ComplexDiseaseGenetics_David_Morris_mbbs_2024.pdf

Complex Disease Genetics Dr David Morris Department of Medical and Molecular Genetics [email protected]  Aims: To introduce the concept of complex genetic disorders which, in contrast to single gene disorders, are infl...

Complex Disease Genetics Dr David Morris Department of Medical and Molecular Genetics [email protected]  Aims: To introduce the concept of complex genetic disorders which, in contrast to single gene disorders, are influenced by a large number of factors, both genetic and environmental. Learning objectives – Introduction to the contribution of genetic variation to common disease; – Case studies of the genetic contribution common complex disorders, including Alzheimer disease, breast cancer and coronary disease – The use of genetics in predicting risk of complex diseases.  What is a complex disease? All traits (including diseases) can be plotted somewhere on the triangle below Simple (“monogenic”, “Mendelian”) e.g. Breast cancer (BRCA1) Complex See “Genetics of Complex Disease” Garland Science. Section 2: Defining Complex Disease  Mendelian disease Cystic Fibrosis Disease affecting primarily the lungs, but also the pancreas liver and intestine – Lung disease results from clogging of the airways due to mucus build-up, decreased mucociliary clearance, and resulting inflammation – Expected life expectancy ~40 years (better than it used to be) Pattern of segregation in families indicates an autosomal recessive Mendelian disease.  Complex Diseases Raised risk in families – but increase in risk may be slight compared with population risk No clear mode of inheritance Multiple genes contribute to disease risk Environmental effects may also contribute Gene-gene, gene-environment interactions Examples Inflammatory bowel disease, depression, schiozphrenia, multiple sclerosis, asthma, rheumatoid arthritis, diabetes, SLE(Lupus), heart disease Most diseases with a considerable public health impact have a genetic component  Assessment of genetic contribution to a disorder Twin studies MZ – comparing disease concordance in monozygotic (MZ) twins and dizygotic (DZ) twins – Partitions trait into genetic, common environment and ? (individual) environment components DZ – Estimate heritability ‘the proportion of the total variance of a trait caused by additive genetic factors’ – e.g. The heritability of schizophrenia is ~80%. ? Family studies – Is there an increased risk to first degree relatives of cases compared to the population risk? ? Do most diseases have a genetic contribution? Higher concordance in monozygotic than dizygotic twins Implies genetic component contributes to disease risk  Spectrum of genetic effects Rare Common variants variants Mendelian Mix of common and Common variants disorders rare variants Polygenic Breast cancer susceptibility to Rare variants, (BRCA1, most disorders single gene BRCA2) disorders Cardiovascular Alzheimer disease disease Huntingto ALS n’s disease Rheumatoid Epilepsy arthritis Cystic fibrosis Autism Schizophrenia SMA Depression  1. Breast cancer Major genes: BRCA1, BRCA2 Mutations in these genes are rare, with many different mutations in each gene High penetrance: – 65% penetrance by age 70 for BRCA1, 45% for BRCA2. – Lower penetrance for ovarian cancer In European populations, approximately – 1 in 1000 people are carriers of a BRCA1 mutation, and – 1 in 800 people are carriers of a BRCA2 BRCA1 and BRCA2 account for 500,000 SNPs tested P-value threshold for significance: 5e-08  A Brief History of Complex Disease Mapping Candidate gene association studies (not much genotyping) – 1950’s onwards – Early studies dictated by knowledge of “classical markers” (e.g. blood groups, HLA types) Linkage studies (low resolution genotyping) – 1980’s onwards – Genome-wide linkage (1990’s) dictated by knowledge of genetic linkage maps of 100’s of microsatellite markers, and PCR methods for typing them Genome-wide association studies (higher resolution genotyping) – 2005 onwards – Dictated by knowledge of millions of SNP markers, and high- throughput methods for typing them Next Generation Sequencing studies (GWAS-by-sequencing) – 2010 onwards – Dictated by knowledge of 1000’s of genomes, and high-throughput methods for sequencing them – Tests each causal variant directly  Example: coronary artery