The Genetic Basis of Disease

The Genetic Basis of Disease
Basics of Biology (4) The Genetic Basis of Disease

Course Content Introductory Content Specific Content
Genetic Variation and Disease Variation and Diversity Types of mutations and the dynamics of variation in the population (Molecular) phenotypes What genetic variation does to The Machinery and how it causes disease Basics of Biology Cell Biology: The Machinery How cells are organized and work Genetics: The Information How the machinery is encoded in the genome and how the genome is inherited Specific Content DNA sequencing and related approaches Sample Preparation Chemistry Bioinformatics Genetic Testing Types of tests Test interpretation Testing in the future

Specific Examples Genetics 2 Functional Effects of Variation
How broken machinery instructions are inherited How they cause phenotypes Functional Effects of Variation Synthesis errors Regulation errors Transmission Genetics Genotype to phenotype Inheritance patterns and effects Genetic Basis of Disease Mendelian disease Common disease Cancer, the special case Specific Examples

Functional effects of mutations
synthesis errors

DNA RNA Protein, potentially error-prone
5’-AGTCGCGTAATGATCAGatagttagcgagGTCGAAATTGTGATAAGA-3’ 5’-cagtAGTCGCGTAATGATCAGATAGTTAGCGAGGTCGAAATTGTGATAAGAtcag-3’ Transcription (by RNA polymerase) DNA RNA 5’-AGTCGCGTAATGATCAGGTCGAAATTGTGATAAGA-3’ Splicing (by spliceosome) mRNA M I R S K L Translation (by ribosome) Protein

Multiple copies, usually (except in splicing)
Proteins are not perfect machines; they have error rates. Therefore: all biological processes make mistakes. DNA 5’-cagtAGTCGCGTAATGATCAGATAGTTAGCGAGGTCGAAATTGTGATAAGAtcag-3’ A few to hundreds of copies 5’-AGTCGCGTAATGATCAGatagttagcgagGTCGAAATTGTGATAAGA-3’ RNA Physical conversion! I.e., 1 to 1 mRNA 5’-AGTCGCGTAATGATCAGGTCGAAATTGTGATAAGA-3’ A few to hundreds of copies Protein M I R S K L

For example, Translation error?
Proteins are not perfect machines; they have error rates. Therefore: all biological processes make mistakes. DNA 5’-cagtAGTCGCGTAATGATCAGATAGTTAGCGAGGTCGAAATTGTGATAAGAtcag-3’ A few to hundreds of copies 5’-AGTCGCGTAATGATCAGatagttagcgagGTCGAAATTGTGATAAGA-3’ RNA Physical conversion! I.e., 1 to 1 mRNA 5’-AGTCGCGTAATGATCAGGTCGAAATTGTGATAAGA-3’ A few to hundreds of copies Protein M L R S K L M I R S K L M I R S K L M I R S K L

But if the error is in the DNA ...
Mutation! DNA 5’-cagtAGTCGCGTAATTATCAGATAGTTAGCGAGGTCGAAATTGTGATAAGAtcag-3’ 5’-AGTCGCGTAATTATCAGatagttagcgagGTCGAAATTGTGATAAGA-3’ RNA A few to hundreds of copies None of them will get translated properly! The highlighted bases are examples of signals in the nucleic acid sequences. Signal = recognition site of a protein.

Mutations can affect any step of the process
DNA 5’-cagtAGTCGCGTAATGATCAGATAGTTAGCGAGGTCGAAATTGTGATAAGAtcag-3’ Transcription (by RNA polymerase) 5’-AGTCGCGTAATGATCAGatagttagcgagGTCGAAATTGTGATAAGA-3’ RNA Splicing (by spliceosome) mRNA 5’-AGTCGCGTAATGATCAGGTCGAAATTGTGATAAGA-3’ Translation (by ribosome) Protein M I R S K L

DNA RNA Protein error summary
In most cases there are multiple copies of a macromolecule so that if one is screwed up the others provide necessary function But if change is in the DNA – a mutation – all molecules from that copy of the gene are defective In diploid organisms mom can potentially cover for pop. Or pop for mom. That makes it interesting. This is generally true, not just for DNA RNA Protein

Regulatory Element Mutations
Whether a gene product is made Regulatory Element Mutations

Transcriptional regulation
Promoter Stop! Transcription A gene Repressor (with transcription factor bound) Enhancer (with transcription factors bound) Off Promoter Insulator (with insulator protein bound) Another gene S!

Stop! 5’-agctgacgatGATTACAttacgc-3’ Off S!

Stop! No transcription! 5’-agctgacgatGACTACAttacgc-3’ Off S! 5’-agctgacgatTCAGCACCATGGACAGCGCCttacgc-3’

Stop! No transcription! 5’-agctgacgatGACTACAttacgc-3’ Off Transcription! S! 5’-agctgacgatTCAGCACCAT----AGCGCCttacgc-3’

Types of regulatory mutations
Loss of Function Abrogation of transcription factor binding by the usual mutational mechanisms point mutation or small indel eliminates amino acid – nucleotide interaction site, loss of binding outright deletion of element Splice site mutation results in incorrect joining of exons in the mRNA Mutation in untranslated region causes translational error Gain of Function: Wrong place or wrong time Regulatory region from one gene is juxtaposed to the promoter of another by a deletion or rearrangement Or a new regulatory element is created de novo by a mutation If negative element, turns off gene If positive element, turns on gene

Molecular Phenotypes RNA Transcription defect
RNA not expressed at all no protein made at all RNA not expressed in one of several tissues no protein made in that cell type RNA expressed in wrong tissue or wrong time protein made where or when it shouldn’t be Processing (splicing) defect Bad RNA premature stop codon, truncated protein Protein Translation defect protein not made or made at level Protein product defect wrong amino acid potentially misfolded binding or catalysis defect premature stop codon truncated protein C-terminus incorrect start codon truncated protein N-terminus

The genetics of Functional effects
Genotype to Phenotype The genetics of Functional effects

Pattern Formation ... 2 days later
Read more on Wikipedia: “Limb Development”

... Tissue Organization ... Pigment cells Rods Cones Various neurons
Light Read more on Wikipedia: “Retina” and “Photoreceptor cell”

... Cellular Function, Outward-facing ...
Hungry Brainy Selfless Brawny R.I.P.

... Intracellular Function
Read more on Wikipedia: “Human cell” and “Cell (biology)”

Biology Neuronal pathfinding Cell adhesion Energy metabolism Oxygen transport Intracellular signaling Hormone synthesis Immune functions Eyesight Germ cell production DNA replication Cell-cell communication Nerve cell insulation Proprioception Bone density Fingernail strength Etc Multilayered cell biological regulation via protein-protein interactions Catalysis of chemical reactions involving small molecules Protein and nucleic acid modifications Gazillions of multifaceted and deeply hierarchical chemical interactions

Biology Executors

Biology Regulators

Biology

Molecular Defects in Muscle
Defective Troponin or Tropomyosin or Titin or Myosin7 (among others) can cause Cardiomyopathies (Heart muscle abnormalities) Examples of molecular defects: Wrong amino acid at a certain place in the chain (“Missense”) Truncation of the chain (“Nonsense”) Examples of results of defects: Protein does not fold correctly Protein is missing a key part Protein cannot interact with its other protein partners or with small molecules essential for its function

Biology

Biology’s Genes

Biology’s Genes $ $

$ Biology’s Genes Molecular phenotype of lesion $ $ $ $ $ Impact
Effect type Transcriptional Regulation Splicing Translation (e.g., frameshift) Missense mutation Outright Deletion TReg MM Spl Del Hi Med Lo

Mutations in Genes $

Functional consequences of genetic variation
$

Functional consequences of genetic variation
$?

