Showing posts with label genetics. Show all posts
Showing posts with label genetics. Show all posts

Thursday, 12 March 2026

Bishopblog catalogue (updated 12 Mar 2026)

Source: http://www.weblogcartoons.com/2008/11/23/ideas/

Those of you who follow this blog may have noticed a lack of thematic coherence. I write about whatever is exercising my mind at the time, which can range from technical aspects of statistics to the design of bathroom taps. I decided it might be helpful to introduce a bit of order into this chaotic melange, so here is a catalogue of posts by topic.

Language impairment, dyslexia and related disorders
The common childhood disorders that have been left out in the cold (1 Dec 2010) What's in a name? (18 Dec 2010) Neuroprognosis in dyslexia (22 Dec 2010) Where commercial and clinical interests collide: Auditory processing disorder (6 Mar 2011) Auditory processing disorder (30 Mar 2011) Special educational needs: will they be met by the Green paper proposals? (9 Apr 2011) Is poor parenting really to blame for children's school problems? (3 Jun 2011) Early intervention: what's not to like? (1 Sep 2011) Lies, damned lies and spin (15 Oct 2011) A message to the world (31 Oct 2011) Vitamins, genes and language (13 Nov 2011) Neuroscientific interventions for dyslexia: red flags (24 Feb 2012) Phonics screening: sense and sensibility (3 Apr 2012) What Chomsky doesn't get about child language (3 Sept 2012) Data from the phonics screen (1 Oct 2012) Auditory processing disorder: schisms and skirmishes (27 Oct 2012) High-impact journals (Action video games and dyslexia: critique) (10 Mar 2013) Overhyped genetic findings: the case of dyslexia (16 Jun 2013) The arcuate fasciculus and word learning (11 Aug 2013) Changing children's brains (17 Aug 2013) Raising awareness of language learning impairments (26 Sep 2013) Good and bad news on the phonics screen (5 Oct 2013) What is educational neuroscience? (25 Jan 2014) Parent talk and child language (17 Feb 2014) My thoughts on the dyslexia debate (20 Mar 2014) Labels for unexplained language difficulties in children (23 Aug 2014) International reading comparisons: Is England really do so poorly? (14 Sep 2014) Our early assessments of schoolchildren are misleading and damaging (4 May 2015) Opportunity cost: a new red flag for evaluating interventions (30 Aug 2015) The STEP Physical Literacy programme: have we been here before? (2 Jul 2017) Prisons, developmental language disorder, and base rates (3 Nov 2017) Reproducibility and phonics: necessary but not sufficient (27 Nov 2017) Developmental language disorder: the need for a clinically relevant definition (9 Jun 2018) Changing terminology for children's language disorders (23 Feb 2020) Developmental Language Disorder (DLD) in relaton to DSM5 (29 Feb 2020) Why I am not engaging with the Reading Wars (30 Jan 2022)

Autism
Autism diagnosis in cultural context (16 May 2011) Are our ‘gold standard’ autism diagnostic instruments fit for purpose? (30 May 2011) How common is autism? (7 Jun 2011) Autism and hypersystematising parents (21 Jun 2011) An open letter to Baroness Susan Greenfield (4 Aug 2011) Susan Greenfield and autistic spectrum disorder: was she misrepresented? (12 Aug 2011) Psychoanalytic treatment for autism: Interviews with French analysts (23 Jan 2012) The ‘autism epidemic’ and diagnostic substitution (4 Jun 2012) How wishful thinking is damaging Peta's cause (9 June 2014) NeuroPointDX's blood test for Autism Spectrum Disorder ( 12 Jan 2019) Biomarkers to screen for autism (again) (6 Dec 2022)

Developmental disorders/paediatrics
The hidden cost of neglected tropical diseases (25 Nov 2010) The National Children's Study: a view from across the pond (25 Jun 2011) The kids are all right in daycare (14 Sep 2011) Moderate drinking in pregnancy: toxic or benign? (21 Nov 2012) Changing the landscape of psychiatric research (11 May 2014) The sinister side of French psychoanalysis revealed (15 Oct 2019) A desire for clickbait can hinder an academic journal's reputation (4 Oct 2022) Polyunsaturated fatty acids and children's cognition: p-hacking and the canonisation of false facts (4 Sep 2023)

Genetics
Where does the myth of a gene for things like intelligence come from? (9 Sep 2010) Genes for optimism, dyslexia and obesity and other mythical beasts (10 Sep 2010) The X and Y of sex differences (11 May 2011) Review of How Genes Influence Behaviour (5 Jun 2011) Getting genetic effect sizes in perspective (20 Apr 2012) Moderate drinking in pregnancy: toxic or benign? (21 Nov 2012) Genes, brains and lateralisation (22 Dec 2012) Genetic variation and neuroimaging (11 Jan 2013) Have we become slower and dumber? (15 May 2013) Overhyped genetic findings: the case of dyslexia (16 Jun 2013) Incomprehensibility of much neurogenetics research ( 1 Oct 2016) A common misunderstanding of natural selection (8 Jan 2017) Sample selection in genetic studies: impact of restricted range (23 Apr 2017) Pre-registration or replication: the need for new standards in neurogenetic studies (1 Oct 2017) Review of 'Innate' by Kevin Mitchell ( 15 Apr 2019) Why eugenics is wrong (18 Feb 2020)