disease (CAD) (Nikpay M et al., Nature Genetics 2015) Genome-wide association study of – 60,801 CAD cases and – 123,504 controls – Meta-analysis of 48 studies Identified 58 SNPs associated with CAD Each SNP increases risk only slightly CAD risk is cumulative effect across SNPs Many further SNPs to be identified for CAD  CAD GWAS results (Nikpay et al., 2015) Genotyping >500K SNPs, need very stringent p-value for significance P=5 x 10^-8 used as threshold (p=0.00000005) SNP rs17087335 on chromosome 4 is significantly associated with CAD SNP is between genes REST and NOA1 SNP alleles G, T Allele T has frequency 0.21 Allele T increases risk of CAD, with odds ratio 1.06 SNPs associated with polygenic diseases have very small effect  CAD genetic associations And Polygenic risk scores (PRS) Over 200 SNPs have been associated with CAD (PMID: 36474045) Many, many more associated SNPs likely to be detected Risks conferred by SNPs are low, and not highly predictive for risk of disease Additional rare variants in genes such as LDLR, APO5 Not detected by these genome-wide studies of common variants Account for small proportion of genetic component of CAD Can we combine information across SNPs to estimate disease risk? Polygenic risk scores (PRS) are now becoming popular, but not yet showing clinical utility as for most traits we have not yet explained all the heritability and we do not understand the environmental risk fully Using epidemiological studies, we can examine how genetic factors and environmental factors (healthy living) contribute to risk of coronary disease Genes Environment Genes + Environment  Coronary artery disease (Khera et al., New Engl J Med, 2016; PMID 27959714) (Khera et al., Nature Genetics, 2018; PMID 30104762) Do genetic and environmental risk factors increase risk of coronary artery disease events? – Events: myocardial infarction, coronary revascularization, death from coronary causes Genetics: Low genetic risk High genetic risk – Polygenic risk scores – Summed genetic risk across 52 variants associated with heart disease Healthy lifestyle: score 1 for each factor: – Current smoking Environmental risk – Obesity (BMI > 30) score: – Unhealthy diet Low risk: 0-1 – Physical activity less than once weekly Intermediate: 2 High risk: 3-4  CAD relative risk from genetic + environmental factors Environmental risk High (Score = ≥ 3) 1.82 2.54 3.50 Intermediate (Score =2) 1.16 1.54 2.24 Low (Score ≥ 0-1) 1 1.33 1.90 Low genetic Intermediate High genetic risk genetic risk risk (lowest 20%) (mid 60%) (highest 20%) Genetic risk score  CAD relative risk from genetic + environmental factors Environmental risk High (Score = ≥ 3) 1.82 2.54 3.50 Intermediate (Score =2) 1.16 1.54 2.24 Low Baseline (Score ≥ 0-1) 1 1.33 1.90 Low genetic Intermediate High genetic risk genetic risk risk (lowest 20%) (mid 60%) (highest 20%) Genetic risk score  CAD relative risk from genetic + environmental factors Environmental risk High (Score = ≥ 3) 1.82 2.54 3.50 Intermediate (Score =2) 1.16 1.54 2.24 Low (Score ≥ 0-1) 1 1.33 1.90 Low genetic Intermediate High genetic risk genetic risk risk (lowest 20%) (mid 60%) (highest 20%) Genetic risk score  Genetic and environmental risk factors Genetic and environmental risk factors work together to predict coronary disease Cumulative effect across both sources These risks are not predictive enough for an individual’s risk, but gives useful population level information Low genetic risk – Poor environment will still increase risk High genetic risk – Can protect against CAD by ensuring healthy environment  Summary A complex disease/trait involves many genetic loci AND the Environment – One locus only explains a small % of the disease risk Terms such as penetrance, Odds ratios and relative risk are important. Complex disease genetics is challenging… – Mapping is difficult (You may observe an associated marker, but which gene and which pathway is involved???) – We are still a long way from a complete understanding of all genetic and environmental factors for any given complex disease – Most associations are for common variants with small effects …But progress is being made – The current number of known risk loci for many diseases is now > 100 – The molecular aetiology is now much better understood – New drugs/treatments are taking advantage of this new knowledge