On March 30, 2020, the COVID-19 health service utilization forecasting team at the University of Washington's Institute for Health Metrics and Evaluation (IHME) published national and state-level projections of the number of COVID-19 deaths in the United States anticipated in the next four months (i.e. by July 30, 2020). At the time of writing (May 5, 2020), less than half of the time between that paper's publication and the date to which its predictions apply has passed, but comparing these predictions to the actual number of deaths reported so far is still useful in determining whether the models have significantly underestimated the number of deaths. Data regarding the cumulative number of predicted COVID-19 deaths per state was obtained from the IHME's published paper on medRxiv, as were the corresponding lower and upper bounds accompanying each state-level prediction. These predictions covered all 50 states and the District of Columbia. They were then compared to the reported total number of deaths based on data from the COVID Tracker (as of May 5, 2020). The findings indicate that most of the included regions still have not surpassed the number of COVID-19 deaths predicted that they would experience cumulatively, though seven states (Connecticut, New Jersey, New York, Massachusetts, Maryland, Rhode Island, and Illinois) and the District of Columbia have already done so. Nationally, the total number of deaths reported in the United States as of May 5, 2020 is 62,698, which represents about 77% of the 81,111 deaths predicted in the United States by July 30, 2020. The total national number of deaths has increased at a rate of about 1,726 deaths per day since the IHME report was published. If this trend continues at the same rate until July 30, the number of Americans who would have died from COVID-19 at that point would be 213,689, more than twice the IHME prediction. 
It is a thing of no great difficulty to raise objections against another man's oration,—nay, it is a very easy matter; but to produce a better in its place is a work extremely troublesome.  -PlutarchIntroduction Although many twin studies have been conducted (which is quite an understatement; there are almost 9,000 hits for "twin study" on PubMed!), there have long been critics who argue that they are scientifically worthless. Obviously, the behavior geneticists who conduct these studies with the aim of separating the influences of genes from that of environment are none too happy about people calling one of their favorite research designs fatally flawed. So how do they respond, and are their responses more compelling than the original criticisms? I will dig into these in this article (which will be long, so be warned).First, I should define what a twin study is in this context. When the phrase "twin study" is used, it is almost always used to refer to a study using the so-called "classical twin design" (abbreviated CTD). So for the rest of this paper, I will use the phrase "twin study" to refer only to studies using the CTD. This type of study involves comparing monozygotic (MZ) twins and dizygotic (DZ) twins with the purpose of estimating how much of the variation in a given trait (i.e. %) is due to genes, a.k.a. "heritability". The way this is done in the CTD is by calculating concordance (the degree of similarity between each twin in a pair; referred to as correlation if a disease is being studied rather than a normal trait) on the trait in question for the monozygotic twins and dizygotic twins separately, and comparing the two concordances. (Important note: monozygotic twins-aka "identical twins"-are assumed to share 100% of their genes, while dizygotic twins-aka "fraternal twins"-only share 50% of theirs. More on the first of these assumptions later.) These concordances are then converted into an estimate of heritability (in this case, narrow-sense heritability, h2, which only includes additive genetic effects) using this formula (known as Falconer's formula):h2 = 2(rMZ - rDZ)Where rMZ and rDZ are, respectively, the phenotypic correlation (for traits) or concordance (for diseases) between MZ twins, and that between DZ twins, on the trait/disease of interest.\cite{Mayhew_2017}Thus, when this formula is used (which it typically is in twin studies), if the concordance is higher among monozygotic twins than among dizygotic twins, this is taken to be due to genetic differences between the two sets of twins, and will yield a nonzero heritability estimate. In contrast, if the concordance rates were the same between MZ and DZ twins on the trait, it would indicate an estimated narrow-sense heritability of zero for the trait, under the CTD model\cite{Guo2001}. This is obvious from looking at the formula above, where rMZ and rDZ being equal makes the value of h2 equal to 0. Note that the extent to which h2 > 0 is often conflated with the extent to which a trait is under "genetic influence" in the twin study literature.Final note: there are two other components to this model: shared and nonshared environment. The three components of the model, to recap, are: additive genetic variance (A), shared/common environment (C), and nonshared environment (E). Thus it is often called the "ACE model".