Adult height is a highly heritable trait. Here we identified 31.6 million sequence variants by whole-genome sequencing of 8,453 Icelanders and tested them for association with adult height by imputing them into 88,835 Icelanders. Here we discovered 13 novel height associations by testing four different models including parent-of-origin (|β|=0.4–10.6 cm). The minor alleles of three parent-of-origin signals associate with less height only when inherited from the father and are located within imprinted regions (IGF2-H19 and DLK1-MEG3). We also examined the association of these sequence variants in a set of 12,645 Icelanders with birth length measurements. Two of the novel variants, (IGF2-H19 and TET1), show significant association with both adult height and birth length, indicating a role in early growth regulation. Among the parent-of-origin signals, we observed opposing parental effects raising questions about underlying mechanisms. These findings demonstrate that common variations affect human growth by parental imprinting. Adult height has a strong genetic component and is highly heritable. Here the authors whole-genome sequence 8,453 Icelanders and find novel parent-of-origin derived associations in IGF2-H19 and DLK1-MEG3. Height is an easily assessed trait in humans, with a narrow-sense heritability estimated to be 80% refs , 2, 3, 4IGF2 and the closely linked long noncoding RNA (lncRNA) growth suppressor H19, which are reciprocally imprinted genes located on the distal end of chromosome 11 that have been intensively studied both as a model system for understanding mechanisms of genomic imprinting and growthIGF2 gene expression is associated with the growth disorders Beckwith–Wiedemann syndrome and Silver–Russell dwarfism 20Previously, we performed GWAS for various diseases and other traits based on variants identified through whole-genome sequencing (WGS) of Icelanders (median read depth of 20X) and subsequent imputation into individuals from the same population. This led to the uncovering of associations of many low frequency and rare sequence variants with a large number of traits678910111618Parent-of-origin effects and genomic imprinting have been shown to contribute to variation in complex traits in both animal models and humans2223252627in utero303233Adult height is to some extent determined by factors Here, we search for sequence variants associated with adult height in the Icelandic population and their effect on birth length using parent-of-origin, additive and recessive models.2 and 165.6±6.3 cm2, respectively. The overall s.d. used in the paper is 6.6 cm)  with imputation information >0.8 were detected through high coverage WGS of 8,453 Icelanders (median depth of 32X in genome build hg38). We imputed the variants into 150,656 individuals genotyped on Illumina microarray single-nucleotide polymorphism (SNP) chips, and 294,212 of their first and second-degree relatives. In total, 88,835 Icelanders with adult height measurements were used in the association study  . The imp 6.6 cm) . We asseWe found four signals associating with adult height using parent-of-origin models, 58 under the additive model and one under the recessive model . Of thesIGF2-H19 and one at DLK1-MEG3 imprinted loci, contain minor alleles that reduce height when paternally inherited   refs , 27 (Sup36) refs .IGF2-H19 and one at KCNQ1 loci, are located within a well-studied imprinted region of ∼1.8 Mb on chromosome 11p15   and conditional analysis shows that they are independently associated with height  ref. . The regh height .r2>0.8) (H19 at the 3′ boundary of the IGF2-H19 imprinted domain (pat=−0.12 s.d. (corresponding to −0.8 cm), Ppat=5.9 × 10−13; a chi-squared test was used to calculate P-values for all GWAS associations.). In contrast, when maternally inherited, the same allele is associated with greater height (βmat=0.056 s.d. (0.4 cm), Pmat=9.4 × 10−4). The difference between the paternal and maternal effect of rs147239461[T] is significant (Ppat versus mat=1.2 × 10−13)  , clusterd domain . The min× 10−13) . When pa× 10−13) . We will× 10−13) . Also ofIGF2 . The minor allele G associates with less height when paternally inherited , Ppat=5.1 × 10−11) (mat=0.018 s.d. (0.1 cm), Pmat=0.076) (IGF2-H19(B).The second signal in the telomeric imprinted domain of 11p15 is represented by rs7482510, which is located 28 Kb upstream of IGF2 . It has × 10−11) , but doet=0.076) . The vart=0.076) . Interesβmat=−0.26 s.d. (−1.7 cm), Pmat=2.0 × 10−17). The SNP rs143840904[T] is located in the more centromeric domain at 11p15 in an intron 15 of KCNQ1. This signal was first described in the Sardinian population and was there represented by six highly correlated variants, (r2>0.70), including rs143840904, that were all at similar frequency  (ref. Iceland=1.78% versus MAFSardinia=9.40%) (r2<0.73) , which along with the paternally expressed long noncoding RNA KCNQ1OT1 participates in maintaining monoallelic expression at the KCNQ1 imprinted domain40The third parent-of-origin association signal at 11p15 is best captured by rs143840904[T] when maternally inherited (−9) ref. . We werea=9.40%) . In contr2<0.73) , and coning site . InteresRTL1 on chromosome 14q32, leading to an amino acid substitution of proline (non-polar) to threonine (polar). This variant is associated with less height when paternally inherited (βpat=−0.12 s.d. (−0.8 cm), Ppat=2.2 × 10−8) (βmat=0.067 (0.4 cm), Pmat=0.0017, Ppat versus mat=7.4 × 10−10)  and is therefore likely to be the causative variant. RTL1 is known to be a paternally expressed gene located within the DLK1-MEG3 imprinted gene cluster  that corresponds to the ovine Callipyge locusRtl1 knockout mice suffer from growth retardationThe final parent-of-origin variant is a low frequency missense variant  in  × 10−8) . Interes× 10−10) . rs41286Padd>1.2 × 10−5)  . This exParent-of-origin effects could be caused by genomic imprinting of the transmitted alleles or by external effects attributable to the full genotypes of the parents – that is both the transmitted and non-transmitted alleles444546http://www.gtexportal.org; accessed 14.07.2016), none of the four variants rs143840904, rs147239461, rs41286560 or rs7482510 had a significant eQTL with a neighbouring gene .To assess the effect of the height associated variants identified under parent-of-origin models on gene expression, we scrutinized data from the Genotype-Tissue Expression (GTEx) project, available for multiple tissues. In GTEx ref.   and adipose tissue (N=675) samples. We assessed 125 genes within ±500 Kb of the variants corresponding to the four parent-of-origin signals. Association was tested under four different models, additive, paternal, maternal and paternal versus maternal. At a significance threshold of 1.0 × 10−4 (P<0.05/4 × 125) we did not observe significant associations of the parent-of-origin variants with gene expression in blood or adipose tissue.In addition, a cis-eQTL analysis was performed in our data based on RNA sequencing of blood   and 2.0% of birth length   , consist39 , consvalues.) .IGF2-H19(A) and rs558226101[T] in TET1, were found to be significant using a threshold of 7.9 × 10−4 (P<0.05/63). As in the case of adult height, rs147239461[T] reduces birth length when paternally inherited ) and increases birth length when maternally inherited , Ppat versus mat=1.0 × 10−4). In s.d. units, the effect of rs147239461 on birth length is twice its effect on adult height , Padult=1.6 × 10−8, birth=0.86 s.d. (2.2 cm), Pbirth=5.3 × 10−4, TET1 gene , has no strong correlates in our data  and is not present in the 1000 Genomes phase 1 data . TET1 has not been reported to affect height in humans  but the association of this missense variant with adult height and birth length reported here supports a role of TET1 in growth regulation in humans, beginning during in-utero development and probably continuing after birth. TET1 is reported to play a critical role in genomic erasure of paternal imprints in the female germ lineTet1 paternal knockout mice suffer placental, fetal and postnatal growth defects as a result of hyper-methylation of differentially methylated regions of paternally imprinted genesThe 63 variants found to be associated with adult height in this study were tested for association with birth length under the appropriate models . Two vart height , Fig. 3.IGF2-H19 and one in RTL1) in three instances, and one of the variants associates with height under the recessive model (LECT2). The other nine novel signals associate with height under an additive model and eight of them are represented by low frequency or rare sequence variants.Among 13 novel height associations, the association is primarily with the paternally inherited allele . Although imprinted genes comprise a small subset of all genes   , they haIGF2-H19(A) and rs41286560[T] in RTL1 at DLK1-MEG3, the minor allele is associated with less adult height when paternally inherited and greater height when maternally inherited. The same kind of effect for rs147239461[T] at IGF2-H19(A) was also observed with birth length indicating that this phenomenon is already at play in utero. Furthermore, we previously reported a distinct 11p15 variant associated with type 2 diabetes, where the minor allele confers risk when paternally inherited but is protective when maternally inheritedIGF2-H19 domain is normally tightly regulated and under the control of a differentally methylated ICR making it vulnerable to genetic and epigenetic variations. rs147239461 at IGF2-H19(A) is located downstream from H19 35 Kb from the H19-ICR. Therefore, it is unlikely that the variant directly disrupts methylation of the ICR. However, since the variant and three of its strong correlates are located at a CTCF binding site it might affect methylation at the ICR and thus disturb the balanced expression of imprinted genes in the region.Generally, it is assumed that parent-of-origin imprinting leads to either paternal or maternal mono-allelic gene expression5423RTL1 (Retrotransposon-like 1) on chromosome 14p32 is an intronless gene with an overlapping maternally expressed antisense transcript, which contains seven miRNAs regulating the expression of this gene through an RNA interference mediated mechanismmiR127 that has been linked to placentomegaly in mice when maternally knocked outRTL1 itself. Paternal Rtl1 knockout mice develop growth retardation at the fetal stage that persists into adulthoodRTL1 has not been linked to growth defects in humans.IGF2 and the neighbouring growth suppressor gene H19 at 11p15 represents a mechanism that is consistent with the parental conflict theory and has been widely used as the basis for its interpretation. Interestingly, imprinting disorders involved in both retarded and excessive growth map to chromosome 11p15 and are linked to depressed versus elevated expression of IGF2 .Height measurements from 88,835 Icelanders were collected in deCODE‘s obesity, cancer and resident assessment instrument studies. Height was either measured using a stadiometer with the subjects wearing no shoes or self-reported on questionnaires by individuals. Birth length measurements were collected from 12,645 Icelanders in deCODE‘s obesity study. This group has very little overlap with the group of individuals with adult height measurements 14The whole genomes of 8,453 Icelanders were sequenced using Illumina technology to a mean depth of at least 10X (median 32X). SNPs and indels were identified and their genotypes were called for all samples simultaneously using the Genome Analysis Toolkit Imputation of untyped variants into the mix of typed variants is now regular procedure in human geneticsN=80,546) or first and second degree relatives of chip-typed individuals that were not chip-typed themselves . The group of individuals with birth length measurements consisted of chip-typed individuals  and first and second degree relatives of chip-typed . A total of 31.6 million variants were used in the association analysis under an additive model and parent-of-origin models. Both parent-of-origin models  were tested for variants for which Icelandic genealogy was used to assign parental origin to phased haplotypes Long-range phasing of haplotypes using surrogate parents allows for accurate phasing of Icelandic samples and the Icelandic genealogy coupled with the large fraction of chip-typed individuals in the population enabled the determination of the parent-of-origin for the genotypes  . For parent-of-origin models, we note that the number of variants that reside in or are adjacent to (±250 kb) known or predicted imprinted genes  was 1.3 million . For recessive analysis, the number of tested variants for which we had homozygotes for the minor allele was 19.2 million. All of the tested variants had imputation information over 0.8.Using genealogic information, the sequence variants were imputed into un-typed relatives of the chip-typed to further increase the sample size for association analysis and increased the power to detect associations. Individuals with height measurements were either chip-typed individuals N=80,54 or firstpes refs . InitialN=165)  from the mean value of adult height in the sample set (N=165) .Our dataset contains samples obtained using three different library preparation methods from Illumina. In addition sequencing was performed using three different types of Illumina sequencing instruments.Standard TruSeq DNA library preparation method. Illumina GAIIx and/or HiSeq 2,000 sequencers.TruSeq DNA PCR-free library preparation method. Illumina HiSeq 2,500 sequencers.TruSeq Nano DNA library preparation method. Illumina HiSeq X sequencers.x or HiSeq 2,000 instruments, respectively. Paired-end libraries were sequenced using 2 × 76, 2 × 101 or 2 × 120 cycles of incorporation and imaging with Illumina SBS kits, TruSeq v5 for the GAIIx. For the HiSeq 2,000, 2 × 101 cycles with SBS kits v2.5 or v3 were employed. Each library was initially run on a single lane on a GAIIx for validation, assessing optimal cluster densities, insert size, duplication rates and comparison to chip genotyping data. Following validation, the desired sequencing depth (10X to 30X) was then obtained using either sequencing platform. Targeted raw cluster densities ranged from 500–800 K−1 mm−2, depending on the version of both the sequencing chemistry and the data imaging/analysis software packages . Real-time analysis involved conversion of image data to base-calling in real-time.Sample preparation and sequencing using the standard TruSeq DNA library preparation method. Approximately 1 μg of genomic DNA, isolated from frozen blood samples, was fragmented to a mean target size of ∼300–400 bp using a Covaris E210 instrument. The resulting fragmented DNA was end repaired using T4 and Klenow polymerases and T4 polynucleotide kinase with 10 mM dNTP followed by addition of an ‘A' base at the ends using Klenow exo fragment (3′ to 5′-exo minus) and dATP (1 mM). Sequencing adaptors containing ‘T' overhangs were ligated to the DNA products followed by agarose (2%) gel electrophoresis. Fragments of about 450–500 bp were isolated from the gels (QIAGEN Gel Extraction Kit), and the adaptor-modified DNA fragments were PCR enriched for ten cycles using Phusion DNA polymerase (Finnzymes Oy) and a PCR primer cocktail needed for paired-end sequencing. Enriched libraries were purified using AMPure XP beads. The quality and concentration of the libraries were assessed with the Agilent 2,100 Bioanalyzer using the DNA 1,000 LabChip. Libraries were stored at −20 °C. Sequencing-by-synthesis (SBS) was performed on either Illumina GAIISample preparation and sequencing using the TruSeq DNA PCR-free method. Paired-end libraries for sequencing were prepared according to the manufacturer's instructions . In short, ∼1 μg of genomic DNA, isolated from frozen blood samples, was fragmented to a mean target size of 350 bp using a Covaris E210 ultrasonicator followed by clean-up using AmPure XP purification beads. Blunt-end DNA from the resulting fragments was generated using a mix of 3′>5′ exonuclease and 5′>3′ polymerase activities, respectively, followed by 5′-phosphorylation using T4 polynucleotide kinase. Size-selection of the blunt-end fragments was done using a two-step purification strategy with different ratios of the AmPure XP purification beads (0.6X and 1X). Finally, 3′-adenylation and ligation of barcoded adaptors was performed, followed by clean-up with magnetic beads. The quality and concentration of the libraries were assessed with the Agilent 2,100 Bioanalyzer using the DNA 1,000 LabChip (Agilent). Barcoded libraries were stored at −20 °C. All steps in the workflow were monitored using an in-house laboratory information management system with barcode tracking of all samples and reagents. All samples were first pooled (12–24 plex) and sequenced on Illumina′s MiSeq instruments (2 × 25 cycles) to assess quality and effective concentration of sequencing libraries. Subsequent deep sequencing was done on HiSeq 2,500 instruments, were each sample was sequenced on 3 lanes, generating >100 Gb of raw data and at least 30X coverage. Sequencing was done using TruSeq v3 reagents, paired-end 2 × 100 cycles. System operation and base calling in real-time was done using HCS 2.2.38 and RTA 1.18.61.Sample preparation and sequencing using the TruSeq Nano DNA method. The sample preparation workflow was essentially the same as described above for the TruSeq DNA PCR-free method, except the input amount was 100 ng of genomic DNA (instead of 1 μg) and following clean-up of adaptor ligated DNA, the samples were enriched by 8-cycles of PCR using a PCR primer cocktail, followed by Ampure XP bead clean-up. The quality and concentration of the libraries were assessed with the Perkin Elmer LabChip GX instrument using the HT DNA HiSens reagent kit. Sequencing was done using the HiSeq X HD reagent kit. Each sample was loaded onto the HiSeq X instrument at a concentration of 300 pM and sequenced to high depth (>30X). System operation and base calling in real-time was done using HCSX 3.1.26 and RTA2 2.3.9.Adult height measurements and birth length measurements were corrected for year of birth and standardized separately for each of the sexes to have a standard normal distribution. Measured and self-reported heights were corrected separately.Four different genetic models were tested: additive, recessive and parent-of-origin . Parent-of-origin models were performed by testing the paternal and maternal alleles separately. For SNPs that associated with height under multiple models, the appropriate model was concluded to be the one that gave the most significant height association. All tests reported in the present study are two-sided.y be the vector of quantitative measurements, and let g be the vector of expected allele counts for the SNP being tested. We assume the quantitative measurements follow a normal distribution with a mean that depends linearly on the expected allele at the SNP and a variance covariance matrix proportional to the kinship matrix:A generalized form of linear regression was used to test for association of adult height and birth length with SNPs. Let Whereα, β and σ2 involve inverting the kinship matrix. If there are n individuals in the cluster, then this inversion requires O(n3) calculations, but since these calculations only need to be performed once the computational cost of doing a genomewide association scan will only be O(n2) calculations; the cost of calculating the maximum likelihood estimates if the kinship matrix has already been inverted.is the kinship matrix as estimated from the Icelandic genealogical database. It is not computationally feasible to use this full model and we therefore split the individuals into smaller clusters. The maximum likelihood estimates for the parameters 2 statistics from our GWAS scan against LD score and used the intercept as a correction factor. The LD scores were downloaded from a LD score database . The estimated correction factor for adult height was 1.48 for the additive model, 1.15 for the recessive model, 1.22 for the maternal model and 1.23 for the paternal model , 9.8 × 10−8 for moderate-impact variants  and 5.3 × 10−10 for low-impact variants . For the parent-of-origin association analysis, to test if an parental allele had effect in a direction opposite to the genome-wide significant parental allele, the threshold of significance was corrected for the number of genome-wide significant signals observed in our data under the parent-of-origin models (threshold=0.05/4).The threshold for genome-wide significance was corrected for multiple testing using a class-specific Bonferroni procedure based on predicted functional impact of classes of variantsP-value) as covariate. Variants were concluded to belong to an independent signal if their adjusted P-value was genome-wide significant. Conditional analysis was repeated for each area until a result with no genome-wide significant adjusted P-value was attained.We performed conditional analysis for each ±2 Mb area that contains at least one variant with genome-wide