Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Whole-genome analyses of whole-brain data: working within an expanded search space


Large-scale comparisons of patients and healthy controls have unearthed genetic risk factors associated with a range of neurological and psychiatric illnesses. Meanwhile, brain imaging studies are increasing in size and scope, revealing disease and genetic effects on brain structure and function, and implicating neural pathways and causal mechanisms. With the advent of global neuroimaging consortia, imaging studies are now well powered to discover genetic variants that reliably affect the brain. Genetic analyses of brain measures from tens of thousands of people are being extended to test genetic associations with signals at millions of locations in the brain, and connectome-wide, genome-wide scans can jointly screen brain circuits and genomes; these analyses and others present new statistical challenges. There is a growing need for the community to establish and enforce standards in this developing field to ensure robust findings. Here we discuss how neuroimagers and geneticists have formed alliances to discover how genetic factors affect the brain. The field is rapidly advancing with ultra-high-resolution imaging and whole-genome sequencing. We recommend a rigorous approach to neuroimaging genomics that capitalizes on its recent successes and ensures the reliability of future discoveries.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Whole-brain GWAS.
Figure 2: Testing genetic associations in an image.
Figure 3: Approaches to imaging genetics.


  1. Lambert, J.C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat. Genet. 45, 1452–1458 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Ripke, S. et al. Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat. Genet. 45, 1150–1159 (2013). This is one of the largest and most successful GWAS aiming at identifying genetic risk factors for psychiatric disease.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Stein, J.L. et al. Identification of common variants associated with human hippocampal and intracranial volumes. Nat. Genet. 44, 552–561 (2012). This paper, along with three others published simultaneously, reported the first large-scale imaging genetics studies. Analyzing scans from over 20,000 individuals in aggregate, measures derived from brain images were shown to be viable and promising traits for genome-wide search. Successful GWAS discoveries were replicated among the collaborating consortia.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Bis, J.C. et al. Common variants at 12q14 and 12q24 are associated with hippocampal volume. Nat. Genet. 44, 545–551 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Strittmatter, W.J. et al. Apolipoprotein E: high-avidity binding to beta-amyloid and increased frequency of type 4 allele in late-onset familial Alzheimer disease. Proc. Natl. Acad. Sci. USA 90, 1977–1981 (1993). Genetically informed priors were used to pinpoint ApoE4 as a risk haplotype associated with late-onset Alzheimer's disease. Twenty years later, this is perhaps the disease risk allele with the highest odds ratio for any common neurological disorder. Neuroimaging studies have since mapped the effects of ApoE4 on various brain traits (volume differences, cortical thinning patterns, shape variations, white matter pathology, etc.) in many cohorts of young and elderly individuals.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Shaw, P. et al. Cortical morphology in children and adolescents with different apolipoprotein E gene polymorphisms: an observational study. Lancet Neurol. 6, 494–500 (2007). This was one of the first papers to show that ApoE4 carriers had different brain morphology when they were young, around 50 years before the typical age of onset of Alzheimer's disease.

    Article  CAS  PubMed  Google Scholar 

  7. Potkin, S.G. et al. Hippocampal atrophy as a quantitative trait in a genome-wide association study identifying novel susceptibility genes for Alzheimer's disease. PLoS ONE 4, e6501 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Flint, J. & Munafo, M.R. Candidate and non-candidate genes in behavior genetics. Curr. Opin. Neurobiol. 23, 57–61 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hibar, D.P. et al. Meta-analysis of structural brain differences in bipolar disorder: the ENIGMA-Bipolar Disorder. in Proc. Organization of Human Brain Mapping (2013).

  10. Turner, J.A. et al. A prospective meta-analysis of subcortical brain volumes in schizophrenia via the ENIGMA Consortium. in Proc. Organization of Human Brain Mapping (2013).

  11. Hibar, D.P. et al. ENIGMA2: genome-wide scans of subcortical brain volumes in 16,125 subjects from 28 cohorts worldwide. in Proc. Organization of Human Brain Mapping (2013).

