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Genomics and Precision Medicine #
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Alternate Name
유전체학과 정밀의학
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Start Date

Coursera 코스 "유전체학과 정밀의학"

Table of Contents

Week 1: Human Genome Strcture, Function, and Variation #

The structure of the human genome and how things work #

Human genome

How is gene expression regulated?

Human genetic variation - structural variation #

Big changes of chromosome

  • Translocation: 1/600 newborn
  • CNVs: deletions or duplications
    • 68% of CNVs overlap with genes
    • recessive CNVs
    • Size and location are pathogenic

Human genetic variation - single nucleotide variants #


  • Random mutations arise natually during cell division
  • gemline and somatic
  • 50 to 100 de novo mutations in average newborn


  • Ancestral and minor allele
  • Allele frequency, rare(<0.5%, 60M no of SNVs in human pupulation), low frequency(0.5-5%, 20M), common variants(>5%, 10M)
  • Average person has ~4M DNA variants
  • Minor allele frequency of ethnic group tells the human migration
  • Minor > 50% means that allele is beneficial in that's environment

Consquences of single nucleotide variants in genes #

Types of SNVs

  • Synnymous (slient)
  • Non-synomous (missense)
  • Premature stop (nonsense)
  • Frameshift

Average person ~350 loss of function SNVs

Architecture of human genetic variation #


  • Only partial combination of SNVs
  • Recombination breaks up haplotype
  • Linkage disequilibrium (LD)
  • LD allows prediction of alleles

Week 2: Applying Genomics to Medicine #

Background and Mendelian inheritance of disease #

Meldelian disorder

  • present in ~2-3% of all newborn
  • OMIM (Online Mendelian Inheritance in Man)

3 major inheritance patterns

  1. Autosomal dominant: 50%
  2. Autosomal recessive
  3. X-linked

It's not so simple

  • New mutation
  • Mosaicism - The exception to every cell having the same DNA
  • Decreased Penetrance - Disease genotype without phenotype
  • Disease in Carriers of Recessive Disorders - Not always "silent"

Increased Disease Risk in Carriers

Genetic testing for Mendelian diseases #

Matching the test to the variant

. Mutations affected 1 - ~100bp Mutations affecting > ~100bp
Specific to particular genes Targetted gene sequencing / SNP for genotyping Deletion/Duplication scanning of one or more genes
Whole genome approach WES / WGS Cytogenomic array to scan genome for CNVs

Newborn Screening #

A latent or early symptomatic stage exists during which intervention imporves outcomes.

Successful newborn screening requires:

  1. Acquiring the samples and performing the test
  2. Having a system for notifying families whose infants test positive
  3. Providing follow-up definitive testing and, if confirmed
  4. Instituting appropriate management and services

It's phenotype based

  • Deafness
  • Cystic fibrosis
  • Hypothyroidism
  • Galactosemia
  • Hemoglobinopathies
  • Fatty(organic, amino) acid disorder: >40 disorders by Tandem Mass spectrometry
  • Congenital Adrenal Hyperplasia
  • Biotinidase deficiency
  • Severe combined immunodeficiency (T-cell lymphopenia)

Carrier Testing #

Goal is to identify asymptomatic carriers with no family history of disease

Ethnicity based

Ethnicity Target disease
Ashkenazi Jewish Tay Sachs disease, Canavan disease, Cystic fibrosis, familial dysautonomia
Louisiana Cajun, Fr Canadian Tay Sachs disease
Caucasian Cystic fibrosis
Africans, African American Sickle cell anemia, Beta thalassemia
Southeast Asian Alpha thalassemia
Mediterranean Beta talassemia

Week 3: Next-gen sequencing for solving diagnostic dilemmas #

Whole Genome Analysis #

A genome-wide search for disease-causing variants

Clinical interpretation of variants #

Typical individual differences from reference

  • ~5-10M SNVs (varies by population)
    • 40-100k SNVs in coding exons
      • 10-12k synonymous (no amino acid change)
      • 8-11k non-synonymous, in 4-5K genes
  • 200-500k indels (1-1000 bp, varies by population)
    • ~150 in-frame indels in exons
    • ~200-250 shift the reading frame of an exon
  • 500-1000 CNVs (>1000 bp)

Annotation of variants

  • Basic
    • Gene name (if in a gene)
    • Chromosome location of the change (position in reference genome)
    • Location of the change within the mRNA/cDNA
    • Location of the amion acid change in the protein
    • Effect on protein
  • Variant dependent methods
    • Allele frequency in general population
  • Disease-dependent methods

Predicting the effect of a variant is CHALLENGING

  • Probably damaging
    • Stop-loss
    • Stop-gained
    • Frameshift
    • Splice disruptor
  • Possibly damaging
    • Non synonymous
    • In-frame In/Del
  • Likely not damaging
    • 5'/3' UTR
    • Synonymous
    • Intergenic
    • Intronic
    • Non-coding genes

Predicting the effect of non-synonymous variants

  • Evolutionary conservation
  • Protein structure
  • Amino acid properties

Typical classification scheme

  • Known pathogenic
  • Likely pathongenic
  • Variant of unknown significance (VOUS or VUS)
  • Likely benign
  • Benign

Using NGS for diagnostic dilemmas #

Dignostic Odyssey - delay and expensive

Intractable inflammatory bowel disease (WES)

  • ~16,000 total variants, ~1,500 were "novel"
  • ~7,100 non-synonymous substitutions, premature stops, ~1,100 were novel
  • 136 variants fit autosomal recessive(AR) or X-linked(XL), 1 variant altered a conserved amino acid.

In a trio, if child affected,

  1. Dominant: One copy of gene affected
  2. Ressive: Both copies of gene affected
    • Homozygote
    • Compound heterozygote
    • X-linked
  3. New mutation

Practical Aspects #


  • Unanticipated Pathogenic Variants

Week 4: Methods for Dissecting the Genetic Basis of Complex Diseases #

Background #

Complex diseases

  • Mendelian: clear inheritance pattern, but complex no clear
  • Phenocopies: Non geneic form of disease
  • Incomplete penetrance: Not all genetically susceptible people develop disease
  • Variable expressivity: Same genetic factor causes multiple phenotypes
  • Genetic heterogeneity: Mutations in different genes can lead to same disease

Symptoms suggestive of a genetic condition

  • Earlier age at onset of disease than expected
  • Condition in the less often affected sex
  • Family history with multiple generations affected
  • Disease in the absense of known risk factors

GWAS methods #

GWAS approach

  1. Genotyping: haplotype tagging
  2. Study population: Cohort, Case control, Confounding and bias
  3. Analysis and interpretation: Significance testing, Measures of effect, External validity

GWAS platform, SNP array

  • 1,000,000 SNPs in one experiment
  • Direct and indirect capture of 'all' common variants by using 'tag' SNPs
  • Coverage of SNP array
    • Asians/Europeans: 67-89%
    • African: p46-66%

Study designs

  • Common observational studies
    • Cohort
    • Case-control
  • Common biases
    • Confounding
    • Misclassification bias

Race is a common confounder in GWAS

GWAS analysis #

Armitage trend test

Hypothesis testing and p values.

  • What does a p<0.05 mean?: <5% probability that the observation is due to chance (i.e. a false positive) --> "statistically significant"

Need for correction for multiple testing

Manhattan plot showing genome-association

Resons for association

  • True association (true positive)
  • False association (false positive)

Properties of a valid association

  • Not due to chance
  • Free of bias
  • Reproducible

GWAS interpretation #

Calculation of risk

  • Risk = incidence of disease
  • Can be calculated from cohort studies

Calculation of a relative risk

  • Relative risk = ratio of two risks
  • Measures the 'effect' of the variant on risk of disease
  • xx fold increased risk of disease

In case-control study, we can calculate the ODDS of disease

  • Odds ration = ratio of two odds
  • xx fold increased odds of disease

Odds ratio vs. relative risk

  • don't do a lot of epidemiological -> same thing
  • rare disease -> approximate each other
  • common disease -> Odds ratios overestimate relative risk

What do we know about the genetics of common, complex diseases? #

Missing heritability, limitaions of GWAS... what we're missing

  • Common SNPs not tagged well
  • Rare variants
  • Other types of variants (CNVs, etc)
  • Epistatic effects (Epistasis, gene-gene interaction)
  • Effets of gene x environment interaction

Week 5: Clinical applications of genomics - Predictive Testing for Common, Complex Diseases #

Background, Analytic and Clinical Validity #

Predictive (predisposition) testing: predict whether a person likely to get disease

Genetic tests for complex diseases

ACCE Model: How the variance is ready for clinical use ( )

Analytic validity: How accurately and reliably the laboratory assay measures the genotype (sensitivity and specificity), CLIA

Clinical validity: How consistently and accurately the test detects or predicts the disease

  1. Clinical sensitivity: Among people with disease, how many test positive
  2. Clinical specificity: Among people without disease, how many test negative
  3. Positive predictive value (PPV): Among those with a positive test, how many will develop disease (penetrance) - BRCA1 mutation PPV is ~60%
  4. Negative predictive value (NPV): Among those with a negative test, how many will remain disease-free - NLA-DQ test has 100% NPV for Celiac disease

Ciinical utility

  • Does it improve health outcomes?
  • Balance of benefits and risks

Case Scenario - Coronary Artery Disease Example #

Coronary artery disease (관상동맥질환)

Key concepts

  • A genetic test is not necessarily measuring the causal variant, but rather, a genetic marker that may be linked to the causal variant.
  • Results of genetic test may have utility in motivating changes in risk factors or improve medication adherence.
  • In some cases, family history remains a better predictor of disease than any specific genetic test.
  • In the U.S. most genetic tests are considered laboratory developed tests (LDTs) and are currently not regulated by the FDA.

Case Scenario - Diabetes Example #

Diabetes mellitus

Key concepts

  • Multi-marker genetic risk panels should be interpreted with causion
  • Genes identified to date poorly explain genetic underpinnings of disease
  • Several resources exist to find objective information/guidance on tests
  • Genetic testing should not distract from modifiable risk factors (diet, lifestyle, medication)

Case Scenario - Alzheimer's Example #

Alzheimer's disease

  • GINA - what it does and doesn't cover
  • Personal utility is increasingly being recognized as rationale for genetic testing
  • Where to find information about available tests
  • Utility of targeted variant analysis versus sequencing entire coding region

Week 6: Clinical applications of genomics - Pharmacogenomics #

Background - Genetic factors affecting pharmacokinetics and pharmacodynamics #

Adverse Drug Reactions (ADR): unintended and noxious, although individually rare, are collectively common

Pharmacogenomics: using a patient's genomics information to improve the efficacy and/or reduce the side effects of drugs

Pharmacokinetics: how the drug concentration changes as it moves through the body (ADME)

  1. Absorption: orally, intravenously, inhaled
  2. Distribution: circulatory system, transporters
  3. Metabolism: enzymes
  4. Elemination: urine, feces, breath

Many drugs are metabolized by the polymorphic Cytochrome P450 enzymes.

  • UM: Ultrarapid metabolizer - extra copies
  • EM: Extensive metabolizer (normal)
  • IM: Intermediate metabolizer - one non-functioning allele
  • PM: Poor metabolizer - two non-functioning alleles

Codeine metabolism

  • codein ->(CYP2D6)-> morphine -> target
  • PM can not convert codein to morphine, so higher dose required

Warfarin metabolism

  • Active compound -> effect to target ->(CYP2C9)-> inert metabolite -> elimination
  • PM can not convert to inert, so lower dose required (due to it's toxic side effects)

Pharmacodynamics: how the drug exerts its effect on the body (potency)

VKORC1, target of courmatin derivatives (e.g. Warfarin): varnant upstream leads to reduced expression

Off target effects - Avacavir hypersensitivity

  • Drug affects target, but also interacts with unintended target
  • Abacavir binds to host HLA-B in patients with HLA-B*5701 genotype
  • Patients shoud avoid this drug

What pharmacogenetic tests are available? #


  • 158 drug-biomarker pairs
  • 12% of 385 drugs approved 1998-2012
  • Not all PGx markers in drug label are clinically valid
  • Tumor markers is 34%
  • CYP450s is 34% (CYP2C9, CYP2C19, CYP2D6)
  • HLA genes increasingly associated with severe adverse events

PharmGKB - PGx biomarker levels

  • Testing required
    • HLA - Carbamazapine
    • CFTR - lvacaftor
    • CYP2D6 - Tetrabenazine
    • OTC, POLG - Valproic acid
    • CYP2D6 - Pimozide
  • Testing recommended
    • HLA - Abacavir
    • TPMT - Azathioprine
    • CYP2C19 - Clopidogrel
    • CYP2D6 - Dextromethorphan/quinidine
  • Actionable
  • Informational

Is my patient a candidate for pharmacogenomics testing? #

CFTR genotype-devendent efficacy of Ivacaftor

  • Cystic fibrosis 85% is ▵508F
  • W1282X
  • G551D (4%) <- Ivacaftor effective only

Where to find?

  1. PLoS Currents: Clinical validity and utility
  2. GAPP Knowledge Base

Evaluating PPV and NPV of test

  • Rare disease or outcomes can never lead to high PPV, no matter how good the sensitivity/specificity of the test

Where to get testing done and how to interpret the result? #

PharmGKB: Manually curated pharmacogenomics knowledge base including information from drug label, clinical testing labs and dosing guidlines.

GTR (Genetic Testing Registry)

CPIC (Clinical Pharmacogenomics Implementation Consortium)

Week 7: Clinical application of genomics - cancer management #

Background and cancer biology #

Cell division (mitosis) is regulated by cell-cycle controllers, growth factors and their receptors that induce or inhibit proliferation --> Cancer cells have acquired ability to bypass growth signalling and lose growth control.

Mutated genes

  1. Oncogenes
  2. DNA repair enzymes
  3. Tumor suppressors
  4. Chromatin modifiers


  • Normally involved in cell cycle and growth signalling
  • 'On' switch
  • When mutated become Oncogenes
  • Gain of function
  • Promote proliferation

DNA repair during replication and exposure to carcinogens

  • Excision repair
  • Recombinational repair
  • Mismatch repair

Tumor suppressors

  1. Slow cell growth
  2. 'Off' switch
  3. Signal apoptosis of damaged cells
  4. Mutated - loss of function

Chromatin modifiers - Epigenetics

  • Mutations in epigenetic machinery
  • Global hypomethylation
  • Promoter specific hypermethylation

Hereditary cancer #

Familial Recurence Risk

  • High risk: Testicular cancer, Thyroid cancer (8~9 times)
  • Others 1~2 times

Features of hereditary cancers

  • In the individual patient
    • Multiple primary tumors in the same organ or different organs
    • Bilateral primary tumors in paired organs or multifocality within a single
    • Yonger-than-usual age at tumor diagnosis
    • Tumors with rare histology
    • Tumors occurring in the sex not usually affected
  • In the patient's family
    • First-degree relatives with same tumor history

Genetic basis of hereditary breast cancer

  • First degree family history increases risk 2-fold
  • Only 5-10% of breast cancer is hereditary
  • Inheritance is complex at the disease level, but some individual genes behave in Medelian fashion
  • BRCA1/BRCA2 - Hereditary Breast and Ovarian Cancer

Genetic basis of hereditary colon cancer

  • Family history increases risk 2-4 fold
  • FAP (Familial adenomatous polyposis): Mutations in APC gene
  • HNPCC - Lynch Syndrome

Knudson's 2 Hit hypothesis for tumor suppressor genes

  • Tumor suppressors are recessive at cellular level
  • Inherited cancers behave as dominant trait

Should my patient undergo genetic testing?

Disease Gene Models
Lynch syndrome MLH1, MSH2, MSH6 .
Brease cancer, Ovarian cancer BRCA1, BRCA2 .
Melanoma CDKN2A .
Pancreatic cancer . .
L-Fraumeni syndrome TP53 .
Cowden syndrom PTEN .

Genetic testing for known cancer susceptibility genes

  • A negative test result is no guarantee that cancer WILL NOT develop
  • A positive test result is no guarantee that cance WILL develop
  • Risk management strategies for a postive test
    • Surveillance
    • Hormone therapy
    • Lifestyle changes
    • Propylatic surgery

Tumor genetic landscape #

Measuring tumor gene expression

  • One or a few genes at a time: e.g. HER2, ER, PR
  • Genome-wide measures (Expression microarrays, RNA-seq)

Measuring somatic mutations in tumors

  • Sequencing DNA from tumor-normal pairs

COSMIC database

How many genes are mutated in the average solid tumor?

  • 33-66 genes in an average solid tumor
  • Mostly SNVs
  • Variable by cancer type

Drivers and passengers

  • Driver mutations provide a selective growth advantage
  • Passenger mutations have no effect on neoplastic growth
  • How to identify driver genes?
    • Mountains and hills (frequency)
    • Patterns

How many driver genes exist?

  • 3284 tumors sequenced
  • 294,881 mutations reported
  • 125 mutation driver genegens identifid
    • 71 tumor suppressor genes
    • 54 oncodones

Metastatic disease

Clinical applications - prognosis and treatment response #

Breast cancer prognostic markers

  • Oncotype DX
    • A 21-gene expression score (16 prognostic genes and 5 housekeeping)
    • Scale of 0-100, strata of low, intermediate or high risk
    • Predicts 10-year risk of distant recurrence in Estrogen Receptor (ER) positive breast cancers (may benefit from adding chemo to their hormone treatment)
    • Predicts responsiveness to CMF (Cyclophosphamide, Methotrexate and 5-Fluorouracil) chemotherapy
  • MammaPrint
    • A 70-gene expresison profile
    • Regardless of estrogen receptor (ER) status, with tumors of less than 5 cm
    • Distinguishes those predicted to have good prognosis (no relapse within 5 years) from poor prognosis (relapse within 5 years)

HER2-positive breast cancer

  • The HER2 gene is amplified in 20% of breast cancers
  • Referred to as HER2-positive cancers
  • Make more HER2 protein than HER2-negative cancers
  • The extra HER2 protein causes increased signal pathway activation, which contributes to the uncontrolled growth and survival of these cancers.

Chronic myelogenous leukemia (CML)

  • Blood cancer caused by reciprocal translocation (Philadelphia Chromosome), resulting in oncogenic BCR-ABL gene fusion
  • BCR-ABL found in 95% of CML
  • Multiple targeted medications have been created which specifically inhibit this oncogene (imatinib, dasatinib, nilotinib)
  • Previously median survial 4 years, now 20-25 years

KRAS mutations in Colorectal cancer

  • Anti-EGFR therapies cetuximab (Eritux) and panitumumab (Vectibix) don't work in KRAS-mutated cancers

Vemurafenib and malignant melanoma

  • Mutant BRAF (~50% of melanomas)
  • BRAF inhibitors (Vemurafenib) block constitutive activation of downstream pathway to decrease cellular proliferation

Different cancers may share common genetic alterations

  • 60% of melanomas respond to the BRAF inhibitor vemurafenib
  • 100% of hairy cell (HC) leukemias response to BRAF inhibitor
  • 5-10% colorectal cancers no response to BRAF inhibitor

Challenges in Molecular Profiling fo rTargeted Treatment

  • Bioinformatics
  • What does it mean when a mutation normally associated with an inherited cancer is found in a tumor sample?
  • Tumor heterogeneity
  • Tumor targets identified but therapy not approved or reimbursed for that indication

Advances in cancer genomics

  • Continued development of targeted treatments
  • New treatment paradigms - immunotheraphy
  • Improved diagnosis - liquid biopsies

Hyungyong's retrospective #

제출 시간을 잘못 알고 있어서 퀴즈 제출 하나 놓치고는 이수 못하는 거 아닌가 걱정이 많았다. (PM 12:00은 밤이 아니라 낮임) 다행히도 간신히 통과함. Certificate 임상에서의 Persnolized medicine에 대한 체계적인 소개를 받을 수 있었다는데 큰 의의가 있었다. 특히 암유전체에 대해 몰랐던 것들이 많았음. Coursera 시스템도 꽤 괜찮았는데, 특히 이수 후, 클릭 한번으로 Linkedin 프로필에 바로 등록되는 건 매우 이채로왔다. 기분도 매우 좋음. 여세를 모아 꾸준히 실력향상~ --Hyungyong Kim,

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