Protocadherin 15 (PCDH15), which encodes a member of the cadherin super family that contributes to neural development and function, has been cited as a risk gene for neuropsychiatric disorders. Recently, rare variants of large effect have been paid attention to understand the etiopathology of these complex disorders. Thus, we evaluated the impacts of rare, single-nucleotide variants (SNVs) in PCDH15 on SCZ or ASD. First, we conducted coding exon-targeted resequencing of PCDH15 with next-generation sequencing technology in 562 Japanese patients (370 SCZ and 192 ASD) and detected 16 heterozygous SNVs. We then performed association analyses on 2,096 cases  and 1,917 controls with six novel variants of these 16 SNVs. Of these six variants, four  were ultra-rare variants  that may increase disease susceptibility. Finally, no statistically significant association between any of these rare, heterozygous PCDH15 point variants and SCZ or ASD was found. Our results suggest that a larger sample size of resequencing subjects is necessary to detect associations between rare PCDH15 variants and neuropsychiatric disorders.Both schizophrenia (SCZ) and autism spectrum disorders (ASD) are neuropsychiatric disorders with overlapping genetic etiology.  Schizophrenia (SCZ) and autism spectrum disorders (ASD) are neurodevelopmental in origin. While SCZ and ASD are regarded as separate clinical entities, etiological, clinical, and genetic overlap between them have been discovered ,2. GenetPCDH15 is a member of the largest group in the cadherin superfamily that involved in generating neural diversity for neuronal differentiation and synapse formation [PCDH15 is primarily recognized as a gene that forms tip-link filaments in sensory hair cells and associated with Usher syndrome type 1F (OMIM 602083) [PCDH15 as relevant to neurocognitive processes [PCDH15 is expressed throughout the brain and central nervous system (CNS) during embryogenesis [PCDH15 were recently identified in ASD [PCDH15 is a promising candidate risk gene for neuropsychiatric disorders because several neuropsychiatric disorders including SCZ, ASD, and BD share genetic risk factors [PCDH15 variants in cases of neuropsychiatric disorders.ormation . PCDH15  602083) . Notably 602083) . A GWAS rocesses . In miceogenesis , and infogenesis . Rare, ed in ASD  and bipod in ASD ,28. Thes factors ,27,29,30PCDH15 variants might confer susceptibility to neuropsychiatric pathogenesis. To increase statistical power and detect shared risk, we combined SCZ and ASD samples in a study cohort [PCDH15 coding exons in 562 Japanese patients; we then conducted single-variant association analysis in an independent case-control set comprising 4,013 samples to identify putative variants with large effect.Our hypothesis was that rare y cohort ,31,32. FDiagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) for SCZ or ASD. Healthy control subjects were selected from the general population and had no history of mental disorders based on questionnaire responses from the subjects themselves during the sample inclusion step. The study was explained to all participants and/or their parents both verbally and in writing. Written informed consent was obtained from the participants and from the parents of the patients under 20 years old. All procedures performed in this study involving human participants were approved by the Ethics Committee of the Nagoya University Graduate School of Medicine and conducted in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.Two independent Japanese sample groups were used in this study. For the targeted-resequencing discovery cohort, 370 SCZ  and 192 ASD  individuals participated. For genetic association analysis, the case control sample set comprised 1,714 SCZ , 382 ASD , and 1,917 control subjects . All subjects were unrelated, living on the mainland of Japan, and self-identified as Japanese. All patients fulfilled the criteria listed in ® dsDNA BR Assay Kit  on a Qubit® 2.0 Fluorometer  following the manufacturer’s recommended protocol.DNA was extracted from peripheral blood or saliva from each SCZ, ASD, and control participant. For DNA extraction, we used the QIAamp DNA Blood Kit or Tissue Kit . The quantity of extracted DNA was estimated using the Qubit™ was used to resequence the PCDH15 coding regions  via the protocols described in the Ion AmpliSeq™ Library Preparation User Guide , Ion PGM™ Template OT2 200 Kit , and Ion PGM™ Sequencing 200 Kit . After target-specific PCR amplification, amplicons were purified and pooled. Libraries were then prepared to obtain 200-bp PCR fragments flanked by adaptor and barcode sequences; these sequences allowed sequencing and sample identification respectively. The concentration of each library was determined with the Ion Library TaqMan Quantitation Assay Kit . Amplified libraries were subjected to emulsion PCR and subsequent enrichment for template-positive Ion Sphere™ particles (ISPs) with the Ion OneTouch™ system . ISPs were enriched and sequenced in a 200-bp configuration run using 318 chips .The next-generation sequencing technology on the Ion Torrent PGM™ version 4.4  to generate sequence reads filtered according to the pipeline software quality-controls and to remove poor signal reads. Reads assembling and variant identification were performed with the Ingenuity Variant Analysis software™ (http://www.ingenuity.com/variants) from Ingenuity Systems using Fastq files containing sequence reads and the Ion Ampliseq Designer BED file software to map the amplicons with default parameters, . Candidate variants were defined as exonic or splice-site variants with allele frequencies of ≤1% in the following six public exome databases: dbSNP Build 139 (http://www.ncbi.nlm.nih.gov/projects/SNP/), the 1000 Genomes Project (http://www.1000genomes.org), NHLBI ESP exomes (http://evs.gs.washington.edu/EVS/), the Human Genetic Variation Database (http://www.genome.med.kyoto-u.ac.jp/SnpDB/), the Exome Aggregation Consortium (http://exac.broadinstitute.org) and the Genebook (http://atgu.mgh.harvard.edu/~spurcell/genebook) [http://genetics.bwh.harvard.edu/pph2/) [in silico prediction of functional consequences. Additional clinical variant annotations were obtained from NCBI ClinVar  [http://tools.genes.toronto.edu) [http://www.h-invitational.jp/evola/search.html) [De novo analysis was performed when DNA samples from parents were available.Sequence reads were run through a data analysis pipeline on the Ion Torrent platform-specific pipeline software, Torrent Suiteenebook) , 27. To enebook)  and Polyu/pph2/)  were uselinvar/) . To analnto.edu) . Evolutich.html) . De novoSanger sequencing with the ABI 3130xl Genetic Analyzer , and standard methods were used to confirm each candidate variant. Sequence analysis software version 6.0  was used to analyze all sequence data. Primer sequences for validating each variant are available in An ABI PRISM 7900HT Sequence Detection System  and TaqMan assays with custom probes were used to genotype putative deleterious variants. Custom probe sequences are available in http://pngu.mgh.harvard.edu/~purcell/gpc/) [We computed the effective sample size and statistical power using a web browser program, Genetic Power Calculator developed by Purcell et al. (ll/gpc/) .http://www.ddbj.nig.ac.jp) under the accession number DRA004490.Nucleotide sequence data reported have been deposited in the DNA Data Bank of Japan (DDBJ) databases (PCDH15 exon and exon-intron boundary in genomic DNA isolated from Japanese patient sample (n = 562). Of 17 SNVs and three indels detected by the Ion Torrent PGM™, one SNV and three indels were not validated by Sanger sequencing. In total, we evaluated one splice-site variant and 15 missense variants  was (result not shown).Of the 15 missense variants, 12 were predicted to be damaging with on tools . No varide novo variants were found in these 16 cases.We were able to determine inheritance status for 16 cases. Among these 16 cases, 10 involved mother-to-son variant transmission, three involved father-to-son transmission, and three others involved father-to-daughter transmission . An affein silico predictions and because each was not registered in any of the public databases described in the Materials and Methods. p.M60I was included in the association analysis because it was previously detected in a Japanese boy with developmental delay and hearing loss [in silico analysis nor absent from the Human Genetic Variation Database. Each of these six SNVs was located in a genomic region that is highly conserved among eight vertebrate species  and the splice-site variant (c.3010-1G>C) as novel ones because they were predicted to be damaging or to affect splicing based on ing loss , althoug species . Brief iPCDH15 with case-control analysis. Post hoc calculation of statistical power based on a minor allele frequency of 0.00048  and controls , we computed a statistical power of >80% using the following parameters: disease prevalence of 0.01, observed rare-allele frequency of 0.0021, odds ratio for dominant effect of ≥ 3.59, and type I error rate of 0.0083 . An overview and each phenotype of genetic association analysis of the six novel variants are presented in  p.M60I,  revealedPCDH15 variants to neuropsychiatric disorders and susceptibility to these disorders. We conducted targeted resequencing of coding exons in PCDH15 for 562 