The Alleles that Contribute to Risk or Disease
M S D N Regulatory (usually transcription; usually mild) Missense (range from neutral to severe) Nonsense (usually severe) Splicing (usually severe) Big deletion (usually severe) Allele frequency in population Impact of lesion

Next slides: De novo Mendelian Dominant Recessive Multigenic Allele frequency in population Impact of lesion

De novo Allele frequency in population Impact of lesion
‘Zero’ allele frequency Huge impact, syndromic Impact of lesion

Mendelian trait - Dominant
Allele frequency in population Low allele frequency Big impact Impact of lesion

Reduced Penetrance due to “genetic background” Allele frequency in population Low allele frequency Big impact Impact of lesion

Mendelian trait - Recessive
Allele frequency in population Homozygote Ok Impact of lesion

Allele frequency in population Homozygote Not Ok Impact of lesion

Allele frequency in population Compound Heterozygote Not Ok Impact of lesion

Oligogenic (few loci) Allele frequency in population Impact of lesion

Multigenic (many loci)
Allele frequency in population Impact of lesion

Next slides: De novo Mendelian Dominant Recessive Multigenic Allele frequency in population Impact of lesion

Case studies

Case studies Not usually inherited (phenotype is too severe) Mendelian
Balanced Translocations Deletion Syndromes Mendelian Nail Patella Syndrome (haploinsufficiency of a transcription factor) Sickle cell (’gain of function’) Cystic Fibrosis (recessive loss of function) ‘Interited’ Cancers (loss of function with a twist) Bardet-Biedl syndrome Distributed additive effects; Multigenic phenotypes Type 2 diabetes Height

De novo Allele frequency in population Impact of lesion
‘Zero’ allele frequency Huge impact, syndromic Impact of lesion

Mitotic Metaphase Chromosomes

Chromosome Banding

All other chromosomes omitted
Chromosome features Normal karyotype All other chromosomes omitted p telomere q telomere centromere Landmarks 9 14 16 9: 138,394,717 bp 14: 107,043,718 bp 16: 90,338,345 bp Lengths p (short) arm q (long) arm Redin et al (2017). The genomic landscape of balanced cytogenetic abnormalities associated with human congenital anomalies. Nature Genetics 49(1):36 doi: /ng.3720

One case Normal karyotype All other chromosomes omitted Normal karyotype from one parent Aberrant karyotype from the other parent 9 14 16 Redin et al (2017). The genomic landscape of balanced cytogenetic abnormalities associated with human congenital anomalies. Nature Genetics 49(1):36 doi: /ng.3720

Another case Normal karyotype All other chromosomes omitted Normal karyotype from one parent Aberrant karyotype from the other parent 2 8 X Redin et al (2017). The genomic landscape of balanced cytogenetic abnormalities associated with human congenital anomalies. Nature Genetics 49(1):36 doi: /ng.3720

Balanced Translocations
Total number of patients = 273 Percentage of patients Sex Male 58.2 Female 41.8 Cosegregation De novo 67.4 Unknown 27.5 Inherited, segregating 5.1 Phenotype Percentage Neurological defects 80.2 Head, neck, or craniofacial defects 51.3 Skeletal defects 42.4 Musculature defects 26.0 Growth defects 23.4 Limb defects 20.9 Abdomen defects 19.8 Eye defects Hearing defects 19.0 Genitourinary defects 18.3 Integument defects Cardiovascular defects 15.0 Respiratory defects 11.0 Neurological defects 80.2 Developmental delay 58.2 Behavior disorders 18.7 Epilepsy Hypotonia 15.0 ASD or autistic features 11.4 High-functioning ASD 1.5 Redin et al (2017). The genomic landscape of balanced cytogenetic abnormalities associated with human congenital anomalies. Nature Genetics 49(1):36 doi: /ng.3720

Deletion syndromes: DiGeorge’s
~10% inherited autosomal dominant More generally: 22q11.2 deletion syndrome Ca. one in 4000 prevalence Size between 1 to 3 Mb ‘Catalyzed’ by repetitive regions 30-50 Genes with variety of functions Syndrome: Craniofacial abnormalities Cognitive impairments Multiple medical challenges ~90% de novo

Allele frequency in population Low allele frequency Big impact Impact of lesion

“The Lmx1 locus is haploinsufficient”
Nail Patella Syndrome Stop! Transcription Lmx1: a transcription factor (Regulates hundreds of genes) UTR CDS Loss of function allele UTR CDS “The Lmx1 locus is haploinsufficient”

