School of Agriculture, Food and Ecosystem Sciences - Research Publications

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    Bootstrapping of gene-expression data improves and controls the false discovery rate of differentially expressed genes
    Meuwissen, THE ; Goddard, ME (E D P SCIENCES, 2004)
    The ordinary-, penalized-, and bootstrap t-test, least squares and best linear unbiased prediction were compared for their false discovery rates (FDR), i.e. the fraction of falsely discovered genes, which was empirically estimated in a duplicate of the data set. The bootstrap-t-test yielded up to 80% lower FDRs than the alternative statistics, and its FDR was always as good as or better than any of the alternatives. Generally, the predicted FDR from the bootstrapped P-values agreed well with their empirical estimates, except when the number of mRNA samples is smaller than 16. In a cancer data set, the bootstrap-t-test discovered 200 differentially regulated genes at a FDR of 2.6%, and in a knock-out gene expression experiment 10 genes were discovered at a FDR of 3.2%. It is argued that, in the case of microarray data, control of the FDR takes sufficient account of the multiple testing, whilst being less stringent than Bonferoni-type multiple testing corrections. Extensions of the bootstrap simulations to more complicated test-statistics are discussed.
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    Mapping multiple QTL using linkage disequilibrium and linkage analysis information and multitrait data
    Meuwissen, THE ; Goddard, ME (BMC, 2004)
    A multi-locus QTL mapping method is presented, which combines linkage and linkage disequilibrium (LD) information and uses multitrait data. The method assumed a putative QTL at the midpoint of each marker bracket. Whether the putative QTL had an effect or not was sampled using Markov chain Monte Carlo (MCMC) methods. The method was tested in dairy cattle data on chromosome 14 where the DGAT1 gene was known to be segregating. The DGAT1 gene was mapped to a region of 0.04 cM, and the effects of the gene were accurately estimated. The fitting of multiple QTL gave a much sharper indication of the QTL position than a single QTL model using multitrait data, probably because the multi-locus QTL mapping reduced the carry over effect of the large DGAT1 gene to adjacent putative QTL positions. This suggests that the method could detect secondary QTL that would, in single point analyses, remain hidden under the broad peak of the dominant QTL. However, no indications for a second QTL affecting dairy traits were found on chromosome 14.
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    A practical approach for minimising inbreeding and maximising genetic gain in dairy cattle
    Haile-Mariam, M ; Bowman, PJ ; Goddard, ME (BMC, 2007)
    A method that predicts the genetic composition and inbreeding (F) of the future dairy cow population using information on the current cow population, semen use and progeny test bulls is described. This is combined with information on genetic merit of bulls to compare bull selection methods that minimise F and maximise breeding value for profit (called APR in Australia). The genetic composition of the future cow population of Australian Holstein-Friesian (HF) and Jersey up to 6 years into the future was predicted. F in Australian HF and Jersey breeds is likely to increase by about 0.002 and 0.003 per year between 2002 and 2008, respectively. A comparison of bull selection methods showed that a method that selects the best bull from all available bulls for each current or future cow, based on its calf's APR minus F depression, is better than bull selection methods based on APR alone, APR adjusted for mean F of prospective progeny after random mating and mean APR adjusted for the relationship between the selected bulls. This method reduced F of prospective progeny by about a third to a half compared to the other methods when bulls are mated to current and future cows that will be available 5 to 6 years from now. The method also reduced the relationship between the bulls selected to nearly the same extent as the method that is aimed at maximising genetic gain adjusted for the relationship between bulls. The method achieves this because cows with different pedigree exist in the population and the method selects relatively unrelated bulls to mate to these different cows. Selecting the best bull for each current or future cow so that the calf's genetic merit minus F depression is maximised can slow the rate of increase in F in the population.
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    Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index
    Speliotes, EK ; Willer, CJ ; Berndt, SI ; Monda, KL ; Thorleifsson, G ; Jackson, AU ; Allen, HL ; Lindgren, CM ; Luan, J ; Maegi, R ; Randall, JC ; Vedantam, S ; Winkler, TW ; Qi, L ; Workalemahu, T ; Heid, IM ; Steinthorsdottir, V ; Stringham, HM ; Weedon, MN ; Wheeler, E ; Wood, AR ; Ferreira, T ; Weyant, RJ ; Segre, AV ; Estrada, K ; Liang, L ; Nemesh, J ; Park, J-H ; Gustafsson, S ; Kilpelaenen, TO ; Yang, J ; Bouatia-Naji, N ; Esko, T ; Feitosa, MF ; Kutalik, Z ; Mangino, M ; Raychaudhuri, S ; Scherag, A ; Smith, AV ; Welch, R ; Zhao, JH ; Aben, KK ; Absher, DM ; Amin, N ; Dixon, AL ; Fisher, E ; Glazer, NL ; Goddard, ME ; Heard-Costa, NL ; Hoesel, V ; Hottenga, J-J ; Johansson, A ; Johnson, T ; Ketkar, S ; Lamina, C ; Li, S ; Moffatt, MF ; Myers, RH ; Narisu, N ; Perry, JRB ; Peters, MJ ; Preuss, M ; Ripatti, S ; Rivadeneira, F ; Sandholt, C ; Scott, LJ ; Timpson, NJ ; Tyrer, JP ; van Wingerden, S ; Watanabe, RM ; White, CC ; Wiklund, F ; Barlassina, C ; Chasman, DI ; Cooper, MN ; Jansson, J-O ; Lawrence, RW ; Pellikka, N ; Prokopenko, I ; Shi, J ; Thiering, E ; Alavere, H ; Alibrandi, MTS ; Almgren, P ; Arnold, AM ; Aspelund, T ; Atwood, LD ; Balkau, B ; Balmforth, AJ ; Bennett, AJ ; Ben-Shlomo, Y ; Bergman, RN ; Bergmann, S ; Biebermann, H ; Blakemore, AIF ; Boes, T ; Bonnycastle, LL ; Bornstein, SR ; Brown, MJ ; Buchanan, TA ; Busonero, F ; Campbell, H ; Cappuccio, FP ; Cavalcanti-Proenca, C ; Chen, Y-DI ; Chen, C-M ; Chines, PS ; Clarke, R ; Coin, L ; Connell, J ; Day, INM ; den Heijer, M ; Duan, J ; Ebrahim, S ; Elliott, P ; Elosua, R ; Eiriksdottir, G ; Erdos, MR ; Eriksson, JG ; Facheris, MF ; Felix, SB ; Fischer-Posovszky, P ; Folsom, AR ; Friedrich, N ; Freimer, NB ; Fu, M ; Gaget, S ; Gejman, PV ; Geus, EJC ; Gieger, C ; Gjesing, AP ; Goel, A ; Goyette, P ; Grallert, H ; Graessler, J ; Greenawalt, DM ; Groves, CJ ; Gudnason, V ; Guiducci, C ; Hartikainen, A-L ; Hassanali, N ; Hall, AS ; Havulinna, AS ; Hayward, C ; Heath, AC ; Hengstenberg, C ; Hicks, AA ; Hinney, A ; Hofman, A ; Homuth, G ; Hui, J ; Igl, W ; Iribarren, C ; Isomaa, B ; Jacobs, KB ; Jarick, I ; Jewell, E ; John, U ; Jorgensen, T ; Jousilahti, P ; Jula, A ; Kaakinen, M ; Kajantie, E ; Kaplan, LM ; Kathiresan, S ; Kettunen, J ; Kinnunen, L ; Knowles, JW ; Kolcic, I ; Koenig, IR ; Koskinen, S ; Kovacs, P ; Kuusisto, J ; Kraft, P ; Kvaloy, K ; Laitinen, J ; Lantieri, O ; Lanzani, C ; Launer, LJ ; Lecoeur, C ; Lehtimaeki, T ; Lettre, G ; Liu, J ; Lokki, M-L ; Lorentzon, M ; Luben, RN ; Ludwig, B ; Manunta, P ; Marek, D ; Marre, M ; Martin, NG ; McArdle, WL ; McCarthy, A ; McKnight, B ; Meitinger, T ; Melander, O ; Meyre, D ; Midthjell, K ; Montgomery, GW ; Morken, MA ; Morris, AP ; Mulic, R ; Ngwa, JS ; Nelis, M ; Neville, MJ ; Nyholt, DR ; O'Donnell, CJ ; O'Rahilly, S ; Ong, KK ; Oostra, B ; Pare, G ; Parker, AN ; Perola, M ; Pichler, I ; Pietilaeinen, KH ; Platou, CGP ; Polasek, O ; Pouta, A ; Rafelt, S ; Raitakari, O ; Rayner, NW ; Ridderstrale, M ; Rief, W ; Ruokonen, A ; Robertson, NR ; Rzehak, P ; Salomaa, V ; Sanders, AR ; Sandhu, MS ; Sanna, S ; Saramies, J ; Savolainen, MJ ; Scherag, S ; Schipf, S ; Schreiber, S ; Schunkert, H ; Silander, K ; Sinisalo, J ; Siscovick, DS ; Smit, JH ; Soranzo, N ; Sovio, U ; Stephens, J ; Surakka, I ; Swift, AJ ; Tammesoo, M-L ; Tardif, J-C ; Teder-Laving, M ; Teslovich, TM ; Thompson, JR ; Thomson, B ; Toenjes, A ; Tuomi, T ; van Meurs, JBJ ; van Ommen, G-J ; Vatin, V ; Viikari, J ; Visvikis-Siest, S ; Vitart, V ; Vogel, CIG ; Voight, BF ; Waite, LL ; Wallaschofski, H ; Walters, GB ; Widen, E ; Wiegand, S ; Wild, SH ; Willemsen, G ; Witte, DR ; Witteman, JC ; Xu, J ; Zhang, Q ; Zgaga, L ; Ziegler, A ; Zitting, P ; Beilby, JP ; Farooqi, IS ; Hebebrand, J ; Huikuri, HV ; James, AL ; Kaehoenen, M ; Levinson, DF ; Macciardi, F ; Nieminen, MS ; Ohlsson, C ; Palmer, LJ ; Ridker, PM ; Stumvoll, M ; Beckmann, JS ; Boeing, H ; Boerwinkle, E ; Boomsma, DI ; Caulfield, MJ ; Chanock, SJ ; Collins, FS ; Cupples, LA ; Smith, GD ; Erdmann, J ; Froguel, P ; Greonberg, H ; Gyllensten, U ; Hall, P ; Hansen, T ; Harris, TB ; Hattersley, AT ; Hayes, RB ; Heinrich, J ; Hu, FB ; Hveem, K ; Illig, T ; Jarvelin, M-R ; Kaprio, J ; Karpe, F ; Khaw, K-T ; Kiemeney, LA ; Krude, H ; Laakso, M ; Lawlor, DA ; Metspalu, A ; Munroe, PB ; Ouwehand, WH ; Pedersen, O ; Penninx, BW ; Peters, A ; Pramstaller, PP ; Quertermous, T ; Reinehr, T ; Rissanen, A ; Rudan, I ; Samani, NJ ; Schwarz, PEH ; Shuldiner, AR ; Spector, TD ; Tuomilehto, J ; Uda, M ; Uitterlinden, A ; Valle, TT ; Wabitsch, M ; Waeber, G ; Wareham, NJ ; Watkins, H ; Wilson, JF ; Wright, AF ; Zillikens, MC ; Chatterjee, N ; McCarroll, SA ; Purcell, S ; Schadt, EE ; Visscher, PM ; Assimes, TL ; Borecki, IB ; Deloukas, P ; Fox, CS ; Groop, LC ; Haritunians, T ; Hunter, DJ ; Kaplan, RC ; Mohlke, KL ; O'Connell, JR ; Peltonen, L ; Schlessinger, D ; Strachan, DP ; van Duijn, CM ; Wichmann, H-E ; Frayling, TM ; Thorsteinsdottir, U ; Abecasis, GR ; Barroso, I ; Boehnke, M ; Stefansson, K ; North, KE ; McCarthy, MI ; Hirschhorn, JN ; Ingelsson, E ; Loos, RJF (NATURE PUBLISHING GROUP, 2010-11)
    Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.
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    Hundreds of variants clustered in genomic loci and biological pathways affect human height
    Allen, HL ; Estrada, K ; Lettre, G ; Berndt, SI ; Weedon, MN ; Rivadeneira, F ; Willer, CJ ; Jackson, AU ; Vedantam, S ; Raychaudhuri, S ; Ferreira, T ; Wood, AR ; Weyant, RJ ; Segre, AV ; Speliotes, EK ; Wheeler, E ; Soranzo, N ; Park, J-H ; Yang, J ; Gudbjartsson, D ; Heard-Costa, NL ; Randall, JC ; Qi, L ; Smith, AV ; Maegi, R ; Pastinen, T ; Liang, L ; Heid, IM ; Luan, J ; Thorleifsson, G ; Winkler, TW ; Goddard, ME ; Lo, KS ; Palmer, C ; Workalemahu, T ; Aulchenko, YS ; Johansson, A ; Zillikens, MC ; Feitosa, MF ; Esko, T ; Johnson, T ; Ketkar, S ; Kraft, P ; Mangino, M ; Prokopenko, I ; Absher, D ; Albrecht, E ; Ernst, F ; Glazer, NL ; Hayward, C ; Hottenga, J-J ; Jacobs, KB ; Knowles, JW ; Kutalik, Z ; Monda, KL ; Polasek, O ; Preuss, M ; Rayner, NW ; Robertson, NR ; Steinthorsdottir, V ; Tyrer, JP ; Voight, BF ; Wiklund, F ; Xu, J ; Zhao, JH ; Nyholt, DR ; Pellikka, N ; Perola, M ; Perry, JRB ; Surakka, I ; Tammesoo, M-L ; Altmaier, EL ; Amin, N ; Aspelund, T ; Bhangale, T ; Boucher, G ; Chasman, DI ; Chen, C ; Coin, L ; Cooper, MN ; Dixon, AL ; Gibson, Q ; Grundberg, E ; Hao, K ; Junttila, MJ ; Kaplan, LM ; Kettunen, J ; Koenig, IR ; Kwan, T ; Lawrence, RW ; Levinson, DF ; Lorentzon, M ; McKnight, B ; Morris, AP ; Mueller, M ; Ngwa, JS ; Purcell, S ; Rafelt, S ; Salem, RM ; Salvi, E ; Sanna, S ; Shi, J ; Sovio, U ; Thompson, JR ; Turchin, MC ; Vandenput, L ; Verlaan, DJ ; Vitart, V ; White, CC ; Ziegler, A ; Almgren, P ; Balmforth, AJ ; Campbell, H ; Citterio, L ; De Grandi, A ; Dominiczak, A ; Duan, J ; Elliott, P ; Elosua, R ; Eriksson, JG ; Freimer, NB ; Geus, EJC ; Glorioso, N ; Haiqing, S ; Hartikainen, A-L ; Havulinna, AS ; Hicks, AA ; Hui, J ; Igl, W ; Illig, T ; Jula, A ; Kajantie, E ; Kilpelaeinen, TO ; Koiranen, M ; Kolcic, I ; Koskinen, S ; Kovacs, P ; Laitinen, J ; Liu, J ; Lokki, M-L ; Marusic, A ; Maschio, A ; Meitinger, T ; Mulas, A ; Pare, G ; Parker, AN ; Peden, JF ; Petersmann, A ; Pichler, I ; Pietilainen, KH ; Pouta, A ; Riddertrale, M ; Rotter, JI ; Sambrook, JG ; Sanders, AR ; Schmidt, CO ; Sinisalo, J ; Smit, JH ; Stringham, HM ; Walters, GB ; Widen, E ; Wild, SH ; Willemsen, G ; Zagato, L ; Zgaga, L ; Zitting, P ; Alavere, H ; Farrall, M ; McArdle, WL ; Nelis, M ; Peters, MJ ; Ripatti, S ; vVan Meurs, JBJ ; Aben, KK ; Ardlie, KG ; Beckmann, JS ; Beilby, JP ; Bergman, RN ; Bergmann, S ; Collins, FS ; Cusi, D ; den Heijer, M ; Eiriksdottir, G ; Gejman, PV ; Hall, AS ; Hamsten, A ; Huikuri, HV ; Iribarren, C ; Kahonen, M ; Kaprio, J ; Kathiresan, S ; Kiemeney, L ; Kocher, T ; Launer, LJ ; Lehtimaki, T ; Melander, O ; Mosley, TH ; Musk, AW ; Nieminen, MS ; O'Donnell, CJ ; Ohlsson, C ; Oostra, B ; Palmer, LJ ; Raitakari, O ; Ridker, PM ; Rioux, JD ; Rissanen, A ; Rivolta, C ; Schunkert, H ; Shuldiner, AR ; Siscovick, DS ; Stumvoll, M ; Toenjes, A ; Tuomilehto, J ; van Ommen, G-J ; Viikari, J ; Heath, AC ; Martin, NG ; Montgomery, GW ; Province, MA ; Kayser, M ; Arnold, AM ; Atwood, LD ; Boerwinkle, E ; Chanock, SJ ; Deloukas, P ; Gieger, C ; Gronberg, H ; Hall, P ; Hattersley, AT ; Hengstenberg, C ; Hoffman, W ; Lathrop, GM ; Salomaa, V ; Schreiber, S ; Uda, M ; Waterworth, D ; Wright, AF ; Assimes, TL ; Barroso, I ; Hofman, A ; Mohlke, KL ; Boomsma, DI ; Caulfield, MJ ; Cupples, LA ; Erdmann, J ; Fox, CS ; Gudnason, V ; Gyllensten, U ; Harris, TB ; Hayes, RB ; Jarvelin, M-R ; Mooser, V ; Munroe, PB ; Ouwehand, WH ; Penninx, BW ; Pramstaller, PP ; Quertermous, T ; Rudan, I ; Samani, NJ ; Spector, TD ; Voelzke, H ; Watkins, H ; Wilson, JF ; Groop, LC ; Haritunians, T ; Hu, FB ; Kaplan, RC ; Metspalu, A ; North, KE ; Schlessinger, D ; Wareham, NJ ; Hunter, DJ ; O'Connell, JR ; Strachan, DP ; Schadt, H-E ; Thorsteinsdottir, U ; Peltonen, L ; Uitterlinden, AG ; Visscher, PM ; Chatterjee, N ; Loos, RJF ; Boehnke, M ; McCarthy, MI ; Ingelsson, E ; Lindgren, CM ; Abecasis, GR ; Stefansson, K ; Frayling, TM ; Hirschhorn, JN (NATURE PUBLISHING GROUP, 2010-10-14)
    Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.
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    Genes influencing milk production traits predominantly affect one of four biological pathways
    Chamberlain, AJ ; McPartlan, HC ; Goddard, ME (EDP SCIENCES S A, 2008)
    In this study we introduce a method that accounts for false positive and false negative results in attempting to estimate the true proportion of quantitative trait loci that affect two different traits. This method was applied to data from a genome scan that was used to detect QTL for three independent milk production traits, Australian Selection Index (ASI), protein percentage (P%) and fat percentage corrected for protein percentage (F% - P%). These four different scenarios are attributed to four biological pathways: QTL that (1) increase or decrease total mammary gland production (affecting ASI only); (2) increase or decrease lactose synthesis resulting in the volume of milk being changed but without a change in protein or fat yield (affecting P% only); (3) increase or decrease protein synthesis while milk volume remains relatively constant (affecting ASI and P% in the same direction); (4) increase or decrease fat synthesis while the volume of milk remains relatively constant (affecting F% - P% only). The results indicate that of the positions that detected a gene, most affected one trait and not the others, though a small proportion (2.8%) affected ASI and P% in the same direction.
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    Testing the neutral theory of molecular evolution using genomic data: a comparison of the human and bovine transcriptome
    MacEachern, S ; McEwan, J ; Mather, A ; McCulloch, A ; Sunnucks, P ; Goddard, M (EDP SCIENCES S A, 2006)
    Despite growing evidence of rapid evolution in protein coding genes, the contribution of positive selection to intra- and interspecific differences in protein coding regions of the genome is unclear. We attempted to see if genes coding for secreted proteins and genes with narrow expression, specifically those preferentially expressed in the mammary gland, have diverged at a faster rate between domestic cattle (Bos taurus) and humans (Homo sapiens) than other genes and whether positive selection is responsible. Using a large data set, we identified groups of genes based on secretion and expression patterns and compared them for the rate of nonsynonymous (dN) and synonymous (dS) substitutions per site and the number of radical (Dr) and conservative (Dc) amino acid substitutions. We found evidence of rapid evolution in genes with narrow expression, especially for those expressed in the liver and mammary gland and for genes coding for secreted proteins. We compared common human polymorphism data with human-cattle divergence and found that genes with high evolutionary rates in human-cattle divergence also had a large number of common human polymorphisms. This argues against positive selection causing rapid divergence in these groups of genes. In most cases dN/dS ratios were lower in human-cattle divergence than in common human polymorphism presumably due to differences in the effectiveness of purifying selection between long-term divergence and short-term polymorphism.
