Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explain one-fifth of heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ~2,000, ~3,700 and ~9,500 SNPs explained ~21%, ~24% and ~29% of phenotypic variance. Furthermore, all common variants together captured the majority (60%) of heritability. The 697 variants clustered in 423 loci enriched for genes, pathways, and tissue-types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/beta-catenin, and chondroitin sulfate-related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry 1-3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific 4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may Reprints and permissions information is available at http://www.nature.com/reprints.

More than one million copies of the ∼300-bp Alu element are interspersed throughout the human genome, with up to 75% of all known genes having Alu insertions within their introns and/or UTRs. Transcribed Alu sequences can alter splicing patterns by generating new exons, but other impacts of intragenic Alu elements on their host RNA are largely unexplored. Recently, repeat elements present in the introns or 3′-UTRs of 15 human brain RNAs have been shown to be targets for multiple adenosine to inosine (A-to-I) editing. Using a statistical approach, we find that editing of transcripts with embedded Alu sequences is a global phenomenon in the human transcriptome, observed in 2674 (∼2%) of all publicly available full-length human cDNAs (n = 128,406), from >250 libraries and >30 tissue sources. In the vast majority of edited RNAs, A-to-I substitutions are clustered within transcribed sense or antisense Alu sequences. Edited bases are primarily associated with retained introns, extended UTRs, or with transcripts that have no corresponding known gene. Therefore, Alu-associated RNA editing may be a mechanism for marking nonstandard transcripts, not destined for translation

We have completed a second-generation linkage map that incorporates sequence-based positional information. This new map, the Rutgers Map v.2, includes 28,121 polymorphic markers with physical positions corroborated by recombination-based data. Sex-averaged and sex-specific linkage map distances, along with confidence intervals, have been estimated for all map intervals. In addition, a regression-based smoothed map is provided that facilitates interpolation of positions of unmapped markers on this map. With nearly twice as many markers as our first-generation map, the Rutgers Map continues to be a unique and comprehensive resource for obtaining genetic map information for large sets of polymorphic markers.Accurate and comprehensive linkage maps continue to be critical for linkage analyses (Daw et al. 2000;Barber et al. 2006;Fingerlin et al. 2006;Dietter et al. 2007), positional cloning projects, and even for some aspects of genome-wide association analyses (Maniatis et al. 2002;Tapper et al. 2005). Previously, we constructed the first-generation combined linkage-physical map (Rutgers Map v.1; Kong et al. 2004) containing 14,759 markers, genotyped in a mixture of CEPH (Center d'Etude du Polymorphisme Humain) (Dausset et al. 1990) and deCODE (Kong et al. 2002) families. Now, we have pooled this data set with 13,666 singlenucleotide polymorphisms (SNPs) genotyped in the CEPH reference pedigrees at the companies Applied Biosystems, Affymetrix, and Illumina. We used the pooled data to construct a secondgeneration combined linkage-physical map (Rutgers Map v.2), which has nearly twice the number of markers and increased marker density relative to the Rutgers Map v.1. The physical positions of 28,121 markers were corroborated by recombinationbased data, making the Rutgers Map v.2, to our knowledge, the most dense and accurate linkage map of the human genome.The Rutgers Map v.2 also provides three novel features that are not generally offered by other publicly available maps. First, we have estimated approximate 95% confidence intervals for the size of all 24,145 map intervals, both on the sex-averaged and sex-specific maps. This feature may be useful for assessing sensitivity of an analysis to map uncertainty and for combining the information in the Rutgers Map v.2 with map estimates derived from independent studies. In addition, we have applied local regression to create a smoothed version of the Rutgers Map that separates all markers by non-zero map distances. Overall, this alternative map should provide better estimates of map distance since nearly half of the map intervals in the Rutgers Map v.2, while physically distinct, show no evidence of recombination. Third, the smoothed map facilitates interpolation of map positions for markers that are not on our map. For example, a cMscale map position can be easily estimated for any of the millions of SNP markers that have not been genotyped in the CEPH reference pedigrees and hence are not present on any of the CEPHbased linkage maps. Results Markers and genotype dataThe ...

Though relatively modest in size, this is the largest placebo-controlled trial done to date in patients with Parkinson disease (PD) and depression. Nortriptyline was efficacious in the treatment of depression and paroxetine CR was not. When compared directly, nortriptyline produced significantly more responders than did paroxetine CR. The trial suggests that depression in patients with PD is responsive to treatment and raises questions about the relative efficacy of dual reuptake inhibitors and selective serotonin reuptake inhibitors.

HRV biofeedback appears to be a useful adjunctive treatment for the treatment of MDD, associated with large acute increases in HRV and some chronic increases, suggesting increased cardiovagal activity. It is possible that regular exercise of homeostatic reflexes helps depression even when changes in baseline HRV are smaller. A randomized controlled trial is warranted.

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide1,2. Although 58 genomic regions have been associated with CAD to date3–9, most of the heritability is unexplained9, indicating additional susceptibility loci await identification. An efficient discovery strategy may be larger-scale evaluation of promising associations suggested by genome-wide association studies (GWAS). Hence, we genotyped 56,309 participants using a targeted gene array derived from earlier GWAS results and meta-analysed results with 194,427 participants previously genotyped to give a total of 88,192 CAD cases and 162,544 controls. We identified 25 new SNP-CAD-associations (P < 5x10-8, in fixed effects meta-analysis) from 15 genomic regions, including SNPs in or near genes involved in cellular adhesion, leucocyte migration and atherosclerosis (PECAM1, rs1867624), coagulation and inflammation (PROCR, rs867186 [p.Ser219Gly]) and vascular smooth muscle cell differentiation (LMOD1, rs2820315). Correlation of these regions with cell type-specific gene expression and plasma protein levels shed light on potential novel disease mechanisms.

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