2026 · Nature Genetics
Large-scale exome analyses reveal new rare variant contributions in amyotrophic lateral sclerosis
Verdict
UNSCIENTIFIC
Neither the paradigm nor the study inside it passes. The verdict is unscientific.
1·Study methodology · inside the paradigm
UNSCIENTIFIC
Does not follow the scientific method within Genomics / DNA Model.
2·Paradigm · Genomics / DNA Model
UNSCIENTIFIC
Fails the tests. This decides the verdict.
How the verdict is decided
The verdict grades the whole picture: the paradigm this study assumes, and how the study was carried out inside it. A study can follow the scientific method rigorously and still be unscientific, because rigor inside a paradigm only shows the conclusion was reached carefully. It cannot verify the premise the paradigm rests on. So the paradigm decides the verdict. The study’s own score is kept because it shows how the conclusion was built.
1·Study methodology
Did this study test its claim with methods that are independent, falsifiable, and non-circular?
UNSCIENTIFIC
This paper claims that rare DNA variants in specific genes contribute to ALS risk, identified by sequencing exomes from thousands of people with and without the disease. All three tests fail.
Independently Verifiable
Fail
Sequencers produced fluorescent signals from extracted DNA, and software interpreted those signals as genetic variants using the DNA model. Nobody could see a variant. They saw signals on a screen and the model told them what the signals meant.
Falsifiable
Fail
If the study found no variant associations, the field would call it insufficient power or missing heritability, not evidence that DNA variants do not cause ALS. The design offered a null the paradigm would never accept as a disproof.
Non-Circular
Fail
The reference genome, the variant calling pipeline, the gene models, and the annotation tools were all built from the DNA model the study claims to validate.
Why
They took DNA from roughly 18,000 people diagnosed with ALS and 200,000 controls. Sequencing machines produced fluorescent signals from that DNA. Software aligned those signals to a reference genome assembled from prior sequencing using the DNA model. Software called variants using a GATK pipeline built on the same model. Software annotated those variants using snpEff and Ensembl gene models, both constructed from the DNA framework. They counted how often certain variants appeared more in cases than controls and called the statistical associations new risk genes. The variant classifications, the impact predictions, and the pathogenicity scores were all generated by tools built from the model. At no point did anyone see a variant damage a neuron. They saw signals on a screen and the model told them what the signals meant. The premise that DNA variants cause disease went in untested and came out looking like a fact.
2·Paradigm · Genomics / DNA Model
Does the framework this study assumes pass the three tests?
UNSCIENTIFIC
Independently Verifiable
Fail
The DNA model's entities, genes and variants, only exist as model-dependent interpretations of fluorescent signals. Nobody has watched a variant cause a neuron to die.
Falsifiable
Fail
When gene therapy fails to cure disease, nobody concludes the DNA model is wrong. They call it poor delivery, wrong target, or complex biology. This paper's null associations would be absorbed the same way.
Non-Circular
Fail
The sequencer, the reference genome, the variant caller, and the gene databases were all built from the DNA model the paradigm claims to validate.
Why
The DNA model claims that a molecule contains a hereditary code and that variations in that code cause disease. Nobody has observed a variant kill a motor neuron. The sequencer produces fluorescent signals. The model tells you those signals represent variants in genes. The reference genome was assembled using the model. The variant callers were built using the model. The gene databases were constructed using the model. When a gene therapy fails, the paradigm calls it a delivery problem. When an association is not found, the paradigm calls it insufficient power. When most disease risk is unexplained, the paradigm calls it missing heritability or oligogenic risk. The model has an absorber for every possible negative result. This paper's claim that rare variants contribute to ALS risk is the model running its own process and reporting the output as evidence. The premise went in untested and came out looking like a discovery. The model treats ALS as a genetic defect rather than as the body's response to terrain damage, and no result from this study could challenge that framing.
From the paper
All raw sequencing data were aligned to the GRCh38 reference genome using BWA-mem according to the functional equivalence pipeline described by Regier et al.
Variants were annotated using snpEff, dbscSNV and Ensembl Release v.105 gene models.
Rare variant analyses identified several new risk genes, with replication confirming association of YKT6 and supporting HTR3C, GBGT1 and KNTC1.
We identified 15 exome-wide significant variants across 11 distinct genes (P < 1.83 × 10−7)
Source
Large-scale exome analyses reveal new rare variant contributions in amyotrophic lateral sclerosis
Hop, Paul J.; Kooyman, Maarten; Kenna, Brendan J.; Zwamborn, Ramona A. J.; van Eijk, Kristel R.; Wang, Yan; van Dijk, Charlotte H.; Bekema, Erwin; van Rheenen, Wouter; Beele, Paul; van Vugt, Joke J. F. A.; Project MinE ALS sequencing Consortium; Van Damme, Philip; van den Berg, Leonard H.; de Carvalho, Mamede; NYGC ALS Consortium; FALS sequencing Consortium; Smith, Bradley N.; GTAC Consortium; Khleifat, Ahmad Al; Iacoangeli, Alfredo; Cooper-Knock, Johnathan; Smith, Bradley N.; Topp, Simon; van der Kooi, Anneke J.; Fominykh, Vera; Drory, Vivian; Lerner, Yossef; Shovman, Yehuda; Rowe, Dominic B.; Williams, Kelly L.; McLaughlin, Russell L.; Hurt, Jessica; Huang, Yunfeng; Chen, Chia-Yen; Tsai, Ellen; Runz, Heiko; Aronica, Eleonora; Groen, Ewout J. N.; van Es, Michael A.; Pasterkamp, R. Jeroen; Farhan, Sali M. K.; Garton, Fleur C.; McRae, Allan F.; McCombe, Pamela A.; Henderson, Robert D.; Fan, Dongsheng; Šlachtová, Lenka; Høyer, Helle; Nishimura, Agnes L.; Cauchi, Ruben J.; Brylev, Lev; Rogelj, Boris; Koritnik, Blaž; Zidar, Janez; Salas, Teresa; Mora Pardina, Jesus S.; Gotkine, Marc; Povedano, Monica; Corcia, Philippe; Vourc’h, Patrick; Couratier, Philippe; Weber, Markus; Kiernan, Matthew C.; Pamphlett, Roger; Blair, Ian P.; de Carvalho, Mamede; Başak, Nazli A.; Ingre, Caroline; Andersen, Peter M.; Zinman, Lorne; Rogaeva, Ekaterina; MacKenzie, Ian R.; Dupre, Nicolas; Rouleau, Guy A.; Traynor, Bryan J.; Ticozzi, Nicola; Chiò, Adriano; Silani, Vincenzo; Hardiman, Orla; Phatnani, Hemali; Harms, Matthew B.; Dalgard, Clifton L.; Glass, Jonathan D.; Landers, John E.; Van Damme, Philip; Morrison, Karen E.; Shaw, Pamela J.; Shaw, Chris E.; Al-Chalabi, Ammar; van den Berg, Leonard H.; Kenna, Kevin P.; Veldink, Jan H.
2026 · Nature Genetics
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