2022Äê5ÔÂ23ÈÕ£¬ÉúÎïÐÅϢѧרҵÆÚ¿¯Briefings In BioinformaticsÔÚÏß·¢±íÁ˸´µ©´óѧÐìÊ黪ÍŶӵÄÑо¿³É¹û¡°MultiWaverX: Modeling latent sex-biased admixture history ¡±¡£¸ÃÏ×÷Ìá³öÁËÒ»ÖÖз½·¨MultiWaverX£¬¿ÉÍÆ¶ÏÈËȺÐÔ±ðÆ«ÏòÐÔ»ìºÏÀúÊ·£¬²¢Ó¦Óø÷½·¨·ÖÎöºÍÖØ¹¹ÁËÖÐÑǵØÇø¡¢Öж«µØÇøÒÔ¼°ÃÀÖÞ´ó½µÄ17¸öÈËȺµÄÐÔ±ðÆ«ÏòÐÔ»ùÒò½»Á÷ÀúÊ·¡£

ÐÔ±ðÆ«ÏòÐÔʵ¼ÊÉϹ㷺´æÔÚÓÚÈËȺ»ùÒò½»Á÷¹ý³ÌÖУ¬¼´Ìض¨×æÔ´µÄÄÐÅ®ÒÅ´«¹±Ï×´æÔÚ²îÒì¡£·ÇÒáÃÀ¹úÈË£¨African Americans£©ºÍÀ­¶¡ÒáÃÀÖÞÈË£¨Hispanic Americans or Latino American£©ÎªÑ§½çÊìÖªµÄ´æÔÚÐÔ±ðÆ«ÏòÐÔ»ìºÏµÄÈËȺ¡£ÐÔ±ðÆ«ÏòÐÔ»ìºÏµÄÑо¿¶ÔÁ˽âÈËȺÐγɺÍÑÝ»¯Àú³Ì¡¢Àí½âÏÖ´úÈËÀàÒÅ´«²îÒì¡¢ÒÔ¼°Ö¸µ¼Ò½Ñ§Ñо¿¶¼ÓÐÖØÒªµÄÀíÂÛÒâÒåºÍÓ¦ÓüÛÖµ¡£È»¶ø£¬ÊÜÏÞÓÚ·ÖÎö·½·¨£¬ÈËȺÑÝ»¯ÀúÊ·ÉÏ´í×Û¸´ÔӵĻùÒò½»Á÷ÖдæÔÚµÄÐÔ±ðÆ«ÏòÐÔ³¤ÆÚÒÔÀ´Î´µÃµ½³ä·ÖÑо¿£¬ÌرðÊÇһЩ¾­Àú¹ý¶à´Î¶ø¸´ÔÓ»ùÒò½»Á÷ÀúÊ·µÄÈËȺ£¬Ôø¾­·¢ÉúµÄ²»Í¬·½ÏòµÄÐÔ±ðÆ«Ïò»ìºÏÍùÍù±»ºöÊÓ¡£MultiWaverXµÄÌá³öÔںܴó³Ì¶ÈÉÏΪÕâЩÎÊÌâµÄ½â¾öÌṩÁËз½·¨ºÍÐÂ˼·¡£

ͼ 1. MultiWaverX Ëã·¨Á÷³Ìͼ

MultiWaverXÊÇÔÚÑо¿ÍŶÓǰÆÚÌá³öµÄËã·¨MultiWaver»ù±¾Ä£Ðͺͷ½·¨µÄ»ù´¡ÉϽøÒ»²½·¢Õ¹¶øÀ´£¬ÌرðÊÇÖ²ÈëÁËÐÔ±ðÆ«ÏòÐÔ»ìºÏÀúÊ·ÍÆ¶ÏÄ£¿é¡£Æä¾ßÌåËã·¨¿ÉÒÔ·ÖΪÒÔÏÂÈý¸ö²½Ö裨ͼ1£©£º£¨1£©»ùÓÚ³£È¾É«ÌåµÄ׿ÏÈÆ¬¶Î³¤¶È·Ö²¼ÐÅÏ¢£¬ÀûÓÃ×î´óÆÚÍûËã·¨£¨EM algorithm£©»ò¶þ·ÖËÑË÷Ëã·¨£¨Binary Search algorithm£©¹À¼Æ²»Í¬»ìºÏÄ£ÐÍÏ»ìºÏʱ¼äºÍ³£È¾É«Ìå»ìºÏ±ÈÀýµÈ²ÎÊý£¬½ø¶øÀûÓÃËÆÈ»±È¼ìÑ飨Likelihood ratio test£©»ò±´Ò¶Ë¹ÐÅÏ¢×¼Ôò£¨Bayes Information Criterion£©Ñ¡Ôñ×îÓÅ»ìºÏģʽ¡££¨2£©ÔÚµÚÒ»²½È·¶¨µÄ»ìºÏģʽÏ£¬»ùÓÚXȾɫÌåµÄ׿ÏÈÆ¬¶Î³¤¶È·Ö²¼ÐÅÏ¢¹À¼ÆXȾɫÌåµÄ»ìºÏ±ÈÀý¡££¨3£©Õë¶Ôÿ¸ö׿ÏÈÈËȺµÄÿ²¨»ìºÏʼþ£¬½áºÏ³£È¾É«ÌåºÍXȾɫÌåµÄ»ìºÏ±ÈÀý¼ÆËãÄÐÐÔ¹±Ï×±ÈÀý£¬´Ó¶øÅжÏÐÔ±ðÆ«ÏòÐÔ·½ÏòÒÔ¼°³Ì¶È¡£Ïà±ÈÓÚ´«Í³·½·¨£¬MultiWaverXÓÐÈçÏÂÁ½¸öÓÅÊÆ£ºÊ×ÏÈ£¬¸Ã·½·¨¿ÉÒÔ׼ȷµØ¹À¼Æ»ìºÏ²¨ÊýÒÔ¼°Ã¿²¨»ìºÏʼþµÄ»ìºÏʱ¼ä¡¢»ìºÏ±ÈÀýºÍÐÔ±ðÆ«ÏòÐÔ»ìºÏ²ÎÊý£¬ÎªºóÐøÐÔ±ðÆ«Ïò»ìºÏÀúÊ·µÄ¾«Ï¸»¯Öع¹´òÏ»ù´¡£»Æä´Î£¬¸Ã·½·¨³ä·ÖÀûÓó£È¾É«ÌåÓëXȾɫÌå¹²ÏíÀúʷʼþµÄ¹æÂÉ£¬Í¨¹ýÊý¾ÝÁ¿Ïà¶Ô¸ü·á¸»µÄ³£È¾É«ÌåÍÆ¶ÏÈËȺ»ìºÏģʽ£¬½ø¶ø¹À¼ÆÐÔ±ðÆ«ÏòÐÔ²ÎÊý£¬¿ÉÒÔÓÐЧ¿Ë·þÓÉÓÚXȾɫÌå½Ï¶Ì£¬Êý¾ÝÁ¿½ÏС´øÀ´µÄÄ£ÐÍÍÆ¶Ï²»Îȶ¨µÄȱÏÝ¡£ÏµÍ³µÄÄ£ÄâÑéÖ¤Êý¾Ý±íÃ÷£¬MultiWaverXÔÚ²»Í¬»ìºÏģʽϹÀ¼ÆÐÔ±ðÆ«ÏòÐÔ²ÎÊý¾ùÓнϸߵÄ׼ȷÐÔ£¬ÔÚÓ¦¶Ô¸÷ÀàÊý¾ÝÔëÉùʱҲ±íÏÖ³öÒ»¶¨µÄÎȽ¡ÐÔ¡£´ËÍ⣬Ñо¿ÍŶÓÒÀ¾Ý»ìºÏ¹ý³ÌÖÐÌØ¶¨×æÏÈÈËȺÄÐÐÔ¹±Ï×±ÈÀýµÄ±ä»¯Ç÷ÊÆ£¬½øÒ»²½½«ÐÔ±ðÆ«ÏòÐÔ»ìºÏÄ£Ð͹éÄÉΪÒÔÏÂÎåÖÖ£¨Í¼2£©£ºÎȶ¨Ä£ÐÍ£¨steady model£©¡¢ÔöǿģÐÍ (enhanced model)¡¢¼õÈõÄ£ÐÍ£¨dilution model£©¡¢²¨¶¯Ä£ÐÍ£¨turnover model£©¡¢µÖÏûÄ£ÐÍ£¨cancellation model£©¡£ÆäÖУ¬ÐÔ±ðÆ«ÏòµÖÏûÄ£ÐÍ×îÎªÌØÊ⣬ÐÔ±ðÆ«ÏòÐźÅÔÚ¾­Àú¶à´Î·½ÏòÏà·´µÄ»ìºÏʼþºóµÃÒÔµÖÏû¡£Ñо¿ÍŶÓÔÚ·ÖÎöʵ¼ÊÊý¾Ýʱ£¬·¢ÏÖÖйúÎ÷±±ÉÙÊýÃñ×å¹þÈø¿Ë×åÊǸûìºÏÄ£Ð͵Ĵú±íÈËȺ¡£¹þÈø¿Ë×åÖ÷Òª¾ÓסÓÚÖйúÎ÷±±µØÇø£¬ÆäÖ÷ÒªÒÅ´«³É·ÖÀ´×Ô¶«ÑǺÍÅ·ÖÞ׿ÏÈÈËȺ£¬ÇÒ»ìºÏ±ÈÀý±ÈÔÚ³£È¾É«ÌåºÍXȾɫÌåˮƽÉϾùΪ60£º40£¬ÈôʹÓô«Í³·½·¨½øÐÐÍÆ¶Ï£¬½á¹û¾ùΪÎÞÐÔ±ðÆ«Ïò»ìºÏ¡£¶øÍ¨¹ýMultiWaverX·ÖÎö£¬Ñо¿ÍŶӷ¢ÏÖ¸ÃÈËȺ³ÊÏÖ³öÔçÆÚÅ·ÖÞÄÐÐÔΪÖ÷£¨Ô¼3000Äêǰ£©£¬½üÆÚ¶«ÑÇÄÐÐÔΪÖ÷£¨Ô¼750Äêǰ£©µÄÁ½²¨ÐÔ±ðÆ«ÏòÐÔ»ìºÏÀúÊ·¡£

ͼ 2. ÐÔ±ðÆ«ÏòÐÔ»ìºÏÄ£ÐÍ·ÖÀ༰Á÷³Ìͼ

ÏÖ´úÈËÀàÀúÊ·½ø³Ì´í×Û¸´ÔÓ£¬µÛ¹úµÄÐËÆðÓëË¥°Ü£¬Å«Á¥Ã³Ò×ÓëÕ½Õù£¬ÈËȺµÄÀ©ÕÅÓëǨáã¶¼¶ÔÈËȺÒÅ´«»ìºÏ²úÉúÁËÉîÔ¶µÄÓ°Ïì¡£Ó¦ÓÃÉÏ£¬Ñо¿ÍŶÓѡȡȫÇòµäÐÍ»ìºÏÈËȺ·Ö²¼ÇøÓò£¬ÖÐÑÇ¡¢Öж«ÒÔ¼°ÃÀÖÞ´ó½ΪÀý£¬·Ö±ð½âÎöÆäÐÔ±ðÆ«ÏòÐÔ»ìºÏÀúÊ·£¬Öع¹ÁËÈ«ÇòÈËȺÐÔ±ðÆ«Ïò»ìºÏͼÆ×£¨Í¼3£©¡£ÖÐÑǵØÇøÎ»ÓÚÅ·ÑÇ´ó½µÄ¸¹Ðĵشø£¬¶ÔÓÚ´Ù½ø¶«Î÷·½ÎÄ»¯¡¢¾­¼ÃÓë»ùÒò½»Á÷Æð×ÅÖØÒªµÄ×÷Ó㬴ӹÅÖÁ½ñ£¬Å·ÑÇ´ó½Éϸ´ÔÓµÄÈË¿ÚÁ÷¶¯ÀúÊ·Ò²²»¶ÏËÜÔì×ÅÕâÆ¬ÍÁµØµÄÒÅ´«¶àÑùÐÔ¡£Çàͭʱ´úÅ·ÖÞÈËȺ¶«Ç¨¡¢¹«ÔªÇ°334ÖÁ324Äê¼äµÄÑÇÀúɽ´ó¶«Õ÷ÒÔ¼°Ê¼ÓÚ¹«ÔªÇ°130Äê¼äµÄ¹ÅË¿³ñ֮·¶¼´Ù½øÁËÔçÆÚÅ·ÑǴ󽲻ͬ¹ú¼Ò¡¢²»Í¬ÎÄÃ÷Ö®¼äµÄ½»Á÷ÓëÅöײ¡£¹«Ôª13ÊÀ¼Í³õ£¬Ãɹŵ۹úµÄÐËÆðÒÔ¼°³É¼ªË¼º¹Î÷Õ÷½øÒ»²½Íƶ¯Á˶«Î÷·½ÔÚ¾­¼Ã¡¢ÎÄ»¯¡¢×ڽ̵ȷ½ÃæµÄ½»Á÷¡£Ñо¿ÍŶӻùÓÚÖÐÑǵØÇø»ìºÏÈËȺµÄ·ÖÎö½á¹û±íÃ÷¸ÃµØÇø»ìºÏʼþ¿ÉÒÔ´óÖ·ÖΪÁ½²¨£¬ÆäÖнϾÃÔ¶µÄÒ»²¨·¢ÉúÔÚ¾à½ñ2500ÖÁ3300Äê¼ä£¬Ö÷Òª±íÏÖΪŷÖÞÄÐÐÔÓë¶«ÑÇÅ®ÐÔΪÖ÷µÄ»ìºÏ£¬¶ø½ÏΪ½üÆÚµÄÒ»²¨´óÖÂΪ¾à½ñ500ÖÁ900Äêǰ£¬ÆäÐÔ±ðÆ«ÏòÖ÷ҪΪ¶«ÑÇÄÐÐÔΪÖ÷µÄ»ìºÏ¡£¹«Ôª7ÊÀ¼Í£¬°¢À­²®Å«Á¥Ã³Ò×ÓëÀ©ÕÅ´Ù½øÁËÑÇÅ··ÇÈý´óÖÞÄÚ¸÷¸ö·â½¨ÎÄÃ÷Ö®¼äµÄ¾­¼ÃÎÄ»¯½»Á÷£¬Íƶ¯ÁËÓ¡¶ÈÑóºÍµØÖк£ÇøÓòº£ÉÏóÒ׵ķ±ÈÙÓë·¢Õ¹£¬¶øÎ»ÓÚÑÇÅ··ÇÈýÖÞ½»½çµÄÖж«µØÇøÒ²Îª½øÒ»²½Á˽âÈËÀà½ø»¯ÀúÊ·ÌṩÁËÖØÒªÐÅÏ¢¡£»ùÓÚÖж«µØÇø»ìºÏÈËȺµÄÑо¿½á¹û±íÃ÷Æä»ìºÏʱ¼ä´óÖÂΪ¾à½ñ1600Äêǰ£¬ÇÒ»ìºÏÆ«ÏòÖ÷Òª±íÏÖΪŷÖÞÄÐÐÔÓë·ÇÖÞÅ®ÐÔ¡£15ÊÀ¼ÍÄ©£¬Ëæ×ŵØÀí´ó·¢ÏÖ¡¢Ðº½Â·µÄ¿ª±ÙÒÔ¼°¿ç´óÎ÷ÑóÅ«Á¥Ã³Ò×µÄÐËÆð£¬´óÖÞÖ®¼äÏà¶Ô¹ÂÁ¢µÄ״̬½øÒ»²½±»´òÆÆ¡£»ùÓÚÃÀÖÞ´ó½»ìºÏÈËȺµÄ·ÖÎö±íÃ÷¸ÃµØÇøÈËȺ»ìºÏʱ¼ä´óÖ´¦ÓÚ¾à½ñ400ÖÁ500Äêǰ£¬ÇÒÐÔ±ðÆ«ÏòΪŷÖÞÄÐÐÔÓë·ÇÖÞÅ®ÐÔ»òÃÀÖÞԭסÃñÈËȺŮÐÔΪÖ÷µÄ»ìºÏ¡£ÒÔÉÏÀúʷʼþ¾ù¶ÔÏÖ´úÈËÀàÐÔ±ðÆ«ÏòÐÔ»ìºÏ²úÉúÁËÉîÔ¶µÄÓ°Ï죬MultiWaverXµÄÌá³öҲΪ½øÒ»²½½âÎöÊÀ½çÈËȺÒÅ´«¶àÑùÐÔÐγɺÍÑÝ»¯»úÖÆÌṩÁËÐµķ½·¨ºÍ˼·¡£

ͼ 3. ÏÖ´úÈËÀàÑÝ»¯ÀúÊ·ÉÏÐÔ±ðÆ«ÏòÐÔ»ìºÏģʽʾÒâͼ

Öйú¿ÆÑ§ÔºÉϺ£ÓªÑøÓ뽡¿µÑо¿Ëù²©Ê¿Ñо¿ÉúÕÅÈð¡¢±±¾©½»Í¨´óѧÊýѧÓëͳ¼ÆÑ§ÔºÄßÐñÃô¸±½ÌÊÚ¡¢¹ú¿Æ´ó±ÏÒµÉúÔ·ïDz©Ê¿Îª¸ÃÂÛÎĹ²Í¬µÚÒ»×÷Õߣ¬¸´µ©´óѧÉúÃü¿ÆÑ§Ñ§Ôº/¸½ÊôÖÐɽҽԺÐìÊ黪½ÌÊÚΪͨѶ×÷Õß¡£¸´µ©´óѧÉúÃü¿ÆÑ§Ñ§Ôº¡¢¸½ÊôÖÐɽҽԺ¡¢ÈËÀà±íÐÍ×éÑо¿ÔºÎªÍ¨Ñ¶µ¥Î»¡£¸ÃÏ×÷»ñµÃÁ˹ú¼Ò×ÔÈ»¿ÆÑ§»ù½ðί¡¢Öйú¿ÆÑ§ÔºÏȵ¼×¨Ïî¡¢Ó¢¹ú»Ê¼Òѧ»áÅ£¶Ù»ù½ð¡¢ÉϺ£ÊпÆÎ¯¡¢ÖÐÑë¸ßУ»ù±¾¿ÆÑÐרÏîµÈ¶àÏî»ù½ðµÄ×ÊÖú¡£

ÂÛÎÄÁ´½Ó£ºhttps://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbac179/6590437?redirectedFrom=fulltext&login=false