nhaliday + scaling-up   61

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
study  bio  preprint  GWAS  state-of-art  embodied  genetics  genomics  compressed-sensing  high-dimension  machine-learning  missing-heritability  hsu  scitariat  education  🌞  frontier  britain  regression  data  visualization  correlation  phase-transition  multi  commentary  summary  pdf  slides  brands  skunkworks  hard-tech  presentation  talks  methodology  intricacy  bioinformatics  scaling-up  stat-power  sparsity  norms  nibble  speedometer  stats  linear-models  2017  biodet 
september 2017 by nhaliday
A combined analysis of genetically correlated traits identifies 107 loci associated with intelligence | bioRxiv
We apply MTAG to three large GWAS: Sniekers et al (2017) on intelligence, Okbay et al. (2016) on Educational attainment, and Hill et al. (2016) on household income. By combining these three samples our functional sample size increased from 78 308 participants to 147 194. We found 107 independent loci associated with intelligence, implicating 233 genes, using both SNP-based and gene-based GWAS. We find evidence that neurogenesis may explain some of the biological differences in intelligence as well as genes expressed in the synapse and those involved in the regulation of the nervous system.

...

Finally, using an independent sample of 6 844 individuals we were able to predict 7% of intelligence using SNP data alone.
study  bio  preprint  biodet  behavioral-gen  GWAS  genetics  iq  education  compensation  composition-decomposition  🌞  gwern  meta-analysis  genetic-correlation  scaling-up  methodology  correlation  state-of-art  neuro  neuro-nitgrit  dimensionality 
july 2017 by nhaliday
10 million DTC dense marker genotypes by end of 2017? – Gene Expression
Ultimately I do wonder if I was a bit too optimistic that 50% of the US population will be sequenced at 30x by 2025. But the dynamic is quite likely to change rapidly because of a technological shift as the sector goes through a productivity uptick. We’re talking about exponential growth, which humans have weak intuition about….
https://gnxp.nofe.me/2017/06/27/genome-sequencing-for-the-people-is-near/
https://gnxp.nofe.me/2017/07/11/23andme-ancestry-only-is-49-99-for-prime-day/
gnxp  scitariat  commentary  biotech  scaling-up  genetics  genomics  scale  bioinformatics  multi  toys  measurement  duplication  signal-noise  coding-theory 
june 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
Human genome - Wikipedia
There are an estimated 19,000-20,000 human protein-coding genes.[4] The estimate of the number of human genes has been repeatedly revised down from initial predictions of 100,000 or more as genome sequence quality and gene finding methods have improved, and could continue to drop further.[5][6] Protein-coding sequences account for only a very small fraction of the genome (approximately 1.5%), and the rest is associated with non-coding RNA molecules, regulatory DNA sequences, LINEs, SINEs, introns, and sequences for which as yet no function has been determined.[7]
bio  sapiens  genetics  genomics  bioinformatics  scaling-up  data  scale  wiki  reference  QTL  methodology 
may 2017 by nhaliday
Sequencing a genome for less than the cost of an X-ray? Not quite yet
A $100 genome will cost $100 in the same way that the $1,000 genome costs $1,000. As in, it won’t, at least not soon. “The $1,000 genome” — which sequencer makers began promising about five years ago — “costs us $3,000,” said Richard Gibbs, founder of the Baylor College of Medicine Human Genome Sequencing Center and one of the leaders of the original Human Genome Project in the 1990s.
news  org:sci  scaling-up  data  scale  genetics  genomics  biotech  money  efficiency  bioinformatics  cost-benefit  frontier  speedometer  measurement 
april 2017 by nhaliday
Embryo editing for intelligence - Gwern.net
https://twitter.com/pnin1957/status/917693229608337408
My hunch is CRISPR/Cas9 will not play a big role in intelligence enhancement. You'd have to edit so many loci b/c of small effect sizes, increasing errors. Embryo selection is much more promising. Peoples with high avg genetic values, of course, have an in-built advantage there.
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february 2017 by nhaliday
Information Processing: Epistasis vs additivity
On epistasis: why it is unimportant in polygenic directional selection: http://rstb.royalsocietypublishing.org/content/365/1544/1241.short
- James F. Crow

The Evolution of Multilocus Systems Under Weak Selection: http://www.genetics.org/content/genetics/134/2/627.full.pdf
- Thomas Nagylaki

Data and Theory Point to Mainly Additive Genetic Variance for Complex Traits: http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1000008
The relative proportion of additive and non-additive variation for complex traits is important in evolutionary biology, medicine, and agriculture. We address a long-standing controversy and paradox about the contribution of non-additive genetic variation, namely that knowledge about biological pathways and gene networks imply that epistasis is important. Yet empirical data across a range of traits and species imply that most genetic variance is additive. We evaluate the evidence from empirical studies of genetic variance components and find that additive variance typically accounts for over half, and often close to 100%, of the total genetic variance. We present new theoretical results, based upon the distribution of allele frequencies under neutral and other population genetic models, that show why this is the case even if there are non-additive effects at the level of gene action. We conclude that interactions at the level of genes are not likely to generate much interaction at the level of variance.
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february 2017 by nhaliday
Information Processing: Search results for compressed sensing
https://www.unz.com/jthompson/the-hsu-boundary/
http://infoproc.blogspot.com/2017/09/phase-transitions-and-genomic.html
Added: Here are comments from "Donoho-Student":
Donoho-Student says:
September 14, 2017 at 8:27 pm GMT • 100 Words

The Donoho-Tanner transition describes the noise-free (h2=1) case, which has a direct analog in the geometry of polytopes.

The n = 30s result from Hsu et al. (specifically the value of the coefficient, 30, when p is the appropriate number of SNPs on an array and h2 = 0.5) is obtained via simulation using actual genome matrices, and is original to them. (There is no simple formula that gives this number.) The D-T transition had only been established in the past for certain classes of matrices, like random matrices with specific distributions. Those results cannot be immediately applied to genomes.

The estimate that s is (order of magnitude) 10k is also a key input.

I think Hsu refers to n = 1 million instead of 30 * 10k = 300k because the effective SNP heritability of IQ might be less than h2 = 0.5 — there is noise in the phenotype measurement, etc.

Donoho-Student says:
September 15, 2017 at 11:27 am GMT • 200 Words

Lasso is a common statistical method but most people who use it are not familiar with the mathematical theorems from compressed sensing. These results give performance guarantees and describe phase transition behavior, but because they are rigorous theorems they only apply to specific classes of sensor matrices, such as simple random matrices. Genomes have correlation structure, so the theorems do not directly apply to the real world case of interest, as is often true.

What the Hsu paper shows is that the exact D-T phase transition appears in the noiseless (h2 = 1) problem using genome matrices, and a smoothed version appears in the problem with realistic h2. These are new results, as is the prediction for how much data is required to cross the boundary. I don’t think most gwas people are familiar with these results. If they did understand the results they would fund/design adequately powered studies capable of solving lots of complex phenotypes, medical conditions as well as IQ, that have significant h2.

Most people who use lasso, as opposed to people who prove theorems, are not even aware of the D-T transition. Even most people who prove theorems have followed the Candes-Tao line of attack (restricted isometry property) and don’t think much about D-T. Although D eventually proved some things about the phase transition using high dimensional geometry, it was initially discovered via simulation using simple random matrices.
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november 2016 by nhaliday
Molecular Psychiatry - A genome-wide analysis of putative functional and exonic variation associated with extremely high intelligence
We did not observe any individual protein-altering variants that are reproducibly associated with extremely high intelligence and within the entire distribution of intelligence. Moreover, no significant associations were found for multiple rare alleles within individual genes. However, analyses using genome-wide similarity between unrelated individuals (genome-wide complex trait analysis) indicate that the genotyped functional protein-altering variation yields a heritability estimate of 17.4% (s.e. 1.7%) based on a liability model. In addition, investigation of nominally significant associations revealed fewer rare alleles associated with extremely high intelligence than would be expected under the null hypothesis. _This observation is consistent with the hypothesis that rare functional alleles are more frequently detrimental than beneficial to intelligence._
study  genetics  iq  enhancement  GWAS  org:nat  QTL  biodet  behavioral-gen  tails  extrema  homo-hetero  scaling-up  spearhead 
october 2016 by nhaliday
Wiring the Brain: The dark arts of statistical genomics
GCTA+GWAS

This is where GCTA analyses come in. The idea here is to estimate the total contribution of common risk variants in the population to determining who develops a disease, without necessarily having to identify them all individually first. The basic premise of GCTA analyses is to not worry about picking up the signatures of individual SNPs, but instead to use all the SNPs analysed to simply measure relatedness among people in your study population. Then you can compare that index of (distant) relatedness to an index of phenotypic similarity. For a trait like height, that will be a correlation between two continuous measures. For diseases, however, the phenotypic measure is categorical – you either have been diagnosed with it or you haven’t.
explanation  methodology  genetics  population-genetics  bio  enhancement  GWAS  variance-components  🌞  scaling-up  bioinformatics  genomics  nibble  🔬  article  GCTA  tip-of-tongue  spearhead  pop-structure  psychiatry  autism  disease  models  map-territory  QTL  concept  levers  ideas  biodet 
october 2016 by nhaliday
The Future of Genetic Enhancement is Not in the West | Quillette
https://qz.com/750908/the-future-of-genetic-enhancement-is-in-china-and-india/

If it becomes possible to safely genetically increase babies’ IQ, it will become inevitable: https://www.washingtonpost.com/news/volokh-conspiracy/wp/2015/07/14/if-it-becomes-possible-to-safely-genetically-increase-babies-iq-it-will-become-inevitable/

Baby Genome Sequencing for Sale in China: https://www.technologyreview.com/s/608086/baby-genome-sequencing-for-sale-in-china/
Chinese parents can now decode the genomes of their healthy newborns, revealing disease risks as well as the likelihood of physical traits like male-pattern baldness.
https://gnxp.nofe.me/2017/06/16/the-cultural-revolution-that-will-happen-in-china/
https://gnxp.nofe.me/2017/07/26/the-future-will-be-genetically-engineered/
http://www.nature.com/news/china-s-embrace-of-embryo-selection-raises-thorny-questions-1.22468
http://infoproc.blogspot.com/2017/08/embryo-selection-in-china-nature.html

China launches massive genome research initiative: https://news.cgtn.com/news/7767544e34637a6333566d54/share_p.html

research ethics:
First results of CRISPR gene editing of normal embryos released: https://www.newscientist.com/article/2123973-first-results-of-crispr-gene-editing-of-normal-embryos-released/
http://www.bbc.com/news/health-32530334
https://www.technologyreview.com/s/608350/first-human-embryos-edited-in-us/
http://infoproc.blogspot.com/2017/07/first-human-embryos-edited-in-us-mit.html
https://news.ycombinator.com/item?id=14912382
http://www.nature.com/nature/journal/vaop/ncurrent/full/nature23305.html
caveats: https://ipscell.com/2017/08/4-reasons-mitalipov-paper-doesnt-herald-safe-crispr-human-genetic-modification/

https://www.scientificamerican.com/article/first-crispr-human-clinical-trial-gets-a-green-light-from-the-u-s/
http://www.nature.com/news/crispr-gene-editing-tested-in-a-person-for-the-first-time-1.20988
https://news.ycombinator.com/item?id=12960844
So this title is a bit misleading; something like, "cells edited with CRISPR injected into a person for the first time" would be better. While CRISPR is promising for topological treatments, that's not what happened here.
https://www.scientificamerican.com/article/chinese-scientists-to-pioneer-first-human-crispr-trial/
China sprints ahead in CRISPR therapy race: http://science.sciencemag.org/content/358/6359/20
China, Unhampered by Rules, Races Ahead in Gene-Editing Trials: https://www.wsj.com/articles/china-unhampered-by-rules-races-ahead-in-gene-editing-trials-1516562360
U.S. scientists helped devise the Crispr biotechnology tool. First to test it in humans are Chinese doctors
https://twitter.com/mr_scientism/status/955207026333929472
https://archive.is/lJ761

https://www.npr.org/sections/health-shots/2018/01/24/579925801/chinese-scientists-clone-monkeys-using-method-that-created-dolly-the-sheep
https://twitter.com/0xa59a2d/status/956344998626242560
https://archive.is/azH4S
https://twitter.com/AngloRemnant/status/956348983303114753
https://archive.is/RclJG
https://twitter.com/AngloRemnant/status/956352891287228416
https://archive.is/BfHuV

http://www.acsh.org/news/2017/03/07/did-gene-therapy-cure-sickle-cell-disease-10950

lol: http://www.theonion.com/infographic/pros-and-cons-gene-editing-56740

Japan set to allow gene editing in human embryos [ed.: (for research)]: https://www.nature.com/articles/d41586-018-06847-7
Draft guidelines permit gene-editing tools for research into early human development.
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august 2016 by nhaliday
23andMe Pulls Off Massive Crowdsourced Depression Study | Hacker News
nice discussion of GWASes, related statistical issues, and sequencing hardware in the comments
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august 2016 by nhaliday

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