Nail Patella Syndrome Lmx1: a transcription factor Transcription
Stop! Transcription Lmx1: a transcription factor (Regulates hundreds of genes) Transcriptional regulatory element Promoter Transcription stop Intron Exon Splice signals Coding sequence Untranslated region Start codon Stop codon UTR UTR CDS CDS CDS UTR

Nail Patella Syndrome Partial deletion downstream of promoter causes either: 1. Transcript degraded 2. If transcript made and spliced (aberrantly), truncated protein Nonsense mutation: C to T at position 306 in coding sequence causes Tyrosine to Stop Codon at position 102 of protein sequence UTR CDS UTR CDS Outright deletion of the whole locus UTR UTR CDS CDS CDS UTR Missense mutation: G to T at position 335 in coding sequence causes Cysteine to Phenylalanine in position 114 of protein sequence

Sickle-cell anemia a b b a a b
Glu to Val at pos 6 in beta hemoglobin causes aggregation

Allele frequency in population Homozygote Ok Impact of lesion

Allele frequency in population Homozygote Not Ok Impact of lesion

Allele frequency in population Compound Heterozygote Not Ok Impact of lesion

Cystic Fibrosis UTR UTR CDS CDS CDS UTR delta508

Cystic Fibrosis UTR CDS UTR CDS UTR UTR CDS CDS CDS UTR delta508

Bardet-Biedl Syndrome
Chromosomal location Gene 1p35.2 CCDC28B 1q43-q44 SDCCAG8 2p15 WDPCP 2q31.1 BBS5 3p21.31 LZTFL1 3q11.2 ARL6 4q27 BBS7 BBS12 7p14.3 PTHB1 8q22.1 TMEM67 9p21.2 IFT74 9q33.1 TRIM32 10q25.2 BBIP1 11q13.2 BBS1 12q21.2 BBS10 12q21.32 CEP290 14q31.3 TTC8 15q24.1 BBS4 16q13 BBS2 17q22 MKS1 20p12.2 MKKS 22q12.3 IFT27

“Inherited Cancers” Daughter Dad

“Inherited Cancers” Mendelian cancer predisposition genes:
Not ok? Mendelian cancer predisposition genes: Cellular recessive / Organismal dominant

“Inherited Cancer” Genes
BRCA1 Mostly breast and ovarian cancer. ~80% lifetime risk Double-stranded break repair and cellular differentiation BRCA2 MLH1 Lynch syndrome. Mostly colorectal cancer. ~80% lifetime risk Small-mutation DNA repair MS6H Li-Fraumeni syndrome. Many types of cancer. ~90% lifetime risk Cell death! P53 Mention Rb (cell cycle) - Retinoblastoma

Multigenic (many loci)
Allele frequency in population Impact of lesion

(Genome-wide) Association Study (GWAS)

Diabetes and Height Diabetes As of 2015: 83 associations
Average risk increase = 13% In 45 of the 83 associations the risk allele is the major allele! >150 small-effect loci “Normal” variation, common variants Explains most of the population A handful of large-effect loci Outlier variation, rare variants Few people Wang X, Strizich G, Hu Y, Wang T, Kaplan RC, Qi Q. Genetic markers of type 2 diabetes: Progress in genome-wide association studies and clinical application for risk prediction. J Diabetes Jan;8(1): doi: / (Review)

Summary: Genetic Architecture of Disease
High-impact variation is strongly selected against and therefore rare Most extreme, syndromic, phenotypes are due to de novo changes with massive impact (e.g., translocations or big deletions) High-impact alleles of single loci usually cause recessive or dominant disease, depending on the affected system Lower-impact variation can persist in the population May collude to cause ‘common’ diseases Is the basis for ‘normal’ phenotypic variation

Cancer (Somatic mutations)

Inherited vs Sporadic Cancers

Cancer Disease of “cell number” that overrides homeostatic mechanisms
Inherited mutations (“germline”) may predispose to cancer by diverse mechanisms, e.g. loss of negative regulator of cell division mutation rate increase Inherited predisposition is rare because those alleles are selected against Sporadic cancers are more common because the germline predisposition allelele is only one of several ‘hits’: Multiple somatic mutations “drive” increase in cell number and are necessary, which is the basis for the: Multistep model of carcinogenesis

