Missing Heritability 2023 PDF

Missing Heritability 20 November 2023 15:46 Main Ideas Notes Notes Puzzle of complex psychiatric and behavioural genetics ○ Findings from molecular genetic studies account only for a small proportion of the variance attributable to genetic effects a s estimated by quantitative genetic (twin and adop...

Missing Heritability 20 November 2023 15:46 Main Ideas Notes Notes Puzzle of complex psychiatric and behavioural genetics ○ Findings from molecular genetic studies account only for a small proportion of the variance attributable to genetic effects a s estimated by quantitative genetic (twin and adoption) studies Are heritability estimates wrong? ○ Potentially no, because: ▪ both twin and adoption methods produce comparable estimates ▪ For most traits these have been robustly replicated several times over, in different populations If we have heritable traits, why cannot we reliably identify genes associated with these traits? ○ Hundreds or even thousands of genes are involved - each with such small effect that most of the genome -wide association studies (GWAS) to date have not been sufficiently powered to pick them up GWA: Small effect sizes ○ Largest effect sizes for any genetic variant are very small ▪ e.g. 1% effect size for FTO gene and body weight – and this is uncommonly large! ○ Power to exclude large effect sizes ▪ There are no genes that have large effects that impact the whole population ▪ i.e. gene variants that are common, do not individually account for a large proportion of variance ○ GWA: Difficult to find associations; difficult to replicate ▪ Very large samples needed 'missing heritability' gap ○ Heritability of most traits ~ 40-60% ○ Largest GWA associations ~ 1% per SNP, 10 -15% of variance predicted by polygenic scores ○ Why does putting all known SNP associations together account for only a portion of heritability? ▪ Still left with ‘missing heritability’ ○ Problems in accounting for the ‘missing heritability’ have spurred the development of a new methodology for identifying gene variants in human populations called genome-wide complex trait analysis (GCTA) Genome-wide Complex Trait Analysis (GCTA) ○ Estimates genetic influence from DNA in any large sample of unrelated individuals - SNP heritability Notes As sample size increases, ability to reliably detect effects of SNPs has increased If scientists had not pooled resources and investigated larger samples, would have erroneously concluded that common SNPs account for only about 3% of variation on IQ and EA. GCTA vs Twin estimates of genetic influence in TEDs ○ GCTA SNP heritability ~ half of twin study heritability for cognitive abilities 1 ▪ This give us insights into genetic architecture of a trait: ▪ limits of additive effects of common variants – once we are confident that our studies are adequately powered Univariate genetic analysis ○ SNP heritability ○ Identifies the degree of variance accounted for by SNPs ○ Extracts a tiny signal from a lot of noise ▪ Large confidence intervals ▪ Need extremely large sample sizes ○ Underestimates heritability because limited to: ▪ What is sampled on an SNP microarrays (microarrays can vary in coverage) ▪ Current DNA arrays use common SNPs □ Not rare variants ▪ Additive genetic effects □ No gene-gene interaction or gene-environment interaction modelled ○ GCTA cannot estimate full h2 if there are rare variants and nonadditive effects ▪ covers only common SNPs ▪ covers only additive genetic effects What are rare variants? ○ A genetic variant that alters gene function, but affects only a small proportion of the population ▪ Can have a large effect for those affected (larger impact than SNP) ○ Genetic mutations that occur post conception (de novo) can be among rare variants that contribute to twin h2 estimates Future of genomic research for complex traits ○ We know that genes are important ▪ DNA sequence variation ▪ Many common genes of very small effect size ▪ Also rare variants and non-additive effects ○ Genetics is the fastest-moving area of science ○ The missing heritability gap will be narrowed over the coming years Comparing twin and SNP heritability estimates gives us useful information ○ If SNP heritability estimate results are similar to twin results: ▪ This suggests that heritability estimates from twin studies for a particular trait are entirely due to additive effects of common variants ▪ Tells us to what extent GWAS using current arrays can identify genes ○ If we have large samples, yet SNP h2, as derived from GCTA, only accounts for a small proportion of the twin heritability estimates… ▪ Then we need to look beyond current GWAS techniques when we ‘hunt’ for genes ▪ i.e. additive effects of common SNPs alone do not explain all of the genetic variation driving individual differences on a trait Twin study heritabilities ○ GCTA confirms twin studies: moderate genetic influence for diverse abilities ○ GCTA heritability (provided the study is well powered) is the lower limit of heritability ○ The critical 'missing heritability' for GWA is GCTA heritability ○ ○ It is not possible for common SNPs to account for more variance on a trait that what is provided via a GCTA analysis PSYC0036 Genes and Behaviour Page 1 PSYC0036 Genes and Behaviour Page 2 Summary PSYC0036 Genes and Behaviour Page 3 PSYC0036 Genes and Behaviour Page 4