circumference GWAS.⁵ A complete overview of the included studies is shown in Table S1 and their population characteristics are presented in Table S2. Each contributing study was approved by their institutional review boards or local ethical committees. Written informed consent was obtained from all study participants.

## Genotyping

Genotyping of individuals was performed on commercially available arrays, and imputed to 1000 Genomes (1KG) or Haplotype Reference Consortium (HRC) imputation panels (Table S3). Quality control was performed using the EasyQC software.⁵¹ In each study, genetic variants with an imputation quality $r^2$ below 0.3 and a minor allele frequency (MAF) below 0.001 were excluded. Additionally, variants were filtered on study level requiring ($r^2 \times MAF \times N$) > 5.

## Phenotyping

Different methods were used to measure human head size across studies. Briefly, either head circumference was measured, or intracranial volume was measured on computed tomography (CT) or magnetic resonance imaging (MRI) scans. In total, human head size was measured using intracranial volume measured on CT or MRI scans in respectively 1,283 and 84,171 individuals, and using head circumference in 20,524 individuals (Table S4). These measures have previously shown to be phenotypically and genetically correlated.⁵,⁶,⁶¹ Genetic correlations between our MRI scans and head circumference measurements was 0.75. Together, this allowed us to perform a combined meta-analyse of different measures of head size.

# METHOD DETAILS

## Genome-wide association studies

GWAS were performed for each study adjusted for age, age² (if significant), gender, eigenstrat PC1-4 (if significant), study-specific adjustments and case-control status (if applicable). In a second model, additional adjustment for height were made. The METAL software⁵² was used to perform a sample size weighted Z score meta-analysis. After meta-analysis, genetic variants available in less than 5,000 individuals were excluded. Comparable betas were derived using the formula $Z_{score} \times \sqrt{\frac{1}{N \times 2 \times MAF}}$ as was done previously.⁶² Genomic inflation and polygenic heterogeneity were assessed using the LD score regression software⁵³ by comparing the genomic control inflation factor and the LD score regression intercept (Table S5).

GWAS meta-analyses were performed separately for African, Asian and European samples. We also performed a transancestral meta-analysis. Since the analyses in non-European samples were underpowered, we additionally used an inverse-variance weighted method to test the combined effects of the lead variants in the non-European samples. This analysis was performed using the gtx package as implemented in R.

## Functional annotations

Regional association plots were made with the LocusZoom software.⁵⁴ The Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA GWAS) platform⁵⁵ was used to derive the independent genomic loci and genetic lead variants, and to functionally annotate the identified genetic variants. Additionally, enrichment for KEGG⁹ biological pathways was assessed for genes located nearby the identified genetic loci using the default options in FUMA, using hypergeometric tests. Genotype-Tissue Expression (GTEx) v7 was used to identify expression quantitative trait loci (eQTL) for the lead genetic variants and variants in LD ($r^2$ > 0.6).

We performed a transcriptome-wide association study (TWAS) using the association statistics from the European-only head size GWAS summary statistics and weights from 21 publicly available gene expression reference panels. We focused on the gene expression weights from blood (Young Finns Study, YFS), arterial (GTEx), brain (GTEx, CommonMind Consortium (CMC)) and peripheral nerve tissues (GTEx). Precomputed SNP-expression weights in the 1-Mb window were obtained for each gene in the reference panel, including the highly-tissue specific splicing QTL (sQTL) information on gene isoforms in the dorsolateral prefrontal cortex (DLPFC, CMC). Using the SNP-expression weights, SNP-trait effect estimates and the SNP correlation matrix, we used the TWAS-Fusion⁵⁶ to estimate the association statistic between the predicted expression and head size (TWAS Z score). Transcriptome-wide significant genes (eGenes) and the corresponding QTLs (eQTLs) were determined using Bonferroni correction in each reference panel, based on the average number of features (4,320 genes) tested across all the reference panels.⁵⁶ Finally, using a prior association probability of $1.1 \times 10^{-5}$ and colocalization analysis (COLOC)⁶³ for each locus we estimated the posterior probability of a shared causal variant (PP4>0.75) between the gene expression and trait association. eGene regions with eQTLs not reaching genome-wide significance in the head size GWAS were considered putatively novel TWAS signals. Furthermore, functional validation of the eGenes was performed by integrating eQTL with the functional genomics feature from the RegulomeDB.⁶⁴ A RegulomeDB probability score greater than 0.5 and closer to 1 indicates the likelihood of the eQTL having a gene-regulatory role. Finally, accounting for pairwise correlation between the gene expression features we conducted the multiple degree of freedom omnibus analysis, to test for the shared effect of eGenes across the different gene reference panels. A significance threshold of $p < 3.48 \times 10^{-6}$ accounting for the number of genes ($N = 14,385$) tested was used to identify significant eGenes in the omnibus test.
circumference GWAS.⁵ A complete overview of the included studies is shown in Table S1 and their population characteristics are presented in Table S2. Each contributing study was approved by their institutional review boards or local ethical committees. Written informed consent was obtained from all study participants.

