each other . rs42063e tissue . Therefovon Willebrand factor A domain containing 3A (vWA3A) gene. Depending on the transcript isoform, it is located in the intron 1, 2, or even before the 5’-UTR. Proteins containing von Willebrand domains are involved in basal membrane formation, cell migration, cell differentiation, adhesion, haemostasis, signaling, chromosomal stability, malignant transformation and immune defenses. Although vWA3A is differentially expressed in the blood, brain, lungs, ovaries and testes [ABCC1 explains 3.1% of the REACTadj variance, with an allele substitution effect of 0.877 points.The second marker in this region, rs109589165, showed the second lowest p-value in this GWAS . This SNP is located within the KIAA1429 intron 1. This gene codes for a spliceosome-associated protein that is putatively involved in mRNA methylation and splicing regulation [KIAA1429 involvement in temperament. This SNP explains 2.6% of REACTadj variance and has an allele substitution effect of 0.355 points.rs110729726 is located inside gulation . This isadj over the 5% FDR threshold. This marker maps to a gene-rich region on BTA5. Two functional candidate genes were identified in this region. This marker maps 10kb upstream from the Netrin 4 (NTN4) gene, which codes for a member of the family of laminin-related proteins. Netrins are involved in axon guidance and cell migration during development [NTN4 acts directly on axon development and morphogenesis, making it an interesting candidate for reactivity.rs29002595 is the last marker associated with REACTelopment . NTN4 orelopment . TherefoSNRPF, also codes for a spliceosomal protein. Alterations in the RNA binding function have already been associated with behavioral disturbances in mice [SNRPN deletions cause Prader-Willi syndrome, characterized by polyphagia and temper tantrums, which are time-limited crises of aggressive and violent behavior that subside, succeeded by the calm and agreeable temperament described for these patients as typical [adj variance; the allele substitution effect is 0.866 points.Another gene in this region,  in mice . In huma typical . rs29002As most of the associated markers in the present study are intronic, we tested for the presence of miRNA recognition sites and alternative splicing using the programs miRBase and ASSP (Alternative Splice Site Predictor). No significant allele substitution effects produced by these SNPs were identified. Therefore, the QTLs identified here probably reflect the effects of variants, in linkage disequilibrium to the SNPs tested, which contribute to reactivity in Guzerat –74.POU1F1, DRD3, VWA3A, ZBTB20, EPHA6, SNRPF and NTN4 implicated in the development or function of the neural system. They are, therefore, strong candidates for temperament phenotypes, more specifically, reactivity, anxiety and aggression. Together, these QTLs explain approximately 20.5% of reactivity variance, give a better understanding of temperament biology and open possibilities for new studies in the field.This is the first study to use GWAS to investigate the genetic basis of reactivity in Guzerat. The QTLs identified here are located inside or close to genes such as S1 FileFig A—Quantile—quantile plot of observed and expected—log10 obtained in GRAMMAR-Gamma association analysis. Box A—Exclusion criteria for markers and samples. Table A—Markers excluded from analysis. Table B—Individuals excluded from analysis.(DOCX)Click here for additional data file.S1 DatasetFile REACTadj_110117.txt–REACTadj values per individual. File GWASresults_110117.txt–GWAS results for each marker tested in the Grammar-gamma model.(ZIP)Click here for additional data file.


Drosophila melanogaster provides a powerful system to investigate the genetic underpinnings of variation in startle behavior. Since mechanically induced, startle responses and environmental conditions can be readily quantified and precisely controlled. The 156 wild-derived fully sequenced lines of the Drosophila Genetic Reference Panel (DGRP) were used to identify SNPs and transcripts associated with variation in startle behavior. The results validated highly significant effects of 33 quantitative trait SNPs (QTSs) and 81 quantitative trait transcripts (QTTs) directly associated with phenotypic variation of startle response. We also detected QTT variation controlled by 20 QTSs (tQTSs) and 73 transcripts (tQTTs). Association mapping based on genomic and transcriptomic data enabled us to construct a complex genetic network that underlies variation in startle behavior. Based on principles of evolutionary conservation, human orthologous genes could be superimposed on this network. This study provided both genetic and biological insights into the variation of startle response behavior of Drosophila melanogaster, and highlighted the importance of genetic network to understand the genetic architecture of complex traits.Startle behavior is important for survival, and abnormal startle responses are related to several neurological diseases.  Genome-wide association studies (GWASs), which examine genetic variants across the genome by associations with variation in traits2, can reveal the extent of functional genetic variation in natural populations. There remains, however, a substantial gap in our understanding of how the concerted effects of SNPs across the genome relate to phenotypic variation. In addition to GWAS, gene expression levels have been used as quantitative traits for eQTL mapping to DNA markers3. Such studies enable us to categorize cis and trans effects of SNPs on gene expression. Thus, eQTL analysis is often incorporated as a component of GWAS to support causality of candidate genes.With the diminishing cost of high-throughput technologies, including rapid genome-sequencing methods and whole genome transcript profiling, the focus of genomic sciences is shifting from data production to data interpretation4. Drosophila melanogaster is able to serve as a valuable model to assess the genetic architecture of complex traits and, based on evolutionary conservation of fundamental principles, insights from this model system can be applied to human quantitative traits and diseases5. To date, GWASs have been conducted for both organismal and gene-expression traits using wild-derived inbred fly lines with fully sequenced genomes6. Numerous loci identified in these studies are novel, including predicted transcripts of unknown function. In addition, sex- and environment-specific effects and epistasis are prominent in the genetic architecture of many complex traits, such as chill coma recovery and starvation resistance8. Many genes identified by GWASs to date play roles in early development, while others are computationally predicted without annotation9. System genetic approaches that combine analyses of variation at the DNA level with transcriptional variation and variation in organismal phenotype allow the construction of gene networks associated with complex traits in Drosophila10.An ever-increasing number of human disease genes will be identified in the coming years along with a deeper understanding of the genetic risk factors for complex diseases. Genetic studies of human diseases have limitations, due to uncontrolled genetic backgrounds, diverse environmental exposures, difficulty to precisely quantify phenotypes, especially for behavioral traits, and the extent of linkage disequilibrium within the human genome11. This could be attributed to a preponderance of a vast number of rare alleles that evade detection even with large sample sizes. Here, we present analyses of variation in startle response, a universal survival behavior, to investigate fundamental features of the genotype-phenotype relationship that underlies the manifestation of complex traits.Although GWASs have identified hundreds of common variants associated with complex traits and susceptibility loci have been reported for many diseases, the overall genetic risk explained by these loci remains modest12 and a previous study showed evidence for epistasis both at the level of QTLs and P-element-induced mutations13. As might be expected, genes required for nervous system development were implicated for startle-induced locomotion14. An analysis of epistatic interactions among a subset of these genes showed a network of genes associated with neurodevelopment12.A simple, high-throughput and reproducible assay to quantify startle-induced locomotion has been developed and used to map candidate genes corresponding to QTLs. Transcriptional analysis indicated that a large fraction of the genome affects this trait15, and is difficult to be detected both in genome scans and experiments13. To detect pairwise or higher-order interactions, powerful computational capability and appropriate statistical approaches are needed16. Epistasis was indicated in various of traits of Drosophila, such as aggressive behavior17. Since some genes are differentially expressed between males and females, the sex-biased expression is a potential mechanism that can maintain genetic variation during the course of evolution6. Sex-specific genetic architecture is quite common and natural variations in the autosome can also affect traits differentially in males and females in many species18. Although the contributions of genes to a phenotype can often be well established, little is known about the regulation and interactions of these genes19. Taking into account regulatory networks will provide a better understanding of the genetic mechanisms that orchestrate complex traits20.As an important feature of the genetic architecture of complex traits, epistasis can generate novel phenotypesDrosophila, which took into account gene-gene interactions and sex-specific effects. Previous studies focused on SNPs or transcripts association with phenotypes, and we firstly integrated QTSs and QTTs for network analysis in Drosophila. We found that several QTSs were associated with QTTs, and some QTTs were associated with other QTTs, which provided more information of the regulatory process of startle response.Here, we combined analyses of SNPs and transcripts to elucidate genetic networks associated with startle behavior in Drosophila melanogaster Genetic Reference Panel (DGRP)21. The transcriptome data of 156 lines were also downloaded from the Mackay Laboratory (http://dgrp2.gnets.ncsu.edu/)22. All these lines were derived from inbred lines by the Raleigh, NC, USA population for 20 generations of full-sib mating to produce viable, fertile inbred lines21. Transcript data were obtained from 3- to 5-day-old mated males and females for each of DGRP lines by Affymetrix Drosophila 2.0. All samples were frozen between 1 and 3 pm. The RNAs were extracted from two independent pools (25 flies/sex/line), and hybridized to 10 µg fragmented cRNA to each array21. Startle-induced locomotion was quantified as the number of seconds each fly was active in the 45 s period immediately following the disturbance. There were two replicated measurements (20 flies/sex/replicate) per line. The replicates for each line were assessed on different days. All flies were reared and all phenotypes assessed under standard culture conditions  unless otherwise specified. The sample size for all experiments was N = 20 measurements/sex/genotype13.We downloaded the published genotype data of 156 inbred lines from the k-th genotypes in the h-th gender (yhk) can be expressed by the following mixed linear model,μ is the population mean; qi is the i-th locus effect for both genders with coefficient uik; qqij is the epistasis effect of the i-th locus by j-th locus for both genders with coefficient; uik; eh is the effect of the h-th gender ; qeih is the locus by gender interaction effect of the i-th locus in the h-th gender with coefficient uikh; qqe is the epistasis-sex interaction effect of the i-th locus by j-th locus in the h-th sex with coefficient uijkh; and εkh is the random residual effect of the k-th breeding line in the h-th gender.We used a mixed linear model to detect individual and epistatic loci of SNPs or transcripts significantly associated with variation in startle behavior in the DGRP lines. The genetic model for phenotypic value of the y for association to independent variants of quantitative trait SNPs (QTSs) or of quantitative trait transcripts (QTTs), and also by setting transcript abundance levels as dependent variants y for association to independent variants of SNPs or of transcripts.This genetic model can be used in association mapping by setting phenotype traits as dependent variants 23. Genetic effects were estimated by software QTXNetwork (http://ibi.zju.edu.cn/software/QTXNetwork/) based on GPU parallel computation24. The phenotypic variance (Vp) is the sum of genotype variance (VG), genotype by environment interaction variance (VGE), and residual variance (Vε), which can be written as follows:Association analyses were conducted for detecting SNPs by QTS modular and transcripts by QTT modular using mixed linear model approachesHeritability is defined as the relative contribution of genetic variance to phenotypic variance with the following estimation model:i-th individual locus using Equation P-value < 0.001) by generalized multifactor dimensionality reduction (GMDR)25. Subsequently, mixed linear model approaches in QTXNetwork were applied for additional screening and identification of significant SNPs by setting a total of 2,000 permutation tests to calculate the critical F-value to control the experiment-wise type I error . The total heritability , which was involved in lipid modification and lysophospholipid acyltransferase activity.We conducted tQTS mapping to detect transcripts associated with QTSs controlling startle response. Table . There wGlcAT-S, gukh, CG13196, l(1)G0196, and CG9044)26. We compared their significance level with previous studies, and most of them were more significant (Table 12. Gene ontology (GO) enrichment analysis showed that glu and bun played an important role in nervous system development, while CdGAPr and gukh were involved in neuron development efl was involved in response to abiotic stimulus and stress response27.Among the genes identified, five have been documented previously to affect startle-induced locomotion  to construct a regulatory network similar patterns shown in Drosophila . Genes RHBDL3, MITD1 and SLC2A8 were jointly associated with function of membrane. Genes NADK and EFTUD1 were jointly associated with function of nucleotide binding. In Figure DNAH5, TRMU, ABCC9 and MAP4K3) were jointly associated with function of ATP binding. In Figure Finally, we searched interactions between homologous genes using ila Figs –S4. The 28. In this study, the total heritability was 93.48% in QTS mapping, and 99.63% in QTT mapping. In addition, the heritability for each transcript in tQTTs was high on average  to the total heritability, while individual loci effects accounted for the most part (99.14%). Sex difference may result from different courtship patterns31. Gene Hsp70Bc was involved in stress response and response to abiotic stimulus, while gene Gr22f was associated with functions like neurological system process and sensory perception27.Recent results of human GWAS have explained relatively small heritability for many complex traits caused by missing heritabilityge Table . It was Drosophila revealed dozens of candidate genes21, but in present study, due to the development of new methods and availability for analyzing larger biological data sets, various intermediate endophenotypes can be interrogated via association studies to identify mechanistic links between genotypic variation and gene expression32. Compared with previous studies on startle response, we replicated five genes that are significantly associated with startle response and firstly integrated SNP and transcript data together to construct a comprehensive genetic network, elaborating how some SNPs and transcripts affected phenotype via transcript. Association mapping of tQTSs and tQTTs can provide insight into the genetic architecture of variation in gene expression. Genes encoding proteins participating in the same pathway or being members of the same protein complex are often co-regulated33. Thus, several tQTTs can regulate more than one QTT and some tQTTs were also controlled by another tQTT  and l(2)efl (corresponding to CRYAB) both have additive effects with CG11034 (corresponding to DPP4; Fig. Drosophila to serve as a guide for studies of human genetics36, and a good approach to explore human complex diseases37.Similar to g Figure . In addiSupplementary Information


The coefficient in a linear regression model is commonly employed to evaluate the genetic effect of a single nucleotide polymorphism associated with a quantitative trait under the assumption that the trait value follows a normal distribution or is appropriately normally distributed after a certain transformation. When this assumption is violated, the distribution-free tests are preferred. In this work, we propose the nonparametric risk (NR) and nonparametric odds (NO), obtain the asymptotic normal distribution of estimated NR and then construct the confidence intervals. We also define the genetic models using NR, construct the test statistic under a given genetic model and a robust test, which are free of the genetic uncertainty. Simulation studies show that the proposed confidence intervals have satisfactory cover probabilities and the proposed test can control the type I error rates and is more powerful than the exiting ones under most of the considered scenarios. Application to gene of PTPN22 and genomic region of 6p21.33 from the Genetic Analysis Workshop 16 for association with the anticyclic citrullinated protein antibody further show their performances. In binary-trait genetic association studies, the relative risk (RR) is commonly employed to show the degree of risk of the genetic variants associated with human complex diseases. The RR has two important features that it compares the probability of an event occurring in an exposed group to that of the event occurring in a non-exposed group and the reference group ensures the proper comparison. As an alternative, the coefficient in a linear regression model can be reported to show the association strength between the genetic variant and the quantitative trait. When the trait values follow the normal distribution or are normally distributed after a certain transformation such as the Box-Cox transformation, the corresponding statistical analyses are valid. However, the assumption of normality is often violated in practice. For example, for the anticyclic citrullinated protein antibody (anti-CCP) to be analyzed later, neither the observed values nor their logarithm transformation versions satisfy the normal assumption due to truncation. Sometimes, even though some transformations can be used, different transformations might result in different conclusions.et al.Xj denotes a random variable taking the phenotype values corresponding to genotype j, j = 0, 1, 2. Based on θj, they constructed the maximal test considering three genetic models: recessive, additive and dominant models. Also, Brunner and Munzelθj and their confidence intervals, especially under the alternative hypothesis that the genetic variant is associated with the trait, which is of great interest to investigators when a positive finding has been found.The Kruskal-Wallis test24i = Pr(Y0 < Yi)/[1 − Pr(Y0 < Yi)], where Yi denote the trait value that the subjects taking in the group with genotype i, i = 1, 2. Compared to θj, j = 0, 1, 2, NO has three salient features. First, it gives a comparison of the difference between two groups; the second is that the reference group with genotype value 0 can guarantee a reasonable comparison; third, it equals 1 under the null hypothesis. This paper is organized as follows. In the “Results” section, simulations and real data analysis are conducted to illustrate the performances of the proposed procedures. Some further topics and issues are present in the “Discussion” section. In the “Methods” section, we describe the NO, give its point and confidence interval estimates, and construct the test statistic for a given genetic model and a robust test, which is free of genetic models. At last some technical details are given in the In this paper, we define the nonparametric odds (NO) as λi.i.d. following a truncated generalized extreme value distribution (a heavy-tailed distribution), tGEV with the shape parameter 0, the location parameter 0, the scale parameter 1 and the truncated point 0. We consider β0 = 0.5, We conduct simulation studies to explore the performances of the proposed procedures. Consider the linear model β1 = 0.25 and MAF = 0.15, the cover probabilities of the Standard Interval, the Wilson Interval and the Log-Delta Interval for λ1 are 95.3%, 95.1% and 95.1%, respectively, which is very close to the nominal level of 95%. When β1 = 0.5 and MAF = 0.30, the cover probabilities of the the Standard Interval, the Wilson Interval and the Log-Delta Interval for λ2 are 94.6%, 94.9% and 94.9%, respectively. The Wilson Interval has the smallest IL and the standard interval have the largest IL among them.β1 = 0.25 and the genetic model is recessive, the empirical CP of the Standard Interval, the Wilson Interval and the Log-Delta Interval are equal to 94.2%, 94.8% and 94.9%, respectively, and the IL of the Wilson interval is (1.313), which is smaller than those of the Standard Interval (1.363) and the Log-Delta Interval (1.319). In general, all the three confidence interval estimation procedures can be used in practice. If we want to choose one, the Wilson Interval is recommended.ZR, ZA and ZD compared with the exiting Kruskal-Wallis test, F test and Jonckheere-Terpstra test under a given genetic model. To make a fair comparison, we construct the Kruskal-Wallis, F and Jonckheere-Terpstra tests under thee genetic models separately. For the simplicity of notations, we denote the Kruskal-Wallis test, F test and Jonckheere-Terpstra test under the recessive model by KW-R, F-R and JT-R, respectively, under the additive model by KW-A, F-A, and JT-A, respectively, and under the dominant model by KW-D, F-D and JT-D, respectively. Besides three MAFs of 0.15, 0.30 and 0.45, we also consider MAF of 0.05. The nominal significance level is set to be 0.05. ZR, ZA, and ZD test are 0.043, 0.048, 0.055, 0.041, 0.048, 0.051, 0.035, 0.041, 0.042, 0.041, 0.051, and 0.054, respectively. We next explore the empirical type I error rates of the robust tests: Konietschke et al.’s test−4. Similar results are observed, see lm in the package stats to compute the statistical significance of the F test, the function jonckheere.test in the package clinfun to compute that of the Jonckheere-Terpstra test, and the function nparcomp in the package nparcomp to compute that of the KLH test.We first evaluate the empirical type I error rates of the the proposed β0 = 0.50, ε ~ tGEV. n = 1,500 and 2,000 replicates are conducted to calculate the power. The nominal significance level is 0.05. ZR, ZA, ZD, KW-R, KW-A, KW-D, F-R, F-A, F-D, JT-R, JT-A, and JT-D. It indicates that the proposed tests is more powerful than the other tests, especially under the recessive and additive model, sometimes there is more than 10% power increase. For example, when the MAF is 0.10, β1 = 0.50 and the genetic model is additive, the empirical powers of ZA, KW-A, F-A, and JT-A are 0.585, 0.419, 0.311, and 0.453, respectively, and when the MAF is 0.25, β1 = 0.50 and the genetic model is recessive, the empirical powers of ZR, KW-R, F-R, and JT-R are 0.717, 0.618, 0.390, and 0.552, respectively.We conduct simulation studies to compare the power among the above tests. We use the same simulation settings as above and set ZA and MAX3. Since for MAF of 0.05 and that being less than 0.15, KLH cannot control the type I error rate under the nominal significance level of 0.05 and 5 × 10−4, respectively, we do not include it here to make a fair comparison. From β1 = 0.5, and the genetic model is recessive, the powers of the KW-A, F-A, ZA, and MAX3 are 0.693, 0.276, 0.493, and 0.770, respectively. Under the additive model, ZA performs the best among them, it is reasonable since the data are generated under it. So, MAX3 is the most robust test among them.et al.−16) nor their logarithm transformation ones  follow the normal distribution. It is most likely that the observed anti-CCP measurements come from a truncated distribution since the anti-CCP is often measured to confirm RA and so it is not measured in controls. So the statistical analysis under the normal assumption is not reasonable.It is well known that the genetic variants are deleterious to rheumatoid arthritis (RA)13ZA, and MAX3. To evaluate the accuracy of the derived formulas, we also include 100,000 permutations to calculate the p-value of MAX3 in the table. The p-value results using permutation is very close to that derived from the asymptotic distribution. We find that the minimum p-value for 25 SNPs is 0.018 using MAX3. Under the significance level of 0.05, and after the Bonferroni correction, no significance reveals. The reason might be that we do not use the data whose anti-CCP measures are less than 20.053. For the genomic region of 6p21.22, there is one SNP rs2734583, which is significant under the significance level of 0.05, and the Bonferroni correction. The p-values using KW-A, F-A, ZA and MAX, are 0.0004, 0.0009, 0.0014, and 0.0008, respectively.Here we only use the genotype data in cases since the anti-CCP values are missing in controls. Linear regression model is a classical approach to evaluate the association between the genetic variants and the continuous traits, where the coefficient in the model indicates the magnitude of the risk of a candidate SNP. When the trait values are normally distributed, most statistical analysis techniques are available. When it is violated, although the linear model can still be done, the estimated coefficient can not measure the real strength of the deleterious SNP. The log-transformation or the Box-Cox transformation is often employed to handle the non-normal quantitative trait. However, different transformations might result in different conclusions. In this work, we define the NR and NO, and give their point and confidence interval estimates under the general framework. Then we define the recessive, additive and dominant genetic models using NO and propose the corresponding point and confidence interval estimates. We also provide three test statistics under different genetic models and a robust test being free of genetic model. Extensive simulation studies shows that the cover probabilities of the proposed procedures are satisfactory, because they are close to the nominal level, and the proposed test is powerful than the exiting procedures under a given genetic model. Applications to GAW16 data for anti-CCP further show the performances of the proposed procedures.There are several issues that can be further investigated. First in this work we study only the scenario for the common allele under the hypothesis of common disease common allele. Under the hypothesis of common disease rare allele, how to extend the proposed procedure to this situation is worth considering, where the large sample theory does not hold. Secondly, we focus on the biallelic marker, the proposed procedures can be readily extended to multiallelic locus, and we can similarly define the NR and NO and construct the confidence intervals of NO. Thirdly, it is well known that family-based association studies is less susceptible to the confounding factors than population-based studies, so a trio design with two parents and an offspring could be considered in the future.Confounding factors can not be ignored and adjusting for covariates is an important issue in population-based genetic association studies. For example, The hidden population structure can lead to many false-positive findings and correcting for population stratification has become a routine in genome-wide association studies (GWASs)16i1 and group with genotype i2)/In the proposed NR and NO, we do not consider the ties among the observations because the probability of the event that there are ties in the observations for the phenotype taking continuous values is zero. However, if there are ties among the sample, we can add A1 under the additive model, the linear combination of To calculate λn0,n1,n2} goes to infinity. However, how many samples we need to get an accurate estimation? Based on the simulations, 1,000 more sample size is needed to get a reasonable estimate. In the current GWASs, the sample size is tens of thousands. For example, the Wellcome Trust Case Control Consortium (2007)et al.’s (2009)In the nsortium 0720 usedt distribution. Results given in the In the simulations, we consider the situation where the MAF is greater than 0.1, the sample size is larger than 1,000 and the distribution is truncated generalized extreme value distribution. Actually, we also conduct the simulation studies considering that MAF is 0.05, the sample size is 1000 and 500, and the distribution is normal distribution and centralized The proposed NR and NO can estimate the non-parametric disease risk and odds in studies with “prospective sampling” such as a cohort study. However, in case-control studies, the case/control ratio for recruited subjects is different from the true one in the population. The derived tests can still be used to detect the association between the quantitative trait and genetic variants in case-control association studies. Finally, the proposed procedure has been coded in R verion 3.1.1 and is freely requested from the corresponding author.n subjects are enrolled from a source population in a quantitative trait genetic association study. A biallelic SNP is considered here. Assume that the genotype is coded as 0, 1, and 2, corresponding to the count of a certain allele. Let yi and gi denote the trait value and genotype value of the ith subject. For the convenience of notations, let the first n0 subjects have the genotype 0, the second n1 subjects have the genotype 1, and the last n2 subjects possess the genotype 2. Suppose that the group with genotype 0 is the reference group. Denote fi = Pr(Y0 < Yi), where Y0, Y1 and Y2 denote the random variables that take the values in three sets Y0,Y1 and Y2 are continuous. fi shows the probability that the trait values in a group with genotype i is stochastically larger than those in the reference group with gentoype 0, i = 1,2. We call f1 and f2 the nonparametric risk (NR). Then we define the NO as λi = fi/(1 − fi), i = 1, 2. We point out that when any of the f1 or f2 is one, the estimator for λ1 or λ2 is not defined, although this is unlikely to occur in practice. The null hypothesis is H0 : f1 = f2 = 0.5 or λ1 = λ2 = 1. The alternative hypothesis is H1 : f2 ≥ f1 ≥ 0.5 and f2 > 0.5 or equivalently, λ2 ≥ λ1 ≥ 1 and λ2 > 1.Suppose that f1 and f2 are, respectively,The empirical estimators of andThe estimators of the variances of andThe estimator of the covariance between fi at the significance level of 1 − α (0 < α < 1) isFrom the theorem in the α/2 quantile of the standard normal distribution. Since λi is a strictly monotonic increasing function of fi, the Standard Interval of λi iswhere Ni, fi) where Ni represent the number of trials and fi is the success probability. We estimate the efficient Ni by setting i = 1, 2. So, the Wilson Interval has the form of Besides the Standard Interval, we can also construct the Wilson Interval22andi, i = 1, 2. Denote The Log-Delta interval is obtained by using the delta method on the logarithm of λζi at the significance level of 1 − α isThen the confidence intervals of i = exp(ζi) is a strictly monotonic increasing function of ζi, the Log-Delta interval of λi isSince λ1 = λ12 > 1 and λ2 > 1 with λ12 = f12/(1 − f12) and f12 = Pr(Y1 < Y2). In other words, the odds of trait values in the group with genotype 2 relative to those in group with genotype 1 is equal to that of trait values in group with genotype 1 relative to those in group with genotype 0. Based on the definitions, λ1 = λ12 is equivalent to f1 = f12.A genetic model refers to a specific mode of inheritance. The commonly used genetic models in binary trait genetic association studies are recessive, additive and dominant models. For the non-normal distributed quantitative trait, the genetic models and the corresponding point estimates of NO are given in fR1 and fD1 can be obtained. The construction of the confidence interval for λA2 under the additive model is similar to that of Using the notations in whereandThus the variance of H0, where H0. The test statistic under the recessive model is We construct the nonparametric test statistics under the recessive, additive and dominant models to test andH0, Under where Under the additive model, the test statistic is So the two-sided p-value isSimilar to the recessive model, the test statistic under the dominant model is H0, Under 1 and λ2 and give some asymptotical statistical properties. However, they do not consider the genetic model. The test statistic ZA is obtained by combining the data of subjects with genotype 0 and 1, and ZD is obtained by combining the data of subjects with genotype 1 and 2. ZA is obtained by weighting λ1 and λ2. They can be seen the combination of λ1 and λ2 to some extent under the fixed genetic model. When the genetic model is fixed for a given SNP, one can use the above tests to do the association studies. However, the mode of genetic inheritance is often unknown in practice. The test obtained under an improper model might lose power substantially. So, a robust test, which is free of genetic model is preferred. Let’s consider a robust testIn the above section, we define λH0, τ follows a three dimensional normal distributionγRA, γAD and γRD are also presented in the pmvnorm in R pacakge mvtnorm to calculate its p-value.the maximum value of three nonparametric tests. Under How to cite this article: Zhang, W. and Li, Q. Nonparametric Risk and Nonparametric Odds in Quantitative Genetic Association Studies. Sci. Rep.5, 12105; doi: 10.1038/srep12105 (2015).


This allele also significantly influences each of two diseases, diabetes and cancer, in antagonistic fashion in different populations. Combined significance of the pleiotropic effects was p = 6.6×10−21. Causal mediation analyses show that endophenotypes explained only small fractions of these effects. This locus harbors an evolutionary conserved gene-desert region with non-coding intergenic sequences likely involved in regulation of protein-coding flanking genes ZEB2 and ACVR2A. This region is intensively studied for mutations causing severe developmental/genetic disorders. Our analyses indicate a promising target region for interventions aimed to reduce risks of many major human diseases and mortality.Gaining insights into genetic predisposition to age-related diseases and lifespan is a challenging task complicated by the elusive role of evolution in these phenotypes. To gain more insights, we combined methods of genome-wide and candidate-gene studies. Genome-wide scan in the Atherosclerosis Risk in Communities (ARIC) Study  was used to pre-select promising loci. Candidate-gene methods were used to comprehensively analyze associations of novel uncommon variants in Caucasians  located in band 2q22.3 with risks of coronary heart disease (CHD), heart failure (HF), stroke, diabetes, cancer, neurodegenerative diseases (ND), and mortality in the ARIC study, the Framingham Heart Study , and the Health and Retirement Study . We leveraged the analyses of pleiotropy, age-related heterogeneity, and causal inferences. Meta-analysis of the results from these comprehensive analyses shows that the minor allele increases risks of death by about 50% ( Biomedical research and medical care are traditionally focused on individual health conditions in order to postpone, ameliorate, or prevent the accumulation of morbidities in late life. An attractive idea is to find factors, which could reduce burden of not just one disease but a major subset of them to efficiently extend healthy lifespan. Here we focus on the analyses of genetic predisposition to risks of major human age-related diseases and mortality. The analyses highlight a locus in band 2q22.3 associated with risks of coronary heart disease, heart failure, stroke, diabetes, cancer, neurodegenerative diseases, and death. Our analyses indicate a promising target region for interventions aimed to reduce risks of many major human diseases and mortality. The demographic transition on aging of populations in developed countries requires strategies, which could extend healthspan and lifespan, and compress morbidity –3. BreakHeterogeneity is the result of various processes. Of the most familiar sources of heterogeneity, genome-wide association studies (GWAS) commonly handle those associated with evolutionarily selected genetic patterns in populations  and compwidespread phenomenon in humans [Age-related traits are a special case of heterogeneous phenotypes, because they emerge as a new n humans , especian humans , and becn humans ,8–12. Thn humans , i.e., en humans ). An impn humans ,14,15.The age-related heterogeneity implies the potential existence of gene-endophenotype-phenotype pathways  and that these mechanisms can change with age, time, and population composition even if the same genetic variant and trait are considered. This heterogeneity can naturally contribute to non-replication of genetic effects in different populations even in case of populations of the same ancestry and phenotypes –18.The elusive role of evolution in fixing molecular basis of age-related traits can also benefit genetic analyses because it can enhance the basis of pleiotropic influences on different traits, including “apparently