Human Chromosome Y and Haplogroups; introducing YDHS Database

T2T-YAO: A Telomere-to-telomere Assembled Diploid Reference Genome for Han Chinese

Since its initial release in 2001, the human reference genome has undergone continuous improvement in quality, and the recently released telomere-to-telomere (T2T) version - T2T-CHM13 - reaches its highest level of continuity and accuracy after 20 years of effort by working on a simplified, nearly homozygous genome of a hydatidiform mole cell line. Here, to provide an authentic complete diploid human genome reference for the Han Chinese, the largest population in the world, we assembled the genome of a male Han Chinese individual, T2T-YAO, which includes T2T assemblies of all the 22 + X + M and 22 + Y chromosomes in both haploids. The quality of T2T-YAO is much better than those of all currently available diploid assemblies, and its haploid version, T2T-YAO-hp, generated by selecting the better assembly for each autosome, reaches the top quality of fewer than one error per 29.5 Mb, even higher than that of T2T-CHM13. Derived from an individual living in the aboriginal region of the Han population, T2T-YAO shows clear ancestry and potential genetic continuity from the ancient ancestors. Each haplotype of T2T-YAO possesses ∼ 330-Mb exclusive sequences, ∼ 3100 unique genes, and tens of thousands of nucleotide and structural variations as compared with CHM13, highlighting the necessity of a population-stratified reference genome. The construction of T2T-YAO, an accurate and authentic representative of the Chinese population, would enable precise delineation of genomic variations and advance our understandings in the hereditability of diseases and phenotypes, especially within the context of the unique variations of the Chinese population.

Large Region of Homozygous (ROH) Identified in Indian Patients with Autosomal Recessive Limb-Girdle Muscular Dystrophy with p.Thr182Pro Variant in SGCB Gene

The sarcoglycanopathies are autosomal recessive limb-girdle muscular dystrophies (LGMDs) caused by the mutations in genes encoding the α, β, γ, and δ proteins which stabilizes the sarcolemma of muscle cells. The clinical phenotype is characterized by progressive proximal muscle weakness with childhood onset. Muscle biopsy findings are diagnostic in confirming dystrophic changes and deficiency of one or more sarcoglycan proteins. In this study, we summarized 1,046 LGMD patients for which a precise diagnosis was identified using targeted sequencing. The most frequent phenotypes identified in the patients are LGMDR1 (19.7%), LGMDR4 (19.0%), LGMDR2 (17.5%), and MMD1 (14.5%). Among the reported genes, each of CAPN3, SGCB, and DYSF variants was reported in more than 10% of our study cohort. The most common variant SGCB p.Thr182Pro was identified in 146 (12.5%) of the LGMD patients, and in 97.9% of these patients, the variant was found to be homozygous. To understand the genetic structure of the patients carrying SGCB p.Thr182Pro, we genotyped 68 LGMD patients using a whole genome microarray. Analysis of the array data identified a large ~1 Mb region of homozygosity (ROH) (chr4:51817441-528499552) suggestive of a shared genomic region overlapping the recurrent missense variant and shared across all 68 patients. Haplotype analysis identified 133 marker haplotypes that were present in ~85.3% of the probands as a double allele and absent in all random controls. We also identified 5 markers (rs1910739, rs6852236, rs13122418, rs13353646, and rs6554360) which were present in a significantly higher proportion in the patients compared to random control set ( $n=128$ ) and the population database. Of note, admixture analysis was suggestive of greater proportion of West Eurasian/European ancestry as compared to random controls. Haplotype analysis and frequency in the population database indicate a probable event of founder effect. Further systematic study is needed to identify the communities and regions where the SGCB p.Thr182Pro variant is observed in higher proportions. After identifying these communities and//or region, a screening program is needed to identify carriers and provide them counselling.

Assessing the factors influencing the performance of machine learning for classifying haplogroups from Y-STR haplotypes

Two distinct genetic markers, single nucleotide polymorphisms (Y-SNPs) and short tandem repeats (Y-STRs), exist simultaneously in the non-recombining portion of the Y chromosome. Because of their different rates of mutation, Y-STRs and Y-SNPs play distinct roles in forensic and evolutionary genetics. Current approaches to infer haplogroup status rely on genotyping lots of Y-SNP loci. Given the relationship between haplotype and haplogroup of a Y chromosome, a cost-effective strategy of Y-STRs typing had an advantage in haplogroup prediction. Many machine learning algorithms have sprung up for assigning a Y-STR haplotype to a haplogroup. However, a series of issues must be solved before the using of machine learning method in practice. Thus, the k-nearest neighbor (kNN) classifier was built respectively based on different situations in this study. We assessed different factors which may influence the performance of the kNN prediction model for classifying haplogroups. The training set was based on a diverse ground-truth data set comprising Y-STR haplotypes and corresponding Y-SNP haplogroups. Our results showed that combining different levels of haplogroups into the observations or transracial prediction was impractical. Moreover, using more slow mutation Y-STR loci in the category is good for promoting classification accuracy. The preconditions for an effective and accurate haplogroup assignment by the kNN classifier were revealed.

The development of precision medicine in clinical practice

Precision medicine allows a dramatic expansion of biological data, while there is still an urgent need to understand and insight the exact meaning of those data to human health and disease. This has led to an increasing wealth of data unanalyzed. The concept of precision medicine is about the customization of healthcare, with decisions and practices tailored to an individual patient based on their intrinsic biology in addition to clinical “signs and symptoms”. Construction of a standardized model for the integration of data from various platforms is the central mission of the ‘New Disease Management Model’. The model is helpful for the development of new taxonomy of diseases and subtypes, to personalize therapy based on patient genetic profiles. A rapid progression of precision therapy has been made recently. Clinical trials have shown the therapeutic efficacy of discovered and developed therapeutic agents has improved. However, next-generation drugs would be designed for disease subtypes with more specificity, efficacy and lower toxicity.