the sample was drawn from the twins early development study ( teds ) , a longitudinal study of twins born in england and wales between january 1994 and december 1996 ( haworth , davis , & plomin , 2013 ; kovas , haworth , dale , & plomin , 2007 ) . parental consent was obtained prior to data collection , and the project received approval from the institute of psychiatry ethics committee . the discovery sample was drawn from the entire teds sample of over 11,000 twin pairs for whom dna was available from saliva samples . twins with severe birth complications , with medical problems , or whose first language was not english were excluded from the sample . to reduce possible confounding as a result of ancestry effects , the sample was restricted to families who identified themselves as caucasian . after we had implemented perinatal , medical , language , and ethnicity exclusions , one member of each twin pair was selected for the discovery sample . children were selected if they had more than 5 mg dna available and if they had participated in web - based cognitive testing at age 12 ( described in haworth et al . , 2007 ) . if both members of a twin pair fulfilled these two criteria , then the twin with the most dna available was selected . of this sample , 2,329 passed genotyping quality conrol ( qc ) procedures ( detailed in the online supplementary materials ) and had complete data on four receptive language measures included in the cognitive test battery . the replication sample was drawn from the remaining teds sample , after excluding the 4,442 children selected for the discovery sample plus their co - twin if the twin pair was monozygotic ( mz ) . children were selected if they had more than 3 g dna available and if they had taken part in the web - based cognitive testing at age 12 . to maximize the replication sample size and maintain power , both members of dizygotic twin pairs were included if they passed the selection criteria , and the dizygotic co - twins of discovery sample individuals were also included if eligible . only one member of each mz twin pair was selected , and if both members of an mz twin pair fulfilled the selection criteria , then the twin with the most dna was selected . a subset of this sample , consisting of 2,639 children , passed the genotyping qc procedures and had complete data on the four receptive language measures . this subsample , which we used as a secondary replication sample , excluded individuals with twin siblings in the discovery sample and included only one member from each twin pair . our replication approach is somewhat unorthodox because any observed convergence between the discovery sample and the primary replication sample may be spuriously inflated by the nonindependence of these samples . on the other hand , if agreement is observed between the results for the discovery sample and the primary replication sample , then additional replication in our fully independent sample of 1,010 unrelated individuals would be required for us to have confidence in the results . however , if the results from the discovery sample do not replicate in the primary replication sample , this is strong evidence of failure to replicate because this replication sample is highly similar to the discovery sample . reliance on internet - based testing , necessary for assessment of a large sample , led to our focus on measures of receptive language ( haworth et al . , 2007 ) . we used four measures of receptive language skill : ( a ) vocabulary , assessed using the vocabulary multiple choice subtest of the wechsler intelligence scale for children ( 3rd ed . , uk version ; wechsler , 1992 ) ; ( b ) semantics , assessed using level 2 of the figurative language subtest of the test of language competence  expanded edition ( wigg , secord , & sabers , 1989 ) ; ( c ) syntax , assessed using the listening grammar subtest of the test of adolescent and adult language  third edition ( hammill , brown , larsen , & wiederholt , 1994 ) ; and ( d ) pragmatics , assessed using level 2 of the making inferences subtest of the test of language competence  expanded edition ( wiig et al . , 1989 ) . details of the measures were described in detail in dale et al . 's ( 2010 ) article . sample statistics ( ms and sds ) for the individual measures are shown for males and females in supplementary table 1 ; these did not differ significantly from those reported by dale et al . a previous multivariate genetic analysis showed that the four language measures were substantially correlated at a genetic level ( rs = .74.97 ) , indicating that genetic factors that contribute to variation in these measure largely overlap . a general language latent factor , reflecting the common variance among all four measures , free from measure - specific error , was highly heritable ( h = 0.59 ; dale et al . , 2010 ) . because it is not possible to obtain latent language factor scores ( free from measurement error ) for individual participants , we computed simple composite scores for the present analysis . these composite scores yielded a heritability estimate of 0.39 ( 95% confidence interval [ ci ] [ 0.34 , 0.44 ] ; plomin , haworth , et al . , 2013 ) . we adjusted scores for the linear effects of age at time of testing using the residuals from a least - squares linear regression as the phenotype . the distribution of test scores on the receptive language composite for the 2,329 individuals in the discovery sample is shown in figure 1 . genotyping on the affymetrix 6.0 genechip and subsequent qc was carried out as part of the wtccc2 project ( uk ibd genetics consortium et al . , 2009 ) . nearly 700,000 genotyped snps met qc criteria . in additionally , because genotyped snps are thought to  tag causal variants , more than 1 million other snps were imputed using impute ( version 2 ) software ( howie , donnelly , & marchini , 2009 ) in order to increase the chances that common causal variants are represented . details about the genotyping , qc procedures , and imputation method are included in the supplemental material . we conducted gwa analyses using a linear regression approach implemented in snptest ( version 2.0 ; wtccc , 2007 ) under an additive model . this approach uses a frequentist method to account for uncertainty of genotype information ( marcini , howie , myers , mcvean , & donnelly , 2007 ) . because even small differences in allelic frequency within subgroups in the population can generate false - positive results , we used eight principal components representing population ancestry to control for population stratification . we visualized results using manhattan plots , quantile  quantile ( q  q ) plots , and genotype  phenotype plots , generated in r ( r core team , 2012 ) . we also created a regional association plot , using locuszoom ( prium et al . , 2010 ) . the strongest snp association from the gwa analysis of the discovery sample was selected for genotyping in the replication sample using the taqman snp genotyping assay . linear regression was implemented in snptest under an additive model , with sex added as a covariate . in addition , a family - based test of association that accounts for sibling relatedness for the 377 sibling pairs within the primary replication sample of 2,639 individuals was performed in plink ( version 1.07 ; purcell et al . , 2007 ) . the sample was drawn from the twins early development study ( teds ) , a longitudinal study of twins born in england and wales between january 1994 and december 1996 ( haworth , davis , & plomin , 2013 ; kovas , haworth , dale , & plomin , 2007 ) . parental consent was obtained prior to data collection , and the project received approval from the institute of psychiatry ethics committee . the discovery sample was drawn from the entire teds sample of over 11,000 twin pairs for whom dna was available from saliva samples . twins with severe birth complications , with medical problems , or whose first language was not english were excluded from the sample . to reduce possible confounding as a result of ancestry effects , the sample was restricted to families who identified themselves as caucasian . after we had implemented perinatal , medical , language , and ethnicity exclusions , one member of each twin pair was selected for the discovery sample . children were selected if they had more than 5 mg dna available and if they had participated in web - based cognitive testing at age 12 ( described in haworth et al . , 2007 ) . if both members of a twin pair fulfilled these two criteria , then the twin with the most dna available was selected . of this sample , 2,329 passed genotyping quality conrol ( qc ) procedures ( detailed in the online supplementary materials ) and had complete data on four receptive language measures included in the cognitive test battery . the replication sample was drawn from the remaining teds sample , after excluding the 4,442 children selected for the discovery sample plus their co - twin if the twin pair was monozygotic ( mz ) . children were selected if they had more than 3 g dna available and if they had taken part in the web - based cognitive testing at age 12 . to maximize the replication sample size and maintain power , both members of dizygotic twin pairs were included if they passed the selection criteria , and the dizygotic co - twins of discovery sample individuals were also included if eligible . only one member of each mz twin pair was selected , and if both members of an mz twin pair fulfilled the selection criteria , then the twin with the most dna was selected . a subset of this sample , consisting of 2,639 children , passed the genotyping qc procedures and had complete data on the four receptive language measures . this subsample , which we used as a secondary replication sample , excluded individuals with twin siblings in the discovery sample and included only one member from each twin pair . our replication approach is somewhat unorthodox because any observed convergence between the discovery sample and the primary replication sample may be spuriously inflated by the nonindependence of these samples . on the other hand , if agreement is observed between the results for the discovery sample and the primary replication sample , then additional replication in our fully independent sample of 1,010 unrelated individuals would be required for us to have confidence in the results . however , if the results from the discovery sample do not replicate in the primary replication sample , this is strong evidence of failure to replicate because this replication sample is highly similar to the discovery sample . reliance on internet - based testing , necessary for assessment of a large sample , led to our focus on measures of receptive language ( haworth et al . , 2007 ) . we used four measures of receptive language skill : ( a ) vocabulary , assessed using the vocabulary multiple choice subtest of the wechsler intelligence scale for children ( 3rd ed . , uk version ; wechsler , 1992 ) ; ( b ) semantics , assessed using level 2 of the figurative language subtest of the test of language competence  expanded edition ( wigg , secord , & sabers , 1989 ) ; ( c ) syntax , assessed using the listening grammar subtest of the test of adolescent and adult language  third edition ( hammill , brown , larsen , & wiederholt , 1994 ) ; and ( d ) pragmatics , assessed using level 2 of the making inferences subtest of the test of language competence  expanded edition ( wiig et al . , 1989 ) . details of the measures were described in detail in dale et al . 's ( 2010 ) article . sample statistics ( ms and sds ) for the individual measures are shown for males and females in supplementary table 1 ; these did not differ significantly from those reported by dale et al . a previous multivariate genetic analysis showed that the four language measures were substantially correlated at a genetic level ( rs = .74.97 ) , indicating that genetic factors that contribute to variation in these measure largely overlap . a general language latent factor , reflecting the common variance among all four measures , free from measure - specific error , was highly heritable ( h = 0.59 ; dale et al . , 2010 ) . because it is not possible to obtain latent language factor scores ( free from measurement error ) for individual participants , we computed simple composite scores for the present analysis . these composite scores yielded a heritability estimate of 0.39 ( 95% confidence interval [ ci ] [ 0.34 , 0.44 ] ; plomin , haworth , et al . , we adjusted scores for the linear effects of age at time of testing using the residuals from a least - squares linear regression as the phenotype . the distribution of test scores on the receptive language composite for the 2,329 individuals in the discovery sample is shown in figure 1 . genotyping on the affymetrix 6.0 genechip and subsequent qc was carried out as part of the wtccc2 project ( uk ibd genetics consortium et al . , 2009 ) . causal variants , more than 1 million other snps were imputed using impute ( version 2 ) software ( howie , donnelly , & marchini , 2009 ) in order to increase the chances that common causal variants are represented . details about the genotyping , qc procedures , and imputation method are included in the supplemental material . we conducted gwa analyses using a linear regression approach implemented in snptest ( version 2.0 ; wtccc , 2007 ) under an additive model . this approach uses a frequentist method to account for uncertainty of genotype information ( marcini , howie , myers , mcvean , & donnelly , 2007 ) . because even small differences in allelic frequency within subgroups in the population can generate false - positive results , we used eight principal components representing population ancestry to control for population stratification . we visualized results using manhattan plots , quantile  quantile ( q  q ) plots , and genotype  phenotype plots , generated in r ( r core team , 2012 ) . we also created a regional association plot , using locuszoom ( prium et al . , 2010 ) . the strongest snp association from the gwa analysis of the discovery sample was selected for genotyping in the replication sample using the taqman snp genotyping assay . linear regression was implemented in snptest under an additive model , with sex added as a covariate . in addition , a family - based test of association that accounts for sibling relatedness for the 377 sibling pairs within the primary replication sample of 2,639 individuals was performed in plink ( version 1.07 ; purcell et al . , 2007 ) . because a gwa study generates a very large number of associations ( each with its own p value ) , it is useful to compare the distribution of the actual p values derived from the gwa analyses with the distribution to be expected by chance . a q  q plot for the general language factor , which summarizes this comparison , is presented in figure 2 . this plot shows the expected distribution of association test statistics across snps on the x - axis compared to the observed values on the y - axis ( negative log base 10 of the p values ) . the straight line at x = y represents chance association , and the gray areas represent 95% concentration bands that approximate cis on the null . one can see in figure 1 that few associations fall outside the concentration bands , indicating little evidence of true association . an alternative approach to visualizing these results is to use a manhattan plot , shown in figure 3 . each point represents a different snp , laid out across the 22 human autosomes on the x - axis . the negative log base 10 p values are plotted on the y - axis . evidence for a significant association would be indicated if we were able to detect a cluster of snps that form a  tower  ( resembling a manhattan skyscraper ) , the result of nearby snps being in ld with one another and thus all marking the same association signal . in the discovery sample , none of the snps reached the conventional genome - wide significance threshold of p < 5  10 . one snp ( rs12474600 ) on chromosome 2 showed an association just below this threshold ( p = 4.57  10 , b = 0.24 , se = 0.05 , n = 2329 ; the solid red line in figure 3 ) . the 114 strongest associations ( p  1  10 ; the solid blue line in figure 3 ) are detailed in supplementary table 2 . the regional association plot in figure 4 provides a more in - depth view of the chromosome 2 signal . this plot illustrates the associated region in the context of local patterns of ld and nearby genes . specifically , the figure highlights a cluster of 24 snps that are strongly correlated and have a p  1  10 . the strongest associated snp ( rs12474600 , p = 4.57  10 ) is an imputed snp ; however , six of the 24 snps with a p  1  10 in this cluster were genotyped , confirming that the signal in this region is not based purely on imputation . figure 5 is a genotype  phenotype plot in which mean standardized language scores and standard errors are shown for the three genotypes for rs12474600 for the discovery sample of more than 2,300 individuals . for bi - allelic snps , , the sign of the unstandardized beta indicates the direction of the association in relation to the number of copies of allele b. so , for rs12474600 , an a / g snp , the effect size ( b = 0.24 ) indicates that allele b ( g ) is associated with lower language scores compared with allele a ( a ) ; that is , gg homozygotes have lower language scores than aa homozygotes . this is illustrated in figure 5 , which shows that the aa homozygotes have an average standardized language score of 0.62 , more than 0.5 sd higher than the gg homozygotes . the ag heterozygotes scores are significantly lower than the aa homozygotes and significantly higher than the gg homozygotes , as suggested by the nonoverlapping standard error bars for each of the genotypic points . this pattern indicates that the a and g alleles have additive effects . the relatively large standard error for the aa genotype reflects its relatively small sample size , caused by the minor allele frequency ( the frequency at which the less common allele occurs in a population ) of 0.10 . in other words , for rs12474600 , the relatively rare a allele contributes to higher language scores . however , we reiterate that even though receptive language scores differ significantly as a function of genotype ( aa homozygote , ag heterozygote , gg homozygote ) , the overall effect of this snp did not reach genome - wide significance . although this snp was imputed in the discovery sample , for the replication we used a validated taqman assay to genotype the snp . the rs12474600 association in the primary replication sample of 2,639 twelve - year - olds was not significant ( p = .357 ) , and its effect size was negligible ( b = 0.02 , se = 0.04 ) . sibling relatedness that is not accounted for in association analyses may bias standard errors , and so we repeated the analysis taking family structure into account . as would be expected , the secondary replication sample , consisting of 1,010 individuals unrelated to individuals in the discovery sample , yielded similarly negative results ( p = .27 , b = 0.043 , se = 0.07 ) . because rs12474600 was imputed in the discovery sample , it is possible that this snp would not have shown the lowest p value in the discovery sample if it had been genotyped directly . however , this seems unlikely because other snps in ld with rs12474600 that were genotyped directly showed similarly low p values ( see figure 4 ) . in any case , the snp with the lowest p value in the discovery sample did not show any association in our replication sample . because a gwa study generates a very large number of associations ( each with its own p value ) , it is useful to compare the distribution of the actual p values derived from the gwa analyses with the distribution to be expected by chance . a q  q plot for the general language factor , which summarizes this comparison , is presented in figure 2 . this plot shows the expected distribution of association test statistics across snps on the x - axis compared to the observed values on the y - axis ( negative log base 10 of the p values ) . the straight line at x = y represents chance association , and the gray areas represent 95% concentration bands that approximate cis on the null . one can see in figure 1 that few associations fall outside the concentration bands , indicating little evidence of true association . an alternative approach to visualizing these results is to use a manhattan plot , shown in figure 3 . each point represents a different snp , laid out across the 22 human autosomes on the x - axis . the negative log base 10 p values are plotted on the y - axis . evidence for a significant association would be indicated if we were able to detect a cluster of snps that form a  tower  ( resembling a manhattan skyscraper ) , the result of nearby snps being in ld with one another and thus all marking the same association signal . in the discovery sample , none of the snps reached the conventional genome - wide significance threshold of p < 5  10 . one snp ( rs12474600 ) on chromosome 2 showed an association just below this threshold ( p = 4.57  10 , b = 0.24 , se = 0.05 , n = 2329 ; the solid red line in figure 3 ) . the 114 strongest associations ( p  1  10 ; the solid blue line in figure 3 ) are detailed in supplementary table 2 . the regional association plot in figure 4 provides a more in - depth view of the chromosome 2 signal . this plot illustrates the associated region in the context of local patterns of ld and nearby genes . specifically , the figure highlights a cluster of 24 snps that are strongly correlated and have a p  1  10 . the strongest associated snp ( rs12474600 , p = 4.57  10 ) is an imputed snp ; however , six of the 24 snps with a p  1  10 in this cluster were genotyped , confirming that the signal in this region is not based purely on imputation . figure 5 is a genotype  phenotype plot in which mean standardized language scores and standard errors are shown for the three genotypes for rs12474600 for the discovery sample of more than 2,300 individuals . for bi - allelic snps , , the sign of the unstandardized beta indicates the direction of the association in relation to the number of copies of allele b. so , for rs12474600 , an a / g snp , the effect size ( b = 0.24 ) indicates that allele b ( g ) is associated with lower language scores compared with allele a ( a ) ; that is , gg homozygotes have lower language scores than aa homozygotes . this is illustrated in figure 5 , which shows that the aa homozygotes have an average standardized language score of 0.62 , more than 0.5 sd higher than the gg homozygotes . the ag heterozygotes scores are significantly lower than the aa homozygotes and significantly higher than the gg homozygotes , as suggested by the nonoverlapping standard error bars for each of the genotypic points . this pattern indicates that the a and g alleles have additive effects . the relatively large standard error for the aa genotype reflects its relatively small sample size , caused by the minor allele frequency ( the frequency at which the less common allele occurs in a population ) of 0.10 . in other words , for rs12474600 , the relatively rare a allele contributes to higher language scores . however , we reiterate that even though receptive language scores differ significantly as a function of genotype ( aa homozygote , ag heterozygote , gg homozygote ) , the overall effect of this snp did not reach genome - wide significance . although this snp was imputed in the discovery sample , for the replication we used a validated taqman assay to genotype the snp . the rs12474600 association in the primary replication sample of 2,639 twelve - year - olds was not significant ( p = .357 ) , and its effect size was negligible ( b = 0.02 , se = 0.04 ) . sibling relatedness that is not accounted for in association analyses may bias standard errors , and so we repeated the analysis taking family structure into account . as would be expected , the secondary replication sample , consisting of 1,010 individuals unrelated to individuals in the discovery sample , yielded similarly negative results ( p = .27 , b = 0.043 , se = 0.07 ) . because rs12474600 was imputed in the discovery sample , it is possible that this snp would not have shown the lowest p value in the discovery sample if it had been genotyped directly . however , this seems unlikely because other snps in ld with rs12474600 that were genotyped directly showed similarly low p values ( see figure 4 ) . in any case , the snp with the lowest p value in the discovery sample did not show any association in our replication sample . this gwa study of receptive language ability in early adolescence found no evidence for genome - wide significant associations . the snp closest to the conventional genome - wide significance level , rs12474600 , failed to replicate , even though the replication sample consisted of a highly similar sample tested at the same age and with the same measures . in the discovery sample we had 92% power at the p < 5  10 level to detect an association for a causal variant with a minor allele frequency of 20% and a 2% effect size ( purcell , cherny , & sham , 2003 ) . given the estimated power , the results are consistent with a view that there are no detectable common snps associated with receptive language that account for more than 2% of the variance . how can we reconcile the current findings with the robust and relatively high heritability estimates for language ? for example , they may be overinflated due to violations of the equal - environments assumption ( plomin , defries , et al . , 2013 ) . however , similar heritability estimates for language have been reported in other designs , such as pedigree studies ( e.g. , logan et al . , 2011 ) , which have different assumptions and problems . the hypothesis we favor is that the genetic architecture of receptive language , similar to height , weight , and iq , reflects many common snps , each with a very small effect size . support for this view comes from statistical methods that estimate the net effect of genetic influence using genotyped snps in samples of unrelated individuals . the first application of this approach was included in the software package genome - wide complex trait analysis ( gcta ; visscher , yang , & goddard , 2010 ; yang , lee , goddard , & visscher , 2011 ) . gcta uses random genetic similarity between each pair of unrelated individuals to estimate the variance in a phenotype accounted for by the genotyped snps ( visscher et al . although gcta does not identify specific snps that contribute to phenotypic variance , it does provide an estimate of how much variance the relevant snps would account for if one could identify each of them and add up their effects . gcta detects additive effects only of the common variants that are included on commercial genotyping arrays used in gwa studies . because gwa studies are also limited to detecting additive effects of common variants , gcta estimates of genetic influence mark the ceiling for gwa studies ; that is , to the extent that associations with rare variants or gene  gene interactions ( epistasis ) are important , neither gcta nor gwa studies will detect them . however , a recent gcta analysis of cognitive phenotypes in teds yielded a significant snp - based heritability estimate of .29 for the 12-year receptive language composite ( 95% ci [ 0.05 , 0.53 ] , which accounted for three quarters of the twin study heritability estimate of .39 ( 95% ci [ 0.34 , 0.44 ] ; plomin , haworth , et al . , 2013 ) ; that is , about three quarters of the additive genetic variation in receptive language in early adolescence is tagged by common snps on commercial genotyping arrays . the results from the current study are also unsurprising in the light of results for a range of complex genetic disorders and quantitative traits published since this study was conceived in 2007 . even in studies with tens of thousands of participants , research has shown that the largest detectable genetic effect sizes account for less than 1% of the phenotypic variance ( e.g. , for height , lango allen et al . , 2010 ; and for weight , walley , asher , & froguel , 2009 ) . for behavioral traits , the largest effect sizes in the first gwa studies of reading , mathematics , and general cognitive ability assessed as quantitative traits in children comprised less than 0.5% of the variance ( butcher , davis , craig , & plomin , 2008 ; docherty , davis , it follows that extremely large samples will be needed in order to reveal significant genetic associations for language skill , given the stringent thresholds of statistical significance used to establish association in gwa studies at large ( plomin , 2013 ) . 2012 ) proposed that a sample size of 100,000 individuals has statistical power of 80% to discover genetic variants accounting for as little as 0.04% of the variance in a trait at a genome - wide significance level of p < 5  10 . it is unlikely that any single laboratory would be able to attain a sample of this size . carefully designed meta - analyses ( combining p value results ) and mega - analyses ( combining data ) will therefore be crucial in efforts to increase sample size and statistical power . although we have stressed the importance of common snps with tiny effects , another direction for molecular genetic studies of language is to study low - frequency polymorphisms : variants that are not rare but are less common than those tagged by commercially available microarrays ( e.g. , minor allele frequencies between 1% and 5% ; plomin , 2013 ) . these variants may have a spectrum of effect sizes , from very small to intermediate or even large for some individuals , even though their effect overall in the population is miniscule . although the jury remains out on the relative importance of uncommon variants for complex quantitative traits , linkage study findings for language suggest that such variants may be important , especially at the extremes of the language distribution ( e.g. , the cmip and atp2c2 variants ; newbury et al . , 2009 ) . dna arrays for a new wave of gwa studies , which will account for these less common variants , are already being designed . use of such arrays will not be a panacea to standard gwa analysis , however ; very large samples will still be required if individual variants explain a very small proportion of variance in the population ( visscher , goddard , derks , & wray , 2012 ) . finally , whole - genome sequencing methods that determine the complete dna sequence of 3 billion nucleotide base pairs of an individual is the  next big thing  in genomics ( cirulli & goldstein , 2010 ; pasaniuc et al . , 2012 ; plomin , 2013 ; plomin & simpson , in press ) . whole - genome sequencing means that dna variants of any kind  not just common snps  can be detected . for studies of language skill and disorder , maximal power may be gained by oversampling individuals at one or both ends of the extremes , that is , sequencing individuals with very poor language scores , who may be the most likely to carry a high - risk allele burden , and individuals with very good language scores , who may be most likely to carry alleles conferring protection ( guey et al . , 2011 ; d. li , lewinger , gauderman , murcray , & conti , 2011 ) . one limitation of the current study , already mentioned , is the relatively small sample size ; in order to have adequate power to detect common variants that account for 1% of the variance in language abilities , a discovery sample of more than 6,000 individuals is required . a second limitation is that we studied a single facet of language , namely , receptive language skill . although the use of multiple tests augments the reliability of our receptive language scores , molecular genetic studies of language would benefit from multiple adequately normed indices of specific language skills , such as pragmatics , vocabulary , and syntactic skill ( mccardle , cooper , & freund , 2005 ) . third , we did not include the sex chromosome in our analyses because of previous agreements with the wtccc2 , and so any associations in this region will have been missed . the current results notwithstanding , we remain optimistic about the future of molecular genetic research on language skill , although this will require larger samples and new methods . genetic variants that are robustly associated with language will provide essential biological leads for subsequent functional studies that aim to improve understanding of the molecular mechanisms involved in language development . in addition , if we are able eventually to identify a number of genetic variants that are associated with language , composites of these snps could be leveraged to test research questions raised by quantitative genetics , such as the extent to which genetic influences for language disorders overlap for commonly comorbid disorders , such as dyslexia ( the generalist genes hypothesis ; plomin & kovas , 2005 ; see , e.g. , docherty , kovas , petrill , & plomin , 2010 ) . the coming decade will likely be an exciting one for researchers interested in understanding the contribution of genetic factors to language abilities and disabilities .
purposeresearchers have previously shown that individual differences in measures of receptive language ability at age 12 are highly heritable . in the current study , 
 the authors attempted to identify some of the genes responsible for the heritability of receptive language ability using a genome - wide association approach.methodthe authors administered 4 internet - based measures of receptive language ( vocabulary , semantics , syntax , and pragmatics ) to a sample of 2,329 twelve - year - olds for whom dna and genome - wide genotyping were available . 
 nearly 700,000 single - nucleotide polymorphisms ( snps ) and 1 million imputed snps were included in a genome - wide association analysis of receptive language composite scores.resultsno snp associations met the demanding criterion of genome - wide significance that corrects for multiple testing across the genome ( p < 5  108 ) . the strongest snp association did not replicate in an additional sample of 2,639 twelve-year-olds.conclusionsthese results indicate that individual differences in receptive language ability in the general population do not reflect common genetic variants that account for more than 3% of the phenotypic variance . 
 the search for genetic variants associated with language skill will require larger samples and additional methods to identify and functionally characterize the full spectrum of risk variants .