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.
pdf  nibble  study  article  org:nat  🌞  biodet  genetics  population-genetics  GWAS  QTL  distribution  disease  cancer  stat-power  bioinformatics  magnitude  embodied  prediction  scale  scaling-up  variance-components  multi  missing-heritability  effect-size  regression  correlation  data 
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.


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:

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
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.

study  biodet  behavioral-gen  genetics  population-genetics  QTL  missing-heritability  psychiatry  autism  👽  disease  org:nat  🌞  gwern  pdf  piracy  education  multi  methodology  wiki  reference  psychology  cog-psych  genetic-load  genetic-correlation  sib-study  hypothesis-testing  equilibrium  iq  correlation  intricacy  GWAS  causation  endo-exo  endogenous-exogenous 
july 2017 by nhaliday
Genomic analysis of family data reveals additional genetic effects on intelligence and personality | bioRxiv
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

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.

The surprising implications of familial association in disease risk: https://arxiv.org/abs/1707.00014
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.

study  preprint  bio  biodet  behavioral-gen  GWAS  missing-heritability  QTL  🌞  scaling-up  replication  iq  education  spearhead  sib-study  multi  west-hunter  scitariat  genetic-load  mutation  medicine  meta:medicine  stylized-facts  ratty  unaffiliated  commentary  rhetoric  wonkish  genetics  genomics  race  pop-structure  poast  population-genetics  psychiatry  aphorism  homo-hetero  generalization  scale  state-of-art  ssc  reddit  social  summary  gwern  methodology  personality  britain  anglo  enhancement  roots  s:*  2017  data  visualization  database  let-me-see  bioinformatics  news  org:rec  org:anglo  org:biz  track-record  prediction  identity-politics  pop-diff  recent-selection  westminster  inequality  egalitarianism-hierarchy  high-dimension  applications  dimensionality  ideas  no-go  volo-avolo  magnitude  variance-components  GCTA  tradeoffs  counter-revolution  org:mat  dysgenics  paternal-age  distribution  chart  abortion-contraception-embryo 
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
study  sapiens  genetics  genomics  population-genetics  bioinformatics  data  prediction  cost-benefit  scale  scaling-up  org:nat  QTL  methodology  multi  pdf  curvature  convexity-curvature  nonlinearity  measurement  magnitude  🌞  distribution  missing-heritability  pop-structure  genetic-load  mutation  wiki  reference  article  structure  bio  preprint  biodet  variance-components  nibble  chart 
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.
article  bio  biodet  behavioral-gen  genetics  genomics  GWAS  candidate-gene  methodology  QTL  missing-heritability  twin-study  measurement  epigenetics  nonlinearity  error  history  mostly-modern  reflection  wiki  reference  science  bounded-cognition  replication  being-right  info-dynamics  🌞  linearity  ideas  GCTA  spearhead 
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.
study  economics  genetics  variance-components  money  🌞  🎩  longitudinal  compensation  biodet  signal-noise  missing-heritability  wealth  behavioral-gen  microfoundations  class  inequality  europe  nordic  twin-study  correlation  econ-metrics  s-factor  survey  labor  roots  time  gender  gender-diff  interdisciplinary  anthropology  education  human-capital  moments  multi 
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.
study  genetics  iq  QTL  🌞  survey  equilibrium  evolution  biodet  missing-heritability  nibble  roots  big-picture  s:*  behavioral-gen  chart  state-of-art  multi  west-hunter  sapiens  summary  neuro  intelligence  commentary  robust  paternal-age  sensitivity  perturbation  epidemiology  stylized-facts  scitariat  rot 
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
study  genetics  science  variance-components  twin-study  environmental-effects  anglo  embodied  biodet  self-report  signal-noise  missing-heritability 
july 2016 by nhaliday

related tags

2017  abortion-contraception-embryo  age-generation  anglo  anthropology  aphorism  applications  article  attention  autism  behavioral-gen  being-right  biases  big-picture  bio  biodet  bioinformatics  bounded-cognition  brands  britain  cancer  candidate-gene  causation  chart  class  classic  cog-psych  commentary  comparison  compensation  composition-decomposition  compressed-sensing  concept  convexity-curvature  correlation  cost-benefit  counter-revolution  curvature  data  database  developmental  dimensionality  disease  distribution  dropbox  dysgenics  econ-metrics  economics  education  effect-size  egalitarianism-hierarchy  embodied  endo-exo  endogenous-exogenous  enhancement  environmental-effects  epidemiology  epigenetics  equilibrium  error  essay  europe  evolution  explanation  frontier  gcta  gender-diff  gender  generalization  genetic-correlation  genetic-load  genetics  genomics  gwas  gwern  hard-tech  high-dimension  history  homo-hetero  hsu  human-capital  hypothesis-testing  ideas  identity-politics  inequality  info-dynamics  insight  intelligence  interdisciplinary  intricacy  ioannidis  iq  labor  large-factor  latent-variables  len:short  lens  let-me-see  levers  linear-models  linearity  links  list  longevity  longitudinal  machine-learning  magnitude  measurement  medicine  meta:medicine  meta:science  methodology  microfoundations  ml-map-e  models  moments  money  mostly-modern  multi  mutation  neuro  news  nibble  no-go  nonlinearity  nordic  norms  org:anglo  org:biz  org:mat  org:nat  org:rec  paternal-age  pdf  personality  perturbation  phase-transition  piracy  plots  poast  pop-diff  pop-structure  population-genetics  prediction  preprint  presentation  psychiatry  psychology  psychometrics  qtl  race  ratty  recent-selection  reddit  reference  reflection  regression  replication  rhetoric  robust  roots  rot  s-factor  s:*  sampling-bias  sapiens  scale  scaling-up  science  scitariat  self-report  sensitivity  sib-study  signal-noise  simulation  skunkworks  slides  social  sparsity  spearhead  speculation  speedometer  ssc  stat-power  state-of-art  statistics  stats  structure  study  stylized-facts  summary  survey  talks  time  track-record  tradeoffs  twin-study  unaffiliated  variance-components  visualization  volo-avolo  wealth  west-hunter  westminster  wiki  wiring-the-brain  wonkish  🌞  🎩  👽 

Copy this bookmark: