Malevolent Design - The Diseases That Wouldn't Exist if an Intelligent Designer Was Real

Genomic Maps Untangle the Complex Roots of Disease

In another major embarrassment for those creationists who understand it, researchers at the Gladstone Institutes and Stanford University have developed a method for linking the genome of a cell to diseases caused by specific gene variants. They have recently published their findings, open access, in Nature.

Creationists insist that the human genome was intelligently designed, with every outcome the result of “complex specified information” which, according to Discovery Institute Fellow William A. Dembski, constitutes definitive evidence of intelligent design. If this were true, it would follow that genes which cause disease were intelligently designed to cause those diseases.

The difficulty deepens for creationists when one considers that many diseases involve multiple genes, sometimes hundreds or even thousands, all of which must possess the “correct” variants for the disease to emerge. In other words, some diseases not only depend on Dembski’s “complex specified genetic information”, but also conform to Michael J. Behe’s proposed hallmark of intelligent design: irreducible complexity.

Unless creationists invoke an additional creator—one over whom their reputedly omnipotent and omniscient god has no control—their supposedly intelligent designer must have deliberately created these gene variants to produce the suffering they cause.

By contrast, the evolutionary explanation requires no such mental gymnastics. The existence of genetic variants is exactly what evolutionary theory predicts, and provided such variants remain rare within a population, there is little selective pressure to remove them. A genome produced by an omniscient, perfect designer, however, would contain no such variants: the original design would be flawless, as would the mechanisms responsible for replicating it. The very existence of gene variants is therefore evidence against intelligent design.

The technique developed by the research team is sensitive enough to examine the entire genome and determine which genes influence which cell types. This makes it possible to identify which genes contribute to particular diseases. In cases where a single gene is involved, this can be relatively straightforward, but where many genes are implicated, it can be extremely difficult to disentangle their individual effects—precisely the problem this new technique helps to overcome.

Diseases caused by multiple gene variants. Many diseases are polygenic, meaning they arise from the combined effects of multiple gene variants, often interacting with environmental factors. In such cases, no single gene is sufficient to cause the disease on its own; rather, risk increases as particular combinations of variants accumulate. Well-studied examples include the following.

Common complex diseases

• Type 2 diabetes
  Involves variants in hundreds of genes affecting insulin production, insulin sensitivity, fat distribution, and glucose metabolism (e.g. TCF7L2, PPARG, SLC30A8).
• Coronary heart disease
  Influenced by variants affecting cholesterol metabolism, inflammation, blood pressure, and vascular integrity (e.g. APOE, LDLR, PCSK9, many others).
• Hypertension
  Associated with variants in genes regulating kidney function, salt balance, vascular tone, and hormonal control.
• Asthma
  Involves numerous immune-related and airway-development genes, with strong gene–environment interactions (e.g. allergens, pollution).

Neurological and psychiatric disorders

• Schizophrenia
  One of the most polygenic conditions known, involving thousands of variants, each contributing a tiny increase in risk. No single gene is determinative.
• Autism spectrum disorder (ASD)
  A mixture of rare high-impact variants and many common low-impact variants affecting synapse formation, neuronal signalling, and brain development.
• Alzheimer’s disease (late-onset)
  Influenced by many genes involved in lipid transport, immune response, and amyloid processing. APOE ε4 increases risk, but only as part of a much larger genetic background.
• Parkinson’s disease (sporadic forms)
  Involves multiple variants affecting mitochondrial function, protein degradation, and neuronal maintenance.

Autoimmune and inflammatory diseases

• Rheumatoid arthritis
  Involves numerous immune-system genes, especially within the HLA region, plus many non-HLA variants.
• Multiple sclerosis
  Strongly polygenic, with variants influencing immune regulation, inflammation, and nervous system vulnerability.
• Inflammatory bowel disease (Crohn’s disease and ulcerative colitis)
  Involves hundreds of genes affecting immune response, gut barrier function, and interactions with gut microbes.

Cancer

Most common cancers are highly polygenic at the population level.

• Breast cancer
• Prostate cancer
• Colorectal cancer

While rare inherited mutations (e.g. BRCA1/2) can greatly increase risk, the majority of cases involve the cumulative effects of many low-risk variants combined with somatic mutations acquired during life.

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Why this matters for the intelligent design argument

In many of these diseases:

• Hundreds or thousands of gene variants contribute
• Each variant may be harmless on its own
• Disease emerges only when particular combinations occur

This directly contradicts the notion of a genome composed of optimised, purposefully specified components. Instead, it fits precisely with evolutionary expectations: variation, redundancy, historical contingency, and trade-offs.

The research is summarised in a Gladstone Institutes news item by Josh Baxt.

Genomic Maps Untangle the Complex Roots of Disease

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Today’s biomedical researchers are relentlessly searching for genes that drive disease, with the goal of creating therapies that target those genes to restore health.

When a single gene is the culprit, the approach can be rather straightforward. But for the majority of diseases, in which multiple genes—sometimes thousands of them—are implicated, the task of pinpointing the connections becomes far more difficult.

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Listen to this press release.

But a new genomic mapping technique may change that. In a study published in Nature, researchers from Gladstone Institutes and Stanford University leveraged a comprehensive method of probing every gene in a cell to connect diseases and other traits with their underlying genetic machinery. These maps could clarify confusing biology and pinpoint disease-causing genes that are ripe for intervention.

We can now look across every gene in the genome and get a sense of how each one affects a particular cell type. Our goal is to use this information as a map to gain new insights into how certain genes influence specific traits.

Dr. Alexander Marson, MD, PhD, co-lead author
Gladstone-UCSF Institute of Genomic Immunology
San Francisco, CA, USA

Finding the ‘Why’

For decades, researchers have leaned on “genome-wide association studies,” which analyze genomes from thousands of people to statistically link DNA anomalies with diseases and other traits. These projects have provided a wealth of data, but the information isn’t always actionable—particularly when it comes to complex traits that are rooted in many genes.

Even with these studies, there remains a huge gap in understanding disease biology on a genetic level. We understand that many variants are associated with disease; we just don’t understand why.

Dr. Mineto Ota, MD, PhD., first author
Department of Genetics
Stanford University
Stanford, CA, USA.

In some ways, it’s like having a map that shows a starting point and a destination but none of the roads in between, Ota says.

To understand complex traits, we really need to focus on the network. How do we think about biology when thousands and thousands of genes, with many different functions, are all affecting a trait?

Professor Jonathan K. Pritchard, co-lead author
Department of Genetics
Stanford University
Stanford, CA, USA.

To answer that question, the team queried two separate databases.

The first was derived from a human leukemia cell line often used to model red blood cell traits. An MIT researcher with no role in the current study had previously disabled every gene in the cell line, one by one, mapping how each loss affected genetic activity.

Marson and his team combined those findings with data from the UK Biobank, which contains genomic sequences of more than 500,000 people. Ota mined the data for people who had genetic mutations that reduced function in a way that altered their red blood cells.

By combining these datasets, the team was able to comprehensively map the gene networks that affect red blood cell traits, illuminating an incredibly complex genomic landscape. Now they had a starting point, a destination, and the web of roads in between.

They found that some genes act on multiple mechanisms, diminishing some biological activities while boosting others. A good example is SUPT5H, a gene linked to beta thalassemia, a blood disorder that affects hemoglobin production and can cause moderate to severe anemia. The researchers linked SUPT5H to three essential blood-cells programs: hemoglobin production, cell cycle, and autophagy. Importantly, they also highlighted how the gene affects those programs—by either boosting or minimizing gene activity.

SUPT5H regulates all three main pathways that affect hemoglobin. It activates hemoglobin synthesis, slows down the cell cycle, and slows down autophagy, which together have a synergistic effect.

Professor Jonathan K. Pritchard.

Applications for Immunology

The ability to reveal the detailed genetic mechanisms that control cells could have a profound impact on biological discovery and drug development.

