missing-heritability   21

Frontiers | Can We Validate the Results of Twin Studies? A Census-Based Study on the Heritability of Educational Achievement | Genetics
As for most phenotypes, the amount of variance in educational achievement explained by SNPs is lower than the amount of additive genetic variance estimated in twin studies. Twin-based estimates may however be biased because of self-selection and differences in cognitive ability between twins and the rest of the population. Here we compare twin registry based estimates with a census-based heritability estimate, sampling from the same Dutch birth cohort population and using the same standardized measure for educational achievement. Including important covariates (i.e., sex, migration status, school denomination, SES, and group size), we analyzed 893,127 scores from primary school children from the years 2008–2014. For genetic inference, we used pedigree information to construct an additive genetic relationship matrix. Corrected for the covariates, this resulted in an estimate of 85%, which is even higher than based on twin studies using the same cohort and same measure. We therefore conclude that the genetic variance not tagged by SNPs is not an artifact of the twin method itself.
study  biodet  behavioral-gen  iq  psychometrics  psychology  cog-psych  twin-study  methodology  variance-components  state-of-art  🌞  developmental  age-generation  missing-heritability  biases  measurement  sampling-bias  sib-study 
december 2017 by nhaliday
Estimation of effect size distribution from genome-wide association studies and implications for future discoveries
We report a set of tools to estimate the number of susceptibility loci and the distribution of their effect sizes for a trait on the basis of discoveries from existing genome-wide association studies (GWASs). We propose statistical power calculations for future GWASs using estimated distributions of effect sizes. Using reported GWAS findings for height, Crohn’s disease and breast, prostate and colorectal (BPC) cancers, we determine that each of these traits is likely to harbor additional loci within the spectrum of low-penetrance common variants. These loci, which can be identified from sufficiently powerful GWASs, together could explain at least 15–20% of the known heritability of these traits. However, for BPC cancers, which have modest familial aggregation, our analysis suggests that risk models based on common variants alone will have modest discriminatory power (63.5% area under curve), even with new discoveries.

later paper:
Distribution of allele frequencies and effect sizes and their interrelationships for common genetic susceptibility variants: http://www.pnas.org/content/108/44/18026.full

Recent discoveries of hundreds of common susceptibility SNPs from genome-wide association studies provide a unique opportunity to examine population genetic models for complex traits. In this report, we investigate distributions of various population genetic parameters and their interrelationships using estimates of allele frequencies and effect-size parameters for about 400 susceptibility SNPs across a spectrum of qualitative and quantitative traits. We calibrate our analysis by statistical power for detection of SNPs to account for overrepresentation of variants with larger effect sizes in currently known SNPs that are expected due to statistical power for discovery. Across all qualitative disease traits, minor alleles conferred “risk” more often than “protection.” Across all traits, an inverse relationship existed between “regression effects” and allele frequencies. Both of these trends were remarkably strong for type I diabetes, a trait that is most likely to be influenced by selection, but were modest for other traits such as human height or late-onset diseases such as type II diabetes and cancers. Across all traits, the estimated effect-size distribution suggested the existence of increasingly large numbers of susceptibility SNPs with decreasingly small effects. For most traits, the set of SNPs with intermediate minor allele frequencies (5–20%) contained an unusually small number of susceptibility loci and explained a relatively small fraction of heritability compared with what would be expected from the distribution of SNPs in the general population. These trends could have several implications for future studies of common and uncommon variants.

...

Relationship Between Allele Frequency and Effect Size. We explored the relationship between allele frequency and effect size in different scales. An inverse relationship between the squared regression coefficient and f(1 − f) was observed consistently across different traits (Fig. 3). For a number of these traits, however, the strengths of these relationships become less pronounced after adjustment for ascertainment due to study power. The strength of the trend, as captured by the slope of the fitted line (Table 2), markedly varies between traits, with an almost 10-fold change between the two extremes of distinct types of traits. After adjustment, the most pronounced trend was seen for type I diabetes and Crohn’s disease among qualitative traits and LDL level among quantitative traits. In exploring the relationship between the frequency of the risk allele and the magnitude of the associated risk coefficient (Fig. S4), we observed a quadratic pattern that indicates increasing risk coefficients as the risk-allele frequency diverges away from 0.50 either toward 0 or toward 1. Thus, it appears that regression coefficients for common susceptibility SNPs increase in magnitude monotonically with decreasing minor-allele frequency, irrespective of whether the minor allele confers risk or protection. However, for some traits, such as type I diabetes, risk alleles were predominantly minor alleles, that is, they had frequencies of less than 0.50.
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november 2017 by nhaliday
Accurate Genomic Prediction Of Human Height | bioRxiv
Stephen Hsu's compressed sensing application paper

We construct genomic predictors for heritable and extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). Replication tests show that these predictors capture, respectively, ~40, 20, and 9 percent of total variance for the three traits. For example, predicted heights correlate ~0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction.

https://infoproc.blogspot.com/2017/09/accurate-genomic-prediction-of-human.html

http://infoproc.blogspot.com/2017/11/23andme.html
I'm in Mountain View to give a talk at 23andMe. Their latest funding round was $250M on a (reported) valuation of $1.5B. If I just add up the Crunchbase numbers it looks like almost half a billion invested at this point...

Slides: Genomic Prediction of Complex Traits

Here's how people + robots handle your spit sample to produce a SNP genotype:

https://drive.google.com/file/d/1e_zuIPJr1hgQupYAxkcbgEVxmrDHAYRj/view
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september 2017 by nhaliday
Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders : Nature Genetics : Nature Research
Autism spectrum disorder (ASD) risk is influenced by common polygenic and de novo variation. We aimed to clarify the influence of polygenic risk for ASD and to identify subgroups of ASD cases, including those with strongly acting de novo variants, in which polygenic risk is relevant. Using a novel approach called the polygenic transmission disequilibrium test and data from 6,454 families with a child with ASD, we show that polygenic risk for ASD, schizophrenia, and greater educational attainment is over-transmitted to children with ASD. These findings hold independent of proband IQ. We find that polygenic variation contributes additively to risk in ASD cases who carry a strongly acting de novo variant. Lastly, we show that elements of polygenic risk are independent and differ in their relationship with phenotype. These results confirm that the genetic influences on ASD are additive and suggest that they create risk through at least partially distinct etiologic pathways.

https://en.wikipedia.org/wiki/Transmission_disequilibrium_test
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july 2017 by nhaliday
Genomic analysis of family data reveals additional genetic effects on intelligence and personality | bioRxiv
methodology:
Using Extended Genealogy to Estimate Components of Heritability for 23 Quantitative and Dichotomous Traits: http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1003520
Pedigree- and SNP-Associated Genetics and Recent Environment are the Major Contributors to Anthropometric and Cardiometabolic Trait Variation: http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1005804

Missing Heritability – found?: https://westhunt.wordpress.com/2017/02/09/missing-heritability-found/
There is an interesting new paper out on genetics and IQ. The claim is that they have found the missing heritability – in rare variants, generally different in each family.

Some of the variants, the ones we find with GWAS, are fairly common and fitness-neutral: the variant that slightly increases IQ confers the same fitness (or very close to the same) as the one that slightly decreases IQ – presumably because of other effects it has. If this weren’t the case, it would be impossible for both of the variants to remain common.

The rare variants that affect IQ will generally decrease IQ – and since pleiotropy is the norm, usually they’ll be deleterious in other ways as well. Genetic load.

Happy families are all alike; every unhappy family is unhappy in its own way.: https://westhunt.wordpress.com/2017/06/06/happy-families-are-all-alike-every-unhappy-family-is-unhappy-in-its-own-way/
It now looks as if the majority of the genetic variance in IQ is the product of mutational load, and the same may be true for many psychological traits. To the extent this is the case, a lot of human psychological variation must be non-adaptive. Maybe some personality variation fulfills an evolutionary function, but a lot does not. Being a dumb asshole may be a bug, rather than a feature. More generally, this kind of analysis could show us whether particular low-fitness syndromes, like autism, were ever strategies – I suspect not.

