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

Nature Neuroscience volume 17, pages 791–800 (2014)Cite this article

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Abstract

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.

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

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Acknowledgements

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

Author notes

  1. Sarah E Medland and Neda Jahanshad: These authors contributed equally to this work.

Authors and Affiliations

  1. Quantitative Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

    Sarah E Medland

  2. Imaging Genetics Center, Institute for Neuroimaging & Informatics, Keck School of Medicine of University of Southern California, Los Angeles, California, USA

    Neda Jahanshad & Paul M Thompson

  3. Department of Neurology, University of Southern California, Los Angeles, California, USA

    Neda Jahanshad & Paul M Thompson

  4. Program in Medical and Population Genetics, The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA

    Benjamin M Neale

  5. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA

    Benjamin M Neale

  6. Psychiatric and Neurodevelopmental Unit, Massachusetts General Hospital, Boston, Massachusetts, USA

    Benjamin M Neale

  7. Department of Neurology, University of Southern California, Los Angeles, California, USA

    Paul M Thompson

  8. Department of Psychiatry, University of Southern California, Los Angeles, California, USA

    Paul M Thompson

  9. Department of Engineering, University of Southern California, Los Angeles, California, USA

    Paul M Thompson

  10. Department of Radiology, University of Southern California, Los Angeles, California, USA

    Paul M Thompson

  11. Department of Pediatrics, University of Southern California, Los Angeles, California, USA

    Paul M Thompson

  12. Department of Ophthalmology, University of Southern California, Los Angeles, California, USA

    Paul M Thompson

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Correspondence to Paul M Thompson.

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Supplementary information

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Supplementary Table 1 (PDF 212 kb)

Glossary

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.

Allele

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.

Haplotype

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

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.

Locus

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

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.

Exome

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.

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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). https://doi.org/10.1038/nn.3718

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