distinct” ones . StatistGiven specific properties of age-related traits, common methods of GWAS of these traits may be insufficient and more comprehensive methods typical for candidate gene studies may be needed. One strategy to improve genetic analyses of age-related traits is to use genome-wide scan for pre-selection of promising SNPs and more comprehensive methods for detail analyses of these SNPs in large samples.p = 4.6×10−9), CHD by 35% (p = 8.9×10−6), HF by 55% (p = 9.7×10−5), stroke by 25% (p = 4.0×10−2), and ND by 100% (p = 1.3×10−3). This allele also significantly influences each of two diseases, diabetes and cancer, in antagonistic fashion in different populations. Combined significance of the pleiotropic effects was p = 6.6×10−21.Following this approach, we selected promising SNPs from GWAS of the Atherosclerosis Risk in Communities (ARIC) Study see . Then weBasic characteristics of the ARIC, FHS, and HRS genotyped participants relevant to our analyses and the available sample sizes for carriers and non-carriers of the minor allele (rs222826_T in ARIC and FHS and rs222827_A in HRS) are given in p = 1.1×10−7) and death in FHS . Leveraging these pleiotropic associations, the global null hypothesis that neither of these associations is true evaluated using the Fisher’s combined probability test [p = 4.7×10−12. This result implies that the probability of being a false finding in this case is much smaller than that defined by the genome wide significance (pGW = 5×10−8). Multiple testing correction for 15 tests with other phenotypes, which are not included in the Fisher’s test  associated with risks of death in two other studies (ARIC and HRS) and with risks of HF , stroke (FHS), diabetes , and cancer . It increases risks of all diseases and death  which may be a natural source of age-related genetic heterogeneity see the .p = 7.6×10−1; p = 2.3×10−1) and FHSO  cohorts. Formal test shows that multiplicative interaction of the minor allele with these FHS cohorts is significant . Explicating this heterogeneity, the effect size in the FHSO  became the same as in ARIC  and HRS . Important result is that we observe the same effect sizes in three cohorts of younger individuals who were born in the same time period around 1930s-1940s  and TG  in ARIC and BMI  in the FHS original cohort  explaining about 34.4% of the death risk.In FHS , only stIn HRS, the minor allele showed small but significant indirect effects on risk of death through diabetes and cancer . IndirecExplicating age-related heterogeneity in the above sections helped in gaining further insights into genetic predisposition to diseases and death and, as a result, in improving estimates of the effects of the minor allele on these outcomes. Accordingly, pooling genetic effects in different populations should leverage these insights.Explicating age-related genetic heterogeneity in the FHS” above and “The role of endophenotypes” in the p = 3.3×10−4. Further, significant antagonistic effects for cancer in different studies imply significant heterogeneity  and lifespan (risk of death) in four cohorts from three studies, ARIC, FHS, and HRS. To comprehensively characterize these associations, we adopted an analytic strategy which leveraged the analysis of age-related heterogeneity (this concept is detailed in the Introduction), causal mediation analysis, and information on pleiotropic effects.Our initial findings  show conDissimilar associations in different studies may reflect differences in biodemographic structures in these studies . The resAccumulating evidence suggests the importance of age-related heterogeneity in genetic effects. The analyses highlighted the role of age in genetic regulation of BMI , sensitiAn important result of these analyses is that they highlight potential non-stochastic mechanisms, which can contribute to non-replication of genetic associations. Clearly, biodemographic factors are not the only ones that can cause non-replication; other factors  may plaCausal mediation analyses showed that three biomarkers-endophenotypes  significantly moderated the effects between the rs222826_T allele and risks of several diseases and death. They, however, accounted for a small fraction of the genetic effects implying that the major fraction of these effects is not through the selected biomarkers.As expected, lipids showed significant mediating effects explaining a fraction of the total detrimental effects on cardiovascular diseases in ARIC.Favorable association of the rs222826_T allele with BMI in the FHS original cohort partly explained its favorable association with risk of diabetes in the same cohort . This reWe also found that BMI was a significant moderator in the FHS original cohort amplifying effects between the rs222826_T allele and HF or ND. This moderation effect implies that favorable association of this allele with BMI  can partThe mediation analysis of indirect effects of the rs222826_T allele on risks of death through diseases-endophenotypes  providedWe found that the rs222827_A allele and cancer increase the risk of death in HRS additively . HoweverThus, the results from the causal mediation analyses indicate that most of the effects between the minor allele and risks of diseases and death are only partly explained by the selected endophenotypes. These results suggest that such a wide impact of this allele on phenotypes with major contribution to healthspan and lifespan may indicate connections of this variant with some fundamental biological mechanisms (see below) that is in line with the concept of geroscience . Modulatp = 8.3×10−14 to p = 6.6×10−21. The minor allele increases risks of death by about 50%; this estimate is genome-wide significant (p = 4.6×10−9). In addition, the same allele increases risks of CHD by 35% (p = 8.9×10−6), HF by 55% (p = 9.7×10−5), stroke by 25% (p = 4.0×10−2), and ND by 100% (p = 1.3×10−3). This allele is also associated with risks of diabetes (p = 1.6×10−4) and cancer (p = 1.8×10−3). Most of its effects are detrimental as it increases risks of diabetes in younger generations from ARIC, FHSO, and HRS by 35%  and risk of cancer in ARIC and FHS by 23% .A “side effect” of gaining insights into intermediate mechanisms connecting genes with major phenotypes contributing to healthspan and lifespan discussed above is improving statistical estimates . Indeed,The rs222826  SNP is an intergenic variant with MAF of about 2.5% in each of three Caucasian populations in ARIC, FHS, and HRS. This SNP is located on chromosome 2 in band q22.3, which harbors gene desert region . StudiesZEB2  (-1.6 Mb) and ACVR2A (activin receptor type-2A) (+1.7 Mb). Function of ZEB2 can be directed in a tissue- and age-dependent manner by long- (1.2 Mb) and short- (62 Kb) distance enhancers suggesting a conserved regulatory string of enhancers for ZEB2 and possibly ACVR2A [ZEB2 were also observed [r2 = 1 with rs222809; The rs222826/rs222827 SNPs are within an evolutionary conserved gene desert region –42, whicy ACVR2A . Other lobserved ,45. ThesZEB2 gene functions as a regulator of transcription interacting with activated SMADs in the TGF-β signaling pathway and ACVR2A is part of a receptor complex that binds and activates SMAD transcriptional regulators. Accordingly, these genes are linked through SMAD proteins and TGF-β signaling. The ZEB2 gene is one of key regulators of epithelial-to-mesenchymal transition playing a critical role in the development of neural crest and is involved in the development of other organs that are not derived from the neural crest. This gene mediates multiple pathways related to inflammation, aging and carcinogenesis [ACVR2A gene takes part in many distinct pathways by mediating the functions of members of TGF-β superfamily which are involved in a variety of biological functions including development and tissue homeostasis and associated with a wide range of human diseases [The ogenesis . The ACVdiseases ,51.ZEB2  and deletions at 2q22-24 are associated with a Mowat–Wilson syndrome, a complex developmental disorder involving a range of physical symptoms as well as severe intellectual disorders [Various mutations in isorders ,52,53. Disorders ,54.Potential functional importance of this gene desert is supported by the results of our analyses showing extensive pleiotropic effects on major human diseases and strong effect on human survival.The ARIC Study participants [icipants  (aged 45The FHS design has been previously described [rd Generation (3rd Gen) cohort (aged 21–71 at baseline in 2001). Measurements of biomarkers were available at multiple examinations in the FHS/FHSO and the baseline in the 3rd Gen cohort. Data on onsets of diseases and survival were available through 2011. Biospecimens were mostly collected in the late 1980s and through the 1990s from surviving participants [escribed –58. We uicipants ,60. GenoThe HRS design has been previously described [escribed . We usedThe focus of the analyses was on risks of major human diseases available in the data including coronary heart disease (CHD), heart failure (HF), stroke, diabetes, cancer, and neurodegenerative diseases , and risk of death. Biomarkers represented by the traditional risk factors for cardiovascular diseases were used for causal mediation analyses (see below). They included body mass index (BMI), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), triglycerides (TG), systolic blood pressure (SBP), and diastolic blood pressure (DBP).Because genetic variants may play a complex role in age-related traits see the , traditiplink [p = 10−4 for Hardy-Weinberg disequilibrium. MAF filter was >1%. We combined individual p-values across all traits using the Fisher’s combined p-value [p-value pF<3×10−9 = 5×10−8/15.We conducted univariate genome-wide scan using plink  with the p-value . The anaCandidate-gene-like analyses were conducted for the associations of SNPs from a promising pre-selected locus in band 2q22.3 with risks of diseases (see the above section) and death. These analyses were focused on ARIC, FHS, and HRS. The pre-selected SNP rs222826 was directly genotyped in ARIC and FHS. In HRS we used its proxy, rs222827, which was 90 bp away from rs222826. These SNPs were in 100% linkage disequilibrium (LD) in CEU population. These variants were uncommon in Caucasians with MAF of about 2.5% in each of our datasets. We considered a dominant genetic model for the minor allele. coxme package in R) to adjust for potential clustering. Information on both prospective and retrospective onsets of diseases in the FHS was used in these analyses. The use of retrospective onsets in a failure-type model is justified by Prentice and Breslow [glm function in R). Empirical survival age patterns were characterized by the Kaplan-Meier estimator.The data on age at death were available in all studies. The data on onsets of diseases were available in ARIC and FHS. The hazard ratios (HR) of death  and diseases (FHS and ARIC) were evaluated using the Cox proportional hazards mixed effects regression model  associations with rs222826/rs222827. These associations were characterized by a linear mixed effects model (lmer function in lme4 package in R). Measurements of BMI, TC, HDL-C, and TG were log-base-10-transformed to offset potential bias due to skewness of their frequency distributions. They were multiplied by 100 for better resolution. Measurements of SBP, and DBP were not transformed as no significant skewness was observed. In the ARIC and FHS datasets, these endophenotypes were measured on multiple occasions during follow-up of the same individuals. We evaluated the associations for SNPs given the measurements of these endophenotypes for individuals of a given age at each examination with available measurements. We used a three-level mixed effects regression model to account for familial and repeated-measurements correlations. Information on longitudinal measurements has multiple advantages including statistical power gain in the analyses [analyses . In the All statistical tests were adjusted for:  age, sex; (ARIC) field center; (FHS) FHS cohorts and whether the DNA samples had been subject to whole-genome amplification , and  selected biomarkers as endophenotypes for diseases and death, and (ii) diseases as endophenotypes for death . Phenotypic HRS data are available publicly and through restricted access from S1 Fig(PDF)Click here for additional data file.S2 FigRed color denotes regulatory regions and those variants in these regions, which are in LD with rs222826. Inserts show characteristics of SNPs from regulatory regions, which are in LD with rs222826.(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.