Japanese patients and detected 16 heterozygous SNVs as condition-related candidate genes. More rare SNVs were detected from samples of ASD than those of SCZ. Of these 16 SNVs, five SNVs  were selected because they were both predicted to be protein-damaging by in silico analysis and not registered in public databases or found with a very low frequency in ExAC. p.M60I was selected because it previously implicated in developmental delay [a priori calculation indicated our sample size was appropriately powered to determine statistical significance of SNVs. To assume the odds ratio for dominant effect of rare SNVs would be more than 3.59 seems reasonable according to previous studies that reported odds ratios from 1.88 [PCDH15 SNVs and either neuropsychiatric disorder.To our knowledge, this is the first study to investigate the contribution of rare al delay . An inderom 1.88  to 7.1 [rom 1.88 . Of thesrom 1.88 ,41,42,43in silico analysis might change the biological functions of PCDH15. PCDH15 plays an essential role in maintenance of normal retinal and cochlear function [We find it interesting that all protein-coding SNVs observed in the resequencing cohort were located within the PCDH15 extracellular domain , which mfunction . Atypicafunction ,45,46. Tfunction ,47; c.30de novo variants have been the focus of research on SCZ and ASD pathogenesis, inherited variants also contribute substantially to these complex diseases [In this study, all inheritance statuses were either from apparently unaffected parents or of unknown origin, suggesting variable penetrance ; S1 Fig.diseases . In addidiseases ,51. Recediseases ,48,52,53PCDH15 SNVs. First, due to extremely low minor-allele frequencies (< 0.0005) as revealed by the association analysis or to odds ratios lower than expected, we could neither confirm nor dismiss the significance of rare PCDH15 variants in either neuropsychiatric disorder. Post hoc calculations revealed that a larger, higher-powered sample should be sought to reveal relationships between neuropsychiatric disorders and PCDH15 variants. Secondly, we focused on the shared genetic risk to increase the statistical power in this study. Considering that the burden of rare PCDH15 was statistically greater in ASD cases than in SCZ, further research may be needed to provide similarities and differences between SCZ and ASD. Thirdly, we focused on the ENST00000320301 transcript, but PCDH15, like many other neuronal proteins, is structurally diversified through the differential inclusion and exclusion of exons. We did not cover the promoter, untranslated regions, or intronic regions of PCDH15, which contain potentially disease-relevant regions. Fourthly, the lack of DNA from a sufficient number of patient family members prevented us from monitoring variant segregation. Finally, although 81% of the SNVs (13/16) identified in this study were predicted to be protein-disrupting or splicing-altering based on in silico analysis, the exact molecular mechanisms and networks affected by PCDH15 variants in SCZ and ASD remain unclear. Useful model systems that can address these questions will be needed to assess the impact of the SNVs discovered here.There are several explanations for our inability to find statistical evidence for a causative role in SCZ and/or ASD for any of these rare PCDH15 SNVs in Japanese SCZ and ASD patients. We found four ultra-rare variants  that may increase disease susceptibility. No statistically significant association between any rare, heterozygous point PCDH15 variant and neuropsychiatric disorders was detected. A much larger sample size is needed to elucidate the relevance of rare PCDH15 variants to neuropsychiatric disorders.We explored the role of rare S1 FigDiagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria. Note: 1Autism Spectrum Disorder; 2Interectual Disability; 3Attention-Deficit/Hyperactivity Disorder; 4Tic Disorder; 5Epilepsy; 6Schizophrenia.The genotypes of the tested individuals are indicated on the lower-side. All comorbidities were diagnosed by experienced psychiatrists according to (PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 TableNote: A TaqMan probe consists with a FAM or VIC dye label on the 5' end, and nonfluorescent quencher (NFQ) on the 3' end.(PDF)Click here for additional data file.S3 TableNote: Ref, reference; Val, variant. a Based on ENST00000320301; b homozygous for a minor allele / heterozygote / homozygous for a major allele; C P values were calculated by one-tailed Fisher’s exact test.(PDF)Click here for additional data file.


Imprinting effects can lead to parent-of-origin patterns in many complex human diseases. For hypertension, previous studies revealed the possible involvement of imprinted genes. Genetic Analysis Workshop 18 real data, with hypertensive phenotype and genotype of more than 1000 individuals from 20 pedigrees, provided us an opportunity to further substantiate such findings. To test for imprinting effects, we developed a pedigree-parental-asymmetry test taking both affected and unaffected offspring into consideration (PPATu). We carried out a simulation study based on the Genetic Analysis Workshop 18 pedigrees to show that PPATu has well-controlled type I error and is indeed more powerful than the pedigree-parental-asymmetry test (PPAT), an existing method that does not utilize information from unaffected offspring. We then applied PPATu to Genetic Analysis Workshop 18 genome-wide association study data from 20 pedigrees. We identified a number of single-nucleotide polymorphisms showing significant imprinting effects that are within genomic regions that have been previously implicated to be associated with hypertension. Among these imprinted genes, Beckwith-Wiedemann syndrome, Silver-Russell syndrome, Angelman syndrome, and Prader-Willi syndrome are the best known.Genomic imprinting refers to the phenomenon of unequal expression of a heterozygous genotype depending on which parent (father or mother) the imprinted variant is inherited from. It is estimated that approximately 1% of all mammalian genes are imprinted  that considers imbalance of parental origins of the variant allele is simple and powerful. A series of generalizations of PAT, such as the pedigree-parental-asymmetry test (PPAT) for general pedigree data, widen its practical range . However1 and M2, and the disease allele is more likely to be associated with marker allele M1. Let 0, 1, and 2 represent the marker genotypes M2M2, M1M2, and M1M1, respectively. For a child-parents trio, let F, M, and C denote the marker genotypes of the father, mother, and child, respectively. Throughout this article, mating symmetry is assumed; that is, P=P for all f, m = 0,1,2. We also assume that there is no maternal effect; that is, the maternal genotype does not confer additional risk on the child's phenotype.Suppose that the marker of interest has 2 alleles, MN independent pedigrees, and for the ith pedigree, we have i nuunaffected and i naaffected offspring. Define Suppose we have I is the usual indicator function. We can prove that under the null hypothesis of no imprinting effect, E(S) = 0. The unbiased estimator of the variance of S iswhere S statistic; that is, without utilizing information on trios with unaffected offspring. More specifically, The standardized test statistic To evaluate the power of the proposed statistic and to compare with PPAT, we carried out a simulation study under 9 different settings, combinations of 3 sets of haplotype frequencies H1, H2, H3) and 3 imprinting models   genotypes on odd chromosomes and phenotype data, including systolic and diastolic blood pressure (SBP and DBP). In our study, we use a hypertensive binary phenotype; an individual is classified as affected if SBP > 140 mm Hg, or DBP > 90 mm Hg, or on antihypertensive medication at the first examination. There are 20 pedigrees; the sizes range from 27 to 107 individuals. In total, there are only 157 affected offspring, while there are 709 unaffected ones. Hence, based on the experience gained in our simulation, we expect a substantial gain in information for PPATu that makes use of information from both affected and unaffected individuals when compared to PPAT. To reduce the effect of multiple testing, we first used pedigree disequilibrium test (PDT [p values than PPAT. For SNPs detected by both methods, the results for PPATu are more significant. Table Table p values from PDT. The analysis has resulted in larger sets with p values smaller than 0.05, which we call "significant"for easy reference. A search in the NCBI database found 49 genes implicated to be associated with hypertension in previous studies. Figure PLEKHA7 that has been implicated to be associated with hypertension in previous studies [To gain a more global view of the extent of the role of imprinting in hypertension, we also carried out genome-wide testing for imprinting effects without restricting to SNPs with small  studies ,7.In this article, we propose an imprinting test that utilizes both affected and unaffected individuals from general pedigrees, the type of data provided by GAW18. We expect PPATu to be more powerful than the existing test PPAT [The authors declare that they have no competing interests.LS designed the overall study; FZ conducted statistical analyses and drafted the manuscript. Both authors wrote, read, and approved the final manuscript.


However, the genetic architecture underlying the dynamic eggshell quality remains elusive. Here, we measured eggshell quality traits, including eggshell weight (ESW), eggshell thickness (EST) and eggshell strength (ESS) at 11 time points from onset of laying to 72 weeks of age and conducted comprehensive genome-wide association studies (GWAS) in 1534 FITPR2, PIK3C2G, and NCAPG. The homozygotes of advantageously effective alleles on PIK3C2G and ITPR2 possessed the best eggshell quality and could partly counteract the negative effect of aging process. NCAPG had certain effect on eggshell quality for young hens.ESWs at all ages exhibited moderate SNP-based heritability estimates (0.30 ~ 0.46), while the estimates for EST (0.21 ~ 0.31) and ESS (0.20 ~ 0.27) were relatively low. Eleven independent univariate genome-wide screens for each trait totally identified 1059, 1026 and 1356 significant associations with ESW, EST and ESS, respectively. Most significant loci were in a region spanning from 57.3 to 71.4 Mb of chromosome 1 (GGA1), which together account for 8.4 ~ 16.5 % of the phenotypic variance for ESW from 32 to 72 weeks of age, 4.1 ~ 6.9 % and 2.95 ~ 16.1 % for EST and ESS from 40 to 72 weeks of age. According to linkage disequilibrium (LD) and conditional analysis, the significant SNPs in this region were in extremely strong linkage disequilibrium status. Ultimately, two missense SNPs in GGA1 and one in GGA4 were considered as promising loci on three independent genes including Identification of the promising region as well as potential candidate genes will greatly advance our understanding of the genetic basis underlying dynamic eggshell quality and has the practical significance in breeding program for the improvement of eggshell quality, especially at the later part of laying cycle.The online version of this article (doi:10.1186/s12864-015-1795-7) contains supplementary material, which is available to authorized users. A vast number of eggs are produced annually for human consumption worldwide. Avian calcified eggshell as a unique bioceramic material can provide protection to egg contents from physical damage and microorganic invasion , 2. Chanhttp://www.animalgenome.org/cgi-bin/QTLdb/index). However, most previously reported QTLs were identified by low-density linkage analysis with limited markers of microsatellites [In recent years, the genomic , 10, tratellites –24, whictellites , 26. Rectellites . For instellites . The devIt is notable that many complex traits are dynamic varied with the aging process of animals , 30. How2 chicken population. We aimed to explore the associated genomic loci and genes that contribute to the phenotypic variability and dynamic change in eggshell quality traits, and to provide potential breeding tools for the improvement of eggshell quality.In the current study, we conducted GWA analysis on the dynamic eggshell quality traits at 11 time points from onset of laying to 72 weeks old utilizing a 600 K high-density SNP arrays in an Fhsnp2 = 0.46). However, the estimates for EST .Means and standard deviations for eggshell quality traits, including ESW, EST and ESS, at 11 time points from onset of laying to 72 weeks of age are presented in Table P-values for all SNPs affecting eggshell quality traits at 44weeks is given in Fig. Eggshell quality traits were collected at 11 time points from onset of laying to 72 weeks. At each time point, three separate genome-wide association tests were conducted for each eggshell quality trait with univariate method. A total of 1057, 1026 and 1356 genome-wide significant associations were identified with ESW, EST and ESS, respectively  and eggshell strength (ESS) were shown in Additional file The linkage disequilibrium (LD) analysis were then carried out and the results showed the uncovered SNPs in GGA1 were in extremely strong linkage disequilibrium status, especially for the LD block from 64.0 to 67.5 Mb that include half 732/1531) of the total loci , inositol 1,4,5-trisphosphate receptor type 2 (ITPR2), RecQ helicase-like (RECQL), ATP-binding cassette sub-family C member 9 (ABCC9) and cancer susceptibility candidate 1 (CASC1) on GGA1 and non-SMC condensin I complex subunit G (NCAPG) on GGA4. Nonetheless, only two SNPs, rs312347405 and rs316607577, located on PIK3C2G and ITPR2, remained significantly associated with eggshell quality traits after multivariate GWA analysis. Furthermore, as illustrated above, these two SNPs could explain more phenotypic variance than others. Consequently, we first considered PIK3C2G and ITPR2 as the primary candidate genes associated with eggshell quality. The missense mutation in GGA4 that located on NCAPG was posterior putative locus for its roles in affecting early eggshell weight. In addition to missense mutations on exons, the mutations on 3′ or 5′ UTRs were also of biological significance for genetic variation underlying complex traits. We aggregately discovered 15 mutations that located on 3′ or 5′ UTRs corresponding to 13 independent genes   and pathrs312347405 and rs316607577 on PIK3C2G and ITPR2 in GGA1 remained significantly associated with dynamic eggshell quality traits. The loci of rs312347405 on PIK3C2G could explain the most phenotypic variance of eggshell quality traits among the five missense mutations. Hens with homozygotes of advantageously effective alleles (GG) could produce eggs with excellent ESS, which decreased relatively within a narrow range along with the aging process. PIK3C2G encoded proteins belonging to the phosphoinositide 3-kinase (PI3K) family, containing a lipid kinase catalytic domain as well as a C-terminal C2 domain [PIK3C2G possessing the C2 domain acting as lipid binding motif was also implicated in eggshell property. The role of lipid binding proteins in eggshell or during eggshell formation should be re-recognized. PIK3C2G possessing the C2 domain could mediate translocation of proteins to lipid membranes, and also regulate protein-protein interactions in human and mammals [PIK3C2G was considered as candidate gene for eggshell quality.Six significant missense mutations in this genomic region were considered as the most important putative variants. After multivariate GWA analysis, only two loci, 2 domain , which a2 domain . Previou2 domain . Conside2 domain . Further2 domain . Now in  mammals . The int mammals . It coulITPR2) harboring the loci of rs316607577 (exon 25) was the positional and functional candidate gene for eggshell quality revealed by the current GWA analysis. The mutation of rs316607577 was a nonconservative substitution of serine by glycine (S1072G), with the glycine-encoding allele being associated with stronger eggshell. A gene could be inactivated by a mutation either in a control site or in a coding region [rs316447591 on 3′URT of ITPR2 was also the susceptibility locus. ITPR2 was known for its mediation in endoplasmic reticulum (ER) calcium release and inositol 1,4,5-trisphosphate (IP3) could mobilize Ca2+ from intracellular calcium stores to many types of cells [ITPR2 in uterine epithelial cell of chicken had been verified [ITPR2 in uterus during eggshell calcification was significantly higher than that in magnum and duodenum which also possess active calcium metabolism [ITPR2 play roles in the regulation of intra-cellular Ca2+ transportation in uterus and contributed to the process of eggshell calcification. Recently, a genome-wide association study in humans identified ITPR2 as a susceptibility gene for Kashin-Beck disease which is a chronic osteochondropathy [SCNN1b) had effects on eggshell strength [ITPR2 (2.43–11.52 %) in the current study. All the above evidences suggested ITPR2 should be a crucial and promising candidate gene relating to eggshell calcification as well as eggshell mechanical property.The gene of inositol 1,4,5-trisphosphate receptor type 2  located on non-SMC condensin I complex subunit G (NCAPG) gene in GGA4 was discovered in association with eggshell weight for young hens in our GWA analysis. Many other studies reported multiple genomic regions containing this gene were identified to be associated with body weight [NCAPG also involved in influencing eggshell weight. Larger eggs generally owned heavier eggshell. Hence we could not exclude the potential cause that the significant association of NCAPG with ESW was just due to its relevance to egg weight. Nevertheless, we still considered NCAPG as a candidate gene for its consistent association with eggshell weight from 32 to 40 week.Another missense mutation  of the total significant loci. Ultimately, three genes, 2 resource population. For parents, six WL and six DX males were initially mated with 133 DX and 80 WL females and generated 1029 and 552 chicks for F1 generation, respectively. Then 25 males and 407 females from WL/DX cross and 24 cocks and 235 hens from DX/WL cross in F1 generation were used to produce the F2 population, consisting of 3749 chicks in a single hatch originating from 49 half-sib and 590 full-sib families. The hens were kept in individual cages of the same environment with food and water ad libitum at the Jiangsu Institute of Poultry Sciences. Finally, 1534 hens from 49 half-sib families and 550 full-sib families with sufficient phenotypic and pedigree information were selected for SNP genotyping.The reciprocal crosses between a standard breed White Leghorn (WL) and a Chinese indigenous strain Dongxiang chickens (DX) were utilized to develop an FEggshell quality traits including eggshell weight (ESW), eggshell thickness (EST) and eggshell strength (ESS) were measured for the first egg of hens and then at 32, 36, 40, 44, 48, 52, 56, 60, 66 and 72 weeks of age, consisting of 11 time points. Except for the first egg, fresh eggs were collected for 4 successive days to ensure two eggs per hen. Then breaking strength (pole to pole) of each egg was measured vertically with the Eggshell Force Gauge . After that, eggs were broken to remove the internal contents and the remaining eggshells were washed clean with tap water. After drying in the air at room temperature, eggshell weights and eggshell thickness were measured with electronic scale and eggshell thickness gauge  respectively. Descriptive statistics were calculated with R project (R version 3.1.2) using all available records. The function of ‘rntransform’ in the GenABEL package of R project was used for the rank-based inverse normal transformations (INTs) .P < 1 × 10−6 were removed from downstream analysis. Some sporadic missing genotypes were imputed using the BEAGLE v4.0 procedure [R2 > 0.5 were retained. Ultimately, up to 1512 individuals and 435,867 SNPs were valid for the following GWA analysis.Genomic DNA was extracted by standard phenol/chloroform method and genotyped with the 600 K Affymetrix Axiom Chicken Genotyping Array . Affymetrix Power Tools v1.16.0 (APT) software was then used for quality control and genotype calling. Specifically, only samples with dish quality control (DQC) > 0.82 and call rate > 97 % were included into the subsequent analyses. An R script supplied by Affymetrix was run to compute the SNP QC metrics and filter out individual SNPs falling below given thresholds. After these QC steps, 1512 individuals and 532,299 SNPs remained valid. Given the current statistical methods were more powerful for the detection of the associations between phenotypes and autosomal genotypes, all SNPs on sex chromosomes were excluded in QC process. The PLINK v1.90 package  were therocedure , only SNP-values were 8.43 × 10−7  and 1.69 × 10−5 , respectively.Principal component analysis (PCA) implemented in PLINK package was first conducted prior to GWA analysis to eliminate spurious associations resulting from the presence of cryptic relatedness or hidden population stratification. To properly decide the thresholds for genome-wide significant/suggestive associations, a method of simpleM  was usedP-value was calculated for the significance level between SNPs and phenotypes. The univariate linear mixed model is as follows:y is an n × 1 vector of phenotypic values for n individuals, W is an n × c matrix of covariates  including a column vector of 1, α is a c × 1 vector of corresponding coefficients including the intercept, x is an n × 1 vector of marker genotypes at the locus tested, β is the corresponding effect size of the marker and all effects are reported for the minor allele in each marker, u is an n × 1 vector of random polygenic effects with a covariance structure as u ~ N , where K represents a known n × n genetic relatedness matrix derived from SNP markers and Vg is the polygenic additive variance, and ε is an n × 1 vector of random residuals with ε ~ N , where I is an n × n identity matrix, and Ve is the residual variance component. We used the Wald test statistic β = 0, where the best linear unbiased estimate (BLUE) of β and the corresponding sampling variance g and Ve.Univariate association test equipped with an exact mixed model approach in GEMMA v0.94 software  was firsThe manhattan plots and quantile-quantile (QQ) plots were drawn by the “gap” packages  in R proTo further analyze genetic variants affecting the dynamic eggshell quality traits along with aging process, a multivariate association analysis  that dirY is an n by d matrix of d phenotypes for n individuals, W =  is an n × c matrix of covariates  including a column of 1 s, A is a c by d matrix of corresponding coefficients including the intercept, x is an n-vector of marker genotypes, β is a d vector of marker effect sizes for the d phenotypes. It should be noted that G is an n by d matrix of random effects with G ∼ MVNn × d ∼ MVNn × d where Vg is a d by d symmetric positive definite matrix of genetic variance component, and E is an n by d matrix of residual errors with E ∼ MVNn × d where Ve is a d by d symmetric positive definite matrix of residual variance component (K and I are the same in the two models). MVNn × d denotes the n × d matrix normal distribution with mean 0, row covariance matrix V1 (n by n) and column covariance matrix V2 (d by d).where Notably, many SNPs maybe passively significant associated with target traits, resulting from their strong linkage to a really causal mutants. To demarcate independent association signals in a putative region, the conditional analyses as well as the LD analysis were both performed in multivariate and univariate models, through fitting the genotypes of one candidate signal as covariates . In genehsnp2). We also quantified the pair-wise genetic and phenotypic correlations for each trait at multiple time points with the bivariate mixed model [Univariate restricted maximum likelihood (REML) implemented in GCTA v1.24 program  was perfed model . A genetVariant Effect Predictor (VEP) and Biomart tools based on the Galgal4 assembly supported by Ensembl were used for the identification of candidate genes that the significant loci located on  and dete


A usually confronted problem in association studies is the occurrence of population stratification. In this work, we propose a novel framework to consider population matchings in the contexts of genome-wide and sequencing association studies. We employ pairwise and groupwise optimal case-control matchings and present an agglomerative hierarchical clustering, both based on a genetic similarity score matrix. In order to ensure that the resulting matches obtained from the matching algorithm capture correctly the population structure, we propose and discuss two stratum validation methods. We also invent a decisive extension to the Cochran-Armitage Trend test to explicitly take into account the particular population structure.We assess our framework by simulations of genotype data under the null hypothesis, to affirm that it correctly controls for the type-1 error rate. By a power study we evaluate that structured association testing using our framework displays reasonable power. We compare our result with those obtained from a logistic regression model with principal component covariates. Using the principal components approaches we also find a possible false-positive association to Alzheimer’s disease, which is neither supported by our new methods, nor by the results of a most recent large meta analysis or by a mixed model approach.Matching methods provide an alternative handling of confounding due to population stratification for statistical tests for which covariates are hard to model. As a benchmark, we show that our matching framework performs equally well to state of the art models on common variants.The online version of this article (doi:10.1186/s12859-015-0521-4) contains supplementary material, which is available to authorized users. Genome-wide association studies (GWAS), see e.g. ,2 and rePopulation stratification, the characteristic of a population sample to enable the occurrence of spurious statistical findings, rises as a consequence of diverging allele frequencies in combination with varying phenotype prevalences. Note that population structure, that is the mere existence of diverging allele frequencies caused by population subgroups  and P=6.1·10−4 for rs3094078. Adding PC 1 changes this to CI %95(β)= and P=5.0·10−7, while PC 1 also becomes significant. Using MDS covariates leads to virtually identical results. We think it is possible, that an accidental correlation of the leading component with the SNP has boosted a false-positive signal. In this context, external knowledge does not convincingly support association of rs3094078 with AD. The International Genomics of Alzheimer’s Project (IGAP) recently published results of a joint meta-analysis ·10−4, but no signals that come close to genome-wide significance are listed within ±2 MB of our signal. IGAP actually found rs9271192 from HLA-DRB1 from the MHC to be associated with AD at genome-wide significance. This signal, however, resides more than 2.3 Mb away from rs3094078. According to the CEU [r2=0.001). In summary, the IGAP analysis does not strongly support association of our top signal from analysis using principal components with AD and suggests that the results of the matching stratification methods are more realistic.It turns out that the first PC is highly correlated with rs3094078, Pearson correlation analysis . The pub the CEU  referenc(2) tests (section ‘(1)  and tends to pick an over-deflated subsample of asymmetric samples (inflation factor <1). We strongly advise to utilize it for analysis of discordant sib-pair only. If it is possible to detect a clear cluster structure without substructure clustering performs optimal. Also, clustering does not rely on case-control information, therefore it can be used with either binary or quantitative traits. If clusters and substructure are present, the groupwise matching with cluster validation is a good choice. In the case that no cluster-structure can be revealed, we advise to apply the groupwise matching with vicinity validation.LCT with body height. For the same study GC was not able to correct for stratification [We found that, for principal components approaches, the number of needed PCs has to be estimated relatively accurately in order to obtain not-inflated analysis results. It has been claimed  that thefication  and alsofication . It is ufication  nor beenWe wish to point out that matching methods are of particular interest for models, for which the inclusion of covariates is not possible or hard to model. We empirically found that about 7,500 common variants are sufficient to calculate the genetic similarity scores, from which the matchings are derived. Thus the score can be evaluated both genome-wide and window-wise. The latter is of particular interest for finding region-specific matches for rare-variants analysis, which was proposed in . The appINTERSNP [PLINK [All methods described are implemented in INTERSNP , which iINTERSNP . The sofP [PLINK  input fiP [PLINK .


There is currently much debate about how much the genetic heritability of complex traits is due to very rare alleles. This issue is important because it determines sampling strategies for genetic association studies. Several recent theoretical papers based on a pleiotropic model for trait evolution suggest that it is possible that a large proportion of the genetic variance could be explained by rare alleles. This model assumes that mutations with a large effect on fitness also tend to have large positive or negative effects on phenotypic traits. We show that conclusions based on standard diffusion results are generally applicable to simulations of whole genomes with overlapping generations in a finite population, although the variance contribution of rare alleles is somewhat smaller than theoretical predictions. We show that under many scenarios the pleiotropic model predicts trait distributions that are unrealistically leptokurtic. We argue that this imposes a limit on the relationship between fitness and trait effects. A widely sought goal in biology is to understand the genetic architecture underlying phenotypic variation. In human genetics a distinction is made between Mendelian traits, usually affected by rare genes of large effect, and complex traits where the genetic component of their variation depends on a number of polymorphisms at different locations in the genome. Genome-wide association studies (GWAS) have endeavoured to identify these locations, typically for traits associated with disease, yet by and large this has not been very successful in unequivocally identifying a set of specific mutations that explain most of the heritability for any particular trait. It would appear that there are many mutations involved, and the distribution of genetic effects of individual mutations attenuates rapidly so that even with large sample sizes only a small fraction can be identified with reasonable statistical certainty, and together these often explain only a small fraction of heritability: the famous “missing heritability” problemA useful modelling approach for examining these issues is one in which deleterious mutations have random effects on a trait, the magnitude of which depends on their selective effect458910The present study is motivated by two concerns. An advantage of the method of Eyre-Walker is that it gives a useful expression for the density of the variance of a complex trait. However, a possible limitation of using a diffusion model is that an infinite population size is assumed and, potentially more importantly, all loci are assumed to be independent. We use a forward-time Moran model with infinite sites and free recombination to compare directly with the analytical results. A degree of linkage disequilibrium is generated in the model because selection acts on whole genomes rather than individual loci. We show that the variance contribution of low frequency alleles can be lower than previously suggestedS = 4Ns. The trait effect, z, is distributed as:N is the diploid population size, ε is a normally distributed noise term with mean 0, and δ takes the value 1 or −1 with equal probability. Depending on the scaling parameter, τ, z and S can be independent (τ = 0), or more strongly correlated with increasing τ.  This will be referred to below as the power function model. Further details of the computer simulation are provided in the Methods.We implemented a forward-time Moran model which incorporates the relationship between fitness and trait effects as previously describedS, defined by:τ is replaced by parameters A and B, which characterize a saturating function that prevents extreme mutational effects on the trait. The motivation is that there are mechanical, or homeostatic, or developmental constraints that prevent extreme changes. For additional biological realism it might be preferable to apply such a saturating function to the overall trait value of an individual. However this would then imply a non-additive genetic model, which would make the interpretation of the results more complex. The term A as S → As described later, for many parameter values, the model of mutational effects given by (1) results in an equilibrium trait distribution across individuals that is highly leptokurtic. We therefore examined the consequences of an alternative conditional distribution for the trait effect given S and z for each model in N) = 10,000, where N is the equivalent diploid population size, generation time = 1, simulation time = 60,000 generations, genome mutation rate U = 0.1, β (shape parameter of gamma distributed strength of selection) = 0.2ε = 0, standard deviation ε = 1. We chose We implemented 5 different parameter combinations using the original conditional distribution and 3 different combinations using the proposed new function . We prese  or 30 , where  In simulations where the mean selective effect of mutations is high . When selection is weak (model b) or when the absolute trait effect and fitness are uncorrelated (model c), the trait distribution has less extreme tails. In our alternative model we find that when the mutational effects on fitness and absolute trait value are the same for small effect sizes  the distributions are leptokurtic irrespective of a threshold of A = B = 10,000 or A = B = 4,000  we see a more closely normal distribution, similar to the uncorrelated (τ = 0) case for the original model. In this latter model the trait effect associated with rare low-fitness variants is truncated and hence the effects of individual mutations on the trait are less leptokurtic than in the former two models.The distribution of the trait across individuals in the population tends to be highly leptokurtic . The tra = 4,000 . HoweverN) irrespective of their fitness. Thus there is a small accumulation of the lowest frequency class in comparison with the diffusion model.The empirical allele frequency spectrum in the simulations very closely matches the theoretical expectation, with the exception of the lowest frequency, which is slightly higher than expected . This caThe proportion of genetic variance explained by alleles in any frequency class is again broadly similar for simulation and theory . Thus ouτ = 1) in the power-function model also shows an appreciable discrepancy . The results are presented in We calculated the difference in the proportion of the genetic variance due to mutations with frequency less than or equal to 0.01 between the analytical and empirical results for models a–h using numerical integration over the plotted points in To examine the relationship between the kurtosis of the trait distribution and the extent to which rare alleles were responsible for the variance in the trait , we calcThese results show that, for both our models of mutational effects, those scenarios in which rare alleles explain a large proportion of the variance have highly leptokurtic trait distributions, whereas models in which most of the variance is explained by alleles at intermediate frequency are associated with more normal trait distributions . The empStimulated by the difficulties of explaining an appreciable fraction of complex trait variation despite the availability of extensive data on the genome of individuals, there has been a renewal of interest in evolutionary models of quantitative genetic variation89104τ = 0), and the two low correlation cases . Human height, for example, which exhibits high heritabilityOne aim of this current study has been to compare the analytical predictions of the proportion of variance explained by genes in different frequency classes with results from simulations of a finite population. In general we find that the predictions are close, although there is appreciable discrepancy for some parameter values, which may be explained by Hill-Robertson interferenceIt has been notedspectrum  and alsoτ = 0.5 more than half of the additive genetic variance in the trait is caused by very rare alleles . When τ = 0, he finds that most of the additive genetic variance is caused by common mutations, irrespective of the level of population growth.A limitation of the present study is that it does not consider a realistic demography, and although the population size used is approaching current estimates of the human effective population size it does not consider the effect of recent large population size, which will be important in determining the frequencies of recent mutations10τ = 1 or τ = 0, the Eyre-Walker model does not accurately predict results from published studies of Type II diabetes, whereas values of τ between 0 and 0.5 cannot be ruled out. This approach points the way forward for using simulation-based analyses to calibrate models of complex trait architecture using published studies. The conclusions of the diabetes studyτ of 0.5 or lower give levels of kurtosis in the distribution of phenotypic traits that are close to normal once environmental variation is included.Recently it has been shownA further limitation of the present study is that it only explores a fraction of the range of possible joint distributions between mutational effects on fitness and on the trait. Our saturating function model, however, does not appear to produce qualitatively different results from the model of Eyre-Walker. It is likely that the Wishart distribution used by Hill and KeightleyOur study confirms that rare variants can explain a large proportion of the genetic variance of complex traits. However, selection on whole genomes within our finite population framework leads to interference effects that lead to a marginal reduction in the variance contributed by rare variants. In addition we note that for many parameter values the pleiotropic model implies a highly leptokurtic trait distribution, and we argue that this imposes an upper limit on the strength of the relationship between trait effects and fitness effects of mutations, in line with recent observationsN), the length of the simulation, the generation time, the genome mutation rate U, the shape parameter, β, of the gamma distribution for selection, the τ parameter in the conditional distribution for the trait value given by A and B for the case of the new ε . Any values of s generated that are greater than 1 are assigned the value 1 in the simulation . Conditional on S, the trait value is simulated by using either (1) or (2) depending on the model. Trait effects are assumed to be additive across loci. To avoid individuals with negative fitness, fitnesses are assumed to be multiplicative across loci. It should be noted that in the current pleiotropic model there is no need to include trait effects during the running of the simulation, and these could be added afterwards.The marginal fitness for that locus is then ). The aim of the analyses is to compute the contribution by individual loci to the trait variance, which assumes that trait effects are uncorrelated across loci. Loci are not independent due to shared ancestry (with recombination rate 0.5). However, because selection does not act on the trait effects, and because the trait and fitness effects of mutations are uncorrelated, the trait effects are expected to be uncorrelated across loci. However, due to sampling, there will be some residual covariance and rather than ignore this we compute the contribution to the variance by a particular locus as the sum of the column  entries in the trait covariance matrix for this locus. The sum of the elements of the covariance matrix is equal to the total variance in the trait. We show the proportion of the variance in the trait due to covariance terms in The variance contributions were summed for all mutations of the same frequency from 1 to 9,999 out of 10,000 individuals. Unobserved frequencies had zero variance contribution. The variance contribution for each allele frequency (from 0.0001 to 0.9999) was then averaged across the 100 runs (and hence any frequency class that was never observed across the 100 runs had zero variance contribution). Some variance contributions close to zero were negative because of the inclusion of covariance terms.10 transformation of the allele frequencies. The fit was obtained, using the function glkerns, by supplying an estimate of the variance of the mean variance contribution for each allele frequency, x. This estimate was obtained by computing the observed variance of the variance contribution among the 100 replicates, and then dividing by 100 to obtain an estimate of the variance of the mean. A smoothed estimate of this variance was then made by using glkerns with default parameter settings. To do this the square root of this variance (i.e. the standard error) was supplied, and then the smoothed value was squared. This was supplied to glkerns, along with the mean variance contribution for each x. We specified that the values of x were designed rather than random, and chose defaults for all other parameters. The predicted values were multiplied by the coefficient loge(10)x, where x is the allele frequency. At this stage all estimated variance contributions <0 were considered negligible and set equal to 0. This function was then converted to a density by computing an approximate normalising constant using the trapezoidal rule. This can then be compared with the analytical solution obtained by Eyre-Walkerx)/VT, the variance contributed by all mutations of frequency x divided by the total variance in the trait. Bootstrap estimates were computed by simulating 2,000 new data sets obtained by adding re-sampled residuals to the fitted values of the initial fitThe averaged variance contributions for each allele frequency class were smoothed within models using the R lokern package to remove noise ). The smoothing was performed after logx)/VT, the density of the variance, is thusx is the allele frequency and Zeta is the Hurwitz Zeta function, with integration limits of a = 0.0001 and b = 0.9999. We then approximated the Hurwitz Zeta function in R and superimposed the analytical solution into the plot with a coefficient of x.loge(10) to achieve a change of variables to the log10 allele frequency scale. Before plotting we added an additional numerical normalisation. , (2/10000),… etc. Thus even if the relative frequency evaluated at these points is directly proportional to the diffusion density there is potential discrepancy when plotting the theoretical density with that evaluated from the simulations, arising from the approximate nature of the trapezoidal integration used to scale the simulated points. In order to minimise this, to ensure that the theoretical and simulation results are compared on the same scale, we have therefore adjusted the diffusion prediction by also using trapezoidal integration based on the same evaluation points. In this case, for most models, the integration constant for the theoretical density deviates from 1 by <0.001, and is undetectable in the figures, but for model e, because most of the density is concentrated towards zero, there is appreciable discrepancy of around 0.08.)To account for a finite population size we modified the calculation of Eyre-Walker , with the expected variance contribution in the interval , approximated by ΔxV(x), where V(x) is the diffusion solutionx = 1/(2N − 1). In the limit as N → x) → 0, and (2N − 1)T(x) → V(x) in expectation. Our normalised function estimates V(x)/VT, where VT = ∫V(x), because it estimates N → The approach taken for comparing the discrete simulation results with the continuous analytical solutionε analytically and integrating over S numerically using the NIntegrate function. This was then subject to the same change of variables approach, and normalisation using trapezoidal integration, for comparable plotting with the empirical data.The variance contribution under the saturating function model  was obtaTo obtain the site frequency spectrum shown in x with selective strength S.The left hand side of expression (5) is T.L.N. and M.A.B. devised the study together, and contributed to the coding and analyses of the results, and wrote the manuscript together.Accession codes: Relevant code used in preparing this manuscript can be accessed at https://github.com/terinorth/.Supplementary Information


Progress has recently been made in understanding the genetic basis of schizophrenia and other psychiatric disorders. Longitudinal studies are complicated by participant dropout, which could be related to the presence of psychiatric problems and associated genetic risk. We tested whether common genetic variants implicated in schizophrenia were associated with study nonparticipation among 7,867 children and 7,850 mothers from the Avon Longitudinal Study of Parents and Children , a longitudinal population cohort study. Higher polygenic risk scores for schizophrenia were consistently associated with noncompletion of questionnaires by study mothers and children and nonattendance at data collection throughout childhood and adolescence (ages 1–15 years). These associations persisted after adjustment for other potential correlates of nonparticipation. Results suggest that persons at higher genetic risk for schizophrenia are likely to be underrepresented in cohort studies, which will underestimate risk of this and related psychiatric, cognitive, and behavioral phenotypes in the population. Statistical power to detect associations with these phenotypes will be reduced, while analyses of schizophrenia-related phenotypes as outcomes may be biased by the nonrandom missingness of these phenotypes, even if multiple imputation is used. Similarly, in complete-case analyses, collider bias may affect associations between genetic risk and other factors associated with missingness. Schizophrenia is a highly heritable and severely impairing neurodevelopmental disorder with onset typically in early adulthood. In a recent genome-wide association study (GWAS) meta-analysis of 34,241 schizophrenia cases, 45,604 controls, and 1,235 parent-offspring trios, the Schizophrenia Working Group of the Psychiatric Genomics Consortium reported 128 independent genome-wide significant single-nucleotide polymorphisms (SNPs) associated with risk of this disorder . Therefore, if increased risk of psychiatric, behavioral, or cognitive phenotypes is associated with nonparticipation and missing data in longitudinal studies, this could lead to biases in analyses which examine risk factors for psychiatric phenotypes. Bias due to missing data can also be looked upon as a form of collider bias , 14, wheTo date, the issue of whether genetic risk of schizophrenia is directly associated with nonparticipation has not been examined in longitudinal data sets. However, it is an important question that needs to be addressed, especially in light of the recent increased interest in leveraging longitudinal population cohort data to investigate the genetic architecture of psychiatric and behavioral phenotypes . Our maiALSPAC is a large, well-characterized longitudinal data set , 16. ALSFor the purposes of this study, only children who survived to age 1 year, had been enrolled in the study during the first phase of data collection, and had genotype data were included in the sample. This resulted in a sample of 7,867 children with imputed data for 2,543,887 SNPs after all quality control procedures were implemented. Imputation of genetic data was performed using HapMap release 22 in MACH 1.0.16 . MaternaYes/no variables were generated for whether each child had data available at each data collection time point  from 3 sources: clinic attendance, mother-completed postal questionnaires, and child-completed postal questionnaires. Additionally, data on participation at 2 earlier time points for mother-completed questionnaires at ages 1 and 4 years (data available: yes/no) were used. These time points were selected to cover a reasonable spread of ages across childhood and adolescence for the study child, for the 3 types of data collection sources. The postal questionnaires contained questions covering a range of demographic, developmental, psychological, and health issues. Data collected at clinical assessments included cognitive and physiological measures . Figure Family history, child-level, and socioeconomic variables previously shown to be associated with missing data were examined in relation to the above measures of participation. Binary variables for the presence of psychosis/schizophrenia (in first- or second-degree relatives) and maternal history of MDD were used as measures of family history of psychopathology. These were assessed using mother-completed questionnaires, which were completed during pregnancy with the index child. Child-level variables examined were sex and a total score for behavioral and emotional difficulties, as assessed by maternal report using the Strengths and Difficulties Questionnaire, at the approximate age of 4 years . The folP value threshold (PT) used to define risk alleles was P < 0.05, the threshold at which PRS maximally predict caseness in independent samples  explained by PRS. Analyses were performed separately for child PRS and maternal PRS for each of the 3 types of data collection (clinic attendance and child- and mother-completed questionnaires). Secondary analyses explored the addition of covariates, unique child and maternal associations, and whether the association with PRS changed over time. The latter analysis was conducted using generalized estimating equations, including an interaction term for the interaction of PRS with age, to examine change in the association over time.Analyses were performed in Stata, version 13.1 . Logistic regression analyses were used to estimate odds ratios and 95% confidence intervals for missingness per 1-standard-deviation increase in PRS. For each test, the Nagelkerke pseudo-Table There was strong evidence that higher schizophrenia PRS in the children were associated with missing data for all measures and time points Results of multivariable regression analyses of the association between schizophrenia PRS and missing data, after adjustment for family history of MDD or schizophrenia, child variables , and socioeconomic variables , can be found in Table n = 5,262). The correlation between children's and mothers' scores was 0.509. As can be seen from Table In order to further examine the source of the genetic association with nonparticipation, we repeated the analyses while including both child and maternal PRS in the same model, for child-mother pairs with available genetic data (Web Table 2). The results suggested that the association of child PRS with child-completed questionnaires increased over time, but there was no evidence that the association with other data sources changed over time.Finally, we estimated the population-averaged association between schizophrenia PRS and missing data across time points and examined whether this changed over time . TogeGiven the low prevalence of schizophrenia in the population  and the Regardless of the exact mechanisms, the plausible importance of subclinical phenotypes in nonparticipation further supports a quantitative trait model of psychiatric phenotypes, suggesting that there is no clear boundary between the presence and absence of an adverse phenotypic outcome and that genetic risk is probably distributed along a continuous liability scale. Indeed, a recent study of autism and variation in related social-communicative traits in the population supported such a model, by highlighting that both common and rare genetic variants operate on a continuum of behavioral and developmental traits .If genetic risk of schizophrenia is associated with study nonparticipation but schizophrenia phenotype is not , then analyses of schizophrenia as an outcome in longitudinal studies would probably not be biased if genetic risk were included as an exposure in the analytical model. If, on the other hand, a phenotype is associated with nonparticipation independently of genetic risk, then it is likely that analyses of schizophrenia as an outcome will be biased by the nonrandom missingness of this phenotype even with inclusion of genetic risk in the model. Furthermore, given that schizophrenia genetic risk is associated with attrition in cohort studies, complete-case analyses could lead to collider bias, affecting associations between genetic risk score and other exposures, such as substance use, that also lead to nonparticipation.It is clear that further work is needed to elucidate the mechanism through which genetic risk operates to result in a given phenotype, such as nonparticipation in a longitudinal study. The method of PRS analysis provides the potential to help with this process. For instance, the association between increased genetic risk of schizophrenia and schizophrenia as an outcome is partly mediated through family history of psychiatric illness , and as In the secondary analyses including both child and maternal PRS in a single analytical model, maternal PRS appeared not to contribute unique variance to the association with missing data. However, there were consistent associations of child PRS with nonparticipation at all data collection time points. This finding suggests that additional genetic risk factors beyond those carried by the mother influence the likelihood of mothers and children not returning questionnaires or attending the clinic visits.Web Table 3).It is possible that the current study underestimated the effect size of the association between PRS in mothers and nonparticipation because some mothers with higher PRS may have not enrolled in the study at all or may not have provided a DNA sample and so were not part of the analyses. Genetic data were available for 57% of the sample for participants alive at 1 year, and these individuals differed with regard to all covariates except sex in comparison with those without genetic data  were not very high. A third issue is that other potential correlates of nonparticipation ) were not taken into account in this study. The reason for this is that we made an effort to maximize the number of samples that could be analyzed by utilizing correlates of nonparticipation gathered early on in the study. IQ was measured when the children were 7 years old; therefore, including this variable drastically reduces the sample size and power of the analyses. However, when IQ is included, the effect sizes remain comparable to those shown in Table Despite these limitations, the results of this study show a consistent pattern of association between genetic risk of schizophrenia and nonparticipation by study mothers and children in data collection at numerous time points across childhood and adolescence. These results have important implications for genetic studies that utilize psychiatric-related phenotypes ascertained using a longitudinal cohort design and possibly also for studies conducted in the general population, which may suffer from selection biases. The results suggest that persons at higher genetic risk for schizophrenia and related phenotypes are likely to be underrepresented in longitudinal studies, which may decrease the statistical power for any analyses of schizophrenia and other related phenotypes, both in terms of categorical diagnoses and in terms of quantitative population traits. Our results suggest that analyses with schizophrenia as an outcome may be biased by the nonrandom missingness of this phenotype, especially if genetic risk factors are not taken into account. It is possible that the missing-at-random assumption of multiple imputation models may still not be met even after inclusion of variables related to family history, socioeconomic position, and childhood behavioral and emotional problems. These results open up possibilities for future research into nonparticipation in longitudinal studies—for example, regarding whether inclusion of genetic risk scores in imputation of missing data might allow associations to be estimated with less bias.


Blood pressure (BP) is a heritable trait with multiple environmental and genetic contributions, with current heritability estimates from twin and family studies being ~ 40%. Here, we use genome-wide polymorphism data from the Atherosclerosis Risk in Communities (ARIC) study to estimate BP heritability from genomic relatedness among cohort members. We utilized data on 6,365,596 and 9,578,528 genotyped and imputed common single nucleotide polymorphisms (SNPs), in 8,901 European ancestry (EA) and 2,860 African Ancestry (AA) ARIC participants, respectively, and a mixed linear model for analyses, to make four observations. First, for BP measurements, the heritability is ~20%/~50% and ~27%/~39% for systolic (SBP)/diastolic (DBP) blood pressure in European and African ancestry individuals, respectively, consistent with prior studies. Second, common variants with allele frequency >10% recapitulate most of the BP heritability in these data. Third, the vast majority of BP heritability varies by chromosome, depending on its length, and is largely concentrated in noncoding genomic regions annotated as DNaseI hypersensitive sites (DHSs). Fourth, the majority of this heritability arises from loci not harboring currently known cardiovascular and renal genes. Recent meta-analyses of large-scale genome-wide association studies (GWASs) and admixture mapping have identified ~50 loci associated with BP and hypertension (HTN), and yet they account for only a small fraction (~2%) of the heritability. Blood pressure (BP) is an established risk factor for multiple cardiovascular, stroke and renal disease and, worldwide, about one-tenth of adult global death is attributable to high BP or essential hypertension. BP, stuP < 5 × 10-8), and contributing to inter-individual variation in BP [2 is the residual phenotype variance); collectively, these loci explain only a small (<5%) fraction of the phenotypic variance (heritability) [In recent years, technical and analytic genomic advancements have made it feasible to conduct a comprehensive search for genes and regulatory loci underlying a trait of interest. To date, large-scale genome-wide association studies (GWAS), and other genome-wide analyses, have identified ~50 single nucleotide polymorphisms (SNPs) associated with genetic risk factors at high confidence (on in BP . The vasability) . This suability) ,17.common genetic variation to BP heritability? The typical approach to providing this answer has been through summing the contributions of individual SNPs showing genome-wide significant associations. This approach leads to a severe underestimate since GWASs suffer from a high false negative rate in its attempt to control the false positive rate. This false negative rate arises from the majority of genetic effects being too small to reach statistical significance and incomplete linkage disequilibrium between genotyped markers and causal variants.Nevertheless, before entertaining new genetic hypotheses for complex trait architecture it is first necessary to ask what is the total contribution of all estimating a trait’s residual variance explained by all common SNPs taken together and by considering them as random effects in a mixed linear model[Newer statistical methods allow a robust answer to this question by ear model,19. Indeear model. In thisear model. The conThe BP distributions of the 8,901 EA and 2,860 AA individuals are not statistically different from the 6,914 unrelated-EA and 1,737 unrelated-AA, respectively, suggesting that the use of either set would be representative of the population’s BP features . RelatedConsequently, for the remaining analyses we used the set of 8,901 EA and 2,860 AA individuals to maximize the available sample sizes.P = 3.2x10-10) and 0.49 in AA  for SBP, and 0.27 in EA  and 0.37 in AA  for DBP: these were all highly significant estimates (Tables P = 2x10-8) and 0.45 in AA  for SBP, and 0.31 in EA  and 0.29 in AA  for DBP: these were also highly significant estimates. Not observing any major differences between these sets of estimates we used all imputed and genotyped SNPs in subsequent analyses since they represent the largest numbers of markers available.The phenotypic variance explained by considering all imputed and genotyped SNPs for first visit measurement of blood pressure (V1) was 0.20 in EA /0.54 (AA); DBP rcor = 0.39 (EA)/0.35 (AA)), individual chromosomes differed considerably in their contributions to SBP and DBP variation (h2~2.6±1.1%), 4 (h2~2.2±1.0%) and 12 (h2~2.1±1.0%). Likewise, in AA the three chromosomes that account for the largest fraction of genetic variance for SBP were chromosomes 2 (h2~13.0±5.0%), 5 (h2~7.0±4.0%) and 11 (h2~6.4±3.0%), capturing nearly half of the genetic variance. With respect to DBP, the most prominent contributions of genetic variation in EA were from chromosome: chromosomes 2 (h2~2.3±1.1%), 4 (h2~2.2±1.0%) and 11 (h2~2.6±1.0%); whereas for AA, the three chromosomes that accounted for the highest variances, were chromosomes 2 (h2~4.2±4.0%), 5 (h2~8.7±4.5%) and 11 (h2~5.1±3.0%), tagging over 40% of the genetic variance. These results support the notion that well over 50% of the BP heritability can be explained using existing genetic markers.The total phenotypic variance explained can be apportioned by individual chromosomes and clearly demonstrated that although there is a general and significant correlation between chromosome length and variance explained  with greater variation explained by chromosomes 1 (~threefold increase in AA), 4 (>40% increase in both EA and AA), 10 (>60% increase in EA) and 17 (over 60% increase in EA). For DBP, the LTA measurements are smaller than those from first visit values in EA  with the majority of the variation explained by chromosomes 2, 6, 10 and 11 (~ 50% to 75% decrease). In contrast, in AA, the overall variance explained by DBP-LTA is higher than first visit values , with the majority of the variation explained by chromosomes 1, 3 and 12 (~ 30% to 75% increase). Although these estimates have large standard errors, the overall heritability estimates are roughly similar between first visit and LTA measures, yet the chromosomal pattern of the variance explained differ considerably. In other words, issues beyond precision may modulate what we observe for LTA versus V1 heritability [Blood pressure is a naturally varying phenotype so that increasing its precision may lead to improved genetic inferences. Thus, we assessed whether using the BP measurements from multiple (2–4) visits across time, as a Long Term Average (LTA), would lead to different conclusions by reducing measurement error ,26. We utability ,26.One expects that the whole-genome estimate of heritability is the sum of the chromosome-specific estimates unless there is correlation among the chromosomes. In our analyses, the chromosome sum and whole genome estimates are close to identical at 20% (EA)/50% (AA) for SBP and 27%(EA)/39%(AA) for DBP . Thus, BWe next investigated the likely locations of these common alleles modulating BP variation: were they preferentially located within genes or in non-coding genomic regions? To do so we classified all genotyped and imputed SNPs by their location within an annotated promoter, DNase I hypersensitive site (DHS), coding exon, intron or UTR . We perfA second functional annotation that we used was the expression site of the gene closest to the SNP being classified . Given cThere is still considerable debate as to the relative importance of common versus rare variants in the inter-individual variation of complex traits. Contemp2 is the phenotypic variance) so that the variance explained per SNP is 2pq(0.05σ)2 or 0.00105 (0.105%) where p ~ 30% is the (average) minor allele frequency. If this value were typical then our observed 25% heritability in this study can be explained through ~240 common variants at independent loci. We suspect the actual number to be far larger because there is both a statistical distribution of allelic effects, many likely smaller that we detect here, and most alleles will have lower frequencies. Assuming that 1000 or more BP loci lead to inter-individual phenotypic variation would not be exceptional and identifying them will involve GWAS in much larger sample sizes.The BP heritability estimates provided here strongly assert that the majority of inter-individual variation in BP detected today can be attributed to polymorphisms and not rare alleles, since the directly genotyped SNPs used had 1% or greater allele frequency. Genome-wide association studies, to date, have identified ~50 BP loci with a combined effect of ~2% of the phenotypic variance from common alleles with frequency ~30% on average –12. It i2 > 0.3) imputed from them; rarer variants and not well-imputed SNPs are, therefore, excluded. Also, causal SNPs that were not highly correlated with the SNPs on the genotyping array or after imputation were also missed.The analysis here sheds light on some of the properties of these putative causal BP alleles. First, the additive chromosomal-level and the joint genome-level analyses provide near identical estimates suggesting that BP alleles are additive in genetic action  Tables. The above conclusions need to be replicated in independent studies to assess how robust these conclusions are. Nevertheless, the main challenge in complex trait genetics remains the specific identification of the causal non-coding alleles, and the genes they affect, underlying SBP and DBP variability. Although GWASs are increasing in sample size and identifying a greater number of loci it is unlikely that they can achieve saturation identification by statistical detection alone. Our analyses suggest that the vast majority of these alleles are common, distributed across the genome, non-coding and not associated with known cardiovascular or renal genes . Thus, wIn conclusion, we estimated additive genetic variation that is captured by genotyped and imputed SNPs for BP, and partitioned this variation according to chromosome, allele frequency, and gene annotation. We provide compelling evidence that a substantial proportion of variance for blood pressure trait is explained by common SNPs, and thereby, give insights into the genetic architecture of BP trait. However, it is likely that variants other than the ones considered here and those with small effect need to be considered in addition to common SNPs.nd cousins). This led to an exclusion of 2,833 unrelated EA and 1,123 unrelated AA subjects, respectively, resulting in a retained dataset of correspondingly 6,914 and 1,737 genetically “unrelated” EA and AA participants [Phenotype data were available for 15,792 participants from the ARIC (Atherosclerosis Risk in Communities) study, which is a population-based, prospective, epidemiologic study of cardiovascular disease in European ancestry (EA) and African ancestry (AA) volunteers aged 45–64 years at baseline (1987–89), conducted in four US communities . All ARIicipants .Blood pressures were measured by a random zero mercury sphygmomanometer following a standard protocol described elsewhere . The pheTo obtain long-term averaged (LTA) BP traits, we averaged repeated BP measurements for study participants; individuals with four repeated BP measures at least 1 year apart were included in our analyses. At each study visit, we fit a linear regression model by using covariate adjustment in a manner identical to what was done for first visit measures to generate visit-specific BP residuals. These residual values were subsequently averaged over all available visits, and the final averaged residual was the LTA trait analyzed .