Neuroscience
Neuroprognosis in dyslexia (22 Dec 2010) Brain scans show that… (11 Jun 2011)  Time for neuroimaging (and PNAS) to clean up its act (5 Mar 2012) Neuronal migration in language learning impairments (2 May 2012) Sharing of MRI datasets (6 May 2012) Genetic variation and neuroimaging (1 Jan 2013) The arcuate fasciculus and word learning (11 Aug 2013) Changing children's brains (17 Aug 2013) What is educational neuroscience? ( 25 Jan 2014) Changing the landscape of psychiatric research (11 May 2014) Incomprehensibility of much neurogenetics research ( 1 Oct 2016)

Reproducibility
Accentuate the negative (26 Oct 2011) Novelty, interest and replicability (19 Jan 2012) High-impact journals: where newsworthiness trumps methodology (10 Mar 2013) Who's afraid of open data? (15 Nov 2015) Blogging as post-publication peer review (21 Mar 2013) Research fraud: More scrutiny by administrators is not the answer (17 Jun 2013) Pressures against cumulative research (9 Jan 2014) Why does so much research go unpublished? (12 Jan 2014) Replication and reputation: Whose career matters? (29 Aug 2014) Open code: note just data and publications (6 Dec 2015) Why researchers need to understand poker ( 26 Jan 2016) Reproducibility crisis in psychology ( 5 Mar 2016) Further benefit of registered reports ( 22 Mar 2016) Would paying by results improve reproducibility? ( 7 May 2016) Serendipitous findings in psychology ( 29 May 2016) Thoughts on the Statcheck project ( 3 Sep 2016) When is a replication not a replication? (16 Dec 2016) Reproducible practices are the future for early career researchers (1 May 2017) Which neuroimaging measures are useful for individual differences research? (28 May 2017) Prospecting for kryptonite: the value of null results (17 Jun 2017) Pre-registration or replication: the need for new standards in neurogenetic studies (1 Oct 2017) Citing the research literature: the distorting lens of memory (17 Oct 2017) Reproducibility and phonics: necessary but not sufficient (27 Nov 2017) Improving reproducibility: the future is with the young (9 Feb 2018) Sowing seeds of doubt: how Gilbert et al's critique of the reproducibility project has played out (27 May 2018) Preprint publication as karaoke ( 26 Jun 2018) Standing on the shoulders of giants, or slithering around on jellyfish: Why reviews need to be systematic ( 20 Jul 2018) Matlab vs open source: costs and benefits to scientists and society ( 20 Aug 2018) Responding to the replication crisis: reflections on Metascience 2019 (15 Sep 2019) Manipulated images: hiding in plain sight (13 May 2020) Frogs or termites: gunshot or cumulative science? ( 6 Jun 2020) Open data: We know what's needed - now let's make it happen (27 Mar 2021) A proposal for data-sharing the discourages p-hacking (29 Jun 2022) Can systematic reviews help clean up science (9 Aug 2022)Polyunsaturated fatty acids and children's cognition: p-hacking and the canonisation of false facts (4 Sep 2023) Book Review: Unreliable (Csaba Szabo) (Mar 16, 2025) Gold standard science isn't gold standard if it's applied selectively - firearms (Aug 26, 2025) Gold standard science isn't gold standard if it's applied selectively - autism (Aug 27, 2025) Wellcome LEAP's new $50M program (Oct 20, 2025) The dangers of using bibliometrics with polluted data (Nov 21, 2025)  

Statistics
Book review: biography of Richard Doll (5 Jun 2010) Book review: the Invisible Gorilla (30 Jun 2010) The difference between p < .05 and a screening test (23 Jul 2010) Three ways to improve cognitive test scores without intervention (14 Aug 2010) A short nerdy post about the use of percentiles (13 Apr 2011) The joys of inventing data (5 Oct 2011) Getting genetic effect sizes in perspective (20 Apr 2012) Causal models of developmental disorders: the perils of correlational data (24 Jun 2012) Data from the phonics screen (1 Oct 2012)Moderate drinking in pregnancy: toxic or benign? (1 Nov 2012) Flaky chocolate and the New England Journal of Medicine (13 Nov 2012) Interpreting unexpected significant results (7 June 2013) Data analysis: Ten tips I wish I'd known earlier (18 Apr 2014) Data sharing: exciting but scary (26 May 2014) Percentages, quasi-statistics and bad arguments (21 July 2014) Why I still use Excel ( 1 Sep 2016) Sample selection in genetic studies: impact of restricted range (23 Apr 2017) Prospecting for kryptonite: the value of null results (17 Jun 2017) Prisons, developmental language disorder, and base rates (3 Nov 2017) How Analysis of Variance Works (20 Nov 2017) ANOVA, t-tests and regression: different ways of showing the same thing (24 Nov 2017) Using simulations to understand the importance of sample size (21 Dec 2017) Using simulations to understand p-values (26 Dec 2017) One big study or two small studies? ( 12 Jul 2018) Time to ditch relative risk in media reports (23 Jan 2020)

Journalism/science communication
Orwellian prize for scientific misrepresentation (1 Jun 2010) Journalists and the 'scientific breakthrough' (13 Jun 2010) Orwellian prize for journalistic misrepresentation: an update (29 Jan 2011) Academic publishing: why isn't psychology like physics? (26 Feb 2011) Scientific communication: the Comment option (25 May 2011)  Publishers, psychological tests and greed (30 Dec 2011) Time for academics to withdraw free labour (7 Jan 2012) 2011 Orwellian Prize for Journalistic Misrepresentation (29 Jan 2012) Communicating science in the age of the internet (13 Jul 2012) How to bury your academic writing (26 Aug 2012) Schizophrenia and child abuse in the media (26 May 2013) Why we need pre-registration (6 Jul 2013) On the need for responsible reporting of research (10 Oct 2013) Psychology research: hopeless case or pioneering field? (28 Aug 2015) When scientific communication is a one-way street (13 Dec 2016) Time to ditch relative risk in media reports (23 Jan 2020) Book Review. Fiona Fox: Beyond the Hype (12 Apr 2022)

Academic Publishing
Science journal editors: a taxonomy (28 Sep 2010) Time for neuroimaging (and PNAS) to clean up its act (5 Mar 2012) High-impact journals: where newsworthiness trumps methodology (10 Mar 2013)  A short rant about numbered journal references (5 Apr 2013) Desperate marketing from J. Neuroscience ( 18 Feb 2016) Editorial integrity: publishers on the front line ( 11 Jun 2016) A New Year's letter to academic publishers (4 Jan 2014) Journals without editors: What is going on? (1 Feb 2015) Editors behaving badly? (24 Feb 2015) Will Elsevier say sorry? (21 Mar 2015) How long does a scientific paper need to be? (20 Apr 2015) Will traditional science journals disappear? (17 May 2015) My collapse of confidence in Frontiers journals (7 Jun 2015) Publishing replication failures (11 Jul 2015) Breaking the ice with buxom grapefruits: Pratiques de publication and predatory publishing (25 Jul 2017) Should editors edit reviewers? ( 26 Aug 2018) Corrigendum: a word you may hope never to encounter (3 Aug 2019) Percent by most prolific author score and editorial bias (12 Jul 2020) PEPIOPs – prolific editors who publish in their own publications (16 Aug 2020) Faux peer-reviewed journals: a threat to research integrity (6 Dec 2020) Time for publishers to consider the rights of readers as well as authors (13 Mar 2021) Universities vs Elsevier: who has the upper hand? (14 Nov 2021) We need to talk about editors (6 Sep 2022) So do we need editors? (11 Sep 2022) Reviewer-finding algorithms: the dangers for peer review (30 Sep 2022) A desire for clickbait can hinder an academic journal's reputation (4 Oct 2022) What is going on in Hindawi special issues? (12 Oct 2022) New Year's Eve Quiz: Dodgy journals special (31 Dec 2022) A suggestion for e-Life (20 Mar 2023) Papers affected by misconduct: Erratum, correction or retraction? (11 Apr 2023) Is Hindawi “well-positioned for revitalization?” (23 Jul 2023) The discussion section: Kill it or reform it? (14 Aug 2023) Spitting out the AI Gobbledegook sandwich: a suggestion for publishers (2 Oct 2023) The world of Poor Things at MDPI journals (Feb 9 2024) Some thoughts on eLife's New Model: One year on (Mar 27 2024) Does Elsevier's negligence pose a risk to public health? (Jun 20 2024) Collapse of scientific standards at MDPI journals: a case study (Jul 23 2024) My experience as a reviewer for MDPI (Aug 8 2024) Optimizing research integrity investigations: the need for evidence (Aug 22 2024) Now you see it, now you don't: the strange world of disappearing Special Issues at MDPI (Sep 4 2024) Prodding the behemoth with a stick (Sep 14 2024) Using PubPeer to screen editors (Sep 24 2024) An open letter regarding Scientific Reports (Oct 16 2024) What's going on at the Journal of Psycholinguistic Research? (Oct 21, 2024) Finland vs Germany: the case of MDPI (Dec 23, 2024) Tomatoes roaming the fields: another embarrassing paper for MDPI (Jan 18, 2025) IEEE has a pseudoscience problem (Feb 22, 2025) Trouble at t'(review) mill: How MDPI lets down authors (July 21 ,2025) New publishing models will only work if authors embrace them (July 31, 2025) Problems with eLife's new article type: Replication studies (Oct 27, 2025) The inner workings of a paper mill (Nov 8, 2025) An open letter to the BMJ editorial board (Jan 5, 2026) An analysis of PubPeer comments on highly-cited retracted articles (Feb 2, 2026) Stealth corrections are still a threat to academic integrity (Feb 20, 2026) 

Social Media
A gentle introduction to Twitter for the apprehensive academic (14 Jun 2011) Your Twitter Profile: The Importance of Not Being Earnest (19 Nov 2011) Will I still be tweeting in 2013? (2 Jan 2012) Blogging in the service of science (10 Mar 2012) Blogging as post-publication peer review (21 Mar 2013) The impact of blogging on reputation ( 27 Dec 2013) WeSpeechies: A meeting point on Twitter (12 Apr 2014) Email overload ( 12 Apr 2016) How to survive on Twitter - a simple rule to reduce stress (13 May 2018)

Academic life
An exciting day in the life of a scientist (24 Jun 2010) How our current reward structures have distorted and damaged science (6 Aug 2010) The challenge for science: speech by Colin Blakemore (14 Oct 2010) When ethics regulations have unethical consequences (14 Dec 2010) A day working from home (23 Dec 2010) Should we ration research grant applications? (8 Jan 2011) The one hour lecture (11 Mar 2011) The expansion of research regulators (20 Mar 2011) Should we ever fight lies with lies? (19 Jun 2011) How to survive in psychological research (13 Jul 2011) So you want to be a research assistant? (25 Aug 2011) NHS research ethics procedures: a modern-day Circumlocution Office (18 Dec 2011) The REF: a monster that sucks time and money from academic institutions (20 Mar 2012) The ultimate email auto-response (12 Apr 2012) Well, this should be easy…. (21 May 2012) Journal impact factors and REF2014 (19 Jan 2013)  An alternative to REF2014 (26 Jan 2013) Postgraduate education: time for a rethink (9 Feb 2013)  Ten things that can sink a grant proposal (19 Mar 2013)Blogging as post-publication peer review (21 Mar 2013) The academic backlog (9 May 2013)  Discussion meeting vs conference: in praise of slower science (21 Jun 2013) Why we need pre-registration (6 Jul 2013) Evaluate, evaluate, evaluate (12 Sep 2013) High time to revise the PhD thesis format (9 Oct 2013) The Matthew effect and REF2014 (15 Oct 2013) The University as big business: the case of King's College London (18 June 2014) Should vice-chancellors earn more than the prime minister? (12 July 2014)  Some thoughts on use of metrics in university research assessment (12 Oct 2014) Tuition fees must be high on the agenda before the next election (22 Oct 2014) Blaming universities for our nation's woes (24 Oct 2014) Staff satisfaction is as important as student satisfaction (13 Nov 2014) Metricophobia among academics (28 Nov 2014) Why evaluating scientists by grant income is stupid (8 Dec 2014) Dividing up the pie in relation to REF2014 (18 Dec 2014)  Shaky foundations of the TEF (7 Dec 2015) A lamentable performance by Jo Johnson (12 Dec 2015) More misrepresentation in the Green Paper (17 Dec 2015) The Green Paper’s level playing field risks becoming a morass (24 Dec 2015) NSS and teaching excellence: wrong measure, wrongly analysed (4 Jan 2016) Lack of clarity of purpose in REF and TEF ( 2 Mar 2016) Who wants the TEF? ( 24 May 2016) Cost benefit analysis of the TEF ( 17 Jul 2016)  Alternative providers and alternative medicine ( 6 Aug 2016) We know what's best for you: politicians vs. experts (17 Feb 2017) Advice for early career researchers re job applications: Work 'in preparation' (5 Mar 2017) Should research funding be allocated at random? (7 Apr 2018) Power, responsibility and role models in academia (3 May 2018) My response to the EPA's 'Strengthening Transparency in Regulatory Science' (9 May 2018) More haste less speed in calls for grant proposals ( 11 Aug 2018) Has the Society for Neuroscience lost its way? ( 24 Oct 2018) The Paper-in-a-Day Approach ( 9 Feb 2019) Benchmarking in the TEF: Something doesn't add up ( 3 Mar 2019) The Do It Yourself conference ( 26 May 2019) A call for funders to ban institutions that use grant capture targets (20 Jul 2019) Research funders need to embrace slow science (1 Jan 2020) Should I stay or should I go: When debate with opponents should be avoided (12 Jan 2020) Stemming the flood of illegal external examiners (9 Feb 2020) What can scientists do in an emergency shutdown? (11 Mar 2020) Stepping back a level: Stress management for academics in the pandemic (2 May 2020)
TEF in the time of pandemic (27 Jul 2020) University staff cuts under the cover of a pandemic: the cases of Liverpool and Leicester (3 Mar 2021) Some quick thoughts on academic boycotts of Russia (6 Mar 2022) When there are no consequences for misconduct (16 Dec 2022) Open letter to CNRS (30 Mar 2023) When privacy rules protect fraudsters (Oct 12, 2023) Defence against the dark arts: a proposal for a new MSc course (Nov 19, 2023) An (intellectually?) enriching opportunity for affiliation (Feb 2 2024) Just make it stop! When will we say that further research isn't needed? (Mar 24 2024) Are commitments to open data policies worth the paper they are written on? (May 26 2024) Whistleblowing, research misconduct, and mental health (Jul 1 2024) I don't care about journal impact factors but I do care about visibility (Oct 27, 2024) Why I have resigned from the Royal Society (Nov 25, 2024) Seven reasons for keeping Elon Musk as a Fellow of the Royal Society (Feb 12, 2025) The dangers of using bibliometrics with polluted data (Nov 21, 2025)