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    The distribution of the effects of genes affecting quantitative traits in livestock
    Hayes, B ; Goddard, ME (E D P SCIENCES, 2001)
    Meta-analysis of information from quantitative trait loci (QTL) mapping experiments was used to derive distributions of the effects of genes affecting quantitative traits. The two limitations of such information, that QTL effects as reported include experimental error, and that mapping experiments can only detect QTL above a certain size, were accounted for. Data from pig and dairy mapping experiments were used. Gamma distributions of QTL effects were fitted with maximum likelihood. The derived distributions were moderately leptokurtic, consistent with many genes of small effect and few of large effect. Seventeen percent and 35% of the leading QTL explained 90% of the genetic variance for the dairy and pig distributions respectively. The number of segregating genes affecting a quantitative trait in dairy populations was predicted assuming genes affecting a quantitative trait were neutral with respect to fitness. Between 50 and 100 genes were predicted, depending on the effective population size assumed. As data for the analysis included no QTL of small effect, the ability to estimate the number of QTL of small effect must inevitably be weak. It may be that there are more QTL of small effect than predicted by our gamma distributions. Nevertheless, the distributions have important implications for QTL mapping experiments and Marker Assisted Selection (MAS). Powerful mapping experiments, able to detect QTL of 0.1sigma(p), will be required to detect enough QTL to explain 90% the genetic variance for a quantitative trait.
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    Prediction of identity by descent probabilities from marker-haplotypes
    Meuwissen, THE ; Goddard, ME (BIOMED CENTRAL LTD, 2001)
    The prediction of identity by descent (IBD) probabilities is essential for all methods that map quantitative trait loci (QTL). The IBD probabilities may be predicted from marker genotypes and/or pedigree information. Here, a method is presented that predicts IBD probabilities at a given chromosomal location given data on a haplotype of markers spanning that position. The method is based on a simplification of the coalescence process, and assumes that the number of generations since the base population and effective population size is known, although effective size may be estimated from the data. The probability that two gametes are IBD at a particular locus increases as the number of markers surrounding the locus with identical alleles increases. This effect is more pronounced when effective population size is high. Hence as effective population size increases, the IBD probabilities become more sensitive to the marker data which should favour finer scale mapping of the QTL. The IBD probability prediction method was developed for the situation where the pedigree of the animals was unknown (i.e. all information came from the marker genotypes), and the situation where, say T, generations of unknown pedigree are followed by some generations where pedigree and marker genotypes are known.
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    The use of communal rearing of families and DNA pooling in aquaculture genomic selection schemes
    Sonesson, AK ; Meuwissen, THE ; Goddard, ME (BIOMED CENTRAL LTD, 2010-11-22)
    BACKGROUND: Traditional family-based aquaculture breeding programs, in which families are kept separately until individual tagging and most traits are measured on the sibs of the candidates, are costly and require a high level of reproductive control. The most widely used alternative is a selection scheme, where families are reared communally and the candidates are selected based on their own individual measurements of the traits under selection. However, in the latter selection schemes, inclusion of new traits depends on the availability of non-invasive techniques to measure the traits on selection candidates. This is a severe limitation of these schemes, especially for disease resistance and fillet quality traits. METHODS: Here, we present a new selection scheme, which was validated using computer simulations comprising 100 families, among which 1, 10 or 100 were reared communally in groups. Pooling of the DNA from 2000, 20000 or 50000 test individuals with the highest and lowest phenotypes was used to estimate 500, 5000 or 10000 marker effects. One thousand or 2000 out of 20000 candidates were preselected for a growth-like trait. These pre-selected candidates were genotyped, and they were selected on their genome-wide breeding values for a trait that could not be measured on the candidates. RESULTS: A high accuracy of selection, i.e. 0.60-0.88 was obtained with 20000-50000 test individuals but it was reduced when only 2000 test individuals were used. This shows the importance of having large numbers of phenotypic records to accurately estimate marker effects. The accuracy of selection decreased with increasing numbers of families per group. CONCLUSIONS: This new selection scheme combines communal rearing of families, pre-selection of candidates, DNA pooling and genomic selection and makes multi-trait selection possible in aquaculture selection schemes without keeping families separately until individual tagging is possible. The new scheme can also be used for other farmed species, for which the cost of genotyping test individuals may be high, e.g. if trait heritability is low.