Genomics - Understanding human diversity

Duke University, Durham, North Carolina, United States
Nature (Impact Factor: 41.46). 11/2005; 437(7063):1241-2. DOI: 10.1038/4371241a
Source: PubMed


The first edition of a massive catalogue of human genetic variation is now complete. The long-term task is to translate these data into an understanding of the effects of that variation on human health.

1 Follower
2 Reads
  • Source
    • "However, there is substantial variation among the individual human genomes that relates to the replication of genetic sequences and alterations in individual pairs. This variation is common – it has been suggested there may be approximately ten million single nucleotide polymorphisms (SNPs).9 Additionally, all of the variations in human characteristics (or phenotypes) result from the interaction between the genotype and environmental stimuli. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The current dominance of African runners in long-distance running is an intriguing phenomenon that highlights the close relationship between genetics and physical performance. Many factors in the interesting interaction between genotype and phenotype (eg, high cardiorespiratory fitness, higher hemoglobin concentration, good metabolic efficiency, muscle fiber composition, enzyme profile, diet, altitude training, and psychological aspects) have been proposed in the attempt to explain the extraordinary success of these runners. Increasing evidence shows that genetics may be a determining factor in physical and athletic performance. But, could this also be true for African long-distance runners? Based on this question, this brief review proposed the role of genetic factors (mitochondrial deoxyribonucleic acid, the Y chromosome, and the angiotensin-converting enzyme and the alpha-actinin-3 genes) in the amazing athletic performance observed in African runners, especially the Kenyans and Ethiopians, despite their environmental constraints.
    Open Access Journal of Sports Medicine 05/2014; 5:123-127. DOI:10.2147/OAJSM.S61361
  • Source
    • "Coding-region SNPs not only characterize human evolution and diversity (Goldstein and Cavalleri, 2005), but are associated with drug sensitivity (Giacomini et al., 2007), and disease susceptibility (Bell, 2004). Classifying nsSNPs, according to their phenotypic effects has an important insinuation for understanding several diseases and exploring genetic ancestry, evolution and diversity among species (Cargill et al., 1999). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Motivation: Single nucleotide polymorphisms (SNPs) are considered the most frequently occurring DNA sequence variations. Several computational methods have been proposed for the classification of missense SNPs to neutral and disease associated. However, existing computational approaches fail to select relevant features by choosing them arbitrarily without sufficient documentation. Moreover, they are limited to the problem of missing values, imbalance between the learning datasets and most of them do not support their predictions with confidence scores. Results: To overcome these limitations, a novel ensemble computational methodology is proposed. EnsembleGASVR facilitates a two-step algorithm, which in its first step applies a novel evolutionary embedded algorithm to locate close to optimal Support Vector Regression models. In its second step, these models are combined to extract a universal predictor, which is less prone to overfitting issues, systematizes the rebalancing of the learning sets and uses an internal approach for solving the missing values problem without loss of information. Confidence scores support all the predictions and the model becomes tunable by modifying the classification thresholds. An extensive study was performed for collecting the most relevant features for the problem of classifying SNPs, and a superset of 88 features was constructed. Experimental results show that the proposed framework outperforms well-known algorithms in terms of classification performance in the examined datasets. Finally, the proposed algorithmic framework was able to uncover the significant role of certain features such as the solvent accessibility feature, and the top-scored predictions were further validated by linking them with disease phenotypes. Availability and implementation: Datasets and codes are freely available on the Web at All the required information about the article is available through
    Bioinformatics 04/2014; 30(16). DOI:10.1093/bioinformatics/btu297 · 4.98 Impact Factor
  • Source
    • "Twin and other family-based studies have shown that genetic variation explains a large proportion of the phenotypic variation observed in humans. Heritability estimates vary widely among traits, 40% of variation of most complex traits can be explained by inherited factors increasing to over 70% for some diseases such as schizophrenia [3]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: During the initial stages of the genome revolution human genetics was hugely successful in discovering the underlying genes for monogenic diseases. Over 3,000 monogenic diseases have been discovered with simple patterns of inheritance. The unravelling and identification of the genetic variants underlying complex or multifactorial traits, however, is proving much more elusive. There have been over 1,000 significant variants found for many quantitative and binary traits yet they explain very little of the estimated genetic variance or heritability evident from family analysis. There are many hypotheses as to why this might be the case. This apparent lack of information is holding back the clinical application of genetics and shedding doubt on whether more of the same will reveal where the remainder of the variation lies. Here we explore the current state of play, the types of variants we can detect and how they are currently exploited. Finally we look at the future challenges we must face to persuade the human genome to yield its secrets.
    Current Genomics 05/2012; 13(3):213-24. DOI:10.2174/138920212800543101 · 2.34 Impact Factor
Show more