Celebrity scientists/quackery
Three ways to improve cognitive test scores without intervention (14 Aug 2010) What does it take to become a Fellow of the RSM? (24 Jul 2011) An open letter to Baroness Susan Greenfield (4 Aug 2011) Susan Greenfield and autistic spectrum disorder: was she misrepresented? (12 Aug 2011) How to become a celebrity scientific expert (12 Sep 2011) The kids are all right in daycare (14 Sep 2011)  The weird world of US ethics regulation (25 Nov 2011) Pioneering treatment or quackery? How to decide (4 Dec 2011) Psychoanalytic treatment for autism: Interviews with French analysts (23 Jan 2012) Neuroscientific interventions for dyslexia: red flags (24 Feb 2012) Why most scientists don't take Susan Greenfield seriously (26 Sept 2014) NeuroPointDX's blood test for Autism Spectrum Disorder ( 12 Jan 2019) Low-level lasers. Part 1. Shining a light on an unconventional treatment for autism (Nov 25, 2023) Low-level lasers. Part 2. Erchonia and the universal panacea (Dec 5, 2023)

Women
Academic mobbing in cyberspace (30 May 2010) What works for women: some useful links (12 Jan 2011) The burqua ban: what's a liberal response (21 Apr 2011) C'mon sisters! Speak out! (28 Mar 2012) Psychology: where are all the men? (5 Nov 2012) Should Rennard be reinstated? (1 June 2014) How the media spun the Tim Hunt story (24 Jun 2015)

Politics and Religion
Lies, damned lies and spin (15 Oct 2011) A letter to Nick Clegg from an ex liberal democrat (11 Mar 2012) BBC's 'extensive coverage' of the NHS bill (9 Apr 2012) Schoolgirls' health put at risk by Catholic view on vaccination (30 Jun 2012) A letter to Boris Johnson (30 Nov 2013) How the government spins a crisis (floods) (1 Jan 2014) The alt-right guide to fielding conference questions (18 Feb 2017) We know what's best for you: politicians vs. experts (17 Feb 2017) Barely a good word for Donald Trump in Houses of Parliament (23 Feb 2017) Do you really want another referendum? Be careful what you wish for (12 Jan 2018) My response to the EPA's 'Strengthening Transparency in Regulatory Science' (9 May 2018) What is driving Theresa May? ( 27 Mar 2019) A day out at 10 Downing St (10 Aug 2019) Voting in the EU referendum: Ignorance, deceit and folly ( 8 Sep 2019) Harry Potter and the Beast of Brexit (20 Oct 2019) Attempting to communicate with the BBC (8 May 2020) Boris bingo: strategies for (not) answering questions (29 May 2020) Linking responsibility for climate refugees to emissions (23 Nov 2021) Response to Philip Ball's critique of scientific advisors (16 Jan 2022) Boris Johnson leads the world ....in the number of false facts he can squeeze into a session of PMQs (20 Jan 2022) Some quick thoughts on academic boycotts of Russia (6 Mar 2022) Contagion of the political system (3 Apr 2022)When there are no consequences for misconduct (16 Dec 2022)

Humour and miscellaneous Orwellian prize for scientific misrepresentation (1 Jun 2010) An exciting day in the life of a scientist (24 Jun 2010) Science journal editors: a taxonomy (28 Sep 2010) Parasites, pangolins and peer review (26 Nov 2010) A day working from home (23 Dec 2010) The one hour lecture (11 Mar 2011) The expansion of research regulators (20 Mar 2011) Scientific communication: the Comment option (25 May 2011) How to survive in psychological research (13 Jul 2011) Your Twitter Profile: The Importance of Not Being Earnest (19 Nov 2011) 2011 Orwellian Prize for Journalistic Misrepresentation (29 Jan 2012) The ultimate email auto-response (12 Apr 2012) Well, this should be easy…. (21 May 2012) The bewildering bathroom challenge (19 Jul 2012) Are Starbucks hiding their profits on the planet Vulcan? (15 Nov 2012) Forget the Tower of Hanoi (11 Apr 2013) How do you communicate with a communications company? ( 30 Mar 2014) Noah: A film review from 32,000 ft (28 July 2014) The rationalist spa (11 Sep 2015) Talking about tax: weasel words ( 19 Apr 2016) Controversial statues: remove or revise? (22 Dec 2016) The alt-right guide to fielding conference questions (18 Feb 2017) My most popular posts of 2016 (2 Jan 2017) An index of neighbourhood advantage from English postcode data ( 15 Sep 2018) Working memories: A brief review of Alan Baddeley's memoir ( 13 Oct 2018) New Year's Eve Quiz: Dodgy journals special (31 Dec 2022) Retrospective look at blog highlights of 2024 (Jan 1, 2025)

Tuesday, 6 December 2022

Biomarkers to screen for autism (again)


Diagnosis of autism from biomarkers is a holy grail for biomedical researchers. The days when it was thought we would find “the autism gene” are long gone, and it’s clear that both the biology and the psychology of autism is highly complex and heterogeneous. One approach is to search for individual genes where mutations are more likely in those with autism. Another is to address the complexity head-on by looking for combinations of biomarkers that could predict who has autism.  The latter approach is adopted in a paper by Bao et al (2022) who claimed that an ensemble of gene expression measures taken from blood samples could accurately predict which toddlers were autistic (ASD) and which were typically-developing (TD). An anonymous commenter on PubPeer queried whether the method was as robust as the authors claimed, arguing that there was evidence for “overfitting”. I was asked for my thoughts by a journalist, and they were complicated enough to merit a blogpost.  The bottom line is that there are reasons to be cautious about the conclusion of the authors that they have developed “an innovative and accurate ASD gene expression classifier”.

 

Some of the points I raise here applied to a previous biomarker study that I blogged about in 2019. These are general issues about the mismatch between what is done in typical studies in this area and what is needed for a clinically useful screening test.

 

Base rates

Consider first how a screening test might be used. One possibility is that there might be a move towards universal screening, allowing early diagnosis that might help ensure intervention starts young.  But for effective screening in that context, you need extremely high diagnostic accuracy, and accuracy depends on the frequency of autism in the population.  I discussed this back in 2010. The levels of accurate classification reported by Bao et al would be of no use for population screening because there would be an extremely high rate of false positives, given that most children don’t have autism.

 

Diagnostic specificity

But, you may say, we aren’t talking about universal screening.  The test might be particularly useful for those who either (a) already have an older child with autism, or (b) are concerned about their child’s development.  Here the probability of a positive autism diagnosis is higher than in the general population.  However, if that’s what we are interested in, then we need a different comparison group – not typically-developing toddlers, but unaffected siblings of children with autism, and/or children with other neurodevelopmental disorders.   

When I had a look at the code that the authors deposited for data analysis, it implied that they did have data on children with more general developmental delays, and sibs of those with autism, but they are not reported in this paper. 

 

The analyses done by the researchers are extremely complex and time-consuming, and it is understandable that they may prefer to start out with the clearest case of comparing autism with typically-developing children. But the acid test of the suitability of the classifier for clinical use would be a demonstration that it could distinguish children with autism from unaffected siblings, and from nonautistic children with intellectual disability.

 

Reliability of measures

If you run a diagnostic test, an obvious question is whether you’d get the same result on a second test run.  With biological and psychological measures the answer is almost always no, but the key issue for a screener is just how much change there is. Gene expression levels could vary from occasion to occasion depending on time of day or what you’d eaten – I have no idea how important this might be, but it's not possible to evaluate in this paper, where measures come from a single blood sample. My personal view is that the whole field of biomedical research needs to wake up to the importance of reliability of measurement so that researchers don’t waste time exploring the predictive power of measures that may be too unreliable to be useful.  Information about stability of measures over time is a basic requirement for any diagnostic measure.

 

A related issue concerns comparability of procedures for autism and TD groups. Were blood samples collected by the same clinicians over the same period and processed in the same lab for these two groups? Were the blood analyses automated and/or done blind? It’s crucial to be confident that minor differences in clinical or lab procedures do not bias results in this kind of study.

 

Overfitting

Overfitting is really just a polite way of saying that the data may be noise. If you run enough analyses, something is bound to look significant, just by chance.  In the first step of the analysis, the researchers ran 42,840 models on “training” data from 93 autistic and 82 TD children and found 1,822 of them performed better than .80 on a measure that reflects diagnostic accuracy (AUC-ROC – which roughly corresponds to proportion correctly classified: .50 is chance, and 1.00 is perfect classification).  So we can see that just over 4% of the models (1822/42840) performed this well.

 

The researchers were aware of the possibility of overfitting, and they addressed it head-on, saying: “To test this, we permuted the sample labels (i.e., ASD and TD) for all subjects in our Training set and ran the pipeline to test all feature engineering and classification methods. Importantly, we tested all 42,840 candidate models and found the median AUC-ROC score was 0.5101 with the 95th CI (0.42–0.65) on the randomized samples. As expected, only rare chance instances of good 'classification' occurred.”  The distribution of scores is shown in Figure 2b. 

 

 


Figure 2b from Bao et al (2022)

 

They then ran a further analysis on a “test set” of 34 autistic and 31 TD children who had been held out of the original analysis, and found that 742 of the 1822 models performed better than .80 in classification. That’s 40% of the tested models.  Assuming I have understood the methods correctly, that does look meaningful and hard to explain just in terms of statistical noise.  In effect, they have run a replication study and found that a substantial subset of the identified models do continue to separate autism and TD groups when new children are considered. The claim is that there is substantial overlap in the models that fall in the right-hand area under the curve for the red and pink distributions.

 

The PubPeer commenter seems concerned that results look too good to be true. In particular, Figure 2b suggests the models perform a bit better in the test set than in the training set. But the figure shows the distribution of scores for all the models (not just the selected models) and, given the small sample sizes, the differences between distributions does not seem large to me. I was more surprised by the relatively tight distribution of AUC-ROC values obtained in the permutation analysis, as I would have anticipated some models would have given high classification accuracy just by chance in a sample of this size.

The researchers went on to present data for the set of models that achieved .8 classification in both training and test sets. This seemed a reasonable approach to me. The PubPeer commenter is correct in arguing that there will be some bias caused by selecting models this way, and that one would expect  less good performance in a completely new sample, but the 2-stage selection of models would seem to ensure there is not "massive overfitting". I think there would be a problem if only 4% of the 1822 selected models had given accurate classification, but the good rate of agreement between the models selected in the training and test samples, coupled with the lack of good models in the permuted data, suggests there is a genuine effect here. 

 

Conclusion

So, in sum, I think that the results can’t just be attributed to overfitting, but I nevertheless have reservations about whether they would be useful for screening for autism.  And one of the first things I’d check if I were the researchers would be the reliability of the diagnostic classification in repeated blood samples taken on different occasions, as that would need to be high for the test to be of clinical use.

 

Note: I'd welcome comments or corrections on this post. Please note, comments are moderated to avoid spam, and so may not appear immediately. If you post a comment and it has not appeared in 24 hr, please email me and I'll ensure it gets posted. 

 PS. See comment from original PubPeer poster attached. 

Also, 8th Dec 2022, I added a further PubPeer comment asking authors to comment on Figure 2B, which does seem odd. 

https://pubpeer.com/publications/B693366B2B51D143C713359F151F7B#4  


PPS. 10th Dec 2022. 

Author Eric Courchesne has responded to several of the points made in this blogpost on Pubpeer: https://pubpeer.com/publications/B693366B2B51D143C713359F151F7B#5  



 


 

 

 

 

 

 

 

 

Sunday, 23 April 2017

Sample selection in genetic studies: impact of restricted range


I'll shortly be posting a preprint about methodological quality of studies in the field of neurogenetics. It's something I've been working on with a group of colleagues for a while, and we are aiming to make recommendations to improve the field.

I won't go into details here, as you will be able to read the preprint fairly soon. Instead, what I want to do here is to expand on a small point that cropped up as I looked at this literature, and which I think is underappreciated.

It's to do with sampling. There's a particular problem that I started to think about a while back when I heard someone give a talk about a candidate gene study. I can't remember who it was or even what the candidate gene was, but basically they took a bunch of students, genotyped them, and then looked for associations between their genotypes and measures of memory. They were excited because they found some significant results. But I was, as usual, sitting there thinking convoluted thoughts about all of this, and wondering whether it really made sense. In particular, if you have a common genetic variant that has such a big effect on memory, would this really show up in a bunch of students – who are presumably people who have pretty good memories? Wouldn't it rather be the case that what you'd expect would be an alteration in the frequencies of genotypes in the student population?

Whenever I have an intuition like that, I find the best thing to do is to try a simulation. Sometimes the intuition is confirmed, and sometimes things turn out different and, very often, more complicated.

But this time, I'm pleased to say my intuition seems to have something going for it.

So here's the nuts and bolts.

I simulated genotypes and associated phenotypes by just using R's nice mvrnorm function. For the examples below, I specified that a and A are equally common (i.e. minor allele frequency is .5), so we have 25% as aa, 50% as aA, and 25% AA. The script lets you specify how closely these are related to the phenotype, but from what we know about genetics, it's very unlikely that a common variant would have a value more than about .25.

We can then test for two things:
1)  How far does the distribution of genotypes in the sample (i.e. people who are aa, aA or AA) resemble that in the general population? If we know that MAF is .5, we expect this distribution to be 1:2:1.
2) We can assign each person a score corresponding to number of A alleles (coding aa as zero, aA as 1, and AA as 2) and look at the regression of the phenotype on the genotype. That's the standard approach to looking for genotype-phenotype association.

If we work with the whole population of simulated data, these values will correspond to those that we specified in setting up the simulation, provided we have a reasonably large sample size.

But what if we take a selective sample of cases who fall above some cutoff on the phenotype? This is equivalent to taking, for instance, a sample from a student population from a selective institution, when the phenotype is a measure of cognitive function. You're not likely to get into the institution unless you have a good cognitive ability. Then, working with this selected subgroup, we recompute our two measures, i.e. the proportions of each genotype, and the correlation between the genotype and the phenotype.

Now, the really interesting thing here is that, as the selection cutoff gets more extreme, two things happen:
a) The proportions of people with different genotypes starts to depart from the values expected for the population in general. We can test to see when the departure becomes statistically significant with a chi square test.
b) The regression of the phenotype on the genotype weakens. We can quantify this effect by just computing the p-value associated with the correlation between genotype and phenotype.

Figure 1: Genotype-phenotype associations for samples selected on phenotype

Figure 1 shows the mean phenotype scores for each genotype for three samples: an unselected sample, a sample selected with z-score cutoff zero (corresponding to the top 50% of the population on the phenotype) and a sample selected with z-score cutoff of .5 (roughly selecting the top third of the population).

It's immediately apparent from the figure that the selection dramatically weakens the association between genotype and phenotype. In effect, we are distorting the relationship between genotype and phenotype by focusing just on a restricted range. 

Comparison of p-values from conventional regression approach and chi square test on genotype frequencies in relation to sample selection

Figure 2 shows the data from another perspective, by considering the statistical results from a conventional regression analysis, when different z-score cutoffs are used, selecting an increasingly extreme subset of the population. If we take a cutoff of zero – in effect selecting just the top half of the population, the regression effect (predicting phenotype from genotype), shown in the blue line, which was strong in the full population, is already much reduced. If you select only people with z-scores of .5 or above (equivalent to an IQ score of around 108), then the regression is no longer significant. But notice what happens to the black line. This shows the p-value from a chi square test which compares the distribution of genotypes in relation to expected population values in each subsample. If there is a true association between genotype and phenotype, then greater the selection on the phenotpe, the more the genotype distribution departs from expected values. The specific patterns observed will depend on the true association in the population and on the sample size, but this kind of cross-over is a typical result.

So what's the moral of this exercise? Well, if you are interested in a phenotype that has a particular distribution in the general population, you need to be careful when selecting a sample for a genetic association study. If you pick a sample that has a restricted range of phenotypes relative to the general population, then you make it less likely that you will detect a true genetic association in a conventional regression analysis. In fact, if you take a selected sample, there comes a point when the optimal way to demonstrate an association is by looking for a change in the frequency of different genotypes in the selected population vs the general population.

No doubt this effect is already well-known to geneticists, and it's all pretty obvious to anyone who is statistically savvy, but I was pleased to be able to quantify the effect via simulations. It is clear that it has implications for those who work predominantly with selected samples such as university students. For some phenotypes, use of a student sample may not be a problem, provided they are similar to the general population in the range of phenotype scores. But for cognitive phenotypes that's very unlikely, and attempting to show genetic effects in such samples seems a doomed enterprise.

The script for this simulation, simulating genopheno cutoffs.R should be available here: 
https://github.com/oscci/SQING_repo

(This link updated on 29/4/17).






Sunday, 8 January 2017

A common misunderstanding of natural selection

-->

-->
© cartoonstock.com
My attention was drawn today to an article in the Atlantic, entitled ‘Why Do Humans Still Have a Gene That Increases the Risk of Alzheimer’s?’ It noted that there are variants of the apoliprotein gene that are associated with an 8- to 12-fold increased risk of the disease. It continued:
“It doesn’t make sense,” says Ben Trumble, from Arizona State University. “You’d have thought that natural selection would have weeded out ApoE4 a long time ago. The fact that we have it at all is a little bizarre.”

The article goes on to discuss research suggesting there might be some compensating advantage to the Alzheimer risk gene variants in terms of protection from brain parasites.

That is as may be – I haven’t studied the research findings – but I do take issue with the claim that the persistence of the risk variants in humans is ‘a little bizarre’.

The quote indicates a common misunderstanding of how natural selection works. In evolution, what matters is whether an individual leaves surviving offspring. If you don’t have any descendants, then gene variants that are specific to you will inevitably disappear from the population. Alzheimer’s is an unpleasant condition that impairs ability to function independently, but the onset is typically long after  child-bearing years are over. If a disease doesn’t affect the likelihood that you have surviving children, then it is irrelevant as far as natural selection is concerned. As Max Coltheart replied when I tweeted about this: “evolution doesn't care about the cost of living in an aged-care facility”.