Early stage prediction of economic trait performance is important and directly linked to profitability of farm pig production. Genome-wide association study (GWAS) has been applied to find causative genomic regions of traits. This study established a regulatory gene network using GWAS for critical economic pig characteristics, centered on easily measurable body fat thickness in live animals. We genotyped 2,681 pigs using Illumina Porcine SNP60, followed by GWAS to calculate Bayes factors for 47,697 single nucleotide polymorphisms (SNPs) of seven traits. Using this information, SNPs were annotated with specific genes near genome locations to establish the association weight matrix. The entire network consisted of 226 nodes and 6,921 significant edges. For in silico validation of their interactions, we conducted regulatory sequence analysis of predicted target genes of transcription factors (TFs). Three key regulatory TFs were identified to guarantee maximum coverage: AT-rich interaction domain 3B (ARID3B), glial cell missing homolog 1 (GCM1), and GLI family zinc finger 2 (GLI2). We identified numerous genes targeted by ARID3B, associated with cellular processes. GCM1 and GLI2 were involved in developmental processes, and their shared target genes regulated multicellular organismal process. This system biology-based function analysis might contribute to enhancing understanding of economic pig traits. Growth rate traits, such as average daily gain (ADG) and days to 90-kg body weight (DAYS), and production traits, such as backfat thickness (BFAT) and lean percent (PCL), have been typically considered as important traits, as they play a major role in the economic success of Korean pig breeding programs. Moreover, the lifetime total number born (LTTNB), lifetime number born alive (LTNBA), and weaning to estrus interval (WEI) are also economically important for sow longevity and reproduction. To date, these economic traits have been genetically improved successfully based on traditional best linear unbiased prediction (BLUP), and breeding values of economic traits have been used with a selection index to select elite lines in Korean pig breeding.p-values of the GWAS result, but there has been no report of the use of a combination of AWM-methodology and Bayesian approach thus far [Recently, genomic information in the form of dense single nucleotide polymorphism (SNP) marker panels  has become available for genetic evaluation, owing to improvements in genotyping technology and statistical methods. One of its applications is in genome-wide association study (GWAS), which has become a powerful genomics tool to identify genetic loci or genes underlying quantitative traits in domestic animals . The sinthus far ,8,9,10.The objectives of the present study were to: (i) conduct a GWAS using the Bayesian method to investigate the genetic architecture and chromosomal regions associated with economic traits of pigs, including growth rate and production-related traits such as litter size information in Yorkshire population using dense SNP panels and (ii) identify the co-associated regulatory network of the multi-trait Bayesian approach GWAS using the AWM methodology.From 2014 to 2017, 2681 Yorkshire pigs were genotyped using Illumina PorcineSNP60 version 2  comprising 61,565 SNP markers. After excluding SNPs that were unmapped, on sex chromosomes, and those with poor call rates (<0.95), the available number of SNP markers was 47,697. Duplicated animals (n = 60) caused by re-genotyping to obtain acceptable call rates, and animals with lower call rates (n = 30) were removed after comparing their call rates. We also removed animals (n = 19) with call rates <0.90. The parentage test was performed using SEEKPARENTF90 software   with knoAll experimental procedures involving animals were conducted in accordance with the Guide for Care and Use of Animals in Research and approved by the Institutional Animal Care and Use Committee of the National Institute of Animal Science (No. 2015-137).Body weights were measured once during performance testing (at approximately 150 days). The ADG was calculated as the difference in final weight and initial weight divided by the number of days at the time of performance testing. The DAYS was estimated according to the recommendations of the Korean Swine Performance Recording Standards (KSPRS), adjusted from birth to the time of performance testing. The BFAT was calculated based on the average fat thickness values of the shoulder (on the fourth thoracic vertebrae), mid-back (on the last thoracic vertebrae), and loin (on the last lumbar vertebrae) measured using the A-mode (amplitude mode) ultrasound device (PIGLOG 105). The PCL was calculated according to the recommendations of the KSPRS, following previously reported procedures . More dePhenotypic data of 39,518 purebred Yorkshire pigs were collected from three Korea GGP farms between 2012 and 2017. Pedigree data from 99,694 individuals were also used. The BayesB  method wP or QTL . HoweverP or QTL . TherefoP or QTL . In totaAn additive genetic variance of 1.0%, which was estimated as a fraction of the total genetic variance explained by all SNPs, was used as the significance level of putative informative 1-Mb window region. Unlike the single marker regression approach, there is no P-value for significance of SNP marker in Bayesian approaches. Therefore, the posterior probability of inclusion of each SNP marker into the model (model frequency) in MCMC cycles is mostly used as a criterion for detecting QTLs . Bayes fs et al.  for the The AWM consists of rows representing genes and columns representing the additive effect of each trait based on the results of the GWAS . Before Sus scrofa motif information from the CisBP database [vertebrate data from JASPAR [P < 0.001) by calculating their score functions [To provide in silico validation of the gene-gene interactions and validate the whole network, among various available methods, we used bio-informatics analysis that predicts TFs and their target genes . To detedatabase  was mainm JASPAR  were usem JASPAR . To idenm JASPAR  was usedunctions . The topunctions . The claunctions . We performed a GWAS using SNP markers on Illumina PorcineSNP60 based on several parameters estimated by the BayesB method . Bayesian GWAS applies the threshold for the significance of SNP markers based on the derivative of model frequency  . HoweverBMP2 gene on SSC17, which is consistent with our results. The BMP2 gene is a member of the bone morphogenetic protein family that regulates early myogenesis. We also found identical informative 1-Mb window regions between the growth-related traits and SNP located at the 93-Mb position of SSC5 (rs345168974) with 1.40% and 1.12% additive genetic variances for ADG and DAYS traits, respectively.In the present study, the most informative 1-Mb window region was detected on SSC17 at 17 Mb, which explained 1.88% and 2.22% of additive genetic variances for ADG and DAYS traits, respectively. Furthermore, the most significant QTL was found at 17.55 Mb on SSC17 (rs342665431) with the highest BF: 298.75 and 764.87 for ADG and DAYS traits, respectively. A previous study  reportedIGF2 gene on SSC2 has an important role in the development of skeletal muscle and BFAT as well as postnatal muscle regeneration and hypertrophy. Other QTL windows were also detected on SSC2 at 76 Mb, which explained 2.87% and 1.49% of additive genetic variances for two production-related traits. The most significant SNP located on SSC5 at 65 Mb (rs81343150) was identified based on BF (304.09) for BFAT. We found 15 significant QTLs within 12 informative chromosomal regions (significance level > 1.0% additive genetic variance or BF > 20) on SSCs 2, 4, 5, 6, 7, 8, 14, 15, and 16 for BFAT and PCL. The most significant 1-Mb window region explaining 3.51% and 5.87% of additive genetic variances was captured on SSC2 at 162 Mb, including two SNPs (rs81341288 and rs81328276) in BFAT and PCL. The QTL window located on SSC at the beginning, which explained 2.68% and 4.48% of additive genetic variances, included SNPs (rs81317307 and rs81318741) for BFAT and PCL. Furthermore, rs81317307 was the most significant SNP based on BF (1268.40) associated with PCL. Van Laere, et al.  reportedSOD2) gene has been reported to have one polymorphism associated with male infertility [Some candidate chromosomal regions and QTLs associated with reproduction-related traits were identified. We found six significant QTLs within five informative chromosomal regions on SSCs 1, 12, and 16 for reproduction-related traits. Among those genes, the superoxide dismutase 2  × 7 (traits), and each cell represents their e effect a. To visThe whole network created by the PCIT analysis based on AWM information consists of 226 nodes and 6921 edges b. The reARID3B gene affects the regulation of limb development [ARID3B gene in pigs has not been studied comprehensively. The ARID3B has 13 in silico validated target genes , most of which  are involved in the cellular process  encodes a member of the AT-rich interaction domain (ARID) family of DNA-binding proteins . Studieselopment . However process c. Many oubunit G . Moreovesyndrome .AATF, ADAM33, ALS2CL, CHRNA3, CLSTN2, CTTNBP2, DRC1, EPC2, FAM134C, GUCY1A2, ICA1L, KIAA1324L, KIRREL3, LOC100523745, LOC100626814, LRFN2, MAP3K14, PLAT, PPP6R3, and RGL1). Among them, three  were involved in developmental process, similar to GCM1.The second member of the trio TF, GCM1, is a well-known TF involved in the regulation of expression of placental growth factor (PGF) and other placenta-specific genes . Within Drosophila to humans [ADAM33, ALS2CL, ARHGAP39, CHRNA3, DAB1, ENSSSCG00000027019, EPC2, GUCY1A2, KIAA1324L, KIRREL2, LOC100155825, LOC100515685, LRFN2, OXNAD1, PKM, PPFIBP1, PPP6R3, SMARCD1, STAG1, and TNS3), two (DAB1 and KIRREL2) were related to the developmental process. The GCM1 and GLI1 modules shared eight target genes .Finally, GLI2 functions as a transcription regulator in the Hedgehog (Hh) pathway. Sonic Hh (Shh) functions as a conserved morphogen in the development of various organs in metazoans—from o humans . It has o humans . Among tp-value information reported previously. This co-association regulatory network created using BFAT as a key trait, would facilitate the validation of the genetic understanding of other economically important traits in pigs. These biologically non-similar traits network could be very useful for the development of improved breeding strategies in the future. Further studies are needed to clarify the specific molecular or cellular processes of interaction among the TF trios and their target gene networks predicted to determine economically important traits in pigs.This study not only provides a list of chromosomal regions and SNPs associated with economically important traits, but also their candidate associated genes. The information about the SNP markers and chromosomal regions associated with the studied traits could be considered as prior information in a genomic selection model. Additionally, to the best of our knowledge, this is the first study to propose a BF-based regulatory gene network, unlike AWM with


CLOCK) gene and its interaction with different stress factors on migraine. In our cross-sectional study 2,157 subjects recruited from Manchester and Budapest completed the ID-Migraine questionnaire to detect migraine type headaches (migraineID). Additional stress factors were assessed by a shortened version of the Childhood Trauma Questionnaire, the List of Threatening Experiences questionnaire, and a validated questionnaire to identify financial difficulties. Rs10462028 showed no main genetic effect on migraineID. However, chronic stress indexed by financial difficulties showed a significant interaction effect with rs10462028 (p = 0.006 in recessive model) on migraineID. This result remained significant after correction for lifetime bipolar and unipolar depression and was replicated in both subsamples, although only a trend effect was reached after Bonferroni-correction, which is the strictest correction not considering interdependences. Childhood adversity (CHA) and Recent negative life events (RLE) showed no significant gene × stress interaction with rs10462028. In addition, in silico analysis demonstrated that the genetic region tagged by rs10462028 alters the binding of several miRNAs. Our exploratory study suggests that variations in the CLOCK gene, with moderating effect on gene function through miRNA binding, in interaction with financial difficulties might influence the risk of migraine-type headaches. Thus, financial hardship as a chronic stress factor may affect migraine through altering circadian rhythms.Previous studies suggested that both maladaptive stress response and circadian dysregulation might have a role in the background of migraine. However, effects of circadian genes on migraine have not been tested yet. In the present study, we investigated the main effect of rs10462028 of the circadian locomotor output cycles kaput ( Migraine is a debilitating disorder affecting approximately one billion people worldwide  showing phosphorylating effect on Per2 circadian protein  migraine headache and circadian genes has not been investigated yet.CLOCK) working as a transcriptional activator in the circadian clock mechanism. For our investigation we selected rs10462028 of CLOCK  24-h time-keeping capacity arises from a transcriptional/post-translation feedback loop with rhythmic expression of circadian genes  for migraine , RLE, and low socioeconomic status in the form of financial difficulties.n = 1,277) and Budapest, Hungary . The study was held under the aegis of NewMood , an EU funded research program about pathomechanism of depression and related conditions. Our study was approved by the local Ethics Committees  and was carried out in accordance with the Declaration of Helsinki. All participants administered written informed consent to the study. In the present investigation, we selected participants who were of European white origin, completed the questionnaires, and consented to DNA analysis. In every analysis we included subjects showing all the necessary data for the investigated variables. The total subject number was reached after screening for the following data: ethnicity, gender, age, migraine status, and rs10462028 genotype. Further details of the recruitment strategies and response rates can be found in our earlier publications . The background questionnaire assembled information about socio-demographic data, personal and family psychiatric history. Gender, age, ethnicity, and reported lifetime bipolar disorder (MANIC) and lifetime depression (DEPR) data were derived from this validated questionnaire . It is a validated screening tool for migraine  “just getting by”; and (3) “finding it difficult to make ends meet/not able to make ends meet.”The following tools were applied to capture different stress factors. A shortened version of the Childhood Trauma Questionnaire  can be seen in r2 > 0.8) with 70 SNPs covering a region of 132,043 base pairs .According to the 1,000 Genomes databaseCLOCK gene. Thus, we have identified miRNAs with seed regions containing rs10462028, and one of the alternative alleles possibly alters the binding affinity. Further miRNA binding sites were predicted and examined near the SNP  and additionally around rs1801260 polymorphism that is in high LD CLOCK because of its proposed effect on activity, sleep onset, and sleep quantity  binding—therefore here, we aim to predict the possible effects of rs10462028 on miRNA-binding and regulation of the Homo sapiens CLOCK 3-UTR (NM_001267843.1) transcript variant was obtained from the Nucleotide database of NCBI4 with the following parameters: LD ≥0.8 in Population CEU, based on Genotype Data from HapMap CEU, based on Genotype Data from dbSNP: European. We only included 3′-UTR polymorphisms in our further investigation and focused on the CLOCK gene in this study.The sequence of the genecards.org/ website and miRiADMiRNA binding sites around polymorphisms were predicted using TargetScanp and LD 2r-values, to run logistic and linear regression analysis with additive, dominant, and recessive genetic models, PLINK v1.07To calculate Hardy-Weinberg equilibrium (HWE) main analyses we tested the main effect of rs10462028, then the SNP × stress interactions with each of the stress factors  on migraineID in the total sample. Age and gender were covariates in all analyses. To handle possible ancestral differences, subjects with European white origin (determined by self-reported data derived from the Background questionnaire) were included in the analysis, and population (study sites) was a covariate in those analysis where combined samples were used. To assess our nominally significant findings we used three methods: (a) the gold standard Bonferroni correction to adjust p-values for multiple testing in our 12 main analyses  with a Bonferroni-corrected threshold of p ≤ 0.004 (0.05/12); (b) as Bonferroni correction is overly conservative and does not take into account the interdependences between the three genetic models of the same SNP, we used another more lenient corrected p-value taking into account our four hypotheses p ≤ 0.0125 (0.05/4); and (c) finally effects were considered statistically significant in case of reaching a significance value below 0.05 not only in the total sample but also in the two subsamples (Budapest and Manchester separately).In the additional analyses. Lifetime bipolar disorder (MANIC) and lifetime depression (DEPR) were added separately to models as covariates—to control for their potential confounding effect.In case of significant results we also ran CLOCK SNP on the stress factors , MANIC, and DEPR—to recognize its potential effect on these components that may contribute to the connection between the SNP and migraine.Furthermore, we tested the main effects of the p = 0.05 threshold. As post hoc tests, we measured the association of migraine frequencies and the SNP in the categories of stress factors with chi-squared test. To measure differences between our two subsamples in the phenotypic data, we used chi-squared test or independent sample t-test. Logistic regression models  were used to replicate the previously identified main effect of the measured stress factors on migraine.For displaying purposes, we calculated positive likelihood ratios (LR+) by dividing the frequency of migraine cases (symptom carriers) by the frequency of control subjects (non-carriers) in each category of each stress variable which showed significant interaction effects with the SNP. Further statistical analyses were performed with IBM SPSS Statistics 23. All statistical testing adopted a two-tailed n = 2,157), we have 96% power to detect genetic main effects associated with a 1.3 odds ratio (OR) for a disease and 80% power to detect a gene × environment interaction (p = 0.05 two-tailed) that is associated with 1.5 OR for a disease.Quanto 1.2 versionA detailed description of our study sample is described in p = 0.36) and in both subsamples .Rs10462028 was in HWE in the total sample (p < 0.0001) main effects on migraineID with more severe stress increasing the prevalence of migraineID. For detailed results see All three stress factors  showed highly significant .Post hoc chi-squared tests validated that at the most severe FINANC category there was a significantly lower frequency of migraineID among those with AA genotype vs. GG  and AG  genotypes. In addition, at the living very/quite comfortably level of financial difficulties those with AA genotype showed significantly higher migraineID frequency vs. GG genotype  and vs. AG genotype at a tendency level .p = 0.006  the SNP × FINANC interaction remained significant both in the additive and recessive models (and not significant in the dominant model)—with OR-values very similar to the original ones ; and (2) just getting by/finding it difficult or not able to make ends meet (n = 904). These analyses also gave us the opportunity to test the differential effects of the SNP at the favorable and at the adverse end of the financial status spectrum that was suggested at the a priori level. Significant additive interaction effects between the SNP and financial hardship on migraineID were replicated in the total sample and in the Budapest subsample, but only a tendency was found in the Manchester subsample  living very/quite comfortably  in the total sample  with predicted change in binding due to the polymorphisms. The miRNA names, SNP allele effects, and prediction algorithms are summarized in CLOCK rs10462028 was associated with a migraine phenotype in interaction with financial difficulties in a European cohort from Manchester and Budapest. Similarly to our results previous meta-analyses of migraine GWASs did not show a significant main effect of CLOCK  representing multiple stressors.NOS1  had protective effect against depressive symptoms at a low degree of stressors, but it was a risk factor at high stress exposure  affects miRNA binding. The most relevant predicted miRNAs have been mostly connected to different cancer types  and was carried out in accordance with the Declaration of Helsinki. All participants provided their written informed consent to participate in this study.GB, GJ, and JD designed the study and wrote the protocol. DB undertook the statistical analysis and managed the literature search. DB, XG, NE, PP, VA, and LK wrote the first draft of the manuscript. All authors contributed to and have approved the final manuscript.Conference of Hungarian Clinical Neurogenetic Society, 1–2 December 2017, Kecskemet, Hungary (lecture); 30th ECNP Congress, 2–5 September 2017, Paris, France (poster presentation); 13th World Congress of Biological Psychiatry, 18–22 June 2017, Copenhagen, Denmark (poster presentation); 24th Congress of the Hungarian Headache Society, 5–6 May 2017, Siofok, Hungary (lecture); XXVI World Congress of Psychiatric Genetics, 11–15 October 2018, Glasgow, Scotland (poster presentation); Semmelweis University PhD Scientific Days, 25–26 April 2019, Budapest, Hungary (poster presentation). JD variously performed consultancy, speaking engagements and research for Bristol-Myers Squibb, AstraZeneca, Eli Lilly, Schering Plough, Janssen-Cilag and Servier ; he also has share options in P1vital. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.Preliminary data from this study were presented at the following events


Background: The presence of an extra sex chromosome is associated with an increased rate of neurodevelopmental difficulties involving language. The 'double hit' hypothesis proposes that the adverse impact of the extra sex chromosome is amplified when genes that are expressed from the sex chromosomes interact with autosomal variants that usually have only mild effects. We predicted that the impact of an additional sex chromosome on neurodevelopment would depend on common autosomal variants involved in synaptic functions.Methods: We analysed data from 130 children with sex chromosome trisomies . Two comparison groups were formed from 370 children from a twin study. Three indicators of phenotype were: (i) Standard score on a test of nonword repetition; (ii). A language factor score derived from a test battery; (iii) A general scale of neurodevelopmental challenges based on all available information. Preselected regions of two genes,CNTNAP2 andNRXN1, were tested for association with neurodevelopmental outcomes using Generalised Structural Component Analysis.  Results: There was wide phenotypic variation in the SCT group, as well as overall impairment on all three phenotypic measures. There was no association of phenotype withCNTNAP2 orNRXN1 variants in either the SCT group or the comparison groups. Supplementary analyses found no indication of any impact of trisomy type on the results, and exploratory analyses of individual SNPs confirmed the lack of association.Conclusions: We cannot rule out that a double hit may be implicated in the phenotypic variability in children with SCTs, but our analysis does not find any support for the idea that common variants inCNTNAP2 orNRXN1 are associated with the severity of language and neurodevelopmental impairments that often accompany an extra X or Y chromosome.Stage 1 report:http://dx.doi.org/10.12688/wellcomeopenres.13828.2 Developmental language disorder (DLD), a condition in which there are unexplained and persistent difficulties with language acquisition, affects around 7% of children , yet it Rather than recruiting increasingly large numbers to try to find reliable associations between language disorders and genetic variants in genome-wide studies, one way forward is to study rare disorders that have a large impact on the phenotype, which may point to functional pathways involved in more common forms of disorder. One instance of a striking association between a genetic condition and language disorder in children of normal intelligence is provided by the sex chromosome trisomies (SCTs), each of which affects 1–1.5 per 1000 children . In the In a study of children with sex chromosome trisomies identified on prenatal screening,The impact of a trisomy is influenced by distinctive characteristics of the sex chromosomes. In most cases, the phenotypic effects of SCTs are much less severe than the impact of an autosomal trisomy: Down syndrome (trisomy 21) usually leads to intellectual disability, and most other trisomies are lethal. Viable trisomies usually involve small chromosomes with a low gene count (for example the Y chromosome), where the effects associated with altered gene dosage are less severe. An exception to this rule is the X chromosome. The X chromosome has a relatively high gene count, but the impact of a duplication is relatively mild because mechanisms of inactivation have evolved, such that in typical females, only one copy is active, and in effect, both males and females have one set of functional genes from this chromosome. In trisomies that involve the X chromosome, two copies are inactivated, largely negating the presence of additional genetic material. There are, however, exceptions to this rule, with between 12–20% genes escaping inactivation to some extent: These include genes in the pseudo-autosomal region, and other genes that have homologues on the Y chromosome .Neuroligin-4 (NLGN4) is a strong candidate for such a gene, for several reasons  they have been associated with DLD or ASD, and (b) they are in the same functional network asneuroligins.CNTNAPs and Neurexins, interacting with neuroligins in the synaptic cleft.The notion of a ‘double hit’ aetiology has been proposed previously to account for cases where a microdeletion is inconsistently associated with neurodevelopmental disorder , or from self-referral via advertisements on the OSCCI website and our Facebook page. A criterion for inclusion was that the child was aware of their trisomy status. In a previous study .Some twin children had evidence of autism spectrum disorder (N = 15) or intellectual disability (N = 3), and twelve failed a hearing screen on the day of testing, although none of them had any known sensorineural hearing loss. For the current study, because we were interested in a broader phenotype than pure DLD, these cases were retained in the sample.Psychiatric evaluation. In an initial telephone interview, parents were asked about the child’s medical and educational history, including a question about whether anyone had diagnosed the child with a neurodevelopmental disorder such as ASD, developmental language disorder (DLD) or specific language impairment, dyslexia or dyspraxia. In addition, one or both parents were asked to complete the onlineDevelopment and Wellbeing Assessment (DAWBA) ((DAWBA) .Language, literacy and cognitive assessments. All children were seen at home or in a quiet space in their school for a neurocognitive assessment, using the battery of language and nonverbal ability tests shown inWe considered three quantitative phenotypes ranging from a specific measure of a heritable language skill, through a more general language measure, to a measure that potentially indexes a wide range of neurodevelopmental problems:CNTNAP2,CMIP,ATP2C2,KIAA0319, andDCDC2 genes (A) Nonword repetition, which is regarded as a measure of phonological short-term memory. This was singled out as an individual measure because it has previously been identified in twin studies as a good marker of heritable language problems (B) A general language factor derived from the four other language tests . As documented inad hoc basis, using all available information from parental report (seeC) A global measure of burden of neurodevelopmental problems extending beyond language, including autistic features. This was developed on anport see.Global Screening Array-24 (v1)’, which includes 692,824 SNPs including rare and common variations. Data were processed in the IlluminaBeadStudio/GenomeStudio software (v. 2.03) and all SNPs with a GenTrain  score of < 0.5 were excluded at this stage. All genotypes were further filtered usingPLINK software v1.07  were used to collect saliva for DNA analysis from children with SCTs and their parents and available twin pairs. DNA extraction was performed using an ethanol precipitation protocol as detailed in the standard protocol (DNA genotek). All extracted DNA was genotyped on the Infinium ‘Open Science Framework. Families who had expressed interest in the study were interviewed by telephone to assess whether the child met inclusion criteria, and if so, an appointment was made to see the child at home or at school, depending on parental preference. Families were widely dispersed around the UK, including Northern Ireland, Scotland, Wales and England. During the course of recruitment a total of eight research assistants as well as the senior author were involved in assessing children. The assessment was conducted in a single session lasting between 2–3 hours per child, with breaks where needed.Ethical approval was obtained for the study in 2011 from the Berkshire NHS Research Ethics Committee (reference 11/SC/0096), and data collection started in August of that year, finishing in October 2016. Information sheets, consent forms and ethics approval documents are available onR software  and included over 100 SNPs from the genotyping array. In order to avoid false positives with our small sample size, we chose to focus our analysis on regions that have previously been associated with neurodevelopmental disorder, analysing all genotyped SNPs  within these selected regions.We conducted a series of literature searches to prioritise autosomal genes for analysis, focusing on genes that had an association with childhood speech and language disorders and that were relevant for synaptic function see. This leCNTNAP2 (NM_014141), we focused on a region spanning exons 13–14 ). This region includes a cluster of 9 SNPs previously associated with language disorder  were uploaded for 15,936 SNPs genotyped on chromosome 7. Genotypes were phased within Eagle and imputed by Minimac against the Human Reference Panel hrc.r1.1.2016, which includes 64,940 haplotypes of predominantly European ancestry. In total, genotypes were generated for 2,289,829 SNPs across chromosome 7, 513,970 of which had quality scores 0.9. The two SNPs of interest, rs2710102 and rs7794745 had quality scores of 0.9938 and 0.94127 respectively.InNRXN1 (NM_004801). Although this gene met our criteria of being relevant for both synaptic function and neurodevelopmental phenotypes, the studies showing this link involved deletions rather than common variants  but were not pruned for linkage disequilibrium. Previous simulations indicate that the Generalized Structured Component Analysis (GSCA) method is not greatly affected by linkage disequilibrium  approach adapted for genetic analysis , using a critical p-value of .05 to test the pathway fromCNTNAP2 to the Neuro factor, and .025 for the pathway fromNRXN1 to the Neuro factor.We conducted simulations that indicated that this method is feasible with the number of SNPs and phenotypes in our sample see: the perIn addition, we conducted the same analyses with children from the two comparison samples.CNTNAP2 andNRXN1 to the Neurodev factor would indicate significant association in the sex chromosome trisomy sample. We further predicted that any associations in the comparison samples will be similar in direction, but smaller in size and may not reach statistical significance.We predicted that one or both paths fromGene-based analyses. Following the publication of the GSCA analyses described above, we performed gene-based analyses of the 22 candidate genes described in our analysis plan . Through an imputation step, these analyses included all known variants across the 22 candidate genes. We tested for association with each of the three outcome measures of interest; nonword repetition, language factor and neurodevelopmental factor.Genotypes were imputed per chromosome using the Michigan Imputation server  <0.7 in the Michigan Imputation server ‘.info’ file were excluded, as were SNPs that appeared twice in the output . The allele frequencies were checked and monomorphic SNPs were excluded. Hardy Weinberg Equilibrium was checked in vcftools and any SNPs with p<10Tests of association with the three quantitative phenotypes: nonword repetition, language factor and global neurodevelopment index, were performed using Rvtests  which isIn a further analysis we used the SKAT test, which handles both common and rare variants within a single test (MAF upper limit 0.5). Both the Zeggini and the SKAT tests group the variants into a unit, in this case a gene.NRXN1 norCNTNAP2  is variable, whereas in males with XYY,NLGN4Y is fully expressed. Although we did not assessNLGN4X andNLGN4Y directly, one might expect a more severe impact of a double hit in the XYY group, which would be reflected in the epistatic interactions withNLGN. To test this, we reran the GSCA separately for XXX, XXY and XYY subsamples, restricting consideration to those who were diagnosed prenatally, as specified in our protocol. As shown inIn addition, we considered the suggestion byGene-based analyses. After Bonferroni correction for multiple testing none of the p-values were significant across the 22 candidate genes tested . These were analysed in relation to neurodevelopmental outcomes in a cohort of children with sex chromosome trisomies and two comparison twin groups, within a generalised structured component model , focusing on the hypothesis that the effects of common variations in neuroligin/neurexin genes upon language development are amplified in children with a sex chromosome trisomy . Previous studies indicate that for both of these SNPs the minor allele  was associated with poorer neurodevelopmental outcomes  reported positive association, primarily with SNPs rs2710102 and/or rs7794745. TheCNTNAP2 gene has been implicated by both genome-wide screening approaches and targeted replication investigations and is reported to have a moderate effect size upon neurodevelopmental outcomes  in autistic disorder, epilepsy and schizophrenia in the rare variant studies while investigations of common variants focused upon nicotine dependency and working memory. Interestingly, a recent analysis of clinical microarray data showed that deletions in exons near the 5’ end ofNRXN1 were specifically implicated in neurodevelopmental disorders  is 5×10CNTNAP2 and/orNRXN1 and this dataset would be too large to give reliable results with our sample size.In this paper, we employed a gene-based analysis method which adapts a generalized structured component-based path model (GSCA)  for geneOther gene-based tests based upon alternative statistical methods  are available and were applied to a limited number of candidate genes in our updated analyses. Even though these further exploratory investigations allowed a more comprehensive screening of genetic variation than the original analyses, which considered the effects of single SNPs, no significant associations were observed. These findings indicate that a much larger number of genes would need to be screened, which is not possible within the current dataset. As a general rule, the number of participants should be significantly greater than the number of parameters measured. Gene-based methods collapse effects across SNPs reducing the number of parameters to be estimated; however, adequate sample size should be ensured and not based on reducing dimensionality of data to achieve satisfactory power.NLGN4 genes and relate these to neurodevelopmental outcomes. Although this approach would only offer a snapshot of gene expression in time and space, it would be one step closer to contributory mechanisms and would provide a picture of the effects of aneuploidy upon gene expression and vice versa. Similarly, while a SNP-based analysis may tag structural rearrangements (deletions and duplications), an alternative approach would be required to fully assess the role of copy number variants. Copy number variants have been reported to play a role in neurodevelopmental disorder  and it is possible that the effects of a given risk variant may be affected by genetic background, the mechanism of mutation and environmental factors . We cannot reject the double-hit hypothesis on the basis of these analyses alone, but we can conclude that variants in the specific gene regions that we focused on do not appear to explain the phenotypic variation in neurodevelopment that is seen in children with an additional sex chromosome. In order to further the double hit hypothesis, additional analyses that consider many more candidate genes and/or different functional mechanisms will be required. Any such analyses would require larger sample sizes than was possible here and should be carefully controlled and pre-registered to avoid “fishing” experiments.In summary, we did not observe association to either theThe authors confirm that they had no prior access to the full dataset described here.For the Stage 1 Registered Report, the neurodevelopmental data had already been processed by DB and PT to derive the phenotypes to be used in the analysis; DFN had processed the DNA data separately to decide on the genotypes. The key tests proposed in this protocol involved putting the two strands of data together, which was deliberately not done, so that predictions could be derived without being aware of the data.https://doi.org/10.17605/osf.io/pr54gPhenotype data, full tables of covariances and means of all variables, and an R markdown script for generating results are available on Open Science Framework. Dataset 1: SCT genetics: Double hit hypothesishttps://osf.io/u7dsw  in epistasis with specific autosomal genes NRXN1 and CNTNAP2. In my original commentary on the proposal (Version 1) I suggested that the evidence supporting the hypothesis was rather weak. However, it is undeniable that there is a considerable variation in the phenotypic presentation of the sex chromosome aneuploidies , which exceeds variation in the general population in terms of their favoured measures of language competence . We do not yet have any satisfactory explanation for why that should be the case.In this, the third version of their planned test of the ‘double hit’ hypothesis, Newbury In the current report, no association was found between phenotypic variation and specific potentially salient variants of either NRXN1 or CNTNAP2. Intriguingly, previous research that has linked a disordered language phenotype to specific minor allelic variation of CNTNAP2 was not replicated here in a typically developing twin comparison sample. This could be due to a lack of power to detect an effect, as the finding was previously replicated in many studies. Consequently, it seems reasonable to conclude the analysis that employed a novel statistical methodology (critiqued in my previous commentary), which was supposedly suitable for small sample sizes, was probably subject to a Type II error. The authors went on to look at alternative models of effects in a small number of candidate genes that have previously been associated with language-related phenotypes, and hence could modify the impact of any epistatic interactions between their X-linked candidates (NLGN3/4) and NRXN1 or CNTNAP2 variants. There is an image describing how these genes were selected from a SCOPUS search provided in Figure S1. In Appendix 9 an analysis is described for 23 SNPs in NRXN1 and 24 in CNTNAP2 where association was sought with language phenotypes in both the SCA and the twin samples. After Bonferroni correction, no association met statistical significance. In the supplementary material, the SNP analyses of the NRXN1 gene for variants 7-11 were omitted, presumably in error. Gene-based analyses were performed on the 22 genes described in Figure S1 (Appendix 5), which apparently involved ‘all known variants across the 22 candidate genes’ and their potential association with the three key language variables of interest. I am unfamiliar with this mode of analysis so cannot comment on the specific risk of false positive results. Little is said about the outcome of this analysis other than that, after Bonferroni correction, none of the p-values was ‘significant’ – meaning that (presumably) variations in these additional genes did not influence the strength of association that was the subject of the original hypothesis. I posited in my original commentary that CNVs could be impacting on language development in their sample of sex chromosome aneuploidies and acting as vulnerability factors. I am pleased to see that the authors are planning to undertake these analyses in the future. They may also examine the variability in the ‘escape from inactivation’ status of NLGN4X, which lies outside the PAR1 and whose inactivation is believed to be only partial. I also predicted that there would be least variability in the language-related phenotype of the XYY male sample and arguably the most variable phenotypes in the XXX sample, because of the relationship between NLGN4X  and NLGN4Y (fully expressed). In this latest version of their paper the authors state they followed up my suggestion and provide the results of their investigation in Table 3. Table 3 as presented on p11 does not mention NLGN4 but illustrates a completely different analysis relating to NRXN1 and CNTNAP2. It is nevertheless stated that my hypothesis was not supported!Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?I cannot comment. A qualified statistician is required.Are all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Neurodevelopmental disorders, sex chromosomal aneuploidies, genetic syndromes associated with intellectual disability.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Many thanks for these additional comments on our paper.   We have made some modifications to the main text, and in addition offer some replies here to specific points: Power of GSCA. Since writing our original paper, we have done further analysis of the power of GSCA using simulations . Generalized Structured Component Analysis in candidate gene association studies: Applications and limitations.Wellcome Open Research,4, 142.https://doi.org/10.12688/wellcomeopenres.15396.2). A type II error cannot be ruled out, but we have shown that the method works well with the kind of sample size and number of SNPs used here.   1.Omission of data from table. Thanks to the reviewer for for spotting the omission of one set of SNPs in Appendix 9. We have corrected Appendix 9 (on Open Science Framework) to now include the missing SNPs, and have updated the script used to generate the tables.  2,Clarification regarding gene-based analyses.The gene-based analyses involve a cumulative assessment of all genetic variation in a single test allowing for differences in functional variants across individuals. The results indicate that variations across the 22 genes tested do not influence language outcomes in SCT cases or twins.  3.Role of CNVs.    4.American Journal of Medical Genetics Part C: Seminars in Medical Genetics,184(2), 256–266.https://doi.org/10.1002/ajmg.c.31791 Since publishing version 3, we have reported an analysis of CNVs in this sample. Once again we have null findings. See: Mountford, H. S., Bishop, D. V. M., Thompson, P. A., Simpson, N. H., & Newbury, D. F. (2020). Copy number variation burden does not predict severity of neurodevelopmental phenotype in children with a sex chromosome trisomy.Skuse predictions and Table 3. 5.NRXN1 andCNTNAP2 in SCT subgroups, in line with the analyses presented elsewhere in the manuscript. These analyses do not directly testNLGN4 but instead focus upon epistatic interactions with neuroligins. The idea is that if there is more consistent expression ofNLGN4Y, then this might be evident as stronger impact in epistatic interactions. We wanted to credit the reviewer for the idea of expression differences inNLGN4YX and NLGN4Y, but have now reworded to clarify that the analysis in Table 3 is indirect evidence for this effect. Table 3 tests for putative epistatic effects of variations across Newbury and colleagues have extended their studies of variation in neurodevelopmental outcomes in children with sex chromosome trisomies with extensive gene-based analyses. However, none of the gene-based analyses passed the multiple testing threshold (Bonferroni correction) indicating that larger number of genes and samples will need to be screened to find evidence supporting the double-hit hypothesis.I assume the authors mean Indels and multi-allelic variants when referring to Non-SNPs. Could the authors please re-phrase?Within the exploratory analyses paragraph, it reads as if imputation was used to impute also rare variants for rare-variant analyses. “Common and rare variants were further examined using the SKAT test (MAF upper limit 0.5).” Do the authors just mean a rare variant test applied to common variants? Please clarify which variants (MAF range) were analysed with which test.Could the authors please clarify what they mean with the “Zeggini” test? “A rare variant test analysis was performed using Rvtests  to analyse three quantitative phenotypes: nonword repetition, language factor and global neurodevelopment index in the SCT and Twin sample sets using the Zeggini test with a MAF upper limit of 0.01.”The author may consider increasing the number of digits reported for p (Appendix 9). For example, the TWA_global_p for rs2193417_G is given as 0.00 (NRXN1), which is now difficult to evaluate with respect to a multiple testing threshold of 0.0003. I have only minor comments:Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:NAI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. We thank the reviewer for the additional points, which allow us to add further clarification. 1. Yes, the non-SNPs included indels and multi-alleleic SNPs.   2-3.Two analyses were performed within RVTESTS; the SKAT test and the Zeggini test.   The Zeggini test is a test of association. This is an aggregation test which computes a cumulative score of rare variant burden across the gene of interest which is then tested for association to the phenotype of interest. The test is described in doi: 10.1002/gepi.20450.   The Zeggini test is suited to the analysis of rare variants only, while SKAT can handle both common and rare variants within a single test. Accordingly, we applied the Zeggini test to all variants with MAF≤0.01 and the SKAT test to all variants with MAF≤0.5 .. Appendix 9 is not part of the additional content we added in version 3 of the manuscript - which is termed 'Exploratory analyses'. Appendix 9 was part of our original submission and was titled ‘Additional exploratory analyses’. We apologise for confusing labelling of sections. 4 The Stage 2 report summarizes the findings of the proposed investigation, which aimed to test the hypothesis that neuroligin genes on the sex chromosomes  would be expressed in excessive dosage in multiple sex chromosome trisomy (SCT) aneuploidies  and that there could be an epistatic interaction between those genes and a specific autosomal gene on Chromosome 7 (CNTNAP2), variants of which have been shown to be associated with disorders of language in particular. Accordingly, this interaction would explain the deficits in language skills characterizing SCTs, which are relatively more common in XYY than XXX individuals, with XXY males intermediate between the other two conditions. In the original proposal, besides the epistatic interaction with CNTNAP2, the authors also proposed an interaction between NLGN and the autosomal gene NRXN1 (chromosome 2), such that variants in that gene within putative regulatory regions could modify the impact of excessive NLGN expression. Essentially, the purpose of the investigation was to evaluate an intriguing ‘double-hit’ mechanism, whereby the variable impact of sex chromosome aneuploidy on language phenotypes would be explained by some sort of interaction with autosomal genetic variation, in two specific candidates (CNTNAP2 and NRXN1). The locus of the interaction was proposed to be at the synapse, reflected in variable synaptic scaffolding with functional consequences. Underlying this hypothesis was the observation that there is a transmembrane connection in the synaptic scaffolding system between CNTNAPs and Contactins/Catenins and also between Neuroligins/Neurexins as illustrated in Figure 1. Having reviewed the material in the original Stage 1 Proposal, I suggested there should be the most variable language phenotypes within the XXX sample, and the least variable language phenotype in the XYY sample. I expressed skepticism that the overall double-hit hypothesis would be supported by the proposed analysis, and also that the novel statistical methods to be used would truly get around the problem of limited power .th July 2018) states there was wide phenotypic variation observed within the SCT groups, but no overall association could be found with the variants that had been measured on the autosomal genes CNTNAP2 or NXN1. Nor were there any specific associations within each of the trisomies investigated. The latest report reiterates much of the information about the study design that was provided by the Stage 1 report (and its April 2018 revision). The Stage 2 report . Accordingly, all males with XYY would over-express NLGN4Y to the same extent, but females with XXX (in which two X-chromosomes are silenced) would express NLGN4X to a highly variable degree depending on individual differences in X-inactivation status. I was surprised by the comment that, in the context of a discussion about alternative models of effects, that there are many possible mechanisms of functional interaction that would not be detected by the approach taken by this study – such as functionally valid variants that lie outside the coding regions. My understanding is that many SNPs employed in this study were indeed outside coding regions . There is a further comment concerning a mechanism whereby there could be modulation of expression of NLGN4X/Y by autosomal variants: a point made in my original commentary is that the regulation of NLGN4X is complex in relation to X-inactivation, and that its expression in individuals with more than one X chromosome is unpredictable for that reason in particular. This mechanism leading to variability of expression may be more pertinent than modulation by autosomal genes. Overall, the study was conducted with impeccable scientific rigor and exemplifies the benefits of registering a research design with Wellcome Open Research, and seeking peer review before undertaking the proposed analysis.Are the data able to test the authors’ proposed hypotheses by satisfying the approved outcome-neutral conditions ? YesAre the introduction, rationale and stated hypotheses the same as the approved Stage 1 submission? (required) YesDid the authors adhere precisely to the registered experimental procedures? If not, has an explanation been provided regarding any change? YesAre any unregistered post hoc analyses added by the authors justified, methodologically sound and informative? YesIs the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?I cannot comment. A qualified statistician is required.Are all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Neurodevelopmental disorders, sex chromosomal aneuploidies, genetic syndromes associated with intellectual disabilityI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. CNTNAP2 or NRXN1 are associated with the severity of language and neurodevelopmental impairments that often accompany an extra X or Y chromosome.In this Stage 2 Registered Report Newbury and colleagues studied whether the presence of an extra sex chromosome is associated with an increased rate of neurodevelopmental difficulties involving language. The authors investigate here a double-hit hypothesis, specifically that the presence of an additional sex chromosome amplifies the impact of common autosomal genetic variants residing within genes involved in synaptic functionality. The authors specifically focus on the functional network of neuroligins and study common variants within CNTNAP2 and NRXN1, and use complex analysis strategies, such as Generalised Structural Component Analysis, to overcome small sample size. Unfortunately, the sample size was small, and the authors found no support for the idea that common variants in  However, although this study was small, the authors could make suggestions, based on their extensive expertise, how a future study should be conducted that may be able to overcome current limitations. It is highly commendable that authors followed a fully transparent analysis approach, sharing scripts and simulated comparable data. The authors may also want to highlight the transferability of their applied structural equation modelling approach.Are the data able to test the authors’ proposed hypotheses by satisfying the approved outcome-neutral conditions ? Yes.Are the introduction, rationale and stated hypotheses the same as the approved Stage 1 submission? (required) Yes.Did the authors adhere precisely to the registered experimental procedures? If not, has an explanation been provided regarding any change? Yes.Are any unregistered post hoc analyses added by the authors justified, methodologically sound and informative? Yes.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:NAI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


The heritability of a phenotype is an estimation of the percent of variance in that phenotype that is attributable to additive genetic factors. Heritability is optimally estimated in family-based sample populations. Traditionally, this involves use of a pedigree-based kinship coefficient generated from the collected genealogical relationships between family members. An alternative, when dense genotype data are available, is to directly measure the empirical kinship between samples. This study compares the use of pedigree and empirical kinships in the GAW20 data set. Two phenotypes were assessed: triglyceride levels and high-density lipoprotein cholesterol (HDL-C) levels pre- and postintervention with the cholesterol-reducing drug fenofibrate. Using SOLAR , pedigree-based kinships and empirically calculated kinships (using IBDLD and LDAK) were used to calculate phenotype heritability. In addition, a genome-wide association study was conducted using each kinship model for each phenotype to identify genetic variants significantly associated with phenotypic variation. The variant rs247617 was significantly associated with HDL-C levels both pre- and post-fenofibrate intervention. Overall, the phenotype heritabilities calculated using pedigree based kinships or either of the empirical kinships generated using IBDLD or LDAK were comparable. Phenotype heritabilities estimated from empirical kinships generated using IBDLD were closest to the pedigree-based estimations. Given that there was not an appreciable amount of unknown relatedness between the pedigrees in this data set, a large increase in heritability in using empirical kinship was not expected, and our calculations support this. Importantly, these results demonstrate that when sufficient genotypic data are available, empirical kinship estimation is a practical alternative to using pedigree-based kinships. This calculation requires the phenotype measurement, relevant covariates, and a kinship matrix. Traditionally, the kinship matrix is derived from a carefully curated pedigree (or pedigrees) joining together the individuals with phenotypes by their self-reported genealogical relationships. The use of self-reported genealogical relationships has one obvious drawback: incorrectly specified relationships. These pedigree errors can arise for multiple reasons, including paternity, recording errors, as well as cultural differences in the understanding of the definition of biological kinship relationships. In addition, when a cohort of pedigrees is recruited from the same geographical region, it’s possible that there may be unknown kinship connections between seemingly discrete pedigrees.SOLAR  , softwarAccurate biological relationships are necessary for the calculation of phenotype heritability. Uncertainty surrounding pedigree relationships in a data set reduces the power of heritability calculations and leads to inaccurate results at best, or false results at worst.With the availability of dense genotyping array data, a potential solution to this problem is to employ the use of empirical kinship estimates. Empirical kinship is when the kinship between each individual in a cohort is estimated using dense genotyping data from single-nucleotide polymorphism (SNP) arrays or next-generation sequencing. Empirical kinship estimates will overall closely align with the kinship calculated from pedigrees, but, importantly, are also able to clarify pedigree relationships, provide an additional quality-control measure to identify sample swaps or duplicates, identify unknown or distant relationships, and overall remove the need to rely on genealogical records. Furthermore, where individuals are unrelated in a pedigree kinship matrix, some level of empirical kinship can be calculated for all pairs in the data set.Intuitively, the use of a matrix of empirical kinship estimates should improve heritability calculations as the observed kinship measurement is used rather than the kinship expectation based on genealogy. We examined in the GAW20 data set from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study  and IBDLThe distributed GAW20 genotypes of 718,544 autosomal SNPs were converted to their corresponding DNA nucleotide bases and the hg18 mapping coordinates were uplifted to hg19. This resulted in 718,407 SNPs for analysis, with 135 excluded because of failing the conversion to hg19. The pedigree distributed with the GAW20 data set was converted to SOLAR format. The phenotype data distributed with the GAW20 data set was merged into a single SOLAR format phenotype file.P = 0.05 used as the threshold) resulting in 22,697 SNPs for within-pedigree and across-pedigree Prest-Plus analysis.Prest-Plus  was usedLDAK version 4.9  and IBDL2, age2 × sex), study center, smoking, and principal components 1 to 4 . Residualized phenotypes were inverse-normalized in SOLAR to prevent nonnormal distribution errors during analysis, ensuring that all phenotypes had a mean of 0 and SD of unity. Heritability was estimated using SOLAR’s variance components framework. These analyses were completed separately using the pedigree kinship matrix derived from SOLAR and each of the empirical kinship matrices.The 2 phenotypes assessed were triglyceride levels and high-density lipoprotein cholesterol (HDL-C) levels pre- and post-fenofibrate intervention. For an individual, when multiple phenotype measurements were available at the 2 visits pre- or 2 visits post-fenofibrate intervention, these were averaged into single pre- and postintervention phenotype values; otherwise, the single pre- or postmeasurement was used. Phenotypes were analyzed using SOLAR (SOLAR Eclipse version 7.6.4) . All pheP-value threshold of P ≤ 1.00 × 10− 5 was used, and for Bonferroni-corrected genome-wide significance a threshold of P ≤ 6.9 × 10− 8 was applied. Manhattan plots of MGA results were constructed in R using qqman [Single-variant association testing was conducted using measured genotype analysis (MGA) in SOLAR for the 718,407 SNPs available for analysis in the GAW20 data set. This analysis takes into account the nonindependence of participants, using the kinship matrix, incorporating each SNP separately into the analysis model as a covariate measured as a genotype dosage  and evaluating the genotype-specific difference in the phenotype means. For genome-wide suggestive significance a Prest-Plus identified unexpected relationships within the GAW20 data set when assessing relationships within-pedigrees Fig. , and limHeritability estimates using SOLAR identified that both triglyceride levels and HDL-C were significantly and highly heritable pre- and post-fenofibrate intervention Table , regardlMGA of 718,407 SNPs across both triglyceride and HDL-C, pre- and post-fenofibrate intervention identified 1 genome-wide significant SNP, rs247617 on chromosome 16, associated with HDL-C pre- and post-fenofibrate intervention under all 3 kinship models. Figure The analysis presented here using the GAW20 data set from the GOLDN study sought to examine whether the use of empirical kinship for the estimation of phenotype heritability and genetic associations in a data set of related individuals was an improvement over relying on pedigree-based kinship. From this analysis, we determined that empirical kinship is analogous, if not equivalent, to pedigree-based kinship. A limitation of the current data set was the minimal unknown relatedness outside of the known pedigrees. It could be expected that in a data set with greater unknown relatedness, or incorrect relatedness  that heritability estimations from pedigree-based and empirical kinships would be more divergent, with the empirical more accurate.Pedigree-based kinship in this data set resulted in the highest heritability estimates, with empirical kinships from LDAK generating the lowest heritability estimates. IBDLD empirical kinship resulted in heritability estimates most similar to the pedigree-based estimates. Both phenotypes used from this data set, triglyceride and HDL-C measurements, were significantly heritable pre- and post-fenofibrate intervention, indicating a strong genetic component to phenotype variation.CETP (cholesteryl ester transfer protein). The protein product of CETP is found in the plasma and has the role of transferring cholesterol esters from HDL-C to LDL [CETP are reported to be the cause of hyperalphalipoproteinemia 1 (HALP1), a disease characterized by abnormally elevated levels of HDL-C [MGA in SOLAR, accounting for the nonindependence of related samples, identified 1 genome-wide significant SNP, rs247617, associated with HDL-C levels see Fig. . rs24761C to LDL . Defectsof HDL-C , 14. Genof HDL-C . Furtherof HDL-C . Using tTo further examine the strength of using empirical kinship, the known pedigrees in this data set could be selectively broken into smaller pedigrees, to reduce the pedigree kinship matrix. We could then assess whether the triglyceride and HDL-C phenotypes remain significantly heritable, whether genetic associations detected using the full pedigree kinship matrix are replicated and whether in this context whether stronger support is provided for using empirical kinship in phenotype heritability estimation and genetic association studies.CETP.The analysis presented here on the GAW20 data set from the GOLDN study has shown that empirical kinship is a practical alternative to pedigree-based kinships, when dense genotypic data are available, within the limitations of this study of a data set with little unknown kinship. Although we only examined phenotypes with moderate heritability, it is likely that the near functional equivalence of empirical and pedigree relatedness matrices holds across the spectrum of heritabilities. Analytical theory supports this as the expected power across heritabilities is determined by the eigenvalues of the relatedness kernel itself . In this


We apply our method to a genome-wise association meta-analysis for body mass index. In our framework, we are able to use the sample sizes for the individual genomic loci and the minor allele frequencies as covariates. We further evaluate our approach via a number of simulation scenarios. We provide an implementation of this novel method for estimating the proportion of null hypotheses in a regression framework as part of the Bioconductor package swfdr.Modern scientific studies from many diverse areas of research abound with multiple hypothesis testing concerns. The false discovery rate (FDR) is one of the most commonly used approaches for measuring and controlling error rates when performing multiple tests. Adaptive FDRs rely on an estimate of the proportion of null hypotheses among all the hypotheses being tested. This proportion is typically estimated once for each collection of hypotheses. Here, we propose a regression framework to estimate the proportion of null hypotheses conditional on observed covariates. This may then be used as a multiplication factor with the Benjamini–Hochberg adjusted  Multiple testing is a ubiquitous issue in modern scientific studies. Microarrays , next-geH0: μ = μ0 vs some alternative hypothesis, for example, H1: μ = μ1, the null hypothesis may either truly hold or not for each feature. Additionally, the test may lead to H0 either being rejected or not being rejected. Thus, when performing m hypothesis tests for m different unknown parameters, U, T, V, and S, and as a result, also R = V + S, are random variables, while m0, the number of null hypotheses, is fixed and unknown.When performing thousands of hypothesis tests, one of the most widely used frameworks for controlling for multiple testing is the false discovery rate (FDR). For a fixed unknown parameter μ, and testing a single null hypothesis 0.The FDR, introduced by 0 can result in a more powerful procedure compared to the original procedure of m increases, the estimate of π0 improves, which means that the power of the multiple-testing approach does not necessarily decrease when more hypotheses are considered . Similar very recent approaches include work by 0 based on existing covariates, using different approaches. The approach of p-value weighting but conservatively estimates π0 ≡ 1. An overview of the differences between these various approaches for incorporating meta-data and the relationships between them is provided by In this paper, we develop and implement an approach for estimating FDRs conditional on covariates and apply it to a genome-wide analysis study. Specifically, we seek to better understand the impact of sample sizes and allele frequencies in the BMI GWAS data analysis by building on the approaches of 0 and their extensions to consider conditioning on specific covariates; discuss estimation and inference procedures in our FDR regression framework, provide a complete algorithm, and apply it to the GWAS case study; and describe results from a variety of simulation scenarios. Finally, we provide our statement of reproducibility, followed by the discussion. Special cases, theoretical properties of the estimator, and proofs of the results can be found in the The remainder of the paper is organized as follows: We first introduce the motivating case study, a BMI GWAS meta-analysis, which will be discussed throughout the paper. We then review the definitions of FDR and πAs we have described, there are a variety of situations where meta-data could be valuable for improving the decision of whether a hypothesis should be rejected in a multiple testing framework, our focus being on an example from the meta-analysis of data from a GWAS for BMI . Using sA and a. In general, A refers to the variant that is more common in the population being studied and a to the variant that is less common, usually called the minor allele. Each person has a genotype for that SNP of the form AA, Aa, or aa. For example, for a particular SNP, of the four possible DNA nucleotides, adenine, guanine, cytosine, and thymine, an individual may have either a cytosine (C) or a thymine (T) at a particular locus, leading to the possible genotypes CC, CT, and TT. If the C allele is less common in the population, then C is the minor allele. The number of copies of a, which is between 0 and 2, is often assumed to follow a binomial distribution, which generally differs between SNPs.In a GWAS, data are collected for a large number of SNPs in order to assess their associations with an outcome or trait of interest . Each pep-value. While GWAS studies are often very large, having sample sizes of tens of thousands of individuals genotyped at hundreds of thousands of SNPs, due to the small effect sizes being detected, meta-analyses combining multiple studies are often considered  in the HapMap CEU population and results from the regression-based association analyses for BMI, presented as beta coefficients, standard errors, p-values, and sample size for each SNP.We consider data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium, specifically the GWAS for BMI . The GIAp-values on sample sizes within this dataset. As we considered the MAF to be a more intuitive covariate than the EAF, we also converted EAF values >0.5 to MAF = 1 − EAF and changed the sign of the beta coefficients for those SNPs. The MAFs spanned the entire possible range from 0 to 0.5, with a median value of 0.208.We removed the SNPs that had missing EAFs, leading to 2,500,573 SNPs. For these SNPs, the minimum sample size considered was 50,002, the maximum sample size 339,224, and the median sample size 235,717—a relatively wide range. We will now review the main concepts behind the FDR and the a priori probability that a null hypothesis is true, and consider the extension to the covariate-specific FDR and the covariate-specific a priori probability. A natural mathematical definition of the FDR would be:R is a random variable that can be equal to 0, so the version that is generally used is:However, m null hypotheses being considered by 1 ≤ i ≤ m: H01, H02, …, Hm0. For each i, the corresponding null hypothesis Hi0 can be considered as being about a binary parameter θi, such that:We index the i are identically distributed, the a priori probability that a feature is null is:Thus, assuming that θ0 represents the proportion of SNPs which are not truly associated with BMI or, equivalently, the prior probability that any of the SNPs is not associated with BMI.For the GWAS meta-analysis dataset, π0 and FDR to consider conditioning on a set of covariates concatenated in a column vector Xi of length c, possibly with c = 1:We now extend πi, summarized by a p-value iP, 0(xi) and FDR(xi), denoted by Z is a m × p design matrix with p < m and rank(Z) = d ≤ p, which can either be equal to X—the matrix of dimension m × (c + 1), which has the ith row consisting of X, such as polynomial or spline terms. The estimator is similar to:E(iY|Xi = xi), assuming a logistic model. A linear regression approach would be a more direct generalization of Storey’s method, but a logistic model is more natural for estimating means between 0 and 1. In particular, we note that a linear regression approach would amplify relatively small differences between large values of π0(xi), which are likely to be common in many scientific situations, especially when considering GWAS, where one may expect a relatively low number of SNPs to be truly associated with the outcome of interest. In the swfdr package, we provide users the choice to estimate π0(xi) via either the logistic or linear regression model. In Step (d), we consider smoothing over a series of thresholds to obtain the final estimate, as done by qvalue package (xi).Assuming that a hypothesis test is performed for each feature  package , with thconditional on the null, the p-values do not depend on the covariates. Our theoretical results are based on the more restrictive assumption that the null p-values have a Uniform distribution, whereas the distribution of the alternative p-values may depend on the covariates. This means that the probability of a feature being from one of the two distributions depends on the covariates but the actual test statistic and p-value under the null do not depend on the covariates further.We provide further details in the The model we considered for the GWAS meta-analysis dataset models the SNP-specific sample size using natural cubic splines, in order to allow for sufficient flexibility. It also considers three discrete categories for the CEU MAFs, corresponding to cuts at the 1/3 and 2/3 quantiles, leading to the intervals  ,  , and  .0(xi) plotted against the SNP-specific sample size N for the data analysis, stratified by the CEU MAFs for a random subset of 50,000 SNPs. We note that the results are similar for λ = 0.8, λ = 0.9, and for the final smoothed estimate. A 95% bootstrap confidence interval based on 100 iterations is also shown for the final smoothed estimate. Our approach is compared to that of FDRreg package  distribution under the null, while the latter estimates the parameters of the null distribution. Both approaches show similar qualitative trends to our estimates, although the empirical null tends to result in much higher values over the entire range of N, while the theoretical null leads to lower values for smaller N and larger or comparable values for larger N. Our results are consistent with intuition—larger sample sizes and larger MAFs lead to a smaller fraction of SNPs estimated to be null. They do, however, allow for improved quantification of this relationship: For example, we see that the range for 0 without covariates—obtained via the swfdr package, we include a subset of the data—for 50,000 randomly selected SNPs—and show how to generate plots similar to  package . The forThe results for the number of SNPs with estimated FDR ≤0.05 are given in 0(xi), as well as the usefulness of our plug-in estimator, We consider simulations to evaluate how well 0(xi), shown in 0 = 0.9, to illustrate a case where there is no dependence on covariates and scenarios II–IV are similar to the BMI GWAS meta-analysis. Scenarios II–IV are chosen to be similar to the BMI GWAS meta-analysis. Thus, scenario II is a smooth function of one variable similar to 0(x1) = x1. The exact functions are given in Section S5 of the x1 on the logistic scale, whereas for scenarios II–IV we consider a model that is linear in x1 and a model that fits cubic splines with three degrees of freedom for x1, both on the logistic scale. For scenarios III and IV, all models also consider different coefficients for the categories of x2.We focus on five different possible functions πm = 1,000 or m = 10,000 features. For each simulation run, we first randomly generated whether each feature was from the null or alternative distribution, so that the null hypothesis was true for the features for which a success was drawn from the Bernoulli distribution with probability π0(xi). Within each scenario, we allowed for different distributions for the alternative test statistics/p-values: beta distribution for the p-values or normal, t, or Chi-squared distribution for the test statistics. For the beta distribution, we generated the alternative p-values directly from a Beta distribution and the null p-values from a Unif distributions. For the other simulations, we first generated the test statistics, then calculated the p-values from them. For the normally distributed and t-distributed test statistics, we drew the means μi of approximately half the alternative features from a N, with the remaining alternative features from a N distribution, with the mean of the null features being 0. We then drew the actual test statistic for feature i from either a N or T distribution (df = degrees of freedom). Note that 10 degrees of freedom for a t-distribution is obtained from a two-sample t-test with six samples per group, assuming equal variances in the groups. We also considered Chi-squared test statistics with either one degree of freedom (corresponding to a test of independence for a 2 × 2 table) or four degrees of freedom (corresponding to a test of independence for a 3 × 3 table). In this case, we first drew the non-centrality parameter (ncpi) from the square of a N distribution for the alternative and took it to be 0 for the null, then generated the test statistics from χ2.Our first set of simulations considers independent test statistics with either m = 1,000 features. Each panel displays the true function π0(xi) along with the empirical means of qvalue package , the results for the three methods are nearly indistinguishable. For scenarios II–V, the covariates are informative, with both of our approach and the Scott T approach being able to flexibly model the dependence of the function π0 on xi. For scenarios II–III, our approach does show some amount of anti-conservative behavior for the higher values of π0, especially for the spline model fit. For scenario V, both our approach and the Scott T approach show a clear increase of π0 with ix1; given that we are using a logistic model, we are not expecting an exact linear estimate. m = 1,000 case with t-distributed test statistics. The z-values, as opposed to the other methods, which use p-values; as a result, in this case we input the t-statistics into the Scott T approach, leading to a more pronounced anti-conservative behavior in some cases. This is not the case for our approach or the Storey approach, which rely on p-values. m = 10,000 case instead. We note that we see less anti-conservativeness for m = 10,000, as the estimation is based on a higher number of features. For all these simulation frameworks, we note that for scenario I, the overall mean across all simulations for our method was between 0.88 and 0.91, very close to the true value of 0.9. package , and thez-statistics and t-statistics, where these are inputted directly in the methods implemented in the FDRreg package. We see in m = 10,000, with some slight anticonservative behavior for m = 1,000, especially when considering the spline models, also noted from the plots of m = 1,000 in t-statistics and are not applicable to the other scenarios. We always see a gain in power for our method over the BH approach, however, it is often marginal (1–3%) for scenarios I–III, which have relatively high values of π0(xi), which is to be expected, since BH in essence assumes π0(xi) ≡ 1. For scenario IV, however, the average TPR may increase by as much as 6–11% in absolute terms for m = 10,000 while still maintaining the FDR. The gains over the m = 10,000). In scenario V, where the covariate is highly informative, the gains in power of our approach over both BH and Storey are much higher. For the Beta case, the difference in TPR is threefold for m = 1,000 and fivefold for m = 10,000 over Storey. Even for the other cases, which may be more realistic, the differences are between 5% and 9% in absolute TPR over Storey and as high as >20% in absolute TPR over BH.The k ∈ {1, 1.25, 1.5, 2, 3}. The k = 1 case corresponds to scenario V and used a linear function in the logistic regression, whereas the remaining cases used cubic splines with three degrees of freedom. The estimated FDR and TPR for our approach compared to Storey are shown in t-distributed test statistics  and 1,000 simulation runs. The mean estimates of π0(ix1) are shown in 0(ix1) is lower than the estimate obtained from our method, on average. Results were similar when considering splines .Additionally, we explored the case of the “global null”, that is, πm = 1,000 features and several dependence structures for the test statistics (200 simulation runs per scenario). We considered multivariate normal and t distributions, with the means drawn as before and block-diagonal variance–covariance matrices with the diagonal entries equal to 1 and a number of blocks equal to either 20 (50 features per block) or 10 (100 features per block). The within-block correlations, ρ, were set to 0.2, 0.5, or 0.9. For the multivariate normal distribution, as expected, the FDR was generally closer to the nominal value of 5% for 20 blocks than for 10 blocks, as 20 blocks leads to less correlation. Increasing ρ also leads to worse control of the FDR. Interestingly, for the multivariate t distribution, our method often results in conservative FDRs, with the exception of the spline models and of the case with 10 blocks and ρ = 0.9. These same trends are also present for the ρ = 0.5, the empirical null leads to errors in 1% or fewer of the simulation runs; however, for ρ = 0.9 it leads to errors in as many as 33% of the runs. In contrast, the ρ = 0.9 and 10 blocks for the t distribution. We also note that the TPR is generally very low for the multivariate t distributions, except in scenarios IV and V. Overall, while control of the FDR is worse with increasing correlation, as would be anticipated, it is still <0.09 for a nominal value of 0.05 for all scenarios with ρ ∈ {0.2,0.5}, with the control being even better when the estimation uses linear, as opposed to spline, terms.Finally, we used simulations to explore what happens when there are deviations from independence. https://github.com/SiminaB/Fdr-regression).All analyses and simulations in this paper are fully reproducible and the code is available on GitHub  is different, involving a more complicated constrained maximum likelihood solution; it also requires convexity of the set of possible values of π0(xi), which is only detailed in a small number of cases . One specific caveat is that multiplying by the estimate of π0(xi) is equivalent to weighing by 1/π0(xi), which has been shown to not be Bayes optimal (We have introduced an approach to estimating FDRs conditional on covariates in a multiple testing framework, by first estimating the proportion of true null hypotheses via a regression model—a method implemented in the  optimal . Howevert-distribution and cannot be applied in cases where the test statistics come from other distributions, such as the Chi-squared distribution, which may arise from commonly performed analyses; the loss of FDR control for t-statistics has been pointed out before for this approach (p-values. Our approach always shows a gain in TPR over the method of Our motivating case study considers a GWAS meta-analysis of BMI–SNP associations, where we are interested in adjusting for sample sizes and allele frequencies of the individual SNPs. Using extensive simulations, we compared our approach to FDR regression as proposed by approach . In genep-values for all features decrease as the sample size increases. This is because the null is rarely precisely true for any given feature. One interesting consequence of our estimates is that we can calibrate what fraction of p-values appear to be drawn from the non-null distribution as a function of sample size, potentially allowing us to quantify the effect of the “large sample size means small p-values” problem directly. Using an FDR cutoff of 5%, our approach leads to 13,384 discoveries, compared to 12,771 from the Applying our estimator to GWAS data from the GIANT consortium demonstrated that, as expected, the estimate of the fraction of null hypotheses decreases with both sample size and MAF. It is a well-known and problematic phenomenon that p-values and independence of the p-values and the covariates conditional on the null. Assuming that the p-values are independent of the covariates conditional on the null is also an assumption used by p-values stratified on the covariates. In particular, it is necessary for the distribution to be approximately uniform for larger p-values. We perform this diagnostic check in N in p-values potentially being more likely to be considered in additional studies and thus becoming part of larger meta-analyses. It is interesting that the estimated proportion of nulls in 0 on meta-data covariates can thus be a good starting place for understanding possible biases and planning future studies.We note that our approach relies on a series of assumptions, such as independence of In conclusion, our approach shows good performance across a range of scenarios and allows for improved interpretability compared to the 10.7717/peerj.6035/supp-1Supplemental Information 1Click here for additional data file.


Crassostrea gigas) raises the opportunity to test genomic selection strategies for polygenic traits. In this study, a population of 820 oysters (comprising 23 full-sibling families) were genotyped using a medium density SNP array (23 K informative SNPs), and the genetic architecture of growth-related traits  was evaluated. Heritability was estimated to be moderate for the three traits , and results of a GWAS indicated that the underlying genetic architecture was polygenic. Genomic prediction approaches were used to estimate breeding values for growth, and compared to pedigree based approaches. The accuracy of the genomic prediction models (GBLUP) outperformed the traditional pedigree approach (PBLUP) by ∼25% for SL and WW, and ∼30% for SH. Further, reduction in SNP marker density had little impact on prediction accuracy, even when density was reduced to a few hundred SNPs. These results suggest that the use of genomic selection in oyster breeding could offer benefits for the selection of breeding candidates to improve complex economic traits at relatively modest cost.Pacific oysters are a key aquaculture species globally, and genetic improvement via selective breeding is a major target. Genomic selection has the potential to expedite genetic gain for key target traits of a breeding program, but has not yet been evaluated in oyster. The recent development of SNP arrays for Pacific oyster ( Crassostrea gigas) is the most cultivated oyster species worldwide and has been introduced to many countries for aquaculture production    and a hi edulis) ; raises  edulis) .A limited number of quantitative trait locus (QTL) mapping studies have been performed to examine the genetic basis of growth related traits in Pacific oyster , generalThe primary aim of the current study was to evaluate the potential use of genomic prediction in a population of Pacific oysters derived from a commercial hatchery. Several growth-related traits were evaluated as exemplar polygenic traits, including shell length (SL), shell height (SH), and wet weight (WW). The impact of SNP marker density on genomic prediction accuracy was evaluated, alongside strategies for selecting low density panels for potential improvement of genotyping cost-efficiency.Ostreid Herpesvirus, as described in Chaetoceros, T-Isochrysis and Pavlova algae. The remaining crosses (from 14 sires and 14 dams) were obtained as spat from a mass spawning at Guernsey Sea Farms (GSF). Prior to settling, larvae were held in upwelling 2 L bottle system, with aeration and constant supply of feed according to standard hatchery procedure. Post settling, oysters were held at GSF for 3 weeks then delivered overnight to Cefas where they were held on mesh upwelling system in a large 60 L tank, with a constant flow of water.The population used in this study were derived from crosses between broodstock from a commercial oyster hatchery  and were a subset of the samples used for analysis of resistance to 1. Measurements were taken at approximately 6 months. The differences in early life environment may have affected growth rate and were therefore accounted for in the statistical model described below. Parental assignment was performed as described in For both groups of oysters reared at Cefas , spat were handled every 2–3 weeks, when the tanks were cleaned. The oysters were drip fed a constant supply of mixed food including Pavlova, T-Isochrysis and Tetraselmis. Throughout the experiment, all sea water was filtered, UV treated, mixed to a salinity of 25 ppt with RO water, and aerated prior to use. Feed was provided at the rate recommend in the manual for hatchery culture of bivalve mollusc, according to the density of each tank. On several occasions, where algal stocks were low, food was supplemented with shellfish diet 1,800Shell measurements (SL and SH) were taken following a standard protocol for the measurement of oyster shells . Wet weiGenome-wide SNP data were generated using the recently developed Affymetrix SNP array for oysters , as descGenetic parameters for the resistance traits were estimated using a linear mixed model approach fitting animal as a random effect using ASReml 4  with they is the observed trait, u is the vector of additive genetic effects, b is the vector of fixed effect of tank, e is the residual error, and X and Z the corresponding incidence matrices for fixed effects and additive genetic effects, respectively. The (co)variance structure for the genetic effect was calculated either using the pedigree matrix (A)  or genomic matrix (G) N, where σ2 is the genetic variance. Hence, the narrow sense heritability was estimated by the additive genetic variance and total phenotypic variance, as follows:where 2a is the additive genetic variance and σ2p is the total phenotypic variance which is a sum of σa2 + σe2. The genomic relationship matrix used for the analysis was obtained according to where σcheck.markers module to exclude SNPs with a minor allele frequency (MAF) <0.05, call rate <0.90 and significantly deviated from Hardy–Weinberg Equilibrium <1 × 10−6, leaving 13,278 SNPs for downstream analyses. Association analyses were performed using the family-based score test for association (FASTA) using the mmscore function (−6 as determined by Bonferroni correction (0.05/N), where N represents the number of QC-filtered SNPs across the genome, while the suggestive threshold was set as 3.76 × 10−5 (0.5/N), i.e., allowing 0.5 false positive per genome scan. For the BLUPF90 approach, the same data previously filtered by GenABEL were used. Model (1) was fitted using the genomic (G) relationship matrix that was created according to The GWAS were performed for the three growth-related traits using two approaches, first using the GenABEL package  in R andfunction  with theFor the estimation of genomic prediction values, the genotype data was filtered to allow markers with a (MAF) >0.01, which resulted in a higher number of markers in the analysis . Estimated breeding values were obtained using either pedigree-based BLUP (PBLUP) or Genomic best linear unbiased prediction (GBLUP) using the linear model described above. The accuracy of genomic selection was estimated by fivefold cross validation , which were repeated five times. Phenotypes from the validation population were masked and breeding values were estimated using ASReml 4 using the linear mixed model described above (1). Prediction accuracy was calculated as the correlation between the predicted EBVs of the validation set and the actual phenotypes divided by the square root of the heritability estimated in the validation population [∼r/h]. Mean prediction accuracy values obtained from the different sets were computed and compared between the pedigree and genomic approaches.Table 2); Secondly, the low density SNP panel was selected using a strategy of random “thinning” of SNPs from the full dataset .Two different strategies for evaluating the potential of lower marker densities for genomic prediction were applied. First, the low density SNP panel for use in the computing the genomic relationship matrix was selected by a progressive increase of the MAF threshold from 0.01 to 0.475 resulting in a progressive reduction in number of markers . Moderate heritability values were estimated for all traits, ranging from 0.20 (pedigree-based) for length, to 0.35 for weight (G-matrix based), as shown in Table 1. Previous studies have described significant heritability values for growth traits in Pacific oyster, albeit these estimates range in magnitude. For instance, C. gigas, while C. gigas . Additionally, the proportion of variance explained (PVE) by each SNP was low (∼2%) highlighting the absence of any major QTL controlling growth traits in this population. Moreover, BLUPF90 analysis based on consecutive (not overlapping) SNP windows did not detect major QTL in any of the three traits, although a suggestive QTL was detected for SL in a window that explained 1.48% of the genetic variance and located on LG 1 (48.61 cM) , Worth noting that 2 windows located at LG 7 (73.23 cM) and LG 6 (39.94–40.82 cM) were found among the 10 highest scores for the three analyzed traits. These results suggest a polygenic nature of growth-related traits in Pacific oyster, controlled by many loci with small effects as it has been observed in many aquaculture species such as Atlantic salmon  in question. Marker assisted selection (MAS) is most appropriate when major QTL explain a high proportion of genetic variation in the trait, enabling use of a small panel of QTL-linked markers to supplement pedigree-based selection. A good example of this is resistance to the Infectious Pancreatic Necrosis virus (IPNV) in Atlantic salmon, where most genetic variation is explained by a single QTL . HoweverFigure 1), with an increase of ∼25% for WW (from 0.54 PBLUP to 0.67 GBLUP) and SL (from 0.44 PBLUP to 0.54 GBLUP) to ∼30% for SH (from 0.47 PBLUP to 0.60 GBLUP). These results are in agreement with published literature which shows the increase in breeding value prediction accuracy using genomic vs. pedigree prediction in aquaculture species, e.g., Atlantic salmon . Animals were randomly split into training (80%) and validation (20%) sets for cross-validation, and this was repeated five times. The genomic prediction accuracy results highlighted that the higher prediction accuracies were obtained using the genomic information (G-matrix) than using the pedigree information (A-matrix) . Using the random subsets the prediction accuracies ranged from 0.59  to 0.52 (100 SNPs). In all cases, the genomic prediction significantly outperformed pedigree prediction, even for low SNP densities, which has positive implications for future use of low-cost low-density SNP panels for genomic selection. The oyster genome is of a moderate size ∼0.6 gb (Supplementary File S1). This may result in high levels of linkage disequilibrium across large chromosome segments, which could influence the genomic prediction accuracy estimates at low SNP densities. Nonetheless, this may be representative of typical aquaculture breeding schemes that utilize large full-sib families for “sib testing,” and where genomic prediction estimates with low density markers can give high prediction accuracy  threshold, resulting in progressive decrease in SNP number . The three traits were found to be moderately heritable and showed high genetic correlation. Prediction accuracy for all traits was substantially higher using genomic prediction than pedigree-based prediction. Reduction in SNP marker density had little impact on prediction accuracy when the lower density SNP panels were chosen at random, implying only a fraction of the SNPs are required to obtain a marked increase in accuracy relative to pedigree-based prediction. These results suggest that the use of cost-effective genomic selection in oyster breeding could bring major benefits for the selection of polygenic traits, and may have commercial value for traits which are difficult to measure e.g., disease resistance.AG and RH conceived the study. TB and RH designed the experimental structure. TB established and performed the experimental challenge. AG performed DNA extractions, genotype processing, and parentage assignment. AG and OM performed the quantitative genetic analyses. All authors contributed to drafting the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.