  12. Jahanshad, N. et al. Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: a pilot project of the ENIGMA-DTI working group. Neuroimage 81, 455–469 (2013). This paper showed how to combine DTI data from many sources, using a standardized protocol for uniform phenotypic extraction from images. The standardization of data analysis was then shown to result in mostly stable but some unreliable phenotypes. Robust phenotypes will be carried forward into future multisite imaging GWAS meta-analysis studies.

    Article  PubMed  Google Scholar 

  13. Thompson, P.M. et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. doi:10.1007/s11682-013-9269-5 (8 January 2014). This review describes the many projects that imaging consortia can take on and the challenges of merging data sets from a vast variety of sources. The successes and limitations of imaging consortia are discussed, in terms of standardizing protocols, distributed analysis of data, and meta-analysis.

  14. Weiner, M.W. et al. The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimers Dement. 8, S1–S68 (2012).

    Article  PubMed  Google Scholar 

  15. Carrillo, M.C., Bain, L.J., Frisoni, G.B. & Weiner, M.W. Worldwide Alzheimer's Disease Neuroimaging Initiative. Alzheimers Dement. 8, 337–342 (2012).

    Article  PubMed  Google Scholar 

  16. Lehmann, J.M. et al. An antidiabetic thiazolidinedione is a high affinity ligand for peroxisome proliferator-activated receptor gamma (PPAR gamma). J. Biol. Chem. 270, 12953–12956 (1995).

    Article  CAS  PubMed  Google Scholar 

  17. Pearson, E.R. et al. Variation in TCF7L2 influences therapeutic response to sulfonylureas: a GoDARTs study. Diabetes 56, 2178–2182 (2007).

    Article  CAS  PubMed  Google Scholar 

  18. Global Lipids Genetics Consortium. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).

  19. Wonderlick, J.S. et al. Reliability of MRI-derived cortical and subcortical morphometric measures: effects of pulse sequence, voxel geometry, and parallel imaging. Neuroimage 44, 1324–1333 (2009).

    Article  CAS  PubMed  Google Scholar 

  20. Stein, J.L. et al. Voxelwise genome-wide association study (vGWAS). Neuroimage 53, 1160–1174 (2010). Despite null findings, this extended the field of imaging genetics, showing that it is feasible to perform a genome-wide search on tens of thousands of imaging phenotypes. Many researchers have since published on reducing the search space in images or genomes, allowing a less extreme correction for multiple comparisons across such a large number of tests.

    Article  CAS  PubMed  Google Scholar 

  21. Hibar, D.P. et al. Alzheimer's disease risk gene, GAB2, is associated with regional brain volume differences in 755 young healthy twins. Twin Res. Hum. Genet. 15, 286–295 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Jahanshad, N. et al. Genome-wide scan of healthy human connectome discovers SPON1 gene variant influencing dementia severity. Proc. Natl. Acad. Sci. USA 110, 4768–4773 (2013). The study searched the entire set of cortical connections for genetic influences, after reducing the dimensionality of the human connectome for GWAS by focusing on only heritable measures. A successful connectome-wide, genome-wide screen resulted in a significantly associated SNP within the APP pathway that also correlated with brain volumetric differences in the elderly. A SNP in the same gene was found only weeks later, through a GWAS, to associate with cognitive decline in an independent elderly sample.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Sherva, R. et al. Genome-wide association study of the rate of cognitive decline in Alzheimer's disease. Alzheimers Dement. 10, 45–52 (2014).

    Article  PubMed  Google Scholar 

  24. Guerreiro, R. et al. TREM2 variants in Alzheimer's disease. N. Engl. J. Med. 368, 117–127 (2013).

    Article  CAS  PubMed  Google Scholar 

  25. Jonsson, T. et al. Variant of TREM2 associated with the risk of Alzheimer's disease. N. Engl. J. Med. 368, 107–116 (2013).

    Article  CAS  PubMed  Google Scholar 

  26. Plaisier, S.B., Taschereau, R., Wong, J.A. & Graeber, T.G. Rank-rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures. Nucleic Acids Res. 38, e169 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Rajagopalan, P., Hibar, D.P. & Thompson, P.M. TREM2 and neurodegenerative disease. N. Engl. J. Med. 369, 1565–1566 (2013).