\cite{hh2005} The basic idea behind this model is that total phenotypic variance is exactly equal to the sum of each of these three components: A + C + E.But there have long been many criticisms of the specific way that twin studies are conducted, and critics claim that such studies can't really separate genetic and environmental contributions to any trait. But Beatty et al. (2002) inform us that such critics are mistaken: "..., satisfactory responses to critics (see for example, Bouchard, 1993, 1994; Goldsmith, 1983; Lykken, 1995; Martin, Boomsma, & Machin, 1997; Scarr & Carter-Saltzman, 1979; Segal, 1997, 1999) have led contemporary behavior geneticists to describe the twins design as “the perfect natural experiment” (Martin et al., 1997. p. 387)."\cite{Beatty_2002}, (p. 2)Oh, so that's good to know, apparently criticisms of twin studies have already been conclusively refuted. So I'll go back to these sources later in this article to see if they actually conclusively show that critics of the twin method are wrong about its purported uselessness.Criticism 1: The equal environments assumptionFor decades, critics of twin studies have argued that they don't really separate the influence of genes and environment on a specific trait because the validity of their heritability estimates depends on the so-called "equal environments assumption" (abbreviated EEA). This is the assumption that the within-twin similarity of environmental exposures that are relevant for the causation of the trait being studied are equal for MZ and DZ twins. In other words, it assumes that MZ twins experience equally similar environments as do DZ twins, at least with regard to the environmental factors that can cause the trait being studied. If this assumption is wrong, it will lead to heritability estimates that are also wrong: if MZ twins experience more similar environments than DZ twins, this can produce higher concordance rates without any genetic influence on the trait whatsoever.\cite{Guo2001}As you may have guessed, there is a lot of evidence that MZ twins experience more similar environments than do DZ twins (for a summary, see \cite{Simons2012}; see also Joseph 2002, Horwitz et al. 2003, Rende et al. 2005). In fact, even an article by BGists aiming to defend the validity of twin studies acknowledged that "There is overwhelming evidence that MZ twins are treated more similarly than their DZ counterparts" (Evans & Martin 2000). But to be fair, that's not necessarily the same as the kind of fundamental invalidating flaw critics often portray violations of the EEA to be. Typically, behavior geneticists respond to this evidence by making one or more of the following arguments (hereafter referred to as "argument #", where "#" is the number listed corresponding to each argument immediately below):Greater environmental similarity between twins in a pair is not significantly associated with greater phenotypic similarity. This argument is based on the assumption that the EEA does not have to be entirely valid for all environmental factors, but instead just for those that are "trait-relevant", i.e. that have a causal effect on whatever trait is being studied. The practice of defining the EEA as applying only to "trait-relevant" environmental factors is common in behavior genetic research (e.g. \cite{Mitchell2007}, \cite{Bouchard_2002},\cite{Kendler_1993}). This argument further claims that the hypothesis that the EEA is invalid for trait-relevant environmental factors must be tested rather than assumed to always be true, after which studies are cited and/or performed that supposedly test this definition of the EEA. These "EEA-test" studies come in many forms: a) "perceived zygosity" or "misclassified twin" studies, a specific type of twin study designed to test the EEA. Such studies take advantage of the fact that some MZ twins are perceived as being DZ and vice versa. The idea is that if greater genetic similarity between MZ twins is what makes them more similar, then actual zygosity should have a greater impact on twin similarity, i.e. MZ twins should be more phenotypically similar to each other than DZ twins even if the MZ twins are incorrectly perceived as DZ. By contrast, if greater environmental similarity (such as is, presumably, experienced by twins perceived to be MZ, whether they are actually MZ or not) explains the greater phenotypic similarity of MZ twins relative to DZ twins, then MZ twins perceived as DZ should be no more phenotypically similar to each other than are DZ twins correctly perceived as DZ. Indeed, actual zygosity has been found to be more strongly associated with twin concordance in multiple such studies.\cite{Conley_2013} b) studies aimed at correlating physical similarity and phenotypic similarity between twins in a pair, and if no such (statistically significant) correlation is found, then it is concluded that the EEA is "valid" in that it doesn't significantly affect heritability estimates for whatever trait is being studied.\cite{Kendler_1993} c) studies including perceived zygosity as well as other measures of environmental similarity, controlling for such measures, and then comparing the "corrected" MZ and DZ correlations to see if the difference between the two correlations remains significant. Two such studies have found that it does.\cite{LaBuda_1997}\cite{CRONK_2002} Greater environmental similarity between members of an MZ twin pair than a DZ twin pair, rather than causing the greater phenotypic similarity between MZ than DZ twins, is actually due to the MZ twins' greater genetic similarity. This argument posits that because MZ twins are more genetically similar than DZ twins, they are treated more similarly and have more similar behavior, which in turn leads to the environments of MZ twins being more similar than those of DZ twins. Many twin researchers have made this argument since at least the 1950s. It has been widely criticized as circular because it assumes that the greater genetic similarity between MZ than DZ twins causes their greater phenotypic similarity, which is also what it purports to demonstrate.\cite{Joseph_2012} Non-CTD studies that do not rely on the EEA generate similar heritability estimates to CTD studies for the same trait. Ostensibly, the similar results generated by these non-CTD methods "validate" the EEA on which CTD studies depend. The word "validate" or derivatives thereof is often used in this literature (for two recent examples, see \cite{Kendler_2014} and \cite{BARNES_2014}). The methodologies of these studies include twins reared apart, adoption studies, and full- vs. half-sibling studies.\cite{BARNES_2014}Response by critics of behavior geneticsCriticism 1: Response to Argument 1Joseph commonly rebuts this argument by pointing out that it reveals a different standard of logical inference that twin researchers use in assessing twin studies versus family studies. Specifically, according to twin researchers, the greater similarity of environments among MZ compared to DZ twins does not necessarily invalidate the classical twin method as a means of separating genetic and environmental influences, but the greater similarity of environments shared by members of the same family relative to members of the general population does necessarily invalidate the ability of family studies to separate such influences. For this reason, Joseph argues that twin researchers are guilty of special pleading by arguing that standards that apply to family studies should not apply to classical twin studies. As an example, he wrote in 2002 that: "genetic researchers acknowledge that family studies do not prove the existence of genetic factors—since the clustering of a condition among family members could be caused by purely environmental factors. However, no one to my knowledge has argued for a “trait-relevant EEA” for family studies; that is, the claim that family studies prove the existence of genetic influences unless specific environmental factors shared by family members are demonstrated to have a causal relationship to the condition in question. Quite the contrary; family studies are acknowledged to be confounded by the simple fact that family members share a common environment. In the same way, the twin method is confounded by the greater environmental similarity of MZ twins, regardless of whether the specific trait-relevant environmental factors are known."\cite{Joseph_2002}, (p. 76)Joseph also notes that by using this argument and defining the EEA as needing to be "trait-relevant", twin researchers attempt to falsely claim that the burden lies on critics of the classical twin method to prove that MZ environments are more similar than those of DZ twins specifically with regard to those environmental factors that affect a given trait. He also points out some other logical problems with this argument in some of his papers.\cite{jay2013}\cite{Joseph_2002}In addition, Joseph and other researchers have been highly critical of the EEA-test studies cited as part of this argument.\cite{jay2013,Joseph_2012}\cite{Richardson_2005}\cite{Beckwith_2008}Criticism 1: Response to Argument 2Joseph has noted the following about this argument: "...this is a circular argument, because twin researchers' conclusion that MZ-DZ differences are explained by genetics is based on assuming the very same thing."\cite{jay2013}, (p. 5) Furthermore, he points out some other logical issues with it, such as that it portrays "...children (twins) as behaving according to a genetic behavioral blueprint, yet somehow parents and other adults have themselves tossed aside the blueprint and are able to flexibly change their behavior and treatment of others on the basis of the twins' behavior and personalities," which he wittily sums up with the phrase "Genetically-programmed human children, meet your ever-so-flexible human parents." He has also pointed out another problem with this argument, namely that "...even if twins do indeed create more similar environments for themselves because of their greater genetic similarity, MZ pairs could still show much higher concordance for psychiatric disorders than DZ pairs for purely environmental reasons."\cite{Joseph_2012}
AbstractThere has long been controversy concerning whether genes influence human intelligence, and if so, which genes influence it, and to what extent. Many genome-wide association studies (GWASs) have attempted to find single nucleotide polymorphisms (SNPs) that are significantly associated with intelligence to shed light on this issue. My aim is to examine whether these studies have generated replicable findings concerning associations between one or more SNPs and variation in intelligence as it has generally been defined. Data on 17 published intelligence GWASs (3 of which reported no associations whatsoever) were downloaded from the GWAS Catalog and analyzed in the current study. Results show a generally low rate of replication: over 87% of the 2,335 included SNPs were reported only once, and only 4 of the 17 studies included follow-up testing in a replication sample. Of these 4, none found any replicable genome-wide significant hits. The literature was also found to be severely lacking in diversity: all study samples in which ancestry was described were of European and/or British ancestry. Additionally, evidence of a "winner's curse" was found: among the minority of SNPs reported more than once, the associated p-value tended to be higher in subsequent studies than in the study in which it was originally reported, corresponding to weaker SNP-intelligence associations in later studies.IntroductionMany studies have previously reported evidence that the trait of human intelligence is under significant genetic influence. Specifically, it has long been estimated that intelligence has a high heritability (i.e. proportion of variation associated with genetic variation), with twin and family studies typically reporting narrow-sense heritability estimates of between 50% and 80%.\cite{Hill_2018} This conclusion, combined with the prevailing interpretation of heritability estimates as reflecting a genetic basis for a given trait, implies that between 50 and 80% of variation in intelligence is due to additive genetic factors specifically. This is because narrow-sense heritability estimates only include additive genetic effects.\cite{Hill_2018}\cite{Plomin_2014}However, genome-wide association studies (GWASs) have generally failed to reveal genes that account for all, or even most, of the reported heritability of intelligence. Instead, genetic variants reported to be associated with intelligence in GWASs invariably have very small effects, typically explaining no more than 1% of trait variance individually.\cite{Plomin_2014} Even when taken together, only about 20% of the heritability of intelligence can be accounted for by known DNA differences.\cite{Plomin_2018} There are two schools of thought to why the heritability of intelligence remains "missing", that is, unable to be accounted for by even the combination of all SNPs known to be associated with intelligence. The first is that the heritability of this trait, like that of most complex traits, is due to many genes of very small effect (e.g. \cite{Chabris_2015}, \cite{Plomin_2012}). The second is that the original estimates of heritability have been inaccurate, thereby misleading researchers into searching for genes to explain the heritability of IQ, when in fact such heritability estimates may be fatally confounded by environmental factors.\cite{Feldman2018} Distinguishing between these possibilities is difficult, but if the first explanation is true, then we should see a relatively high rate of replication of associations, especially with such higher-powered studies as are needed to detect the expected small effects.\cite{Chabris_2015}In this paper I aim to comprehensively assess whether GWASs of intelligence have generated replicable findings. In doing so, I hope to shed light on the extent to which genetic associations with intelligence reported in GWASs are false positives, as is known to be the case with many candidate gene studies,\cite{Chabris_2012} or whether the identified SNPs in GWASs actually contribute to differences in human intelligence. I will also examine other patterns in the GWAS literature on intelligence, such as whether these studies are done on populations of diverse ancestries, and whether there is a "winner's curse" in this literature with earlier studies reporting stronger associations than later ones.\cite{Xiao2009}MethodsOn April 7, 2019, I downloaded all data on the GWAS Catalog\cite{Buniello2019} for the trait of "intelligence" (URL: https://www.ebi.ac.uk/gwas/efotraits/EFO_0004337). This was done simply by clicking the button on the page linked in the previous sentence labeled "Download Catalog Data". I then assessed whether these associations had been replicable across different studies, as well as whether there was significant ancestral diversity in the populations on which the studies were conducted.Functional annotationConveniently, the GWAS Catalog database, and data downloaded from it, includes information about the functional status of each SNP (in the "CONTEXT" column). Specifically, it categorizes each SNP (with exceptions detailed in the "inconsistent reporting" section below) into exactly one of the following categories: