Transposons and transposon-like repetitive elements collectively occupy 44% of the human genome sequence. In an effort to measure the levels of genetic variation that are caused by human transposons, we have developed a new method to broadly detect transposon insertion polymorphisms of all kinds in humans. We began by identifying 606,093 insertion and deletion (indel) polymorphisms in the genomes of diverse humans. We then screened these polymorphisms to detect indels that were caused by de novo transposon insertions. Our method was highly efficient and led to the identification of 605 nonredundant transposon insertion polymorphisms in 36 diverse humans. We estimate that this represents 25-35% of approximately 2075 common transposon polymorphisms in human populations. Because we identified all transposon insertion polymorphisms with a single method, we could evaluate the relative levels of variation that were caused by each transposon class. The average human in our study was estimated to harbor 1283 Alu insertion polymorphisms, 180 L1 polymorphisms, 56 SVA polymorphisms, and 17 polymorphisms related to other forms of mobilized DNA. Overall, our study provides significant steps toward (i) measuring the genetic variation that is caused by transposon insertions in humans and (ii) identifying the transposon copies that produce this variation.

Although many studies have been conducted to identify single nucleotide polymorphisms (SNPs) in humans, few studies have been conducted to identify alternative forms of natural genetic variation, such as insertion and deletion (INDEL) polymorphisms. In this report, we describe an initial map of human INDEL variation that contains 415,436 unique INDEL polymorphisms. These INDELs were identified with a computational approach using DNA re-sequencing traces that originally were generated for SNP discovery projects. They range from 1 bp to 9989 bp in length and are split almost equally between insertions and deletions, relative to the chimpanzee genome sequence. Five major classes of INDELs were identified, including (1) insertions and deletions of single-base pairs, (2) monomeric base pair expansions, (3) multi-base pair expansions of 2-15 bp repeat units, (4) transposon insertions, and (5) INDELs containing random DNA sequences. Our INDELs are distributed throughout the human genome with an average density of one INDEL per 7.2 kb of DNA. Variation hotspots were identified with up to 48-fold regional increases in INDEL and/or SNP variation compared with the chromosomal averages for the same chromosomes. Over 148,000 INDELs (35.7%) were identified within known genes, and 5542 of these INDELs were located in the promoters and exons of genes, where gene function would be expected to be influenced the greatest. All INDELs in this study have been deposited into dbSNP and have been integrated into maps of human genetic variation that are available to the research community.

Genomic copy number variation (CNV) is a recently identified form of global genetic variation in the human genome. The Affymetrix GeneChip 100 and 500 K SNP genotyping platforms were used to perform a large-scale population-based study of CNV frequency. We constructed a genomic map of 578 CNV regions, covering approximately 220 Mb (7.3%) of the human genome, identifying 183 previously unknown intervals. Copy number changes were observed to occur infrequently (<1%) in the majority (>93%) of these genomic regions, but encompass hundreds of genes and disease loci. This North American population-based map will be a useful resource for future genetic studies.

DNA copy number variation has long been associated with specific chromosomal rearrangements and genomic disorders, but its ubiquity in mammalian genomes was not fully realized until recently. Although our understanding of the extent of this variation is still developing, it seems likely that, at least in humans, copy number variants (CNVs) account for a substantial amount of genetic variation. Since many CNVs include genes that result in differential levels of gene expression, CNVs may account for a significant proportion of normal phenotypic variation. Current efforts are directed toward a more comprehensive cataloging and characterization of CNVs that will provide the basis for determining how genomic diversity impacts biological function, evolution, and common human diseases. 10.1101/gr.3677206

The sequencing of the chimpanzee genome and the comparison with its human counterpart have begun to reveal the spectrum of genetic changes that has accompanied human evolution. In addition to gross karyotypic rearrangements such as the fusion that formed human chromosome 2 and the human-specific pericentric inversions of chromosomes 1 and 18, there is considerable submicroscopic structural variation involving deletions, duplications, and inversions. Lineage-specific segmental duplications, detected by array comparative genomic hybridization and direct sequence comparison, have made a very significant contribution to this structural divergence, which is at least three-fold greater than that due to nucleotide substitutions. Since structural genomic changes may have given rise to irreversible functional differences between the diverging species, their detailed analysis could help to identify the biological processes that have accompanied speciation. To this end, interspecies comparisons have revealed numerous human-specific gains and losses of genes as well as changes in gene expression. The very considerable structural diversity (polymorphism) evident within both lineages has, however, hampered the analysis of the structural divergence between the human and chimpanzee genomes. The concomitant evaluation of genetic divergence and diversity at the nucleotide level has nevertheless served to identify many genes that have evolved under positive selection and may thus have been involved in the development of human lineage-specific traits. Genes that display signs of weak negative selection have also been identified and could represent candidate loci for complex genomic disorders. Here, we review recent progress in comparing the human and chimpanzee genomes and discuss how the differences detected have improved our understanding of the evolution of the human genome. Hum Mutat 28(2), 99-130, 2007. \copyright 2006 Wiley-Liss, Inc.

We examined fixed substitutions in the human lineage since divergence from the common ancestor with the chimpanzee, and determined what fraction are AT to GC (weak-to-strong). Substitutions that are densely clustered on the chromosomes show a remarkable excess of weak-to-strong "biased" substitutions. These unexpected biased clustered substitutions (UBCS) are common near the telomeres of all autosomes but not the sex chromosomes. Regions of extreme bias are enriched for genes. Human and chimp orthologous regions show a striking similarity in the shape and magnitude of their respective UBCS maps, suggesting a relatively stable force leads to clustered bias. The strong and stable signal near telomeres may have participated in the evolution of isochores. One exception to the UBCS pattern found in all autosomes is chromosome 2, which shows a UBCS peak midchromosome, mapping to the fusion site of two ancestral chromosomes. This provides evidence that the fusion occurred as recently as 740,000 years ago and no more than approximately 3 million years ago. No biased clustering was found in SNPs, suggesting that clusters of biased substitutions are selected from mutations. UBCS is strongly correlated with male (and not female) recombination rates, which explains the lack of UBCS signal on chromosome X. These observations support the hypothesis that biased gene conversion (BGC), specifically in the male germline, played a significant role in the evolution of the human genome.

There is an increasing interest in detecting genes, or genomic regions, that have been targeted by natural selection. The interest stems from a basic desire to learn more about evolutionary processes in humans and other organisms, and from the realization that inferences regarding selection may provide important functional information. This review provides a nonmathematical description of the issues involved in detecting selection from DNA sequences and SNP data and is intended for readers who are not familiar with population genetic theory. Particular attention is placed on issues relating to the analysis of large-scale genomic data sets.