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Description : Check out this medical titled "A brief history of imaging genetics” by Jason Stein.

This medical is about imaging genetics, a relatively new field that combines the techniques of genetics and neuroimaging to investigate the relationship between genetic variation and brain function. The history of imaging genetics can be traced back to the early 1990s when functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) were first developed, providing researchers with a new way to study the brain.

The first studies that combined genetics and imaging focused on candidate genes, which are genes that are thought to be involved in specific brain functions or disorders. For example, researchers might study the dopamine receptor gene in individuals with schizophrenia to investigate the role of dopamine in this disorder.

In the early 2000s, advances in genotyping technologies made it possible to study large numbers of genetic variants simultaneously, leading to the development of genome-wide association studies (GWAS). These studies have identified many genetic variants associated with brain structure and function, as well as with psychiatric and neurological disorders.

As the field has advanced, researchers have developed more sophisticated techniques for analyzing and interpreting imaging genetics data, such as machine learning algorithms and network analysis. Today, imaging genetics is a rapidly growing field with the potential to provide new insights into the underlying mechanisms of brain function and dysfunction.

Tags : imaging genetics, genetics, neuroimaging, genetic variation, brain function, Medical Genetics, Medical Genetics

Published on : Jun 01, 2023 | Views : 1374 | Downloads : 0

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Slide 1-

Jason Stein Assistant Professor Department of Genetics & Neuroscience Center UNC-Chapel Hill Introduction to Imaging Genetics, 2018 A brief history of imaging genetics
Slide 2-

99.9% of DNA identical between any two people DNA variation causes changes in height, personality, predisposition to disease, and brain structure. Human genetics
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Types of genetic variation in the human genome Some big questions in imaging genetics Heritability Imaging genetics history Progress in answering the big questions Outline
Slide 4-

23 pairs of chromosomes In a particular part of the chromosome 5 there are many genes Within a gene there are exons, introns, and SNPs Single Nucleotide Polymorphism (SNP) Human genetics: terminology UCSC genome browser http://genome.ucsc.edu/
Slide 5-

Human genetics: terminology Slide courtesy of Sven Cichon If allele G is associated with risk for disease, it is the risk allele. That makes allele A the protective allele.
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Slide courtesy of Sven Cichon Human genetics: terminology
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Genetic variation across size spectrum (Scherer, Nature Genetics, 2007)
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Copy number variations and translocations (de la Torre, Nat Med, 2016) The currently most commonly studied structural variants
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(de la Torre, Nat Med, 2016) Exonic and intronic variation ~97% of the genome does not code for protein (either intergenic or intronic) Mutations in protein coding regions often are easier to functionally interpret
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How many variants exist in each individual? European Asian South American/Hispanic African Each individual has 4-5 million variants different from reference genome >99.9% of those variants are SNPs or short indels (1000 Genomes Project Consortium, Nature, 2015)
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Most detected variants in a population are rare ~64 million have frequency < 0.5% ~12 million have frequency between 0.5 and 5% ~8 million have frequency >5% Most variants within an individual are common 1-4% of variants have frequency < 0.5% ~96% of variants have frequency > 0.5% Frequency of variants (1000 Genomes Project Consortium, Nature, 2015)
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Slide courtesy of Sven Cichon During meiosis, crossing over between homologous chromosomes and recombination occurs. This mixes your chromosome segments between mom and dad before passing them along. This has the effect that genetic variants are inherited in chunks, so there is correlation between alleles at nearby SNPs. Linkage disequilibrium
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The history of mutations (Field, Science, 2016) Mutations happen in the population as a de novo (new mutation) and are necessarily rare. They can undergo positive selection (increasing allele frequency) or purifying selection (decreasing allele frequency) based on their influence on survival or reproduction. They can also undergo random genetic drift, randomly increasing or decreasing in frequency over long periods. We generally do not know the evolutionary histories of each genetic variant in the human population.
Slide 14-

Somatic vs Germline Mutations (Poduri, Science, 2013, Lodato, Science, 2015) We previously assumed the genome sequence is identical between every two cells in the body, which is roughly true. But, each neuron has ~1400 somatic mutations from single cell genome sequencing. Brain-affecting somatic mutations will not be passed on unless they happen before development of gonads.
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Many genetic variants exist in everyone’s genome. Most likely have infinitesimally small effect on any trait Which affect brain structure and function? Relevance of genetic variation to brain structure
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Types of genetic variation in the human genome Some big questions in imaging genetics Heritability Imaging genetics history Progress in answering the big questions Outline
Slide 17-

How does genetic variation lead to disorders of the brain? What cell-types and developmental processes impact brain structure? Do genetic changes that affect brain structure also affect brain function? What genetic changes have led to humans having big brains and unique cognitive capabilities? Some big questions in imaging genetics
Slide 18-

Types of genetic variation in the human genome Some big questions in imaging genetics Heritability Imaging genetics history Progress in answering the big questions Outline
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Identical twins Share 100% of alleles + common environment Fraternal Twins Share 50% of alleles on average + common environment Twins and heritability What percentage of trait/phenotype variance is due to genetic variation? H2 = σ2genetics / σ2phenotype Falconer’s heritability: h2 = 2(rmz – rdz)
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Common misconceptions about heritability High heritability implies genetic determination High heritability means that in the current population given its current environment, genetic variation explains a large amount of phenotypic variation. If the environment changes or is manipulated, the phenotype may change even with a highly heritable trait. Height is highly heritable but improved nutrition is associated with increased height. Large heritability implies genetic variants of large effect High heritability means there are genetic variants influencing a trait. It does not tell you if there are many genetic variants influencing a trait or only one. It does not tell you if those genetic variants influencing a trait have a strong or weak effect. (Visscher, Nat Rev Genet, 2008)
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Most well-measured traits are heritable (Polderman, Nat Genet, 2015) A meta-analysis of 14,558,903 partly dependent twin pairs!
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Heritability of human brain structure (Blockland, Twin Research and Human Genetics, 2012) Most well-measured (larger) brain structure traits are heritable
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Heritability of human brain function (Elliott, bioRxiv, 2017) Brain function measures based on fMRI have particularly low heritability Structure Function
Slide 24-

Types of genetic variation in the human genome Some big questions in imaging genetics Heritability Imaging genetics history Progress in answering the big questions Outline
Slide 25-

Measures of brain structure and function are “closer to the action of the gene” than behaviorally defined neuropsychiatric diseases Measures of brain structure and function are genetically correlated to neuropsychiatric diseases So, we need smaller sample sizes to detect genetic influences on brain than behavior genetic influences on brain will be informative about genetic influences on behavior Assumptions of early imaging genetic studies (Glahn, Hum Brain Mapping, 2007)
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Effect size of a common variant here is > 40% Sample size is small Gene and variant chosen based on prior biological knowledge Crisis of reproducibility in imaging genetics (Hariri, Science, 2002)
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“The literature of imaging genetic studies contains many false positives.” Acknowledgement that we are bad at guessing the particular genes or variants involved in any biological process Uniform significance thresholds established For common variant associations (GWAS) P<5e-8 Replication and meta-analysis required Now associations replicate Solving the crisis of reproducibility (Sullivan, Biological Psychiatry, 2018; Pe’er, Genet Epidemiology, 2008; Dudbridge & Gusnanto, Genet Epidemiology, 2008; McCarthy, Nat Rev Genet, 2008; Flint & Munafo, Curr Opin Neurobiology, 2013)
Slide 28-

(McCarthy, Nat Review Genet, 2008) Different genotyping methods for different types of variants Linkage Genome/exome sequencing; CNV calling Genome-wide association
Slide 29-

Rare variants associated with human brain structure alterations (Gilmore & Walsh, Dev Biol, 2012) Rare recessive variants, predominately affecting centrosomal proteins, can lead to primary microcephaly
Slide 30-