It’s bad new news for medicine and psychiatry, though. It would suggest that what we call a given type of mental illness, like schizophrenia, is really a grab-bag of many different syndromes. The ultimate causes are extremely varied: at best, there may be shared intermediate causal factors. Not good news for drug development: individualized medicine is a threat, not a promise.

see also comment at: https://pinboard.in/u:nhaliday/b:a6ab4034b0d0

https://www.reddit.com/r/slatestarcodex/comments/5sldfa/genomic_analysis_of_family_data_reveals/
So the big implication here is that it's better than I had dared hope - like Yang/Visscher/Hsu have argued, the old GCTA estimate of ~0.3 is indeed a rather loose lower bound on additive genetic variants, and the rest of the missing heritability is just the relatively uncommon additive variants (ie <1% frequency), and so, like Yang demonstrated with height, using much more comprehensive imputation of SNP scores or using whole-genomes will be able to explain almost all of the genetic contribution. In other words, with better imputation panels, we can go back and squeeze out better polygenic scores from old GWASes, new GWASes will be able to reach and break the 0.3 upper bound, and eventually we can feasibly predict 0.5-0.8. Between the expanding sample sizes from biobanks, the still-falling price of whole genomes, the gradual development of better regression methods (informative priors, biological annotation information, networks, genetic correlations), and better imputation, the future of GWAS polygenic scores is bright. Which obviously will be extremely helpful for embryo selection/genome synthesis.

The argument that this supports mutation-selection balance is weaker but plausible. I hope that it's true, because if that's why there is so much genetic variation in intelligence, then that strongly encourages genetic engineering - there is no good reason or Chesterton fence for intelligence variants being non-fixed, it's just that evolution is too slow to purge the constantly-accumulating bad variants. And we can do better.
https://rubenarslan.github.io/generation_scotland_pedigree_gcta/

The surprising implications of familial association in disease risk: https://arxiv.org/abs/1707.00014
https://spottedtoad.wordpress.com/2017/06/09/personalized-medicine-wont-work-but-race-based-medicine-probably-will/
As Greg Cochran has pointed out, this probably isn’t going to work. There are a few genes like BRCA1 (which makes you more likely to get breast and ovarian cancer) that we can detect and might affect treatment, but an awful lot of disease turns out to be just the result of random chance and deleterious mutation. This means that you can’t easily tailor disease treatment to people’s genes, because everybody is fucked up in their own special way. If Johnny is schizophrenic because of 100 random errors in the genes that code for his neurons, and Jack is schizophrenic because of 100 other random errors, there’s very little way to test a drug to work for either of them- they’re the only one in the world, most likely, with that specific pattern of errors. This is, presumably why the incidence of schizophrenia and autism rises in populations when dads get older- more random errors in sperm formation mean more random errors in the baby’s genes, and more things that go wrong down the line.

The looming crisis in human genetics: http://www.economist.com/node/14742737
Some awkward news ahead
- Geoffrey Miller

Human geneticists have reached a private crisis of conscience, and it will become public knowledge in 2010. The crisis has depressing health implications and alarming political ones. In a nutshell: the new genetics will reveal much less than hoped about how to cure disease, and much more than feared about human evolution and inequality, including genetic differences between classes, ethnicities and races.

2009!
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june 2017 by nhaliday
Estimating the number of unseen variants in the human genome
To find all common variants (frequency at least 1%) the number of individuals that need to be sequenced is small (∼350) and does not differ much among the different populations; our data show that, subject to sequence accuracy, the 1000 Genomes Project is likely to find most of these common variants and a high proportion of the rarer ones (frequency between 0.1 and 1%). The data reveal a rule of diminishing returns: a small number of individuals (∼150) is sufficient to identify 80% of variants with a frequency of at least 0.1%, while a much larger number (> 3,000 individuals) is necessary to find all of those variants.

A map of human genome variation from population-scale sequencing: http://www.internationalgenome.org/sites/1000genomes.org/files/docs/nature09534.pdf

Scientists using data from the 1000 Genomes Project, which sequenced one thousand individuals from 26 human populations, found that "a typical [individual] genome differs from the reference human genome at 4.1 million to 5.0 million sites … affecting 20 million bases of sequence."[11] Nearly all (>99.9%) of these sites are small differences, either single nucleotide polymorphisms or brief insertion-deletions in the genetic sequence, but structural variations account for a greater number of base-pairs than the SNPs and indels.[11]

Human genetic variation: https://en.wikipedia.org/wiki/Human_genetic_variation

Singleton Variants Dominate the Genetic Architecture of Human Gene Expression: https://www.biorxiv.org/content/early/2017/12/15/219238
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may 2017 by nhaliday
Missing heritability problem - Wikipedia
The "missing heritability" problem[1][2][3][4][5][6] can be defined as the fact that single genetic variations cannot account for much of the heritability of diseases, behaviors, and other phenotypes. This is a problem that has significant implications for medicine, since a person's susceptibility to disease may depend more on "the combined effect of all the genes in the background than on the disease genes in the foreground", or the role of genes may have been severely overestimated.

The 'missing heritability' problem was named as such in 2008. The Human Genome Project led to optimistic forecasts that the large genetic contributions to many traits and diseases (which were identified by quantitative genetics and behavioral genetics in particular) would soon be mapped and pinned down to specific genes and their genetic variants by methods such as candidate-gene studies which used small samples with limited genetic sequencing to focus on specific genes believed to be involved, examining the SNP kinds of variants. While many hits were found, they often failed to replicate in other studies.

The exponential fall in genome sequencing costs led to the use of GWAS studies which could simultaneously examine all candidate-genes in larger samples than the original finding, where the candidate-gene hits were found to almost always be false positives and only 2-6% replicate;[7][8][9][10][11][12] in the specific case of intelligence candidate-gene hits, only 1 candidate-gene hit replicated,[13] and of 15 neuroimaging hits, none did.[14] The editorial board of Behavior Genetics noted, in setting more stringent requirements for candidate-gene publications, that "the literature on candidate gene associations is full of reports that have not stood up to rigorous replication...it now seems likely that many of the published findings of the last decade are wrong or misleading and have not contributed to real advances in knowledge".[15] Other researchers have characterized the literature as having "yielded an infinitude of publications with very few consistent replications" and called for a phase out of candidate-gene studies in favor of polygenic scores.[16]

This led to a dilemma. Standard genetics methods have long estimated large heritabilities such as 80% for traits such as height or intelligence, yet none of the genes had been found despite sample sizes that, while small, should have been able to detect variants of reasonable effect size such as 1 inch or 5 IQ points. If genes have such strong cumulative effects - where were they? Several resolutions have been proposed, that the missing heritability is some combination of:

...

7. Genetic effects are indeed through common SNPs acting additively, but are highly polygenic: dispersed over hundreds or thousands of variants each of small effect like a fraction of an inch or a fifth of an IQ point and with low prior probability: unexpected enough that a candidate-gene study is unlikely to select the right SNP out of hundreds of thousands of known SNPs, and GWASes up to 2010, with n<20000, would be unable to find hits which reach genome-wide statistical-significance thresholds. Much larger GWAS sample sizes, often n>100k, would be required to find any hits at all, and would steadily increase after that.
This resolution to the missing heritability problem was supported by the introduction of Genome-wide complex trait analysis (GCTA) in 2010, which demonstrated that trait similarity could be predicted by the genetic similarity of unrelated strangers on common SNPs treated additively, and for many traits the SNP heritability was indeed a substantial fraction of the overall heritability. The GCTA results were further buttressed by findings that a small percent of trait variance could be predicted in GWASes without any genome-wide statistically-significant hits by a linear model including all SNPs regardless of p-value; if there were no SNP contribution, this would be unlikely, but it would be what one expected from SNPs whose effects were very imprecisely estimated by a too-small sample. Combined with the upper bound on maximum effect sizes set by the GWASes up to then, this strongly implied that the highly polygenic theory was correct. Examples of complex traits where increasingly large-scale GWASes have yielded the initial hits and then increasing numbers of hits as sample sizes increased from n<20k to n>100k or n>300k include height,[23] intelligence,[24] and schizophrenia.
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may 2017 by nhaliday
The Promises and Pitfalls of Genoeconomics*
This estimator suggests that heritability increases from 0.37 to 0.58 in men as we move from single-year income to a 20-year average. The corresponding figures for women are 0.28 and 0.46. These findings suggest that permanent income is more heritable than single-year income. This conclusion partly seems to reflect the fact that measurement error and transitory shocks generate a downward bias in estimates of heritability (Solon 1992, Zimmerman 1992, Mazumder 2005), consistent with our earlier conjecture that the heritability estimates of many other economic outcomes are downward biased.

Heritability of Lifetime Income: https://ideas.repec.org/p/pra/mprapa/46326.html
Using 15 years of data on Finnish twins, we find that 24% (54%) of the variance of women’s (men’s) lifetime income is due to genetic factors and that the contribution of the shared environment is negligible. We link these figures to policy by showing that controlling for education reduces the variance share of genetics by 5-8 percentage points; by demonstrating that income uncertainty has a genetic component half the size of its variance share in lifetime income; and by exploring how the genetic heritability of lifetime income is related to the macroeconomic environment, as measured by GDP growth and the Gini-coefficient of income inequality.

Genetic and Environmental Influences on Household Financial Distress: http://www.sciencedirect.com/science/article/pii/S0167268117302251
- Financial behaviors are genetically influenced especially at the extremes of SES.
- Personality and cognition are linked to financial distress genetically.
- Within-family factors also link personality and cognition to financial distress.
- Neuroticism is a more important predictor of financial distress at low SES.
- Cognitive ability is a more important predictor of financial distress at high SES.
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january 2017 by nhaliday
J. Intell. | Free Full-Text | Zeroing in on the Genetics of Intelligence
Rare variants and mutations of large effect do not appear to play a main role beyond intellectual disability. Common variants can account for about half the heritability of intelligence and show promise that collaborative efforts will identify more causal genetic variants. Gene–gene interactions may explain some of the remainder, but are only starting to be tapped. Evolutionarily, stabilizing selection and selective (near)-neutrality are consistent with the facts known so far.

Idiot Proof: https://westhunt.wordpress.com/2016/01/07/idiot-proof/
I was looking at a recent survey of current knowledge in psychological genetics. The gist is that common variants – which can’t have decreased fitness much in the average past, since they’re common – are the main story in the genetic architecture of intelligence. Genetic load doesn’t seem very important, except at the low end. Big-effect deleterious mutations can certainly leave you retarded, but moderate differences in the number of slightly-deleterious mutations don’t have any observable effect – except possibly in the extremely intelligent, but that’s uncertain at this point. Not what I expected, but that’s how things look right now. It would seem that brain development is robust to small tweaks, although there must be some limit. The results with older fathers apparently fit this pattern: they have more kids with something seriously wrong, but although there should be extra mild mutations in their kids as well as the occasional serious one, the kids without obvious serious problems don’t have depressed IQ.
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december 2016 by nhaliday
Bias, precision and heritability of self-reported and clinically measured height in Australian twins. - PubMed - NCBI
Self-report height measurements are shown to be more variable than clinical measures. This has led to lowered estimates of heritability in many previous studies of stature.

basically measurement error of any kind can reduce heritability estimates
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july 2016 by nhaliday

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