    PubMed  PubMed Central  Google Scholar 

  28. Dudbridge, F. & Gusnanto, A. Estimation of significance thresholds for genomewide association scans. Genet. Epidemiol. 32, 227–234 (2008). This paper proposed methods to correct for surveying large number of SNPs in performing a GWAS.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Pe'er, I., Yelensky, R., Altshuler, D. & Daly, M.J. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet. Epidemiol. 32, 381–385 (2008).

    Article  PubMed  Google Scholar 

  30. Kiebel, S.J., Poline, J.B., Friston, K.J., Holmes, A.P. & Worsley, K.J. Robust smoothness estimation in statistical parametric maps using standardized residuals from the general linear model. Neuroimage 10, 756–766 (1999).

    Article  CAS  PubMed  Google Scholar 

  31. Hayasaka, S., Phan, K.L., Liberzon, I., Worsley, K.J. & Nichols, T.E. Nonstationary cluster-size inference with random field and permutation methods. Neuroimage 22, 676–687 (2004).

    Article  PubMed  Google Scholar 

  32. Worsley, K.J., Marrett, S., Neelin, P. & Evans, A.C. Searching scale space for activation in PET images. Hum. Brain Mapp. 4, 74–90 (1996). This paper was one of the first to statistically test features at multiple scales in a brain image, allowing powerful detection of signals in PET scans of the brain.

    Article  CAS  PubMed  Google Scholar 

  33. Hua, X. et al. Unbiased tensor-based morphometry: improved robustness and sample size estimates for Alzheimer's disease clinical trials. Neuroimage 66, 648–661 (2013).

    Article  PubMed  Google Scholar 

  34. Purcell, S., Cherny, S.S. & Sham, P.C. Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics 19, 149–150 (2003). This paper reports methods for evaluating the statistical power attainable in a genome-wide association study.

    Article  CAS  PubMed  Google Scholar 

  35. Hibar, D.P., Kohannim, O., Stein, J.L., Chiang, M.C. & Thompson, P.M. Multilocus genetic analysis of brain images. Front. Genet. 2, 73 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Candès, E.J., Romberg, J.K. & Tao, T. Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59, 1207–1223 (2006).

    Article  Google Scholar 

  37. Inkster, B. et al. Histone deacetylase gene variants predict brain volume changes in multiple sclerosis. Neurobiol. Aging 34, 238–247 (2013).

    Article  CAS  PubMed  Google Scholar 

  38. Hibar, D.P. et al. Voxelwise gene-wide association study (vGeneWAS): multivariate gene-based association testing in 731 elderly subjects. Neuroimage 56, 1875–1891 (2011).

    Article  PubMed  Google Scholar 

  39. Shen, L. et al. Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers. Brain Imaging Behav. doi:10.1007/s11682-013-9262-z (5 October 2013).

  40. den Braber, A. et al. Heritability of subcortical brain measures: a perspective for future genome-wide association studies. Neuroimage 83, 98–102 (2013).

    Article  PubMed  Google Scholar 

  41. Glahn, D.C. et al. High dimensional endophenotype ranking in the search for major depression risk genes. Biol. Psychiatry 71, 6–14 (2012). Theoretically, a good phenotype for a successful GWAS must be heritable. To serve as an endophenotype for disease, it must also be strongly implicated in disease. This paper developed an algorithm to rank complex quantitative phenotypes from imaging, as well as other markers, in terms of their potential for success in GWAS.

    Article  PubMed  Google Scholar 

  42. Jahanshad, N. et al. Seemingly Unrelated Regression empowers detection of network failure in dementia. Neurobiol Aging. (in the press).

  43. Yuan, L., Wang, Y., Thompson, P.M., Narayan, V.A. & Ye, J. Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data. Neuroimage 61, 622–632 (2012).

    Article  PubMed  Google Scholar 

  44. Xiang, S. et al. Bi-level multi-source learning for heterogeneous block-wise missing data. Neuroimage doi:10.1016/j.neuroimage.2013.08.015 (27 August 2013).

  45. Glahn, D.C. et al. Genetic control over the resting brain. Proc. Natl. Acad. Sci. USA 107, 1223–1228 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Potkin, S.G. et al. A genome-wide association study of schizophrenia using brain activation as a quantitative phenotype. Schizophr. Bull. 35, 96–108 (2009). Using brain activation as a quantitative phenotype, this paper used functional imaging scans from a consortium to perform one of the first imaging GWAS in a psychiatric disorder.

    Article  PubMed  Google Scholar 

  47. Kochunov, P. et al. Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: comparing meta and mega analytical approaches for data pooling. Neuroimage doi:10.1016/j.neuroimage.2014.03.033 (18 March 2014).

  48. Dennis, E.L. & Thompson, P.M. Typical and atypical brain development: a review of neuroimaging studies. Dialogues Clin. Neurosci. 15, 359–384 (2013).

    PubMed  PubMed Central  Google Scholar 

  49. Lee, A.D. et al. 3D pattern of brain abnormalities in Fragile X syndrome visualized using tensor-based morphometry. Neuroimage 34, 924–938 (2007).

    Article  PubMed  Google Scholar 

  50. Zuk, O. et al. Searching for missing heritability: designing rare variant association studies. Proc. Natl. Acad. Sci. USA 111, E455–E464 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Gratten, J., Wray, N.R., Keller, M.C. & Visscher, P.M. Light after the long dark tunnel: elucidating the genetic architecture of psychiatric disorders through large-scale genomics. Nat. Neurosci. 17, 791–800 (2014).

    Article  CAS  Google Scholar 

  52. Carvill, G.L. et al. Targeted resequencing in epileptic encephalopathies identifies de novo mutations in CHD2 and SYNGAP1. Nat. Genet. 45, 825–830 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Epi4K Consortium & Epilepsy Phenome/Genome Project. De novo mutations in epileptic encephalopathies. Nature 501, 217–221 (2013).

  54. Neale, B.M. et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485, 242–245 (2012). This paper highlighted the importance of de novo genetic mutations in the risk for autism.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. O'Roak, B.J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Sanders, S.J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237–241 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Iossifov, I. et al. De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285–299 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Chattopadhyay, I. & Lipson, H. Data smashing. Preprint at (2014).

  59. Duarte-Carvajalino, J.M. et al. Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship. Neuroimage 59, 3784–3804 (2012).

    Article  PubMed  Google Scholar 

  60. Tibshirani, R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Series B Stat. Methodol. 58, 267–288 (1996).

    Google Scholar 

  61. Kohannim, O. et al. Boosting power to detect genetic associations in imaging using multi-locus, genome-wide scans and ridge regression. in Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on 1855–1859 (IEEE, 2011).

  62. Kohannim, O. et al. Discovery and replication of gene influences on brain structure using LASSO regression. Front. Neurosci. 6, 115 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Wellcome Trust Case Control Consortium et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).

  64. Ikram, M.A. et al. Common variants at 6q22 and 17q21 are associated with intracranial volume. Nat. Genet. 44, 539–544 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Psaty, B.M. et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ. Cardiovasc. Genet. 2, 73–80 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Nymberg, C., Jia, T., Ruggeri, B. & Schumann, G. Analytical strategies for large imaging genetic datasets: experiences from the IMAGEN study. Ann. NY Acad. Sci. 1282, 92–106 (2013).

    Article  CAS  PubMed  Google Scholar 

  67. Paillère Martinot, M.L. et al. White-matter microstructure and gray-matter volumes in adolescents with subthreshold bipolar symptoms. Mol. Psychiatry 19, 462–470 (2014).

    Article  PubMed  Google Scholar 

  68. Eicher, J.D. et al. Genome-wide association study of shared components of reading disability and language impairment. Genes Brain Behav. 12, 792–801 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Potkin, S.G. & Ford, J.M. Widespread cortical dysfunction in schizophrenia: the FBIRN imaging consortium. Schizophr. Bull. 35, 15–18 (2009).

    Article  PubMed  Google Scholar 

Download references


S.E.M. is supported by an Australian Research Council Future Fellowship, 110100548. N.J. and P.M.T. were supported, in part, by US National Institutes of Health R01 grants NS080655, AG040060, EB008432, MH097268, MH085667, MH089722 and MH094343, and grants U01 AG024904 and P41 EB015922. B.M.N. was supported in part by R01 MH101244.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Paul M Thompson.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Table 1 (PDF 212 kb)


Common variant

Common variant generally refers to an allele that segregates in a population at an allele frequency of at least 5%.

Genome-wide association study (GWAS)

A genome-wide association study (GWAS) is an unbiased screen of the genome for genetic variants that present at different frequencies in affected and unaffected individuals, that is, that associate with a phenotype. Although either rare or common variants can now be studied and analyzed for association in a genome-wide way, GWAS has historically referred to a specific, early type of genome-wide study in which a genome-wide set of common polymorphisms (single nucleotide polymorphisms) is analyzed using microarray-based technologies to find disease-associated common alleles.


An allele is one of a number of alternative forms of a gene or locus. The minor allele is the less frequent allele at a locus and the major allele is the more frequent allele.


A haplotype is an arrangement of alleles along a chromosome. In population-based studies, a haplotype refers more specifically to a set of genomically nearby alleles that segregate in populations as a block or unit, as their physical linkage is seldom, if ever, disrupted by recombination.

Single-nucleotide polymorphism (SNP)

A single-nucleotide polymorphism (SNP) is a single base-pair position in the genome that varies between members of a species. The terms polymorphism and SNP generally refer to sequence variations that segregate in a population at an allele frequency of at least 1%.


Polygenic is a term meaning "many genes". A polygenic phenotype is influenced by more than one gene and can refer to common variants with small effects or rare variants with larger effects.

Candidate gene

A candidate gene is a pre-specified gene of potential interest. Candidate gene studies are often distinguished from unbiased genome-wide studies that analyze variation in all or most genes simultaneously.


A locus is a place on a chromosome. A locus may contain one gene, multiple genes or no genes at all.

Rare variant

Rare variant describes variants that are private to individuals and families. In some usage, the term rare variant is used more expansively to include all variants that are not common.

Case-control study

A case-control study is a study design that compares the distribution of a genetic or other variable between individuals affected with a disease (cases) and unaffected individuals (controls).


Heritability refers to the proportion of phenotypic variance of a trait, such as disease liability, that can be attributed to genetic factors.

Copy number variation (CNV)

A common-variant association study (CVAS) is a genome-wide association study to find common variants that present at different allele frequencies in affected and unaffected individuals. The term CVAS has recently been proposed as a replacement for the term GWAS, as rare-variant association studies are also association studies and are also genome wide.

Next generation sequencing (NGS)

Next generation sequencing (NGS) refers to a set of technologies that sequence DNA in massively parallel ways; for example, by optically detecting the incorporation of specific bases into millions of different DNA molecules, spatially segregated on an imageable glass surface, at the same time.


The exome is the part of a genome that encodes proteins, approximately 1% of the human genome.

Whole-exome sequencing (WES)

Whole-exome sequencing (WES) is the targeted enrichment and sequencing of the set of all protein-coding exons and non-coding RNAs in the genome (the exome). WES is performed by selectively capturing the protein-coding part of the genome by hybridization to pre-designed oligonucleotide 'baits'. The captured DNA is then sequenced. Although WES offers a less-complete view of an individual's genome sequence than whole-genome sequencing, WES has been more frequently used because of its substantially lower cost. As the price of sequencing continues to fall, WES may be gradually replaced by whole-genome sequencing.

Whole-genome sequencing (WGS)

Whole-genome sequencing (WGS) is the sequencing of all of the DNA in an individual's genome.

De novo mutation (DNM)

A de novo mutation (DNM) is a mutation that is part of an individual's genome that is not detected in the genome of either parent (although it may have arisen from a mutation in the parental germline). With the exception of de novo mutations in monozygotic twins, or those shared by siblings as a result of germline mosaicism, most new mutations are not shared by relatives and do not contribute to heritability estimates.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Medland, S., Jahanshad, N., Neale, B. et al. Whole-genome analyses of whole-brain data: working within an expanded search space. Nat Neurosci 17, 791–800 (2014).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing