reviews: 
Comments: In this paper, the authors use diffusion models to synthesize genomic data, allowing researchers to access it at a low cost. They expanded the synthesis scale to encompass the entire human genome. To handle long sequences, the authors employed a method similar to Latent-Space Diffusion Model: first splitting the genome into parts. For each part, they used PCA as an encoder/decoder to reduce the dimensionality to 8. The authors applied various methods to test the model's generation accuracy, generalization performance, and efficiency in downstream tasks. Notably, models trained on synthetic data performed almost identically to those trained on real data.
Strengths: As the authors claim, they propose the first model that augments human genomic data at full scale, which is larger compared to previous works. Their experiments demonstrate that their generated data can be used as effectively as real data to train downstream models.
Weaknesses: The paper's most significant weakness is its clarity and writing quality. In the section on previous works, there is only a table without main text, making it difficult to understand how the authors' approach compares to prior research. What are the limitations of previous works beyond their smaller scale? How does the authors' approach relate to existing methods? 

The authors' claim of "using diffusion model" as a novelty is questionable. Diffusion models aren't fundamentally different from other generative models. While using a diffusion model might be justified based on performance, it doesn't constitute a novel contribution in itself.

Lines 92 to 94 are confusing: they state that DDIM is beneficial, but DDPM is also necessary, without propose any solutions. In fact, DDPM is a special case of DDIM with specific alpha and sigma values.

On line 95, the authors mention using classifier-free guidance, claiming it can improve quality. However, classifier-free guidance was developed for conditional generation, not specifically for quality improvement. The authors should clarify their reasoning for this claim.

The experiment section lacks details about the data generation process. Although the model was trained with classifier-free guidance, it's unclear how the data is generated with conditions. The authors should clarify how they generate the data and how they select the conditions for generation.

Several other details also require improvement. The UMAP experiment is distracting and should be removed, as it's highly subjective and doesn't provide useful information. In Figure 2, the horizontal lines are not truly horizontal, and the markers are misplaced. The notations in this figure are unclear, making it difficult to determine which parts they refer to. Figure 3 appears to be a screenshot from Weights & Biases, which is unprofessional. Figure 5 is challenging to read due to its poor layout and formatting.

Regarding the model design, the use of CNNs is questionable due to the lack of translational symmetry. The same vector can represent entirely different information in the 18,279 or 26,624 distinct latent spaces created by PCA.
Recommendation: 5
Reviewer confidence: 3
Soundness/correctness: 2
Clarity: 1