Cancer There are many ways to
Take the brakes off ... Put a brick on the accelerator ... ... of cell growth and division Each cell type has a different subset of brakes or potential accelerators There are some that are common across many cell types and many that are highly specific

Two types from genetic perspective
“Oncogene”. Accelerator. Activating mutation puts brick on it. Activating mutations (usually missense or copy number amplification of the whole genomic locus) turn on growth and division programs all the time. No more regulation. Cell and its daughters keep growing and dividing. !!!! !!!! !!!! !!!! !!!! !!!! membrane !! “Tumor suppressor gene”. Brake. Disabling mutation breaks brake. Diverse normal functions: Cell death (p53) Downregulation of growth signals (Rb) DNA repair (BRCA, MSH) Loss of function mutations: All the same mechanisms as previously discussed for inherited loss of function mutations.

Cell growth and cell death
If activating mutation causes cancer: Oncogene If disabling mutation causes cancer: Tumor suppressor Oncogene Tumor suppressor DNA mem Tumor suppressor Oncogene

Normal homeostasis Growth pathway Death pathway

Normal homeostasis – gone awry..
Growth pathway Death pathway .. not by mutation but simply by error in execution ... .. but then cell death kicks in

ON OFF AND Cancer? Growth pathway Death pathway Break the brake
Turn on activator ON OFF Mdm2 p53 Many genes Highly tissue specific Break the brake Turn on brake’s brake AND

Oncogenes and Tumor Suppressors: “Drivers”
Overrepresentation in tumors Measured by whole-genome or exome sequencing and rigorous statistical analysis of *somatic* mutations “Driver gene” Oncogene Tumor suppressor “Driver” (without ‘gene’) Specific mutation or genomic change

Driver Generalities – somatic mutation
Higher than normal somatic mutation frequency may cause cancer somatic ‘mutations’ may be any of the aforementioned genomic changes, from small mutations (point mutations, small indels) to whole chromosome gains and losses Probability of somatic mutation goes up with number of ancestral cell divisions stem cell divisions brick on accelerator but brake is still functional mutagens higher rate of mutation per cell division increase probability that a driver gene is hit

Driver Generalities – tumor types
There are as many cancers as there are cell types Some cell types are more prone to “brick on accelerator” or “break the brake” than others Generally: Blood tumors appear to require fewer ‘hits’ (one driver can be enough) mutations often happen in stem cells Solid tumors many cell types therefore: enormous variety of characteristics some cell types can only convert to a cancer cell if a very specific gene is mutated some cell types can become cancerous if any largish subset (5-10 genes?) of a very large pool of possible genes are mutated (e.g., colon cancer) some cell types only lose p53 and the rest of the drivers are ‘larger’ changes like massive aneuploidy (chromosome gains and losses) and structural variants many cell types require one or two genes to mutate and the rest is less specific colon adenocarcinoma ’requires’ APC loss liposarcomas ‘require’ MDM2 amplification (p53 degragation) almost all require p53 loss

“case” studies (1) small mutations
Data from TCGA / GDAC Broad Institute “case” studies (1) small mutations

Colorectal carcinoma ~80% of patients have APC loss of function mutations APC inactivation is also one of the most common inherited predispositions APC is a brake on an accelerator pathway ~70% p53 loss of function mutations Tens of other genes disrupted (or, more rarely, activated) in 10-20% of patients Hundreds of other genes disrupted (or, more rarely, activated) in <10% of patients

Breast carcinoma ~30% of patients have PIK3CA activating mutations
PIK3CA is a growth accelerator PIK3CA mutations are very often found in nonmalignant proliferations ~50% p53 loss of function mutations Couple of other genes disrupted or activated in 10-20% of patients Tens of other genes disrupted (or, more rarely, activated) in <10% of patients

Lung adenocarcinoma ~30% of patients have KRAS activating mutations
KRAS is a growth regulator KRAS mutations are often found in other cancers too ~30% p53 loss of function mutations Couple of other genes disrupted or activated in 10-20% of patients Tens of other genes disrupted (or, more rarely, activated) in <10% of patients

Prostate adenocarcinoma
Couple of genes (including p53) disrupted or activated in 10-20% of patients Tens of other genes disrupted (or, more rarely, activated) in <10% of patients Gene fusions may be more important