-5. Of the 839,048 genotyped markers, 656,362 and 772,638 genotyped autosomal SNPs in EA and AA, respectively, passed the initial quality filters and were used for imputation. In this study, sex chromosomes were excluded from the analysis of blood pressure.Quality control on genotypes has been described elsewhere . Neverthet al[y is an N × 1 vector of systolic or diastolic blood pressure measurements with N being the sample size, β a vector for fixed effects such as sex, age, age squared and BMI, X the genotype incidence matrix relating to individuals, VA a vector of random additive genetic effects partitioned on aggregate of all autosomal SNPs estimated from whole-genome markers . The statistical method utilized here is detailed in Yang et al. Briefly-6) autosomal genotypes at 6,365,596 SNPs for EA and 9,578,527 SNPs for AA subjects, including all those directly genotyped and those imputed markers with MAF ≥ 1% (imputation R2 ≥ 0.3). To avoid phenotypic resemblance due to non-additive genetic effects and common environmental influences, we excluded one of each pair of individuals with an estimated genetic relationship >0.025 . Consequently, we created a second dataset that included only 6,914 and 1,737 genetically “unrelated” EA and AA study participants, respectively, to assess whether they impacted our conclusions.A pairwise genomic relationship matrix (GRM) was estimated using the high quality  P > 10p such classes are considered then MA is the GRM estimated from the whole-genome genotyped or imputed markers, ε is a vector of random error effects. These GRMs were fit in a mixed linear model (MLM) to SBP and DBP and restricted maximum likelihood (REML) methods were used to estimate the proportion of variance explained by any predefined set of genetic markers. Two types of analyses were performed that estimated the proportion of variance explained by the sum of the effects from individual chromosomes and by the whole-genome: for the first analysis, we fit 22 pairwise relationship matrices simultaneously  while in the second we merged these relationship matrices into one GRM . The specific partitions we considered were chromosomal number , minor allele frequency (MAF) class , specific functional genomic annotation of SNPs developed by Gusev et al. [p = 6; promoter, DHS, UTRs, coding, intronic, intergenic), functional annotation based on gene expression in known cardiovascular or renal associated-genes [http://www.genome.gov/gwastudies) and the literature  and including all markers in strong LD (r2 ≥ 0.8) with the sentinel SNP using SNAP [The variance estimate from the entire genome can also be partitioned into non-overlapping subsets of SNPs defined by any specific criteria: if v et al.  Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


NGEF and RGS6, respectively, and analyzed the associations between the seven SNPs and central adiposity measured by CT using an additive, dominant, or recessive model. The participants were generally healthy middle-aged men (50.7 ± 5.3 years). In the additive model, the rs11678490 A allele of NGEF was significantly associated with total adipose tissue, visceral adipose tissue (VAT), and subcutaneous adipose tissue . The AA genotype of this SNP in the recessive model showed a more significant association with all adiposity traits, and its association with VAT remained significant even after adjustment for BMI (P = 0.005). In the overall or visceral obesity group analysis, the AA genotype of rs11678490 showed no association with overall obesity (P = 0.148), whereas it was significantly associated with visceral obesity both before (P = 0.010) and after (P = 0.029) adjustment for BMI. In particular, an AA genotype effect was conspicuous between lower and upper groups with 5% extreme VAT phenotypes . However, we found no significant association between SNPs of RGS6 and central adiposity. We identified a visceral-fat-associated SNP, rs11678490 of NGEF, in Korean men. This study suggests that the genetic background of central adiposity and BMI is different, and that additional efforts should be made to find the unique genetic architecture of intra-abdominal fat accumulation.Central adiposity, rather than body mass index (BMI), is a key pathophysiological feature of the development of obesity-related diseases. Although genetic studies by anthropometric measures such as waist circumference have been widely conducted, genetic studies for abdominal fat deposition measured by computed tomography (CT) have been rarely performed. A total of 1,243 participants who were recruited from two health check-up centers were included in this study. We selected four and three single-nucleotide polymorphisms (SNPs) in  Obesity, which is defined as abnormal or excessive fat accumulation, is a major risk factor for the development of hypertension, type 2 diabetes mellitus, dyslipidemia, cardiovascular diseases, and cancers –5. TheseRGS6 and NGEF, influenced the body fat distribution and amount of fat, respectively . We. WeNGEF)P = 0.031). This genetic association remained significant after adjusting for BMI . In addition, we investigated the effect of the association in the 5% extreme group among the total sample. Compared with the 10% extreme group, the effect size of the AA genotype in the 5% extreme group was larger .We also performed a case–control association study of the rs11678490 variant and extreme visceral adiposity group defined as subjects within upper and lower 5% visceral adiposity area (n = 62) and 10%  of total sample, respectively . The meaNGEF and RGS6 on central adiposity traits, including visceral fat, in an Asian population. We measured TAT, VAT, and SAT area using CT, as well as BMI, in Korean adult men, and assessed the genetic associations between NGEF and RGS6 and central adiposity traits using four and three SNPs in the NGEF and RGS6 genes, respectively. We found that the A allele of the intronic SNP rs11678490 of NGEF was associated with TAT, VAT, and SAT in the additive model  and with all adiposity-related traits in the recessive model . After adjusting for BMI, the genetic effect of this SNP regarding VAT in the recessive model remained significant, suggesting the possibility of an abdominal visceral-fat-specific gene. Interestingly, that effect was conspicuous between lower and upper groups with 5% extreme VAT phenotypes . In contrast, we found no significant associations between central adiposity traits and SNPs in RGS6.This study was performed to assess the genetic effects of Reports of visceral fat distribution vary according to ethnicity and sex. Asian populations generally have greater VAT levels than do other populations under conditions such as the same age and WC, despite the presence of substantially lower levels of overall obesity ,20,21. INGEF, which plays a critical role in the formation of neuronal connections. In the additive genetic model, our results for NGEF were consistent with those of Norris et al., raising the possibility of the existence of a total fat-contributing gene. However, in the recessive genetic model, the effect of the SNP on VAT after controlling for BMI remained (P < 0.007). This result suggests a new hypothesis: that NGEF contributes to abdominal visceral fat, as well as to overall adiposity. Interestingly, this possibility was also identified in further association analysis of the visceral obesity group, or groups with 5% and 10% extreme values of VAT distribution. In particular, the effect size of rs11678490, despite the fact that this is a common variant, increased remarkably in the group with extreme values of VAT phenotype. Compared with the results of Norris et al., our distinctive results for NGEF can be explained by several possible factors. First, the assumed genetic model in each study was different. The associations of this SNP in Norris et al.’s study were tested by an additive genetic model [We identified a significant genetic association between VAT and rs11678490 of ic model , whereasic model . TherefoNGEF for adiposity phenotypes using CT data. Most genetic studies of overall or abdominal obesity have used nonspecific anthropometric traits, such as BMI and WC, rather than CT measures, which are associated with a high cost. In this sense, our more precise and refined traits for body fat distribution, including VAT and SAT, may enable the significant replication of the effect of NGEF, despite the relatively smaller sample size compared with those of large-scale GWASs. Moreover, we performed comprehensive genetic analyses of visceral fat for the first time in an Asian population, the results of which suggest the new possibility that a variant of NGEF in the recessive genetic model contributes to the distribution of adiposity, particularly VAT. However, we were unable to identify these associations in women, as only men were included in this study. In 2012, one association study reported a sex-specific genetic effect on visceral fat [NGEF SNP on VAT, further genetic association studies in women are needed.The present study had several new aspects. First, we provided a significant replication result of eral fat . They peNGEF in Korean men.In conclusion, we aimed to investigate the genetic effects of central adiposity with 7 SNPs of two candidate genes in a Korean population. We identified that VAT is associated with SNP rs11678490 of S1 Fig(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.