The translocation was homozygous in each of the four affected individuals studied (46,XY,t(3;10)(p24;q23)2x), whereas healthy parents were heterozygous. Patients exhibit microcephaly, polymicrogyria, agenesis of corpus callosum EOMES levels are absent in affected patients. The chromosome that contains the affected allele has no expression of EOMES. (Baala, Nat Genet, 2007) Balanced chromosomal translocation leading to microcephaly
Slide 31-

(McCarthy, Nat Review Genet, 2008) Different genotyping methods for different types of variants Linkage Genome/exome sequencing; CNV calling Genome-wide association
Slide 32-

Tag SNPs (International HapMap Consortium, Nature, 2003) Because nearby SNP genotypes are correlated, we only need to genotype a subset of all SNPs in existence to, by proxy, measure most SNPs Rare variants genotypes are less correlated with common variants, so this technique only works well for common variants
Slide 33-

Genome-wide association studies: Genotyping (Illumina.com) Arrays (~$100/sample) are cheap in comparison to genome sequencing (~$2000/sample), but only allow you to genotype common variants
Slide 34-

(Balding, Nature Reviews Genetics, 2006) Genome-wide association studies: Association Quantitative Phenotype A/A A/G G/G Slope of line of best fit indicates strength of the association. Significance assessed through linear regression (probability that slope = 0) For disease (binary) traits, use logistic regression vs linear regression – but same idea Repeat for all genotyped (and imputed) SNPs
Slide 35-

Genome-wide association studies: Manhattans Manhattan plots show associations across the entire genome. Each dot represents a SNP Y-axis is significance calculated from association X-axis is location along the genome Because so many comparisons are conducted, must correct for multiple comparisons (by conducting more statistical tests you are more susceptible to false positive results). Common genome-wide significant threshold is P<5x10-8 (McCarthy, Nat Review Genet, 2008)
Slide 36-

Associated locus - interpretation (Lango Allen, Nature, 2010) What you have found What you want to know Variation in a locus of the genome which significantly influences your trait (disease) A mechanism by which genetic variation influences risk for disease Causal variant(s) Causal gene(s) Causal biological pathway(s)
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(Adams, et al., Nat Neurosci, 2016) N ~ 33,000 Intracranial Volume Common variant associations to brain structure ENIGMA, CHARGE, UK Biobank all have led to large replicable results across consortia
Slide 38-

(Adams, et al., Nat Neurosci, 2016; Beck et al., Neuron, 1995) A locus of interest
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(McCarthy, Nat Review Genet, 2008) Different genotyping methods for different types of variants Linkage Genome/exome sequencing; CNV calling Genome-wide association
Slide 40-

Next generation sequencing (Goodwin, Nat Review Genet, 2016) Can simultaneously sequence chopped up versions of the genome within short reads (50-150 bp) Take a picture after each base pair addition. Allows genotyping of every base pair, which is good for finding rare variants More expensive than genotyping chips
Slide 41-

Exome sequencing (Bamshad, Nat Review Genet, 2011) Sequencing ~3% of genome that codes for proteins is much less expensive and enriches for (currently) functionally interpretable results Biotinylated probes Streptavidin pulldown
Slide 42-

(Meyerson, Nat Review Genet, 2010) How to find genetic variation with next generation sequencing
Slide 43-

Association testing in exome or genome seq cases control Gene A Gene A Likely Gene Disrupting Mutation Person 1 Person N Person N+1 Person 2N Aggregate mutations across a feature (like a gene), count number of mutations in cases vs control, conduct a statistical test to determine if significant difference – generally called a burden test
Slide 44-

Few examples so far More to come for sure… Whole genome/exome sequencing in imaging genetics
Slide 45-

Types of genetic variation in the human genome Some big questions in imaging genetics Heritability Imaging genetics history Progress in answering the big questions Outline
Slide 46-

How does genetic variation lead to disorders of the brain? What cell-types and developmental processes impact brain structure? Do genetic changes that affect brain structure also affect brain function? What genetic changes have led to humans having big brains and unique cognitive capabilities? Some big questions in imaging genetics
Slide 47-

47 What makes an endophenotype
Slide 48-

What questions can we answer with endophenotypes? Genes Endo-phenotype Disease Genes Endo-phenotype Disease Mediational model Pleiotropy or Liability-Index Model Mechanistic Study Higher powered genetic search X X X (Kendler and Neale, Mol Psychiatry, 2010)
Slide 49-

49 (Franke*, Stein*, Ripke*, Nat Neurosci, 2016) Common genetic variants have similar effect sizes on brain structure as on height and neuropsychiatric disease risk Effect sizes in brain structure GWAS
Slide 50-

50 (Adams*, Hibar*, Nat Neurosci, 2016) Genetic correlation with neuropsychiatric disorders/traits
Slide 51-

51 Variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg=-0.155, p=0.0034) (Franke*, Stein*, Ripke*, Nat Neurosci, 2016; Hibar*, Adams*, Nat Commun, 2017) Genetic correlation with neuropsychiatric disorders/traits
Slide 52-

52 How do structural neuroimaging phenotypes fare as endophenotypes?
Slide 53-

How does genetic variation lead to disorders of the brain? What cell-types and developmental processes impact brain structure? Do genetic changes that affect brain structure also affect brain function? What genetic changes have led to humans having big brains and unique cognitive capabilities? Some big questions in imaging genetics
Slide 54-

(Silbereis, Neuron, 2016) What developmental processes affect brain structure?
Slide 55-

(Rakic, Nat Rev Neurosci, 2009; Rakic, Science, 1988) Radial unit hypothesis The more neural progenitors that exist, the bigger the cortical surface area as more cortical columns will be created. The more asymmetric divisions of progenitors within a column, the thicker the cortex
Slide 56-

(Buenrostro et al, Nature Methods, 2013; http://res.illumina.com/documents/products/research_reviews/sequencing-methods-review.pdf; de la Torre Ubieta, Stein, Cell, 2018) Common variants alter gene regulation of neural progenitors to influence adult brain
Slide 57-

How does genetic variation lead to disorders of the brain? What cell-types and developmental processes impact brain structure? Do genetic changes that affect brain structure also affect brain function? What genetic changes have led to humans having big brains and unique cognitive capabilities? Some big questions in imaging genetics
Slide 58-

58 (Rakic, Nat Reviews Neurosci, 2009; Herculano-Houzel, PNAS, 2006; Geschwind and Rakic, Neuron, 2013) Human brain Mouse brain 170 billion cells (86 billion neurons) 109 million cells (70 million neurons) Greatest expansion is in surface area of neocortex (1000 fold compared to rodent) as compared to thickness (2 fold compared to rodent) Neocortex is greatly expanded in humans as compared to (most) other animals
Slide 59-

59 (Sousa, Cell, 2017) Neocortex is greatly expanded in humans as compared to other primates
Slide 60-

60 (Lee and Wolpoff, Paleobiology, 2003) At 200 kya, much bigger increase in anatomicall modern human skull size From 2 Mya to 200 kya, slow increase in brain size Changes in human cranial capacity over the past 2M yrs
Slide 61-

61 (Enard, Current Biology, 2016) Evidence supporting gene involvement in brain evolution
Slide 62-

(McCarthy, Nat Review Genet, 2008) Utility of genetic discovery
Slide 63-

Each human genome harbors a great deal of genetic variation. Some genetic variation creates changes in human brain structure. Modern genomic methods and large sample sizes have allowed for the discovery of genetic variants influencing human brain structure. The recent explosion of well-replicated genetic variants associated with brain structure will allow new insight into human evolution, neuropsychiatric disease risk, and brain function, if we can interpret them. Conclusions
Slide 64-

What’s the difference between an allele and a SNP? Why do we use a uniform significance threshold in genome-wide association study? What are two important criteria for a “good” endophenotype? If I increase the number of proliferative progenitors in the developing cortex, what happens to cortical surface area? What are your big questions that we can answer with imaging genetics? Questions!