## Genotyping

Genotyping of individuals was performed on commercially available arrays, and imputed to 1000 Genomes (1KG) or Haplotype Reference Consortium (HRC) imputation panels (Table S3). Quality control was performed using the EasyQC software.⁵¹ In each study, genetic variants with an imputation quality $r^2$ below 0.3 and a minor allele frequency (MAF) below 0.001 were excluded. Additionally, variants were filtered on study level requiring ($r^2 \times MAF \times N$) > 5.

## Phenotyping

Different methods were used to measure human head size across studies. Briefly, either head circumference was measured, or intracranial volume was measured on computed tomography (CT) or magnetic resonance imaging (MRI) scans. In total, human head size was measured using intracranial volume measured on CT or MRI scans in respectively 1,283 and 84,171 individuals, and using head circumference in 20,524 individuals (Table S4). These measures have previously shown to be phenotypically and genetically correlated.⁵,⁶,⁶¹ Genetic correlations between our MRI scans and head circumference measurements was 0.75. Together, this allowed us to perform a combined meta-analyse of different measures of head size.

# METHOD DETAILS

## Genome-wide association studies

GWAS were performed for each study adjusted for age, age² (if significant), gender, eigenstrat PC1-4 (if significant), study-specific adjustments and case-control status (if applicable). In a second model, additional adjustment for height were made. The METAL software⁵² was used to perform a sample size weighted Z score meta-analysis. After meta-analysis, genetic variants available in less than 5,000 individuals were excluded. Comparable betas were derived using the formula $Z_{score} \times \sqrt{\frac{1}{N \times 2 \times MAF}}$ as was done previously.⁶² Genomic inflation and polygenic heterogeneity were assessed using the LD score regression software⁵³ by comparing the genomic control inflation factor and the LD score regression intercept (Table S5).

GWAS meta-analyses were performed separately for African, Asian and European samples. We also performed a transancestral meta-analysis. Since the analyses in non-European samples were underpowered, we additionally used an inverse-variance weighted method to test the combined effects of the lead variants in the non-European samples. This analysis was performed using the gtx package as implemented in R.

## Functional annotations

Regional association plots were made with the LocusZoom software.⁵⁴ The Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA GWAS) platform⁵⁵ was used to derive the independent genomic loci and genetic lead variants, and to functionally annotate the identified genetic variants. Additionally, enrichment for KEGG⁹ biological pathways was assessed for genes located nearby the identified genetic loci using the default options in FUMA, using hypergeometric tests. Genotype-Tissue Expression (GTEx) v7 was used to identify expression quantitative trait loci (eQTL) for the lead genetic variants and variants in LD ($r^2$ > 0.6).

We performed a transcriptome-wide association study (TWAS) using the association statistics from the European-only head size GWAS summary statistics and weights from 21 publicly available gene expression reference panels. We focused on the gene expression weights from blood (Young Finns Study, YFS), arterial (GTEx), brain (GTEx, CommonMind Consortium (CMC)) and peripheral nerve tissues (GTEx). Precomputed SNP-expression weights in the 1-Mb window were obtained for each gene in the reference panel, including the highly-tissue specific splicing QTL (sQTL) information on gene isoforms in the dorsolateral prefrontal cortex (DLPFC, CMC). Using the SNP-expression weights, SNP-trait effect estimates and the SNP correlation matrix, we used the TWAS-Fusion⁵⁶ to estimate the association statistic between the predicted expression and head size (TWAS Z score). Transcriptome-wide significant genes (eGenes) and the corresponding QTLs (eQTLs) were determined using Bonferroni correction in each reference panel, based on the average number of features (4,320 genes) tested across all the reference panels.⁵⁶ Finally, using a prior association probability of $1.1 \times 10^{-5}$ and colocalization analysis (COLOC)⁶³ for each locus we estimated the posterior probability of a shared causal variant (PP4>0.75) between the gene expression and trait association. eGene regions with eQTLs not reaching genome-wide significance in the head size GWAS were considered putatively novel TWAS signals. Furthermore, functional validation of the eGenes was performed by integrating eQTL with the functional genomics feature from the RegulomeDB.⁶⁴ A RegulomeDB probability score greater than 0.5 and closer to 1 indicates the likelihood of the eQTL having a gene-regulatory role. Finally, accounting for pairwise correlation between the gene expression features we conducted the multiple degree of freedom omnibus analysis, to test for the shared effect of eGenes across the different gene reference panels. A significance threshold of $p < 3.48 \times 10^{-6}$ accounting for the number of genes ($N = 14,385$) tested was used to identify significant eGenes in the omnibus test.
## Effects on anthropomorphic measures and regional brain volumes

The LD score regression software⁵³,⁶⁵ was used to assess genetic correlations with adult height,⁶⁶ for both the height-unadjusted and height-adjusted model.

Dual-energy X-ray absorptiometry (DXA) measurements of the UK Biobank imaging subsample (N = 3,313) were used to examine the effect of the identified lead variants on anthropometric measures across the body, i.e., bone area of the arms, legs, pelvis, ribs, spine, trunk and vertebrae L1-L4. In these analyses values more than three standard deviations from the mean were considered outliers and removed from the analyses. We adjusted for age, age,² gender and principal components (model 1), and additionally for height (model 2) to correct for an overall growth effect.

To investigate the effects of the identified variants for head size on growth in specific brain regions, we investigated the overlap between the identified loci for head size and previous genome-wide association studies (GWASs) on brain volumes.⁷,⁶⁷⁻⁷⁰ We also analyzed the associations between the identified lead genetic variants and global volumes (i.e., four brain lobes and lateral ventricle volumes), subcortical volumes (i.e., volumes of eight subcortical structures) and cortical volumes (i.e., volumes of 34 cortical regions of interest) in the UK Biobank (N = 22,145). Volumes were derived using the FreeSurfer 6.0 software. Values more than 3.5 standard deviations away from the mean were considered outliers and removed from the analysis. In the first model, we adjusted for age, age,² gender and principal components, and in the second model additionally for intracranial volume.

Additionally, we took the lead variants specifically associated with one or two subcortical volumes, and investigated their effects on the shape of seven subcortical structures, i.e., amygdala, caudate nucleus, hippocampus, nucleus accumbens, pallidum, putamen and thalamus. The radial distances and log Jacobian determinants were derived using the ENIGMA-Shape package (http://enigma.usc.edu/ongoing/enigma-shape-analysis/). Volumetric outliers more than 3.5 standard deviations from the mean were removed from the analysis.

We performed 10,000 permutations to define the number of independent DXA, brain volumetric and subcortical shape outcomes. We used this number to define our multiple testing adjusted *p* value thresholds for significance, i.e., 0.05/(number of independent outcomes x number of lead genetic variants).

## Genetic correlations

We investigated the genetic correlations with neuropsychiatric traits using the LD score regression software.⁵³,⁶⁵ Genetic correlation analyses were performed for educational attainment,⁷¹ general cognitive function,⁷² all stroke,⁷³ Alzheimer's disease,⁷⁴ frontotemporal dementia,⁷⁵ Parkinson's disease,⁷⁶ anorexia nervosa,⁷⁷ attention-deficit hyperactivity disorder,⁷⁸ autism spectrum disorder,⁷⁹ bipolar disorder,⁸⁰ extraversion,⁸¹ insomnia,⁸² major depressive disorder,⁸³ neuroticism,⁸⁴ obsessive compulsive disorder⁸⁵ and schizophrenia.⁸⁰ Analyses were performed in the entire GWAS dataset as well as in the GWAS set with newly included studies in comparison to the intracranial volume GWAS performed by Adams et al.⁶

We also performed genetic correlation analyses for publicly available cancer GWAS, namely for breast cancer,²⁸ ovarian cancer²⁹ and prostate cancer.³⁰ To obtain information on more cancer types, we additionally included GWAS of cancer registries from the UK Biobank and Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging (GERA).³¹ Of those, we excluded cancer types with less than 1,000 cases, which left the following cancer types to be analyzed: bladder cancer (N_cases = 2,242), breast cancer (N_cases = 17,881), cervical cancer (N_cases = 6,563), colon cancer (N_cases = 3,793), endometrial cancer (N_cases = 2,037), esophageal/ gastric cancer (N_cases = 1,091), kidney cancer (N_cases = 1,338), lung cancer (N_cases = 2,485), malignant melanoma (N_cases = 6,777), non-Hodgkin's lymphoma (N_cases = 2,400), prostate cancer (N_cases = 10,792) and rectal cancer (N_cases = 2,091). Genetic correlations with oral cavity/pharyngeal cancer (N_cases = 1,223) and ovarian cancer (N_cases = 1,259) could not be calculated due to low heritability estimates.

## Enrichment analyses

We performed enrichment analyses of different gene sets: genes within 1 Mb, 100 kb or 10 kb of the identified genetic loci, genes within 10 kb of the identified genetic loci with intragenic genetic variants, and genes within 10 kb of the identified genetic loci with intragenic genetic lead variants. As a reference, we used the rest of the protein-coding genome.

First, the Online Mendelian Inheritance in Man (OMIM) database⁵⁸ was used to retrieve information on genes related to heritable phenotypes affecting head size (Tables S19 and S20). Second, the COSMIC database²⁷ was used to extract Tier 1 cancer genes. Taking the rest of the genome as our reference gene set, we calculated the enrichment of these macrocephaly, microcephaly and cancer genes in the abovementioned gene sets.

Lastly, DOMINO,⁵⁷ a previously developed machine learning tool, was used to assess if the genes in the different gene sets were more often predicted to harbor dominant changes in comparison with genes in the rest of the genome.

Mean autosomal dominance scores were compared with the reference genome using a Mann-Whitney test. Differences in the proportions for the OMIM macro- and microcephaly genes, intellectual disability genes and COSMIC genes were calculated using a Pearson's χ² test.

We performed these analyses for the head size height-unadjusted GWAS results, but also the GWAS in the subset of studies for which height was available, the height-adjusted GWAS and the height GWAS.⁶⁶ For comparison, we also selected the top 67 loci for the height GWAS, so the results were not driven by a difference in the number of associated loci.
<figure><img src="image_18.png" /></figure>

## Effects on anthropomorphic measures and regional brain volumes

The LD score regression software⁵³,⁶⁵ was used to assess genetic correlations with adult height,⁶⁶ for both the height-unadjusted and height-adjusted model.

Dual-energy X-ray absorptiometry (DXA) measurements of the UK Biobank imaging subsample (N = 3,313) were used to examine the effect of the identified lead variants on anthropometric measures across the body, i.e., bone area of the arms, legs, pelvis, ribs, spine, trunk and vertebrae L1-L4. In these analyses values more than three standard deviations from the mean were considered outliers and removed from the analyses. We adjusted for age, age,² gender and principal components (model 1), and additionally for height (model 2) to correct for an overall growth effect.

To investigate the effects of the identified variants for head size on growth in specific brain regions, we investigated the overlap between the identified loci for head size and previous genome-wide association studies (GWASs) on brain volumes.⁷,⁶⁷⁻⁷⁰ We also analyzed the associations between the identified lead genetic variants and global volumes (i.e., four brain lobes and lateral ventricle volumes), subcortical volumes (i.e., volumes of eight subcortical structures) and cortical volumes (i.e., volumes of 34 cortical regions of interest) in the UK Biobank (N = 22,145). Volumes were derived using the FreeSurfer 6.0 software. Values more than 3.5 standard deviations away from the mean were considered outliers and removed from the analysis. In the first model, we adjusted for age, age,² gender and principal components, and in the second model additionally for intracranial volume.

Additionally, we took the lead variants specifically associated with one or two subcortical volumes, and investigated their effects on the shape of seven subcortical structures, i.e., amygdala, caudate nucleus, hippocampus, nucleus accumbens, pallidum, putamen and thalamus. The radial distances and log Jacobian determinants were derived using the ENIGMA-Shape package (http://enigma.usc.edu/ongoing/enigma-shape-analysis/). Volumetric outliers more than 3.5 standard deviations from the mean were removed from the analysis.

We performed 10,000 permutations to define the number of independent DXA, brain volumetric and subcortical shape outcomes. We used this number to define our multiple testing adjusted *p* value thresholds for significance, i.e., 0.05/(number of independent outcomes x number of lead genetic variants).

## Genetic correlations

We investigated the genetic correlations with neuropsychiatric traits using the LD score regression software.⁵³,⁶⁵ Genetic correlation analyses were performed for educational attainment,⁷¹ general cognitive function,⁷² all stroke,⁷³ Alzheimer's disease,⁷⁴ frontotemporal dementia,⁷⁵ Parkinson's disease,⁷⁶ anorexia nervosa,⁷⁷ attention-deficit hyperactivity disorder,⁷⁸ autism spectrum disorder,⁷⁹ bipolar disorder,⁸⁰ extraversion,⁸¹ insomnia,⁸² major depressive disorder,⁸³ neuroticism,⁸⁴ obsessive compulsive disorder⁸⁵ and schizophrenia.⁸⁰ Analyses were performed in the entire GWAS dataset as well as in the GWAS set with newly included studies in comparison to the intracranial volume GWAS performed by Adams et al.⁶

We also performed genetic correlation analyses for publicly available cancer GWAS, namely for breast cancer,²⁸ ovarian cancer²⁹ and prostate cancer.³⁰ To obtain information on more cancer types, we additionally included GWAS of cancer registries from the UK Biobank and Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging (GERA).³¹ Of those, we excluded cancer types with less than 1,000 cases, which left the following cancer types to be analyzed: bladder cancer (N_cases = 2,242), breast cancer (N_cases = 17,881), cervical cancer (N_cases = 6,563), colon cancer (N_cases = 3,793), endometrial cancer (N_cases = 2,037), esophageal/ gastric cancer (N_cases = 1,091), kidney cancer (N_cases = 1,338), lung cancer (N_cases = 2,485), malignant melanoma (N_cases = 6,777), non-Hodgkin's lymphoma (N_cases = 2,400), prostate cancer (N_cases = 10,792) and rectal cancer (N_cases = 2,091). Genetic correlations with oral cavity/pharyngeal cancer (N_cases = 1,223) and ovarian cancer (N_cases = 1,259) could not be calculated due to low heritability estimates.

## Enrichment analyses

We performed enrichment analyses of different gene sets: genes within 1 Mb, 100 kb or 10 kb of the identified genetic loci, genes within 10 kb of the identified genetic loci with intragenic genetic variants, and genes within 10 kb of the identified genetic loci with intragenic genetic lead variants. As a reference, we used the rest of the protein-coding genome.

First, the Online Mendelian Inheritance in Man (OMIM) database⁵⁸ was used to retrieve information on genes related to heritable phenotypes affecting head size (Tables S19 and S20). Second, the COSMIC database²⁷ was used to extract Tier 1 cancer genes. Taking the rest of the genome as our reference gene set, we calculated the enrichment of these macrocephaly, microcephaly and cancer genes in the abovementioned gene sets.

Lastly, DOMINO,⁵⁷ a previously developed machine learning tool, was used to assess if the genes in the different gene sets were more often predicted to harbor dominant changes in comparison with genes in the rest of the genome.

Mean autosomal dominance scores were compared with the reference genome using a Mann-Whitney test. Differences in the proportions for the OMIM macro- and microcephaly genes, intellectual disability genes and COSMIC genes were calculated using a Pearson's χ² test.

We performed these analyses for the head size height-unadjusted GWAS results, but also the GWAS in the subset of studies for which height was available, the height-adjusted GWAS and the height GWAS.⁶⁶ For comparison, we also selected the top 67 loci for the height GWAS, so the results were not driven by a difference in the number of associated loci.
Experimental datasets of brain cells

To assess whether the identified genes in the current study are enriched for genes differentially expressed in human progenitors versus neurons, we utilized differential gene expression data of those cell lines, derived from a previously published sample population (N<sub>donor</sub> = 85 in progenitors and N<sub>donor</sub> = 74 in neurons).<sup>32</sup> Using genes with at least 10 counts in more than 5% of the cell-type specific donors in either cell-type (resulting in 16,172 protein-coding genes out of 28,785 genes in total), we performed a paired differential gene expression analysis with design matrix: model.matrix(~ CellType + as.factor(DonorID) + RIN, data) as described previously,<sup>32</sup> using the limma R package.<sup>86</sup> We detected 1,095/1,420 protein genes upregulated in progenitors/neurons, respectively, for abs(logFC) > 1.5 and adjusted *p* value < 0.05. Performing a hypergeometric test, we evaluated if multiple protein-coding gene sets: head size gene sets with different distances from the lead variants, OMIM macrocephaly and microcephaly genes, and COSMIC tier 1 cancer genes are enriched among the protein-coding genes upregulated in progenitors or neurons.

Using a different approach, scRNA-seq data were used to investigate whether our genes of interest were enriched for genes specific for certain cortical brain cell types. Specifically, scRNA-seq data from the developing human cortex (gestational week 6-22, more than 189,000 cells) were used to identify the top 10% of genes specific for a certain cell type.<sup>34</sup> Using this data, we first performed LD score regression<sup>53</sup> based enrichment analyses of the head size GWAS summary statistics, as previously described.<sup>33,87</sup> Gene specificity was defined as the ratio of expression of a gene in a cell type by the total expression of that gene in all cell types. In parallel, we again tested the enrichment of various gene sets: head size gene sets with different distances from the lead variants, OMIM macrocephaly and microcephaly genes, and COSMIC tier 1 cancer genes, with the top 10% of cell specific genes for each cell type using hypergeometric tests. FDR correction was used to correct for the multiple gene sets tested for enrichment in each cell type.

To determine if regulatory elements of neural progenitors are enriched for the heritability of head size, we performed partitioned
heritability analyses<sup>53,88</sup> using chromatin accessibility profiles from a population of 76 primary human neural progenitor cells and
61 of their differentiated neuronal progenies, as was done previously.<sup>89</sup>

QUANTIFICATION AND STATISTICAL ANALYSIS

Please see the statistical analyses and software in method details.
Experimental datasets of brain cells

To assess whether the identified genes in the current study are enriched for genes differentially expressed in human progenitors versus neurons, we utilized differential gene expression data of those cell lines, derived from a previously published sample population (N<sub>donor</sub> = 85 in progenitors and N<sub>donor</sub> = 74 in neurons).<sup>32</sup> Using genes with at least 10 counts in more than 5% of the cell-type specific donors in either cell-type (resulting in 16,172 protein-coding genes out of 28,785 genes in total), we performed a paired differential gene expression analysis with design matrix: model.matrix(~ CellType + as.factor(DonorID) + RIN, data) as described previously,<sup>32</sup> using the limma R package.<sup>86</sup> We detected 1,095/1,420 protein genes upregulated in progenitors/neurons, respectively, for abs(logFC) > 1.5 and adjusted *p* value < 0.05. Performing a hypergeometric test, we evaluated if multiple protein-coding gene sets: head size gene sets with different distances from the lead variants, OMIM macrocephaly and microcephaly genes, and COSMIC tier 1 cancer genes are enriched among the protein-coding genes upregulated in progenitors or neurons.

Using a different approach, scRNA-seq data were used to investigate whether our genes of interest were enriched for genes specific for certain cortical brain cell types. Specifically, scRNA-seq data from the developing human cortex (gestational week 6-22, more than 189,000 cells) were used to identify the top 10% of genes specific for a certain cell type.³⁴ Using this data, we first performed LD score regression⁵³ based enrichment analyses of the head size GWAS summary statistics, as previously described.³³,⁸⁷ Gene specificity was defined as the ratio of expression of a gene in a cell type by the total expression of that gene in all cell types. In parallel, we again tested the enrichment of various gene sets: head size gene sets with different distances from the lead variants, OMIM macrocephaly and microcephaly genes, and COSMIC tier 1 cancer genes, with the top 10% of cell specific genes for each cell type using hypergeometric tests. FDR correction was used to correct for the multiple gene sets tested for enrichment in each cell type.

To determine if regulatory elements of neural progenitors are enriched for the heritability of head size, we performed partitioned
heritability analyses⁵³,⁸⁸ using chromatin accessibility profiles from a population of 76 primary human neural progenitor cells and
61 of their differentiated neuronal progenies, as was done previously.⁸⁹

QUANTIFICATION AND STATISTICAL ANALYSIS

Please see the statistical analyses and software in method details.