Two blood cancers Chronic myeloid leukemia: BCR-Abl
In-frame fusion gene makes chimeric protein Expressed in BCR pattern: blood cell lineages Protein product has kinase, which is now active and sends persistent cell division signals in the wrong cell type Burkitt’s lymphoma: cMyc-IGH Rearrangement puts B-cell specific regulatory regions upstream of cell cycle maters regulator cMyc Myc now highly expressed in B-cells Turns on the perhaps most powerful cell growth and cell cycle program ON BCR, Chr22 Kina se OFF Abl, Chr9 Kina se Fusion gene ON IGH, Chr8 ON OFF cMyc, Chr14 ON Fusion gene

“case” studies (2) Large changes

Arm-level and ‘focal’ changes
normal HER2 / ERBB2 Regions, ca 100kb to several Mb Colorectal 31 arm, 24 amp, 48 del Breast 28 arm, 28 amp, 42 del Lung 25 arm, 29 amp, 46 del Prostate 25 arm, 28 amp, 35 del PTEN frequently deleted amplified Chromosome arms and similarly scaled changes

Copy number (CNVs) and structural variants (SVs) in BRCA-positive breast cancer
1 2 3 ...... 22 Each row is a patient

Structural variation in a single sarcoma
Mdm2

Cancer summary In most cancers, multiple genes have to be mutated to overcome barriers to unregulated growth or to inactivate cell death; “drivers” Mutations may range from point to whole-chromosome Both copies of tumor suppressor genes have to be inactivated independence of mutations guarantees that the mutations are different; e.g., one point mutation, the other a chromosome arm loss Oncogenes are activated by gain of function mutations, which may only occur in one allele but have to be very powerful, e.g.: gene amplification point mutations that turn on signaling ‘Inherited’ cancers are due to loss-of-function alleles of tumor suppressor genes cell recessive, organism dominant pop is hit already, only mom needs to be inactivated (or vice versa)

Attributions Slide Content Link to file Attribution 19
Limb bud development By Terrasigillata at English Wikipedia, CC BY-SA 3.0, Human embryo By Ed Uthman from Houston, TX, USA - 9-Week Human Embryo from Ectopic Pregnancy, CC BY 2.0 22 Cell By LadyofHats (Mariana Ruiz) - Own work using Adobe Illustrator. Image renamed from Image:Animal cell structure.svg, Public Domain, 29 Actin-myosin interaction By OpenStax - CC BY 4.0 56 Mitosis Onion By Doc. RNDr. Josef Reischig, CSc. - Author's archive, CC BY-SA 3.0, Karyotype By Courtesy: National Human Genome Research Institute - Talking Glossary of Genetics. The pdf version from this web site was used as source for this image file to obtain a better resolution than in the image embedded in the web site, Public Domain cell cycle By Brat Ural - Own work, CC BY-SA 3.0, 57 Cartoon karyotype By Mikael Häggström - References for this description (or part of this) or for the depiction in the file are not provided., Public Domain, Chr17 bands By National Center for Biotechnology Information, U.S. National Library of Medicine - Ideogram is by NCBI's Genome Decoration Page.Data used to describe ideogram is GRCh38.p2 (Genome Reference Consortium Human Build 38 patch release 2 (2014)). Raw data is available at ftp://ftp.ncbi.nlm.nih.gov/pub/gdp/ideogram_9606_GCF_ _550_V1, Public Domain 67 Sickle cell By Diana grib - Own work, CC BY-SA 4.0, 78 GWAS2 By Lasse Folkersen - Own work, CC BY 3.0, 79 GWAS3 By M. Kamran Ikram et al - Ikram MK et al (2010) Four Novel Loci (19q13, 6q24, 12q24, and 5q14) Influence the Microcirculation In Vivo. PLoS Genet Oct 28;6(10):e doi: /journal.pgen g001, CC BY 2.5, 80 GWAS4 By Sanna S et al - Sanna S (2011) Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability. PLoS Genet Jul;7(7):e Epub 2011 Jul 28. doi: /journal.pgen , CC BY-SA 2.5

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