significant association with height in our data. All variants in the area with info>0.9 were included in the analysis with the lead variant . Variants were classified as reported if published variants could account for their effect. Variants were classified as a refinement of a formerly reported signal if they fulfilled all the following three criteria: (i) the refinement variant is moderately correlated with at least one reported variants (0.10<r2<0.80), (ii) the refinement variant demonstrates a significantly stronger association and accounts for the effects of its reported correlates  and (iii) the reported correlates could not fully account for the effect of the refinement variant detected in the present study (adjusted P<1.0 × 10−3) . We concluded a variant to be novel if it was not correlated (r2<0.10) to any variant reported to associate with height under the same model  and conditional analysis revealed that reported height-variants could not account for its effect.To distinguish between reported, refinement and novel signals, we conducted conditional analysis for each variant that represented a genome-wide significant association in our data and reported height associated variants within ±500 Kb. The group of reported variants consisted of (i) the 697 SNPs reported by GIANT (ref. f(1−f)a2, in which f denotes the minor allele frequency of the variant and fh denotes the homozygous frequency of the variant and ar denotes the recessive effect. For variants associating under parent-of-origin models, fraction of variance explained was computed using the formula f denotes the minor allele frequency of the variant, am denotes the effect under the maternal model and ap denotes the effect under the paternal model. Both parent-of-origin effects, am and ap, were only included in the formula if the variant associates with height under both models with a P<1.0 × 10−2. If that is not the case, then the genome-wide significant parent-of-origin effect was only included in the formula and the other parent-of-origin effect was given the value of zero.For computation of fraction of variance explained, we chose only one SNP representing each independent signal. For each variant associating with height under the additive model, the fraction of variance explained was calculated using the formula 2To determine the cause of parent-of-origin effects, non-transmitted alleles were tested with a simple linear regression for each SNP associating with height under a parent-of-origin model. This analysis was limited to chip-typed individuals who had chip-typed parents and height measurements and/or birth length measurements.GWAS results for 2,550,858 variants were computed based on GIANT .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figure 1-19, Supplementary Table 1-9, Supplementary Discussion, and Supplementary Referenceshttp://www.geneimprint.com/) and a publication by Babak et al. (PMID: 25848752).Coordinates of imprinted genes according to the GeneImprint database (Association results for the 63 variants representing independent signals with genome-wide significant association with height.Conditional analysis for 38 variants representing reported independent height signals.Conditional analysis for twelve variants representing independent height signals that show a significant refinement of reported height signals.Conditional analysis for thirteen variants representing novel independent height signals.Regulatory annotation of variants associating with height under parent-of-origin models.


LDHA) likely affects pigeon racing and homing abilities, due to its role in physical and mental performance. Additionally, the adenylate cyclase activating polypeptide 1 gene (ADCYAP1) has been associated with physiological and behavioral shifts that occur during avian migration. In this study, we examined the association between LDHA and ADCYAP1 genotypes with pigeon survivability during racing competitions. Survivability was evaluated through the estimated breeding value (EBV) of each individual’s total race distances during its athletic life. ADCYAP1 was not polymorphic among our samples, while LDHA genotypes were significantly associated with deviated EBV values of longer total race distance; individuals carrying the S+ genotype had higher EBV . Thus, the LDHA locus might be useful for marker-assisted selection, empowering breeders and trainers to maximize pigeon quality. Moreover, data obtained from breeding will also improve our understanding of the genetic mechanism underlying navigation and flight for wild migrating bird species.Pigeon racing is a popular sport worldwide. Pigeons are under continuous selection to improve speed, spatial orientation, and endurance during long flights. However, numerous genetic and non-genetic factors affect survivability and homing ability, making such traits difficult for breeders to control. Polymorphisms in the lactate dehydrogenase A gene ( Columba livia), breeders has been using artificial selection for generating pigeons with preferred appearances and improved homing ability . Next, eij is the residual effect [N], A is the numerator relationship matrix in BLUP [2u is the additive genetic variance, and σ2e is the residual variance. Under this model with Lij, EBVs are also based on a normal distribution like Lij. Heritability (h2) is then defined as σ2u / (σ2u + σ2e).The five race traits were analysed using non-linear categorical (threshold) trait models with the GSTM (Gibbs Sampling Threshold Model) program , based oet al. . This pret al. , 33, 34.n with 1 . For exaThe total number of Gibbs samplings in GSTM was 5,050,000 with a burn-in period of 50,000 and spacing of 2,500; these values are sufficiently large for parameter convergence. Finally, the deviations of EBVs were calculated with an average of 50.0 and a standard deviation of 10.0, and then used for the association study.LDHA genotypes with deviated EBV values (DEBV), in SAS version 9.1.3 . Because S/S and S/L genotypes were low in frequency, we combined all genotypes containing the minor allele (S) as the S+ genotype , while the remainder were categorized as S- genotype .A generalized linear model (GLM) was used for the association analysis between All aspects of the study were performed according to the guidelines established by the Ministry of Education, Culture, Sports, Science, and Technology in Japan (Notice No. 71). The protocol was approved by the Committee on the Ethics of Animal Experiments of the Wildlife Research Center of Kyoto University (Permit No. WRC-2017-002A).h2): 0.23, 0.25, 0.39, 0.28, and 0.20 for Race1, Race2, Race3, Race4, and Race5, respectively. Thus, the five complex traits of interest are heritable, with influence from both genetic and non-genetic factors.The five race traits exhibited moderate to high heritability estimates (12AAA(GA)7 microsatellite in 3′ UTR of ADCYAP1 (GenBank accession number: MF099650). Subjects were not polymorphic at this microsatellite locus. For LDHA, genotyping the microsatellite (TTTAT)3-5 in intron 6 yielded three alleles  and six genotypes . This polymorphic site was previously described in intron 5 [LDHA sequence that added a first non-coding exon .We amplified and sequenced a 155 bp DNA fragment that includes the (GA)intron 5 . The posM allele (0.614) and M/M genotype (0.354) were highest in frequency, while the S allele (0.146) and S/L genotype (0.024) were the lowest (LDHA genotypes were significantly associated with the DEBVs of three race traits (Race3–5). Specifically, S+ individuals exhibited increased survivability (higher DEBV) during racing competitions . The lack of significant relationships between LDHA genotypes and shorter race distances (Race1 and Race2) may be because pigeon survivability matters less at 500–1000 km. Our results corroborate multiple studies that have demonstrated a significant link between LDHA polymorphisms and pigeon racing/homing abilities [In this study, we investigated the association between racing-pigeon survivability and polymorphisms in two genes, the novel bilities , 16, 23.bilities . The hipbilities .M allele frequencies were found in Japanese racing (0.712) than in wild rock pigeons (0.334), a trend that is attributable to the long history of artificial selection for high speed and rapid return during racing competitions [M allele frequency was also higher than S allele frequency, but S/S individuals exhibited greater survivability . This outcome is likely due to our chosen trait of interest and a trade-off between selecting for speed versus selecting for survivability. Specifically, accumulated total race distance in a pigeon’s athletic life involves completing many races safely, a factor primarily reliant on survivability. High survivability indicates excellent navigation ability, heightened endurance, and increased resistance against environmental hazards , but not necessarily high speed. Wild rock pigeons have greater survivability (suggesting higher S frequency) than racing pigeons, given intense natural selective pressure on the former for traits such as foraging, seeking shelter, and predator defense. However, intense and extended artificial selection for high speed in racing pigeons appears to have increased M allele frequency at the expense of S allele.Higher etitions . In the h2 values for our five pigeon race traits were in agreement with previous reports on horse racing performance, which exhibited h2 values of 0.05–0.29 [h2 estimates were higher than the estimate (0.06) from an earlier study on racing pigeons [S alleles, with occasional homozygotes that exhibit high survivability.The moderate to high .05–0.29 , 0.12–0..05–0.29 , and 0.1.05–0.29 , 37, 38. pigeons . This co pigeons . In the LDHA intron 6 affects EBVs of accumulated total race distances. This polymorphic locus is near the GT splice donor site and thus may affects pre-mRNA splicing to influence LDHA gene expression. Alternatively, the microsatellite might be in linkage disequilibrium with some unknown functional locus located in close proximity to LDHA. Other important genes likely to affect pigeon performance are serotonin transporter (5-HTT) and mitochondrial ATP6 genes. 5-HTT affects personality and ATP6 affects energy production and physical fitness and both traits are important for survivability. We had surveyed 5-HTT exon1 and ATP6 among Japanese racing pigeons but we couldn’t find polymorphism within these two genes (data not shown). Future functional genomics and linkage analyses must include more candidate genes  and morS1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


DNA-based predictions for hard-to-measure production traits hold great promise for selective breeding programs. DNA pooling might provide a cheap genomic approach to use phenotype data from commercial flocks which are commonly group-mated with parentage unknown. This study on sheep explores if genomic breeding values for stud sires can be estimated from genomic relationships that were obtained from pooled DNA in combination with phenotypes from commercial progeny.Phenotypes used in this study were categorical data. Blood was pooled strategically aiming at even pool sizes and within sex and phenotype category. A hybrid genomic relationship matrix was constructed relating pools to sires. This matrix was used to determine the contribution of sires to each of the pools and therefore phenotype category by using a simple regression approach. Genomic breeding values were also estimated using the hybrid genomic relationship matrix.We demonstrated that, using pooled DNA, the genetic performance of sires can be illustrated as their contribution to phenotype categories and can be expressed as a regression coefficient. Genomic estimated breeding values for sires were equivalent to the regression coefficients and are a commonly used industry tool.Genotyping of DNA from pooled biological samples offers a cheap method to link phenotypic information from commercial production animals to the breeding population and can be turned into information on the genetic value of stud sires for traits that cannot be measured in the stud environment. Genomic predictions have had a significant impact on livestock breeding systems for which large reference populations with genotypic and phenotypic information exist . The oppAllele frequencies can be estimated from pooled genotype data and subsequently estimated effects of single nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) have been demonstrated to be equivalent to effects of SNPs from individual genotyping , 5. ThisThe objective of this study was to determine whether, in the absence of individual genotypes, pooled DNA genotypes can be used in combination with commercial progeny records on a categorical phenotype to estimate genomic breeding values and to illustrate these in a regression approach as a sire’s genetic contribution to a particular phenotype. This approach could be a cost-effective commercial progeny test to inform stud breeders and commercial producers on the genetic suitability of sires for specific environments.Approval was granted  to use the animals in this study by the Animal Ethics Committee of the Australian Animal Health Laboratory, Victoria. Two thousand six hundred 13 to 14 months old Merino sheep were available for the trial. The sires of the sheep in the study were obtained from a stud in southern New South Wales which had been the sole provider of rams to the commercial property for many years. For management purposes, the sheep were maintained in two groups according to sex, ewes  and wethers .The phenotype of interest in this study is called “dag score” and describes the amount of faecal soiling in the breech area of the sheep. Breech soiling devalues the wool harvested from the sheep, increases time and management costs of routine husbandry practices and can predispose the animal to increased risk of flystrike. Dag score is a heritable trait with a heritability of about 0.35, depending on the age of the animal at the time of phenotype assessment , but is Pool formation within contemporary groups or fixed effects has been suggested as the most effective pooling design . General® Blood and Tissue Kit).Once samples were assigned to pools, samples of 20 μL of whole blood from each individual sample within each pool were combined to create a pooled blood sample. 200 μL of the pooled blood was then used for genomic DNA extraction following manufacturer’s instructions  because it consists of three blocks of relationships, i.e. (1) between pools, (2) between individual sires, and (3) between pools and sires . The firs) plus the number of pools (p) by the number of loci (i), containing B-allele frequencies. The B-allele frequencies for sires were 0.0, 0.5, and 1.0 for the homozygote, heterozygote and other homozygote, respectively, and for pools these were expressed as a real number between 0 and 1. For vector s + p)), the same formula is used as for VanRaden uses matrix f size I . Let vecLet matrix nt}Z=M-P .pi the frequency at locus i [p, but with missing values set to 0.5.Matrix  locus i . For thiThen, the distribution of pool phenotypes is not necessarily a scaled equivalent of the distribution of phenotypes in the population. In our sheep data, pool sizes were fairly even. Although not necessary in this case, matrix The relationship of the sires with pools was regressed on the dag score. The DNA pooling strategy was based on phenotype category, therefore, the linear relationship between genetic contribution of a sire and phenotype categories is expressed in the regression coefficient. In this study, a positive regression slope indicates a higher degree of relatedness of the sire to the pools with higher dag scores. Conversely, a negative slope indicates higher relatedness of the sire to the pools with low dag score. Therefore, the latter would characterize sires that are genetically favorable for dag score.0 is the intercept, β1 and β2 are regression coefficients, i is a random error term.For the regression approach, we fitted the following model:P values of the regression coefficient were empirically validated through permutation testing. Sires’ phenotype data were permuted 100 times within sex. The distribution of counts for the real data compared to the distribution of counts for the permuted data using a Pearson’s Chi squared test with simulation was used to derive the significance level. Results were tabulated, and means were calculated for each sire’s genomic relationship to a pool and for the slope of the relationship between each sire and each pool calculated.Genomic breeding values were estimated using h-GRM in a genomic best linear unbiased prediction (gBLUP) approach in ASReml . The folIt was assumed that An appropriate pooling strategy strikes a balance between cost effectiveness and accuracy. Here, we were not in a position to assess our pooling strategy on accuracy, but previous studies suggested that 64 pools of 46 individuals each estimated SNP effects equally well as individual genotypes . We usedThe interpretation of the genomic relationships between sires provides some insights into the choice of sires for the commercial flock. Genomic relationships between sires, which are the off-diagonal values of the sire-by-sire block of the h-GRM, ranged between 0 and 0.5, with the majority around 0.1 , which indicates that the variation observed between sires in the contributions to pools of different dag scores is likely to be real, as one would expect for a heritable trait.To determine whether the relationships between sires and pools were associated with the phenotypes of the pools, for each sire we regressed the genomic relationships on the pool dag scores. Departure from the null hypothesis, assuming no association between sire-pool relationship and pool phenotype, was assessed by examining the distribution of the significance levels of the regressions. The regressions of sire relationship on dag score were more often significant than would be expected by chance. For example, 10 of 33 sires (30%) were significant at the 5% level Table , far morThe “best” and “worst” sires with respect to the regression coefficients for dag score are illustrated in Fig. The replicate pools for each phenotype category, i.e. two male and two female pools Fig.  show thaThe equivalence of the regression approach compared to genomic estimated breeding values (GEBV) obtained with a gBLUP approach is shown in Fig. A probability-based approach was suggested to estimate a numerator relationship matrix for multiple sire combinations , but genIn the hierarchical structure of most livestock industries, where genetic improvement is undertaken in the stud breeding sector, and the genetic gains flow to the commercial sector through the sale of sires and semen for artificial insemination, no performance information from the commercial sector returns to the stud. This is a lost opportunity to genetically characterize sires across environments. The approach demonstrated in this study has multiple benefits for the stud and commercial sector. It provides an opportunity to apply cost-effective genomic technologies to harvest commercial performance data, which is either routinely measured or assessed during general husbandry procedures or could be obtained with only small additional labour investment. The resulting information on the sires provides the commercial producer with the knowledge of which sires have contributed to particular phenotypes, which can inform future sire or stud selection. Although, in this study sires were evaluated retrospectively, individual genotypes of related current generation sires could be included in a pooling study to obtain information of their genetic merit in a commercial environment .For studs, the proposed pooling approach offers genomic breeding values for their sires for traits that are impossible to obtain in the stud environment, e.g. disease or performance in a commercial environment. It has been demonstrated that significant genotype × environment (G × E) interactions exist in the Merino industry between the stud and commercial level  and the The next step in defining the value proposition of DNA pooling for the livestock industries is to establish the loss in accuracy of pooling versus individual genotyping. Here, we demonstrated that the contribution of sires to phenotypes reflects their genetic merit and it can be translated into a breeding value which is a known industry tool. DNA pooling is a cheap approach that can inform a knowledge gap on commercial performance of sires which can be exploited by stud breeders and producers.Estimates of the genetic merit of sires using pooled DNA from progeny in a commercial production system can be determined using this technique, and it can provide a cost effective option to inform on the performance of sires for traits that cannot be measured in the stud environment, but are important for commercial operations, and to ultimately increase the amount of data available to stud breeders to inform on the genetic value of sires for a range of traits.


Machine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called “short fat data” problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach.Through our simulation study we propose a collective feature selection approach to select features that are in the “union” of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~ 44,000 samples obtained from Geisinger’s MyCode Community Health Initiative .In this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity. The advancements and cost-effectiveness of genotyping and sequencing technologies have led to the ever increasing “short fat data” problem  in applying various machine learning methods to detect epistasis , 2. GeneIn this study, we tested an eclectic set of parametric and non-parametric methods on simulated datasets to first pick a few orthogonal methods to use in selecting features that can be used in downstream analysis of epistasis. We compared these methods based on both efficiency and effectiveness. We observed that different methods tend to select variables based on different important aspects. Thus, we suggest a collective feature selection approach. We propose to select the union of features from the top comparable methods. The concept of taking the input from many algorithms, to select variables as a collective opinion is in line with the “no free lunch” theorem of optimization which states that in searching for candidate solutions, no one algorithm can be specialized to all problems . UnknownIn this section, we will describe the datasets used for simulations and real data analyses as well as the statistical methods applied for conducting feature selection.Disease penetrance: This refers to the strength of the simulated signal or effect size and thus directly corresponds to the heritability of the phenotype. We have used previously simulated data with the same signal strength, these are listed as 0.1_diagonal, 0.5_diagonal and 0.9_diagonal and have been previously explained in Li et al. .Number of disease sites: This refers to the number of SNPs that contributes to the total effect in the dataset.Minor Allele Frequency (MAF): For many genetic interaction studies, it has been shown that MAF highly influences power to detect true interactions. Therefore, we limited our analysis to only common alleles above MAF 0.4 , 20. ForNumber of Samples: We generated 8 simulation scenarios consisting of balanced datasets with 2000 cases and 2000 controls.Number of Variants: We set the number of variants as 100 and 500 to address the computation burden.We simulated multiple data sets consisting of SNPs (single nucleotide polymorphisms), referred to as variables, using an additive genetic encoding  with case-control status to test for binary outcome. Our simulation parameters consisted of various combinations of the following epidemiological characteristics:We simulated (a) main effect only and (b) interaction effect only datasets using a simulation procedure that has been previously explained . As descUsing marginal association as an example, the association of a variable with a binary outcome simply indicates differences of allele frequencies between cases and controls. Using a really simple dummy example as in Table The same logic extends to interacting variables. Only in this case, we are specifying different joint allele frequencies between two SNPs. For example, there are more SNP1 = 2 and SNP2 = 2 combination for controls than cases in Table Therefore, simulated variables with interaction effects were generated in case and control datasets separately. For example, in dataset 1SNP, there is 1 SNP with a main effect and similarly for the interaction effect datasets, such as case2_control2, there are 2 pairs of interaction SNPs simulated in cases (G1 < −>G2 and G3 < −>G4) and 2 interaction pairs in controls (G97 < −>G98 and G99 < −>G100).We simulated another set of datasets with a slightly different architecture to recapitulate the need to consider the varying architectures of complex disease traits. For this simulation, we used GAMETES software to simulate two different genetic architectures . The twoP-value<1e-07. Lower frequency variants (MAF < 0.2) were excluded from analyses as a first filtration step so as to compare our methodology to the simulated datasets. Additionally, studies also suggest that for variants with MAF < 0.2, the interaction effects do not explain much of genetic variance [2 threshold of 0.2 for LD pruning. After genotype QC, the training dataset consisted of 60,232 variants for feature selection.We applied the proposed collective feature selection to a real dataset obtained from the Geisinger MyCode DiscovEHR collaboration , 24. At variance , 20. To To compare and contrast the different methods that can be used to select features with non-additive effects, we chose a wide range of filter and embedded methods. For filter-based feature selection methods we tested MultiSURF* and MDR and for embedded methods we tested Random forests, gradient boosting, LASSO and Elastic Net. In this manuscript, we divided methods into parametric and non-parametric methods. We used this terminology throughout the manuscript to classify the methods tested. Figure L1 regularization on the regression coefficients. Since the main goal of this analysis is to detect features that exhibit interaction effects, we ran LASSO regression to include both additive effects of SNPs in the models and exhaustive pairwise interactions of all SNPs in the model. Below are the equations representing LASSO regularization for single SNP and interactions:LASSO and Elastic Net regularizations are widely accepted methods for feature selection . Least Aλ >  = 0 is the regularization parameter and J(β) is the penalty function.Penalized estimates for the model with additive effects alone can be derived as the solution to the following optimization problem:For LASSO Regularization with additive effects only, the penalty function is the L1 norm and can be expressed as follows:Likewise, penalized estimates for the regression model with additive and SNP-SNP interaction effects can be derived as the solution to the following optimization problem:Again, for LASSO Regularization with additive effects and interactions together, the penalty function can be expressed as follows:p > n), in which case it tends to select at most n predictors. Also, when predictors are correlated, LASSO is outperformed by ridge regression. Thus, we modeled the data with ridge regression in a preliminary part of our analysis but did not include those results in this manuscript since they were similar to those from LASSO. Next, we explored another penalized regression method, the Elastic Net, which works well in selecting a group of correlated variables and does not limit the selection of the number of variables. Elastic Net uses a weighted average of the L1 and L2 norms for its penalty function.LASSO combines variable selection and shrinkage of variables, but it has a drawback when the number of predictors is greater than the number of samples  require optimization of ƛ. Elastic net involves another tuning parameter called Even though parametric methods are simple and easy to understand, they do not always fit the complex nature of biology. Thus, exploring some non-parametric methods is also necessary. Non-parametric methods do not make assumptions about the distribution of variables and underlying genetic architecture. These methods usually work best for “big data” problems. We tested two decision-tree based methods, including Random forests and Gradient Boosting, and we also tested a non-heuristic ReliefF algorithm variation called Multiple Threshold Spatially Uniform ReliefF (MultiSURF*) .https://cran.r-project.org/web/packages/gbm/gbm.pdf). TuRF refers to Tuned ReliefF and it performs feature selection recursively. It is suggested to use TuRF along with ReliefF algorithms to get better performance when using a large number of variables [For our random forests implementation, we used the RANGER R package . We tuneariables , 15, 28.https://ritchielab.psu.edu/software/mdr-download} in similar way to Oki NO et al. [Multifactor Dimensionality Reduction (MDR) has been traditionally applied to several association studies including gene-gene and gene-environment interaction studies , 8. ManyO et al. , where wA plethora of machine learning and feature selection methods have been proposed and tested in various studies , 16, 32.Using this approach, we also propose a pipeline as shown in Fig. Five-fold cross validationWe applied this proposed approach to test for SNPs that are associated with obesity among samples from the MyCode DiscovEHR study . On qual2.ProcessModelling data as described in Step 3, which included 14,925 samples and features selected via collective approach, were divided into 5 equal parts.3.ResultsThe first iteration begins with selecting a training set to generate random population , dividing into sub-populations, and then preforming an analysis on 30 nodes. The grammar for GENN is then used to evaluate the training set using Area Under Curve (AUC) fitness criteria. This step is then repeated 20 times (numsteps) after which migration takes place to select the best solution from all 30 nodes. This process is repeated 4 more times, once for each remaining cross-validation fold to perform 5-fold cross validation as explained in step #1.Training and testing AUC for each network model associated with outcome is reported from all cross validations.We aimed to use TuRF along with MultiSURF to help increase its efficiency. We tested 3 different thresholds, 1%, 5%, and 10%, to iteratively remove that percent of lowest ranking variables at each iteration. Figure It is interesting to note here that MultiSURF* without TuRF performs better for strong effect models (0.5 and 0.9 penetrance) for both 100 SNPs and 500 SNPs datasets. This could be due to the fact that TuRF works better for larger datasets with many variables whereas 500 variables is still considered relatively “small” and can be handled by MultiSURF alone. In this case, TuRF does not help but instead makes it worse. The poor performance of MultiSURF and TuRF could be explained by the algorithm accidentally discarding the important variable or variables in the first iteration.We ranked all MDR generated models based on their training accuracy to select top user-defined percentages of models as explained in model selection. Figure We tested all chosen methods on the two experiments of simulated datasets with different ranges for effect sizes and various additive main effect and interaction effect models as explained in the data section. To compare results, we are using the degree of effectiveness described as “Sensitivity” where a sensitivity of 1 is equivalent to 100% of true positives being selected in the top features. Figure We also estimated the time it would take for most of these methods to run when the number of samples and variants are increased. From our analysis, we estimated that MDR scales linearly with number of samples and quadratically with number of features whereas MultiSURF* scales quadratically with number of samples and linearly with number of features. . Thus, tWe applied collective feature selection on simulated experiment data 2 to obtain the number of features that will be selected from top performing methods. Figure Therefore, the practice of applying a collective approach seems advantageous. Figure FTO and MYO16). The overlap of these variables among the different methods is shown in Fig. The first step in identifying non-linear models associated with obesity  is to perform feature selection. We selected 3 methods  for feature selection as described in the methods section. As shown in Table Five different networks were obtained as a result of applying GENN to identify non-additive interactions associated with BMI outcome. The training and testing area under curve (AUC) for the 5 models are presented in Table P-values for these variants change drastically when BMI, the dependent variable, is adjusted by covariates . Therefore, to identify if there is significant effect of co-variates on SNPs, we tested these variants by running logistic regression with and without adjusting for covariates. Table p-values and betas from regression analyses.We choose the best network from this analysis, which is shown in Fig. FTO gene which has been identified by many GWAS analyses. It is to be noted that FTO variant was not selected by every feature selection method and similar is the case for other variants that are reported in Fig. FTO [TNC, MYO16, and TTBK1 genes. Notably, these three genes have known associations with other phenotypes influenced by BMI, such as TNC with Alzheimer’s and schizophrenia [TTBK1 is also known to be associated with Alzheimer’s disease [MYO16 has been found to be associated with pulse pressure [MYO16 and TNC are not significant when tested for independent main effect (as reported in Table Obesity is a worldwide epidemic and it predisposes to many other metabolic traits and diseases . In our Fig. FTO , 41 variophrenia , 43. TTB disease –46 whilepressure . It is aFTO variant by MultiSURF+TuRF and non-selection of variants in genes MYO16 and TTBK1 by MDR and Ranger respectively. One possible explanation for selection of different features from different algorithm corresponds to the “no free lunch” theorem [Epistatic features of genes are necessary to consider when investigating the genetic etiology of disease traits. Gene-gene interactions are believed to account for hidden genetic variability . Testing theorem  and the One of the limitations of this study is that we tested our analyses for binary outcome in both simulated and natural datasets. Future work would include the application of these methods to quantitative phenotypes. Additionally, in our simulation analyses we were not able to identify any patterns among the SNPs that were selected across methods. One possible reason could be because of the way that noise is simulated in our dataset, we selected all variants at similar MAF. More studies including simulations of different sets of MAF could also help validate this approach further. In addition, the inclusion of other types of underlying models of epistasis would be useful to further discern which orthogonal or complementary methods perform best in a collective feature selection strategy.Although our current study is limited in terms of the simulations we performed, they clearly indicate that different methods select varying features depending on the genetic architecture of the trait. Thus, using a collective approach by selecting union of results from different methods rather than selecting an intersection could help preserve features with non-additive effects during feature selection. We applied our approach to select features that were later tested in an independent dataset to identify networks using GENN. Our model was able to select known signals as well as potential interacting effects of known signals with other variants that could be influencing the risk of obesity.


In the past few years, imputation approaches have been mainly used in population-based designs of genome-wide association studies, although both family- and population-based imputation methods have been proposed. With the recent surge of family-based designs, family-based imputation has become more important. Imputation methods for both designs are based on identity-by-descent (IBD) information. Apart from imputation, the use of IBD information is also common for several types of genetic analysis, including pedigree-based linkage analysis.We compared the performance of several family- and population-based imputation methods in large pedigrees provided by Genetic Analysis Workshop 19 (GAW19). We also evaluated the performance of a new IBD mapping approach that we propose, which combines IBD information from known pedigrees with information from unrelated individuals.Different combinations of the imputation methods have varied imputation accuracies. Moreover, we showed gains from the use of both known pedigrees and unrelated individuals with our IBD mapping approach over the use of known pedigrees only.Our results represent accuracies of different combinations of imputation methods that may be useful for data sets similar to the GAW19 pedigree data. Our IBD mapping approach, which uses both known pedigree and unrelated individuals, performed better than classical linkage analysis. In the last few years, imputation approaches have been widely used in genetic studies, especially in the context of genome-wide association studies and population-based designs. This is a result of an attractive feature of imputation: it increases genetic information at low-cost. Moreover, imputation allows meta-analysis between different studies, especially when different genotyping platforms are used. The main idea of imputation is to: (a) genotype or sequence a small subset of individuals on a dense single nucleotide polymorphism (SNP) panel, (b) genotype remaining individuals on a sparse SNP panel, and (c) use statistical methods for imputation, generally based on hidden Markov models, to impute untyped SNPs from the dense panel into these individuals with only sparse (or no) genotyped SNPs.Imputation approaches have been proposed for both family- and population-based designs. However, the performance of these approaches has not yet been thoroughly evaluated and compared in pedigree data. The availability of the International Haplotype Map Project (HapMap)  and the Identity-by-descent (IBD) underpins both linkage analysis and imputation. For linkage analysis, once IBD has been determined, the pedigree structure is no longer needed for subsequent computations. For imputation, IBD within pedigrees or  across individuals is used as a source of correlation between individuals for family- and population-based imputation.In this study, we evaluated and compared the performance of several family- and population-based imputation approaches in the Mexican American pedigree data provided by Genetic Analysis Workshop 19 (GAW19). We also evaluated the effect on imputation accuracy of the number of selected individuals for sequencing and the way they were selected, randomly or by tailored selection with GIGI-Pick . In addiAll our analyses used chromosome 3. For population-based imputation, we used the whole genome sequence data on the 464 sequenced individuals in a region of 2.5 Mbp  random selection or (b) via GIGI-Pick (a method to help with informative choices) . For GIGBEAGLE–BEAGLE, SHAPEIT–BEAGLE, and SHAPEITped–BEAGLE;MaCH–MaCH, MaCH–minimac, SHAPEIT–minimac, and SHAPEITped–minimac;MaCH–MaCHAdmix, SHAPEIT–MaCHAdmix, and SHAPEITped–MaCHAdmix;IMPUTE2–IMPUTE2, SHAPEIT–IMPUTE2, and SHAPEITped–IMPUTE2.We performed 2-step imputation, with pre-phasing of markers in the first step, and imputation in the second step. The pre-phasing algorithms/programs we used were: BEAGLE , IMPUTE2We performed pedigree-based imputation using GIGI . This meρ1 = sum of correlation/total number of imputed SNPs , and (b) ρ2 = sum of correlation/total number of imputed SNPs in the reference panel (given MAF in reference panel data > 0). Correlation was computed for each SNP between the true and the imputed genotypes in the sparse SNP panel individuals, and then averaged across all imputed SNPs. As a metric of imputation accuracy, the use of correlation, which implicitly adjusts for the MAF of SNPs, is better than the use of concordance, which gives misleading results for rare variants. Other existing measures could also be used but there is no clear consensus in the literature of which performs best in all situations. In any case, our aim was to compare the accuracy between approaches and not to evaluate the accuracy per se for each approach. In this case, the correlation measure provides the necessary information for this comparison for both rare and common variants.We also combined imputation obtained by GIGI and population-based approaches via a flexible framework described elsewhere . We compWe used a set of 1000 IBD graphs , 16 that(MAP4) at 69 cM  on chromosome 3. Trait data were preadjusted for age, sex, and current use of antihypertensive medications, and we used a trait model previously developed [We used the 200 simulated traits of diastolic blood pressure (DBP) with a causal gene ρ1) increased by 0.07 when SHAPEITped was used for pre-phasing rather than BEAGLE. The increase is even more striking (~0.18) for the imputation using minimac and the pre-phasing using SHAPEITped rather than MaCH. Accounting for the pedigree structure information during pre-phasing (SHAPEITped) showed a slight but consistently better imputation accuracy for all approaches and MAF bins .Our results for several MAF bins were: rare variants ∈ , is better than MaCH–MaCH  for rare and uncommon variants, where the difference of imputation accuracy was approximately 0.1. For common variants, both approaches led to similar results. This result stresses the need, in such data, of using imputation programs that allow for admixture in order to improve imputation accuracy.ρ1 = 0.23, 0.27, 0.39, and 0.54, respectively). In addition, MaCH–minimac performed better than MaCH–MaCH, which means that minimac’s imputation algorithm still may be better than the one used in MaCH. The underperformance of GIGI in the GAW19 data for rare variants, and possibly also the common variants, might be the result of using an admixed population. In fact, when GIGI is not able to impute genotypes using pedigree information, it draws them from the pre-specified population MAF. If these frequencies are inaccurate, resulting poor imputation is likely.Surprisingly, MaCH–minimac consistently outperformed MaCH–MaCH. Despite the claim that minimac and MaCH use same imputation algorithms, our results suggest otherwise, and that minimac’s undocumented algorithm may be better. To investigate this result and to determine if the difference might be a result of possible bias in calling the sequence data , we simulated sequence data on the same GAW19 pedigrees but used European ancestry data. We performed analysis with MaCH–MaCH, MaCH–minimac, SHAPEITped–minimac, and GIGI. Interestingly, GIGI performed better than all approaches for rare variants on these data  SHAPEITped–BEAGLE, (b) SHAPEITped–minimac, and (c) SHAPEITped–IMPUTE2. Overall, we show a slight improvement in imputation accuracy from using the combined data, for rare and uncommon variants, but less for common variants. This trend could be seen by the 2 correlation measures, but especially by The selection of reference panel individuals using GIGI-Pick showed a slight increase in imputation accuracy compared to random selection, for the subset of approaches we used Fig. . For allFigure We compared the performance of several imputation approaches in pedigree data provided by GAW19 organizers. Also, we proposed and evaluated the performance of a new IBD mapping approach that combines IBD information from both unrelated and related individuals, by pedigrees, in order to identify genes implicated in complex traits. We showed that using the SHAPEIT program for pre-phasing, with its option that handles pedigree structure, along with the imputation program minimac, led to the best imputation performance for both rare and common variants. This population-based imputation approach outperformed GIGI (a family-based imputation method) not only for common variants but also for rare variants. This result was not expected for rare variants, and might be specific to this admixed data set as indicated by our results from simulated sequence data derived from European samples. Beside this specific result, most of the remaining results observed in GAW19 data were generally consistent with what we observed in our earlier simulations, which makes the results generalizable. On the other hand, our new IBD mapping approach shows promise, as it appeared to perform better than classic linkage analysis that uses only known related individuals in pedigrees.


Background: Single variant approaches have been successful in identifying DNA methylation quantitative trait loci (mQTL), although as with complex traits they lack the statistical power to identify the effects from rare genetic variants. We have undertaken extensive analyses to identify regions of low frequency and rare variants that are associated with DNA methylation levels.Methods: We used repeated measurements of DNA methylation from five different life stages in human blood, taken from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Variants were collapsed across CpG islands and their flanking regions to identify variants collectively associated with methylation, where no single variant was individually responsible for the observed signal. All analyses were undertaken using the sequence kernel association test.Results: For loci where no individual variant mQTL was observed based on a single variant analysis, we identified 95 unique regions where the combined effect of low frequency variants (MAF ≤ 5%) provided strong evidence of association with methylation. For loci where there was previous evidence of an individual variant mQTL, a further 3 regions provided evidence of association between multiple low frequency variants and methylation levels. Effects were observed consistently across 5 different time points in the lifecourse and evidence of replication in the TwinsUK and Exeter cohorts was also identified.Conclusion: We have demonstrated the potential of this novel approach to mQTL analysis by analysing the combined effect of multiple low frequency or rare variants. Future studies should benefit from applying this approach as a complementary follow up to single variant analyses. Genome wide association studies (GWAS) have had a profound influence on the number of complex diseases associated common variants identified. Current endeavours have now shifted to elucidate the functional role of these variants and to better understand the underlying mechanisms by which they influence phenotypic varition. One approach to this has been to determine their impact on DNA methylation, an epigenetic regulation mechanism known to play a key role in many biological processes and disease susceptibility ,2. RecenCollaborative efforts have found success in improving the statistical power to detect disease associated rare variants by pooling large sample sizes together (termed meta-GWAS) ,9. HowevWe have undertaken extensive analyses using repeated measures of methylation data from the Accessible Resource for Integrated Epigenomic Studies (ARIES) project n = 834, Mean Age = 7.49 (Standard Deviation = 0.15), proportion female = 0.50) was selected as the discovery analysis for this study and all results are from this data unless stated otherwise. The imputed genotype dataset for these analyses contained 3,721,682 low frequency variants (MAF ≤ 5%). 1,787,681 of these were rare variants (MAF ≤ 1%).All analyses were undertaken using the ARIES dataset  which inet al. A further 4,407 CpG islands with shore and shelf regions (i.e. islands +4kbs up and downstream).There were 27,176 CpG island regions according to UCSC annotations using the hg19 build of the human reference genome. 10,836 et al. . Of the An illustration of these 3 definitions can be found in et al. .Each collapsed region of variants was analysed in turn with each good quality CpG probe  within et al.  was used . Extending these regions to include variants within adjacent shores provided strong evidence of association for 90 unique regions, 88 of which were not identified when collapsing variants from island regions alone. The top hits for these results can be located in Supplementary Material, Table S2). Q-Q plots for all these results can be found in ALPP (P = 3.62 × 10−17). Results can be found in Supplementary Materials, Table S3–S5.Methylation levels at five positions were influenced by low frequency variants (MAF ≤ 5%) that were restricted to being located within proximal CpG islands .All sets of variants responsible for cis-mQTL effects that survived the correction for multiple comparisons were further evaluated by calculating individual variant effects using linear regression. This was to ensure that no single variant was independently responsible for evidence of association, as well as discerning which variants were collectively responsible for driving the observed signal  subtracting 12,726,079 possible cis-mQTL effects). Due to the computational burden of this analysis, only CpG island and shore regions were evaluated as this region of interest appeared to be the most effective at identifying evidence of cis-mQTL in the previous analysis (88 of the 94 cis-mQTL effects were observed using this region of interest).C21orf5) and cg09050820  with a P-value of 1.50 × 10−15. No observed associations survived the multiple testing correction using a cutoff of 1% MAF. Results can be found in Supplementary Materials, Tables S12 and S13.Only one result survived the conservative multiple testing correction. The observed association was found between 9 low frequency variants (MAF ≤ 5%) located on chromosome 21  (n = 608) . Replication analyses were successful as low p-values were observed for each mQTL (P < 0.01). Results can be found in Evidence of replication for the top 10 hits in the CpG island and shore analysis was evaluated using individuals from two independent cohorts, TwinsUK (n = 847)  and ExetSupplementary Material, Fig. S1). These figures did not suggest outliers were an issue in our analysis, although to be certain we have also undertaken leave one out analyses to discern whether signals were driven by a single variant influenced by extreme measurements of methylation. Variants within regions were firstly pruned using plink software , we firstly generated box and whisker plots to visually inspect possible outliers for the top 10 hits of the CpG island and shore analysis . However, after conditioning our analysis by including the overlapping rare variant as a covariate in our model we did not observe an attenuation in the observe effect (P = 1.38×10−11), suggesting that this analysis was not biased by the overlapping rare variant.To verify that observed associations were not due to an overlapping rare variant in the probe sequence, we identified a subset of 394 individuals from the discovery analysis who had whole genome sequence (WGS) data as part of the UK10K project . For theet al.  in the single variant analysis conducted by Gaunt et al. . TherefoPPP2R2A, C2orf80 and SLC32A1. All of these collapsed mQTL were acting in cis. The results for this analysis can be found in Supplementary Material, Table S16 includes all time points in ARIES where these effects were observed to have a P-value < 1 × 10 − 14.Results from this analysis suggested that methylation levels were influenced independently at 3 loci by low frequency variants (MAF ≤ 5%) after conditioning analyses on reported single mQTL effects. The nearest genes to these loci were We have undertaken a novel approach to mQTL analysis by investigating the combined impact of multiple low frequency and rare variants on DNA methylation. Altogether we identified 95 unique regions of low frequency variants (MAF ≤ 5%) that were collectively associated with DNA methylation. 94 of these were acting in cis (associated with methylation within 1MB distance away) and 1 in trans (greater than 1MB away). Importantly, none of these effects were driven by an individual variant and therefore were not identified in the single variant analysis. Evidence of replication was observed both internally and in external datasets for the top 10 hits of this analysis which supports evidence that these associations are driven by causal effects. We also identified a further 3 loci with previous evidence of an mQTL effect from the single variant analysis, where there was evidence of an independent signal from multiple low frequency variants. This approach was less successful in identifying association signals from regions of rare variation (MAF ≤ 1%), although further studies with larger sample sizes may yield stronger evidence of association for these types of effects.We found that 88 of the 94 unique regions responsible for observed cis-mQTL effects were identified by expanding our region of interest from CpG islands to include flanking shore regions. When conducting a variant collapsing analysis, the definition of a functional unit or genomic region by which variants are collapsed together is crucial to identifying association signals . This isPFKL gene region . Methylation of the ATF-motif in PFKL was observed to be reduced in obese patients compared to non-obese controls in a study investigating epigenetic modifications in terms of the aetiology of type 2 diabetes  was another locus which provided evidence of association in this analysis. This gene was observed to be dysregulated according to methylation status in tumour cell lines according to a recent study . The validity of these approaches should still be useful for future studies with larger sample sizes, in terms of detecting combined effects from rare variants on DNA methylation which would not be identified using single marker approaches. One result which was of interest involved rare variants contributing to a cis effect at the DVL1 loci (P = 6.26×10−14), as it is a previously reported imprinted gene  .All samples in the discovery analysis are taken from the Avon Longitudinal Study of Parents and Children (ALSPAC),14. Blooorg.uk/) . For theTM kit . The Illumina HumanMethylation450 BeadChip (HM450k) was used to measure methylation across the genome and the following arrays were scanned using Illumina iScan, along with an initial quality review using GenomeStudio. A purpose-built laboratory information management system (LIMS) was responsible for generating batch variables during data generation. LIMS also reported quality control (QC) metrics for the standard probes on the HM450k for all samples and excluded those which failed QC. Data points with a read count of 0 or with low signal:noise ratio  were also excluded. Methylation measurements were then compared across timepoints for the same individual and with SNP-chip data (HM450k probes clustered using k-means) to identify and remove sample mismatches. All remaining data from probes was normalised with the Touleimat and Tost  by the Wellcome Trust Sanger Institute  and the Laboratory Corporation of America . Samples were excluded based on incorrect sex assignment; abnormal heterozygosity ; high missingness (>3%); cryptic relatedness (>10% identity by descent) and non-European ancestry . After QC, 500,527 SNP loci were available for the directly genotype dataset. Data for ALSPAC mothers was generated using the Illumina human660W-quad genome-wide SNP genotyping platform  at the Centre National de Génotypage . Samples were excluded due to non-European ancestry, missingness, relatedness, heterozygosity or gender mismatches. PLINK (v1.07)  was usedP > 5 × 10 − 7, MAF ≤ 5% and imputation info score > 0.8. The final imputed dataset for all subsequent analyses contained 3,721,682 loci. 1,787,681 of these had a MAF ≤ 1%.Imputation was performed for all genotyped mothers and children to improve SNP density. ShapeIt (version 2 revision 727) was used to phase genotypes and Impute (version 2.2.2) was used to impute this data using the 1000 genomes reference panel . Genotypes were then filtered to include those with a Hardy-Weinberg equilibrium of The TwinsUK cohort was established in 1992 to recruit monozygotic and dizygotic twins . More thP values < 10 − 4 were excluded. We also removed subjects if the sample call rate was less than 95%, autosomal heterozygosity was outside the expected range, genotype concordance was over 97% with another sample and the sample was of lesser call rate. Imputation of genotypes was carried out using the software IMPUTE  following standard procedures. Normalised intensity data were pooled and genotypes called on the basis of the Illluminus algorithm . No calle IMPUTE .P value of >0.05 or missing values were removed, resulting in 452,874 probes. Blood cell type coefficients were estimated from the methylation data using the method described by Houseman et al.  was used to measure DNA methylation. Details of experimental approaches have been previously described . Normalin et al. . For thiet al. 2016. Submitted). 500ng of DNA from each sample was treated with sodium bisulfite using the EZ-96 DNA Methylation kit . DNA methylation was quantified using the Illumina Infinium HumanMethylation450 BeadChip  run on an Illumina iScan System  following a standard protocol. Samples were randomly assigned to chips and plates to ensure equal distribution of cases and controls across arrays and minimise batch effects. Data were imported in R programming environment using the methylumIDAT function in the methylumi package (https://dnamage.genetics.ucla.edu/)  and Minimac2 (These samples are the first phase of a multi-stage case-control EWAS of schizophrenia (Hannon  package . Our strla.edu/) , gender  package . Genotyp package . Samples package  was usedMinimac2 ,48 with Minimac2  only incDNA Samples from 4,030 UK10K study participants  were subjected to low coverage (6-8x average read depth) whole-genome sequencing (WGS). Sequencing was performed at both the Wellcome Trust Sanger Institute (WTSI) and the Beijing Genomics Institute (BGI). DNA (1-3μg) was sheared to 100–1000 bp using a Covaris E210 or LE220 . Sheared DNA was size subjected to Illumina paired-end DNA library preparation. Following size selection (300–500 bp insert size), DNA libraries were sequenced using the Illumina HiSeq platform as paired-end 100 base reads according to manufacturer’s protocol.Data that passed quality control (QC) was aligned to the GRCh37 human reference used in phase 1 of the 1000 Genomes Project. Reads were aligned using BWA (v0.5.9-r16) . Of the Missing and low-confidence genotypes in the filtered VCFs were refined out using the imputation procedure in BEAGLE 4  with defet al. (CpG islands as defined by UCSC co-ordinatesCpG islands and shores (i.e. islands +2kbs up and downstream)CpG islands, shores and shelves (i.e. islands +4kbs up and downstream).Annotations for UCSC CpG Islands were obtained using the R Package ‘COHCAPanno’ accordinet al.  using thet al.  and ϵ is the error term. SKAT assumes that the genetic effect βj of an individual variants j follows an arbitrary distribution with mean 0 and variant wjτi where τ is a variance component and wj is a weight of variant j. SKAT assumes that j follows a Beta.Regions which had a reported mQTL in close proximity (CpG island ± 5kb) were not analysed here, but in a subsequent analyses conditioning on the reported mQTL effect. The remaining regions which had least 2 variants were analysed using the Sequence Kernel Association Test (SKAT)  with eacet al. . In brie− 14 as undertaken by Gaunt et al.  and ≤ 1% (for rare variants). We used a conservative multiple testing correction of P < 1.0x10 t et al. . This waWe applied the same approach as above but to identify trans-mQTL . Due to the computational demand required for the number of tests, we collapsed variants together only using CpG islands with flanking shore regions, as these regions provided the number of association signals that survived the correction for multiple comparisons in the cis-analysis. This analysis was undertaken using 2 MAF cutoffs of 5% and 1% and evaluated with the same multiple testing correction as before.P > 1 × 10−7 on the basis that these results are supported by their combination with other evidence from time points through the life course.Analyses were initially undertaken using all available individuals from the Childhood time point in ARIES (Mean Age = 7.49 (Standard Deviation = 0.15), proportion female = 0.50), which was designated as our discovery analysis. Results which survived the correction for multiple testing were further evaluated by analysing the same set of variants with methylation values measured at the same probe from all other time points in both children and mothers. These analyses were conducted using each time point in turn and without adjustment for relatedness. Variants which did not exist amongst the sample of mothers were not replaced in these analyses (i.e. we attempted to replicate the effects with available variants without replacement). We did not evaluate all potential cis- and trans-mQTL at all-time points due to the computationally exhaustive number of analyses needed, as well as the potential number of false positive findings incurred by doing so. Evidence that hits replicated at other time points was based on associations with a lower threshold p-value of Replication analyses were conducted using SKAT to evaluate the association between sets of variants and DNA methylation using the same 450k probe ID. Variants which were not eligible or available within the replication cohorts were not included in replication analyses.P-values on the y-axis and the variant removed on the x-axis. Plots were annotated with red lines to show the observed P-value when all variants were analysed (prior to pruning). A blue line was also added to show the P-value threshold used in our study (i.e. P < 1.0 x 10 − 14).We firstly generated box and whisker plots for the top 10 hits of our analysis to discern whether extreme measures of DNA methylation were influencing our results. A leave one out analysis was also undertaken on the top 10 hits to further ensure that our results were not heavily influenced by potential outliers or individual effects. The purpose of this analysis was also to illustrate the combined effect of these sets of variants on methylation. Variants within regions were first of all pruned using plink software , followeet al. had potentially not evaluated these variants in their study when looking at overlapping SNPs  data from the UK10K project . This waing SNPs . Using tFor CpG island regions where an mQTL was identified in the previously undertaken single variant analysis, we undertook conditional analyses to evaluate whether there was an independent effect from regions of low frequency and rare variants at these loci. All CpG island regions not included in the previous analysis were eligible. mQTL results from the single variant analysis had been previously analysed with GCTA  to identr2 values for LD were not used as the range of r2 is dependent on allele frequencies, which could potentially be very different between a common SNP and rare genetic variants. Regions with at least two variants remaining were analysed using SKAT with the corresponding probe, which was associated with the single variant mQTL at this site. The SNP responsible for the mQTL was included as a covariate in the model. Analyses were run using two MAF cutoffs of 5% and 1% as before for the collapsed regions of low frequency variants. P-values lower than 1×10−14 were analysed a further time but including a covariate matrix consisting of all SNPs responsible for an mQTL effect at this locus. This was to ensure results were not tagging a different mQTL signal not accounted for in the initial analysis. Analyses were undertaken using all time points in ARIES. All statistical analyses were undertaken using R statistical software (Low frequency variants (MAF ≤ 5%) were collapsed as before within CpG islands and their flanking shore regions. Variants which were in high linkage disequilibrium (LD) with the mQTL (defined as D’ ≥ 0.8) were removed for these regions. software .Supplementary Material is available at HMG online.