While the study uncovered a number of ways gene networks influence blood cell function, those findings are secondary to the method itself. The research team—and possibly many others in the life science community—can now conduct similar research in a variety of human cells to tease out the molecular signatures that drive disease.

For the Marson lab, which seeks to better understand T cells and other immune mechanisms, this new method could be the wish that grants more wishes.

The genetic burden associated with many autoimmune diseases, immune deficiencies, and allergies are overwhelmingly linked to T cells. We look forward to developing additional detailed maps that will help us really understand the genetic architecture behind these immune-mediated diseases.

Dr. Alexander Marson, MD, PhD

Publication:

Ota, M., Spence, J.P., Zeng, T. et al.
Causal modelling of gene effects from regulators to programs to traits.
Nature (2025). https://doi.org/10.1038/s41586-025-09866-3

Abstract
Genetic association studies provide a unique tool for identifying candidate causal links from genes to human traits and diseases. However, it is challenging to determine the biological mechanisms underlying most associations, and we lack genome-scale approaches for inferring causal mechanistic pathways from genes to cellular functions to traits. Here we propose approaches to bridge this gap by combining quantitative estimates of gene–trait relationships from loss-of-function burden tests¹ with gene-regulatory connections inferred from Perturb-seq experiments² in relevant cell types. By combining these two forms of data, we aim to build causal graphs in which the directional associations of genes with a trait can be explained by their regulatory effects on biological programs or direct effects on the trait³. As a proof of concept, we constructed a causal graph of the gene-regulatory hierarchy that jointly controls three partially co-regulated blood traits. We propose that perturbation studies in trait-relevant cell types, coupled with gene-level effect sizes for traits, can bridge the gap between genetic association and biological mechanism.

Main
Genome-wide association studies (GWAS) and rare variant burden tests have identified tens of thousands of reproducible associations for a wide range of human traits and diseases. These signals have identified many genes that can serve as therapeutic targets^4,5,6; driven discoveries of new molecular mechanisms^7,8, critical cell types⁹ and physiological pathways of disease risks or traits^10,11,12; and enabled genetic risk prediction for complex diseases¹³.

But despite these successes, interpreting the vast majority of associations remains challenging. Aside from coarse-grained analyses such as identifying trait-relevant cell types and enriched gene sets, we lack genome-scale approaches for interpreting the molecular pathways and mechanisms through which hundreds, if not thousands, of genes affect a given phenotype.

One challenge for interpreting genetic associations is the observation that many hits act indirectly, via trans-regulation of other genes^{14,15,16,17,18,19}. This observation is formalized in the omnigenic mode^l3,20, which proposes that, for any given trait, only a subset of genes, referred to as core genes, are located within key molecular pathways that act directly on the trait of interest. Meanwhile, many more genes affect the trait indirectly, by regulating core genes through links in gene-regulatory networks. In this model, we can interpret the effect size of a variant in terms of all paths through the network by which it affects core genes.

The central role of trans-regulation underlying many GWAS hits implies that fully understanding the genetic basis of complex traits requires tools to measure how genetic effects flow through networks. However, until recently, we have had very limited information about gene-regulatory networks in any human cell type, with the main information coming from observational data such as trans-expression quantitative trait locus (trans-eQTL) and co-expression mapping^14,16,21. However, both approaches have important limitations including low power^20,22 and confounding effects of cell-type composition¹⁴ in the case of trans-eQTLs, and ambiguous causality in co-expression analysis^23,24.

Advances in genome editing and single-cell RNA sequencing, including Perturb-seq, now provide new opportunities to measure causal gene-regulatory connections at genome scale^25,26,27,28. In Perturb-seq experiments, a pool of cells is transduced with a library of guide RNAs, each of which causes knockdown (or other perturbation) of a single gene. After allowing the cells time to equilibrate, single-cell sequencing is used to determine which genes were knocked down in each cell and measure the transcriptome of the cell. Critically, Perturb-seq enables measurement of the trans-regulatory effects of each gene in a controlled experimental setting at the genome-wide scale. Recent work has shown that such approaches are a promising tool for interpreting GWAS data, finding that GWAS hits are often enriched in specific transcriptional programs identified by CRISPR perturbations of a subset of genes^{29,30,31,32,33}.

Major challenges remain as we aim to move beyond identifying enriched programs to inferring genome-scale causal cascades of biological information. In this paper, we developed a new systematic approach to this problem. We demonstrate how, by combining loss-of-function (LoF) burden results with Perturb-seq, we can infer an internally coherent graph linking genes to functional programs to traits, and derive biological insight into the key genes and pathways that control these traits (Fig. 1a). The resulting graph helps us to understand not only the trait-relevant pathways but also the functions of genes and programs within the graph, to explain why those genes are associated with the traits. On the basis of our results, we expect that forthcoming efforts to generate perturbation data in a wide variety of cell types will provide a critical interpretative framework for human genetics.

Fig. 1: Study overview and selection of model traits.

a, Overview of the study. The square nodes represent genes, the coloured arrows between genes represent regulatory effects and the arrows from genes to traits represent associations. sgRNA, single guide RNA. b, Heritability enrichment of UKB traits to open chromatin regions in K562. Traits are ordered based on the P value of enrichment, which was estimated using the Jackknife test in S-LDSC. The dashed line indicates the threshold for Bonferroni significance. ATAC-seq, assay for transposase-accessible chromatin using sequencing; Cou, count; HLSR, high light scatter reticulocyte count; MCV, mean corpuscular volume; MRV, mean reticulocyte volume; MSCV, mean sphered corpuscular volume; Per, percentage; RBC, red blood cell; Ret, reticulocyte. c, Schematic of the human haematopoietic tree. Traits of interest are annotated near their relevant cell types. CLP, common lymphoid progenitor; CMP, common myeloid progenitor; GMP, granulocyte–monocyte progenitor; HSC, haematopoietic stem cell; MPP, multipotent progenitor. d, Comparison of heritability enrichment to UKB traits, between MEP and K562 open chromatin regions. P values were estimated using the Jackknife test in S-LDSC. The dashed line indicates the threshold for Bonferroni significance.

Ota, M., Spence, J.P., Zeng, T. et al.
Causal modelling of gene effects from regulators to programs to traits.
Nature (2025). https://doi.org/10.1038/s41586-025-09866-3

Copyright: © 2025 The authors.
Published by Springer Nature Ltd. Open access.
Reprinted under a Creative Commons Attribution 4.0 International license (CC BY 4.0)

Genetic diseases sit uneasily with the claim that the human genome is the product of an intelligent, benevolent designer. Many such diseases do not arise from a single malfunctioning component, but from the interaction of numerous gene variants spread across the genome. These variants often perform useful functions in other contexts and only become harmful in particular combinations. This is precisely what one would expect from an undirected evolutionary process that preserves variation and tolerates trade-offs, but it is profoundly difficult to reconcile with deliberate, foresighted design.

An intelligent designer with perfect knowledge would not need to rely on fragile genetic systems in which normal biological function is balanced precariously against the risk of catastrophic failure. Nor would such a designer construct diseases that require the coordinated involvement of dozens, hundreds, or even thousands of genetic elements to manifest. The existence of polygenic disease means that suffering is not the result of a single error, but of entire networks of interacting genes behaving exactly as they were “designed” to behave.

Appeals to mystery or to a “fallen world” do nothing to resolve this problem. They merely shift responsibility away from the designer while preserving the claim of intelligence without evidence. By contrast, evolutionary biology predicts both genetic variation and its occasionally harmful consequences as unavoidable by-products of mutation, inheritance, and imperfect selection. There is no need to posit intention, foresight, or purpose behind genetic disease.

Far from pointing to intelligent design, the growing ability to map genetic variants to disease strengthens the evolutionary account. It reveals a genome shaped by history, constraint, and compromise, not by optimal engineering. Genetic disease is not an anomaly within evolution; it is exactly what evolution leads us to expect—and exactly what a truly intelligent, benevolent designer would have avoided.

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